Multidimensional Liquid Chromatography Theory and Applications in Industrial Chemistry and the Li

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Theory and Applications in Industrial Chemistry and the Life Sciences Edited by

STEVEN A. COHEN Life Sciences R&D, Waters Corporation Milford, MA 01757, USA

MARK R. SCHURE Theoretical Separation Science Laboratory Rohm and Haas Company Springhouse, PA 19477-0904, USA

A JOHN WILEY & SONS, INC., PUBLICATION

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Theory and Applications in Industrial Chemistry and the Life Sciences Edited by

STEVEN A. COHEN Life Sciences R&D, Waters Corporation Milford, MA 01757, USA

MARK R. SCHURE Theoretical Separation Science Laboratory Rohm and Haas Company Springhouse, PA 19477-0904, USA

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright Ó 2008 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, 201-748-6011, fax 201-748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at 800-762-2974, outside the United States at 317-572-3993 or fax 317-572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www. wiley.com. Library of Congress Cataloging-in-Publication Data: Multidimensional liquid chromatography: theory and applications in industrial chemistry and the life sciences / edited By Steven A. Cohen, Mark R. Schure. p. cm. Includes index. ISBN 978-0-471-73847-3 (cloth) 1. Liquid chromatography. 2. Chemical engineering. 3. Chemistry, Technical. 4. Biochemistry. I. Cohen, Steven A., 1953- II. Schure, Mark R.; 1952QD79.C454M85 2007 543’.84–dc22 2007041576

Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

CONTENTS

Foreword Preface

xiii xv

Contributors

xvii

1 Introduction

1

1.1 Previous Literature Which Covers MDLC 1.2 How this Book is Organized References

PART I

THEORY

2 Elements of the Theory of Multidimensional Liquid Chromatography 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

Introduction Peak Capacity Resolution Orthogonality Two-Dimensional Theory of Peak Overlap Dimensionality, Peak Ordering, and Clustering Theory of Zone Sampling Dilution and Limit of Detection Chemometric Analysis

4 5 6

9

11 11 13 17 19 21 23 24 26 27 v

vi

CONTENTS

2.10 Future Directions References 3 Peak Capacity in Two-Dimensional Liquid Chromatography 3.1 Introduction 3.2 Theory 3.3 Procedures 3.4 Results and Discussion 3.5 Conclusions Appendix 3A Generation of Random Correlated Coordinates Appendix 3B Derivation of Limiting Correlation Coefficient r References 4 Decoding Complex 2D Separations 4.1 4.2

Introduction Fundamentals: The Statistical Description of Complex Multicomponent Separations 4.3 Decoding 1D and 2D Multicomponent Separations by Using the SMO Poisson Statistics 4.4 Decoding Multicomponent Separations by the Autocovariance Function 4.5 Application to 2D Separations 4.5.1 Results from SMO Method 4.5.2 Results from 2D Autocovariance Function Method 4.6 Concluding Remarks Acknowledgments References

PART II COLUMNS, INSTRUMENTATION AND METHODS DEVELOPMENT 5 Instrumentation for Comprehensive Multidimensional Liquid Chromatography 5.1 5.2 5.3 5.4

Introduction Heart-Cutting Versus Comprehensive Mode Chromatographic Hardware 5.3.1 Valves CE Interfaces 5.4.1 Gated Interface for HPLC–CE 5.4.2 Microfluidic Valves for On-Chip Multidimensional Analysis

28 30 35 35 37 41 42 49 50 54 56 59 59 62 68 74 78 81 84 88 88 88

91

93 93 95 97 97 104 104 105

CONTENTS

5.5

Columns and Combinations 5.5.1 Column Systems, Dilution, and Splitting 5.6 Detection 5.7 Computer Hardware and Software 5.7.1 Software Development 5.7.2 Valve Sequencing 5.7.3 Data Format and Storage 5.8 Zone Visualization 5.8.1 Contour Visualization 5.8.2 2D Peak Presentation 5.8.3 Zone Visualization in Specific Chemical (pI) Regions 5.8.4 External Plotting Programs 5.8.5 Difference Plots 5.8.6 Multi-channel Data 5.9 Data Analysis and Signal Processing 5.10 Future Prospects References 6 Method Development in Comprehensive Multidimensional Liquid Chromatography 6.1 6.2 6.3 6.4

Introduction Previous Work Column Variables Method Development 6.4.1 The Cardinal Rules of 2DLC Method Development 6.5 Planning the Experiment 6.6 General Comments on Optimizing the 2DLC Experiment: Speed–Resolution Trade-off Acknowledgment References 7 Monolithic Columns and Their 2D-HPLC Applications 7.1 7.2

7.3

Introduction Monolithic Polymer Columns 7.2.1 Structural Properties of Polymer Monoliths 7.2.2 Chromatographic Properties of Polymer Monolithic Columns 7.2.3 Two-Dimensional HPLC Using Polymer Monoliths Monolithic Silica Columns 7.3.1 Preparation 7.3.2 Structural Properties of Monolithic Silica Columns 7.3.3 Chromatographic Properties of Monolithic Silica Columns

vii

106 108 109 109 110 111 113 115 115 117 117 117 118 118 119 120 121

127 127 128 130 130 132 143 143 144 144 147 147 148 148 150 152 153 154 154 156

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CONTENTS

7.4

Peak Capacity Increase by Using Monolithic Silica Columns in Gradient Elution 7.5 2D HPLC Using Monolithic Silica Columns 7.5.1 RP-RP 2D HPLC Using Two Different Columns 7.5.2 RP–RP 2D HPLC Using Two Similar Columns 7.5.3 Ion Exchange–Reversed-Phase 2D HPLC Using a Monolithic Column for the 2nd-D 7.5.4 IEX-RP 2D HPLC Using a Monolithic RP Capillary Column for the 2nd-D 7.6 Summary and Future Improvement of 2D HPLC References 8 Ultrahigh Pressure Multidimensional Liquid Chromatography 8.1

Background: MDLC in the Jorgenson Lab 8.1.1 Cation Exchange–Size Exclusion 8.1.2 Anion Exchange–Reversed Phase 8.1.3 Cation Exchange–Reversed Phase 8.1.4 Size Exclusion–Reversed Phase 8.2 Online Versus Off-Line MDLC 8.3 MDLC Using Ultrahigh Pressure Liquid Chromatography: Benefits and Challenges 8.3.1 An Introduction to UHPLC 8.3.2 UHPLC for LC LC: High Speed Versus High Peak Capacity 8.3.3 LC UHPLC for Separations of Intact Proteins 8.4 Experimental Details 8.4.1 Instrumentation 8.4.2 Data Analysis 8.4.3 Chromatographic Conditions 8.4.4 Samples 8.5 Results and Discussion 8.6 Future Directions for UHP-MDLC References

PART III

LIFE SCIENCE APPLICATIONS

9 Peptidomics 9.1 State of the Art—Why Peptidomics? 9.2 Strategies and Solutions 9.3 Summary and Conclusions References

158 159 161 164 166 168 171 171 177 177 178 180 181 183 188 189 190 191 191 193 193 194 195 196 196 202 203

205 207 207 208 218 218

CONTENTS

10 A Two-Dimensional Liquid Mass Mapping Technique for Biomarker Discovery 10.1 10.2

Introduction Methods for Separating and Identifying Proteins 10.2.1 pI-Based Methods of Separation 10.2.2 Chromatofocusing-A Column Based pH Separation 10.2.3 Nonporous Separation of Proteins 10.2.4 Electrospray-Time of Flight-Mass Spectrometry 10.2.5 MALDI Peptide Mass Fingerprinting 10.2.6 Data Analysis and Recombination 10.3 Applications 10.3.1 Proteomic Mapping and Clustering of Multiple Samples—Application to Ovarian Cancer Cell Lines 10.3.2 2D Liquid Mass Mapping of Tumor Cell Line Secreted Samples, Application to Metastasis-Associated Protein Profiles 10.3.3 Identification Annotation and Data Correlation in MCF10 Human Breast Cancer Cell Lines 10.4 Summary and Conclusions Acknowledgments References 11 Coupled Multidimensional Chromatography and Tandem Mass Spectrometry Systems for Complex Peptide Mixture Analysis 11.1 SCX-RP/MS/MS 11.2 SCX/RP/MS/MS 11.3 MudPIT 11.4 Alternative First Dimension Approaches 11.5 Conclusion References 12 Development of Orthogonal 2DLC Methods for Separation of Peptides 12.1 12.2 12.3

Introduction Previous Work Developing Orthogonal 2DLC Methods 12.3.1 LC Selectivity for Peptides: Experimental Design 12.3.2 Investigation of 2DLC Orthogonality for Separation of Peptides 12.3.3 Geometric Approach to Orthogonality in 2DLC 12.3.4 Practical 2DLC Considerations in Proteome Research 12.3.5 Evaluation of Selected 2DLC MS/MS Systems

ix

221 221 223 223 225 226 228 229 230 230

230

233 235 237 238 238

243 245 248 251 254 255 255

261 261 263 264 264 266 271 275 276

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CONTENTS

12.3.6 Peak Capacity in 2DLC-MS/MS 12.3.7 Considerations of Concentration Dynamic Range 12.4 Conclusions Acknowledgment References 13 Multidimensional Separation of Proteins with Online Electrospray Time-of-Flight Mass Spectrometric Detection 13.1 13.2 13.3 13.4

Introduction Chromatographic Parameters Analyte Detection and Subsequent Analysis Building a Multidimensional Protein Separation 13.4.1 Selection of an Ion-Exchange–Reversed-Phase Separation System for Protein-Level Separations 13.4.2 Chromatographic Sorbent Considerations 13.4.3 Chromatographic Behavior of Proteins 13.5 Comprehensive Multidimensional Chromatographic Systems 13.6 Coupling 2DLC with Online ESI–MS Detection 13.6.1 Interactions between the Two Dimensions of Chromatography (Step Vs. Linear) 13.6.2 Recognizing Increased Selectivity in 2DLC Separations 13.7 Expanding Multidimensional Separations into a “Middle-Out” Approach to Proteomic Analysis 13.8 Future Directions in Protein MDLC 13.8.1 Protein Chromatography 13.8.2 MS Analysis of Proteins 13.8.3 Data Interpretation 13.9 Conclusion References 14 Analysis of Enantiomeric Compounds Using Multidimensional Liquid Chromatography 14.1 14.2

Online Achiral-Chiral LC-LC Applications 14.2.1 Analysis of Enantiomers in Plasma and Urine 14.3 Amino Acids 14.3.1 Physiological Fluids or Tissues 14.3.2 In Food, Beverages, and Other Products 14.4 Other Applications 14.4.1 Analysis of Enantiomers from Plant and Environmental Sources 14.5 Miscellaneous Applications 14.6 Conclusion References

280 282 284 284 284

291 291 293 293 294 295 295 296 296 299 304 306 308 311 312 313 314 314 315

319 320 323 323 328 328 333 334 334 336 338 339

CONTENTS

xi

PART IV MULTIDIMENSIONAL SEPARATION USING CAPILLARY ELECTROPHORESIS

345

15 Two-Dimensional Capillary Electrophoresis for the Comprehensive Analysis of Complex Protein Mixtures

347

15.1 15.2

Introduction Previous Work 15.2.1 Miniaturized IEF/SDS-PAGE 15.2.2 One-Dimensional Capillary Electrophoresis for Protein Analysis 15.3 Two-Dimensional Capillary Separations for Analysis of Peptides and Proteins 15.3.1 Capillary Liquid Chromatography Coupled with Capillary Electrophoresis for Analysis of Unlabeled Peptides and Proteins 15.3.2 Two-Dimensional Capillary Electrophoresis for Analysis of Proteins 15.3.3 High-Speed Two-Dimensional Capillary Electrophoresis 15.3.4 The Analysis of a Single Fixed Cell 15.4 Conclusions 15.5 Abbreviations References 16 Two-Dimensional HPLC–CE Methods for Protein/Peptide Separation 16.1 Introduction 16.2 Off-line Versus Online 16.3 HPLC Fractionation 16.4 2D HPLC–CE 16.5 CE–MS Detection 16.6 Applications 16.7 Concluding Remarks Acknowledgment References

PART V

INDUSTRIAL APPLICATIONS

17 Multidimensional Liquid Chromatography in Industrial Applications 17.1 17.2

Introduction Principles of Multidimensional Liquid Chromatography as Applied to Polymer Analysis

347 348 348 349 352

352 352 356 358 360 360 360

365 365 366 366 367 368 370 380 381 381

385

387 387 390

xii

CONTENTS

17.3 17.4

Experimental Analysis of Alkylene Oxide-Based Polymers 17.4.1 Amphiphilic Polyalkylene Oxides 17.5 Excipients 17.6 Polyether Polyols 17.7 Analysis of Condensation Polymers 17.8 Polyamides 17.9 Aromatic Polyesters 17.10 Aliphatic Polyesters References

393 395 395 399 403 406 407 414 417 420

18 The Analysis of Surfactants by Multidimensional Liquid Chromatography

425

18.1 18.2

Introduction Analytical Characterization Methods 18.2.1 CE and CGE 18.2.2 SEC 18.2.3 NPLC 18.2.4 RPLC 18.3 Detection Methods 18.4 2DLC 18.4.1 RPLC Coupled to SEC 18.4.2 NPLC Coupled to RPLC 18.5 Conclusions References

425 428 429 430 431 433 434 434 435 435 442 443

Index

447

FOREWORD

The principal rationale for multidimensional separations is that they offer a more effective as well as efficient way to generate high peak capacity and thus permit more complete resolution of complex mixtures. I suspect, however, that there is another motivation that attracts people to multidimensional separations: the resulting twodimensional chromatograms make fascinating pictures. Two-dimensional separation patterns are somehow more satisfying than a series of peaks in a one-dimensional chromatogram. The human mind is highly adept at dealing with complex information presented in the form of images and, despite the complexity, is able to quickly spot differences among such patterns. My own inspiration for pursuing multidimensional separations came from J. Calvin Giddings. I was invited to present a seminar at the University of Utah in May 1987, where I spoke on our current research project concerning liquid chromatography in open-tubular columns. That night over dinner Cal told me that he liked the work I presented and also our work on capillary electrophoresis, and suggested that I consider multidimensional chromatography as a more practical approach for the resolution of complex mixtures. On the trip back to Chapel Hill, I thought of nothing other than Cal’s recommendation and how I might set about to implement it. I was interested in analyzing samples in their entirety by two-dimensional separations, so I did not want to settle for the well-established “heart-cutting” approach, where only a single portion of the effluent from the first separation dimension is subjected to a second dimension of separation. Instead, I wanted to subject the entire sample to the full two dimensions of resolution. Also, I did not want to do two-dimensional separations in space, as in two-dimensional thin-layer chromatography, but to use coupled columns instead. This latter consideration was driven at least in part by the ready coupling to mass

xiii

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FOREWORD

spectrometry that columns (but not slabs) provide. Upon arriving home, I made plans with my graduate student, Michelle Bushey, to initiate a project on protein separation by two-dimensional liquid chromatography. Michelle also went on to develop comprehensive two-dimensional liquid chromatography-capillary electrophoresis in my lab. We searched for a suitable term to differentiate our approach from the “heartcutting’’ style of two-dimensional separations and settled for “comprehensive’’ in order to emphasize that all of the sample components were subjected to the full two dimensions of separation. Multidimensional separations have proven to be quite successful, as evidenced by the wealth of examples of hardware and applications described in this book. It is hoped that increased awareness and use of multidimensional separations will open up possibilities for meaningful analyses of truly complex samples and permit the routine analysis of thousands of components from a single sample in a single run. James W. Jorgenson Chapel Hill, North Carolina, USA September 9, 2007

PREFACE

At least two driving forces have contributed to the recent increased use and development of multidimensional liquid chromatography (MDLC). These include the high resolution and peak capacity needed for proteomics studies and the independent size and chemical structure selectivity for resolving industrial polymers. In this regard, separation science focuses on a system approach to separation as individual columns can contribute only part of the separation task and must be incorporated into a larger separation system for a more in-depth analytical scheme. Separation techniques are increasingly used to resolve molecular structure at a finer and finer scale and in chemical environments that are fundamentally complex. This applies not only to small and medium-sized (<1000 Da) molecules but also to polymers, nanoparticles, and colloidal-sized chemical structures. Of particular importance is when a small change in molecular structure modifies a response or behavior of another molecule. This is often the case in the life sciences where resolution of biomarker molecules can be a key to early disease detection. The complexity of the biomarker problem is high as the biomarker molecule may be present as a minor- or trace-level component (very large dynamic range), and its structure may be a slight modification of something present in far larger concentration (large structural diversity). One often finds that when high resolution separation schemes are utilized, other techniques and disciplines must participate in the scheme of understanding and effectively utilizing the separation with subsequent identification of the resulting zones. A rigorous and often multidimensional detection scheme such as mass spectrometry and/or fluorescence is found both for the life science and industrial polymer applications. Other disciplines including informatics and statistics are often

xv

xvi

PREFACE

found to be integral to the separation/detection scheme study when a full analysis of the information present is to be performed. In these themes we have assembled a number of recent contributions that help define the present state of the art and science of MDLC. The coverage of these topics includes instrumentation, theory, methods development, applications of MDLC in the life sciences, and applications of MDLC in industrial polymer chemistry. We have purposely narrowed the scope of all multidimensional chromatography to those techniques that incorporate separations in the liquid phase and to those in which the use of the comprehensive mode prevails but is not exclusive. This text neither incorporates elements of multidimensional thin-layer chromatography, multidimensional separations in gel media such as those commonly employed for the separation of complex mixtures of proteins, nor the techniques that utilize multidimensional gas chromatography. Some of the same principles apply, particularly in the theory section, but our emphasis is strictly on separations carried out in the liquid phase and by columns, rather than in the gas phase or in planar configurations. We would like to thank the contributors to this book for their chapters and for the ease with which we could work with these authors. We thank Heather Bergman of John Wiley Interscience for the opportunity to contribute this text in the active field of MDLC. Additionally we thank the production team of Brendan Sullivan and Robert Esposito from John Wiley and Sons, and Ekta Handa from Thomson Digital, and our respective wives, Nancy Schure and Donna Cohen, for their infinite patience. We hope that this book serves as a useful guide to the field and that it functions as both a reference and as an enticement to others to enter this field. Mark Schure, Blue Bell, PA, USA Steve Cohen, Hopkinton, MA, USA

CONTRIBUTORS

Hiroshi Aoki Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan Daniel W. Armstrong Department of Chemistry, Iowa State University, Ames, IA, USA Timothy J. Barder Eprogen, Inc., Darien, IL 60561, USA Scott J. Berger Life Sciences R&D, Waters Corporation, Milford, MA 01757, USA Nathan S. Buchanan Department of Chemistry, The University of Michigan, Ann Arbor, MI 48109, USA Kathleen Cho Department of Pathology, The University of Michigan, Ann Arbor, MI 48109, USA Steven A. Cohen Life Sciences R&D, Waters Corporation, Milford, MA 01757, USA Amy E. Daly Waters Corporation, Milford, MA 01757, USA Joe M. Davis Department of Chemistry and Biochemistry, Southern Illinois University at Carbondale, Carbondale, IL 62901-4409, USA

xvii

xviii

CONTRIBUTORS

Francesco Dondi Department of Chemistry, University of Ferrara, 1-44100 Ferrara, Italy Norman J. Dovichi Department of Chemistry, University of Washington, Seattle, WA 98195, USA Charles R. Evans Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA John C. Gebler Waters Corporation, Milford, MA 01757, USA Martin Gilar Waters Corporation, Milford, MA 01757, USA Steven Goodison Department of Pathology, University of Florida, Jacksonville, FL 32209, USA Attila Felinger Department of Analytical Chemistry, University of Pecs, Pecs, Hungary Melissa M. Harwood Department of Chemistry, University of Washington, Seattle, WA 98195, USA Ken Hosoya Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan Tohru Ikegami Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan Haleem J. Issaq Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA Megan Jones Department of Chemistry, University of Washington, Seattle, WA 98195, USA James W. Jorgenson Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Hiroshi Kimura Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan James R. Kraly Department of Chemistry, University of Washington, Seattle, WA 98195, USA Paweena Kreunin Department of Chemistry, The University of Michigan, Ann Arbor, MI 48109, USA

CONTRIBUTORS

xix

David M. Lubman Department of Surgery, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI 48109, USA Egidijus Machtejevas Institute of Inorganic Chemistry and Analytical Chemistry, Johannes Gutenberg University, Duesbergweg 10-14, 55099 Mainz, Germany Nicola Marchetti Department of Chemistry, University of Ferrara, 1-44100 Ferrara, Italy Fred R. Miller Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA Robert E. Murphy Kroungold Analytical, Inc., Encinitas, CA 92024, USA Petra Olivova Waters Corporation, Milford, MA 01757, USA Harald Pasch Deutsches Kunststoff-Institut (German Institute for Polymers), 64289 Darmstadt, Germany Maria C. Pietrogrande Department of Chemistry, University of Ferrara, 1-44100, Ferrara, Italy Frank Rittig BASF Aktiengesellschaft, Polymer Research, 67056 Ludwigshafen, Germany Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA Renee J. Soukup Department of Chemistry, Iowa State University, Ames, IA, USA Nobuo Tanaka Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan Klaus K. Unger Am Alten Berg 40, 64342 Seeheim, Germany Timothy D. Veenstra Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA Yanfei Wang Department of Chemistry, The University of Michigan, Ann Arbor, MI 48109, USA

xx

CONTRIBUTORS

Michael P. Washburn Stowers Institute for Medical Research, 1000 E. 50th St., Kansas City, MO 64110, USA Rong Wu Department of Pathology, The University of Michigan, Ann Arbor, Ml 48109, USA

1 INTRODUCTION Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA

Steven A. Cohen Life Sciences R&D, Waters Corporation, Milford, MA 01757, USA

Techniques commonly referred to as “Multidimensional Chromatography’’ have had a long and interesting history. One of the first examples of using two dimensions to develop higher resolution separations utilized paper chromatography with mobile phases applied at right angles in two separate development cycles. This was introduced by Martin and coworkers (Consden et al., 1944). The novelty of this technique was that the separation space was increased because a component zone was eluting through an area, not just through a one-dimensional line-like separation axis. The concept that a separation could be conducted dimensionally, that is, separations in a line (one dimension) and in an area (two dimensions), led to a number of innovations in developing separation techniques with an ever-increasing ability at resolving component zones. A great deal of thought regarding both the theoretical and experimental work in separation science has taken place since this first experiment in planar chromatography. If extractions are included in an analytical scheme, for example, extractions of cations, anions, and neutrals, higher dimensional separations are possible where the separation dimensions are greater than or equal to three. The dimensional count can be further increased through detectors that detect along a wavelength axis (e.g., a diode array detector for use in ultraviolet spectrometry) or a mass-to-charge ratio axis (as in mass spectrometry). These detectors can be coupled to

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright Ó 2008 John Wiley & Sons, Inc.

1

2

INTRODUCTION

other detectors, for example, when mass spectrometers are coupled together (the socalled MS/MS detector), which increases the dimensionality even higher. Innovations in separation science continued on this theme and provided one of the most powerful separation techniques used in biochemistry, where proteins are separated with isoelectric focusing (IEF) applied in one direction, and gel electrophoresis (GE) applied at a right angle to the first separation direction (O’Farrell, 1975; Celis and Bravo, 1984). In this case, proteins are first separated according to their isoelectric point, measured in pI units, and then according to their molecular weight by gel electrophoresis. The size separation step is usually aided by addition of a surfactant, most typically sodium dodecyl sulfate (SDS), and the gel material is a polyacrylamide formulation. A typical two-dimensional (2D) gel electropherogram of proteins is shown in Figure 1.1. The resolution of this technique is quite impressive; this result was stored in one of many databases containing thousands of protein electropherograms, which have been invaluable in molecular biology and medical research. One reason for the continued use of planar 2D gels is that it gives high resolution and permits a great deal of information to be extracted about proteins. Protein migration provides information on the molecular weight and isoelectric point of the individual components. In addition, proteins can be isolated from the gel, identified, and cataloged. However,

FIGURE 1.1 A 2D-gel electrophoresis map of colorectal epithelia cells proteins from the SWISS- 2DPage database (entry CATD_HUMAN, primary access number P07339) accessible from http://www.expasy.org/swiss-2dpage.

INTRODUCTION

3

this process is time consuming, often taking two or more days for separation and processing. The isolation of the component zones is not simple, and identification of the components can take significantly large amounts of time when the components are digested and further analyzed by liquid chromatography/mass spectrometry to obtain their peptide signatures used for protein identification. This time burden does not permit the planar techniques to be used as fast, sensitive biomarker discovery systems for routine investigations. Identification of trace-level zones is particularly problematic in planar systems. Much effort has been made over the past 15 years to replace planar techniques with modern column-based techniques. The advantages of columns are many including reproducibility, speed, selectivity, and ease of use among others. Another advantage of columns is that they are much easier, in almost all cases, to interface to detectors such as mass spectrometers. The column methods are much faster and are automated so that a much larger number of samples can be processed per unit time. An example of this technology, described in more detail in Chapter 10 by Lubman and coworkers, is shown in Figure 1.2, where the first dimension is from a chromatofocusing column, which gives separations in pI much like isoelectric focusing, only here the pI axis is in bands instead of continuous pI increments. The second dimension is by reversed-phase liquid chromatography (RPLC). This technology is most important in demonstrating the huge capabilities that column chromatography can bring to the separation arsenal. Although predictions regarding the huge peak capacity that would occur when coupling multiple dimensions were in place by 1984, as discussed in the theory chapter (Chapter 2),

FIGURE 1.2 2D liquid protein expression map of the HCT-116 human colon adenocarcinoma cell line. The x-axis is in pI units from 4.0 to 7.0 in 0.2 increments. The y-axis is percent B of the RP-HPLC gradient. The gray scale of the bands represents the relative intensity of each band by UV detection at 214 nm. From Yan et al. (2003) with permission of the American Chemical Society. (See color plate.)

4

INTRODUCTION

FIGURE 1.3 Three-dimensional representation of a tryptic digest of ovalbumin. The threedimensional separation consists of size-exclusion chromatography (first dimension), reversedphase LC (second dimension), and capillary electrophoresis (third dimension). From Moore and Jorgenson, (1995) with permission of the American Chemical Society.

many investigations in polymer separations had shown the power of off-line multidimensional separations. It was the comprehensive coupling of column techniques by Erni and Frei (1978) and Bushey and Jorgenson (1990) that lead the way for the continued development of automated column-based comprehensive multidimensional methods. We use the term comprehensive to denote the repeated application of sampling the kth dimensional column effluent by the (k þ 1)th column in narrow volume elements. In this manner, the procedure resembles a planar separation but run exclusively with column methods. The versatility of columns and the understanding of how to interface columns in a multidimensional instrument were evident in obtaining a three-dimensional separation implemented approximately 10 years ago, as shown in Figure 1.3. Both life science and industrial applications benefit from the combination of multidimensional chromatography and advanced detector technology. Both of these areas of multidimensional liquid chromatography (MDLC) are covered in this book.

1.1 PREVIOUS LITERATURE WHICH COVERS MDLC Two books of interest have covered some of the ground for multidimensional liquid chromatography in various forms. These include the book Multidimensional Chromatography, edited by Cortes (1990) and Multidimensional Chromatography by Mondello et al. (2002). Cortes’ book contains a collection of chapters that discuss many of the aspects of the modern MDLC system. Specifically, Giddings’ chapter covers many of the theoretical underpinnings for the multidimensional techniques including multicolumn

HOW THIS BOOK IS ORGANIZED

5

and planar chromatography modes of operation. The role of peak capacity is specifically discussed and this has been one of the efficacy metrics to judge many of the multidimensional techniques. These developments will be elaborated on in the theory section of this book as new results have been obtained since Giddings’ chapter in 1990. In addition, the chapter on multidimensional high performance liquid chromatography by Cortes and Rothman has been a useful reference for early applications where coupled-column systems were utilized. These were not exclusively comprehensive but employed heart-cutting techniques that solved chemical problems. In the second book, Multidimensional Chromatography by Modello, Lewis, and Bartle, a number of contributions are made that cover MDLC. These include the chapter by Kazakevich and LoBrutto on industrial and polymer applications that use liquid chromatography (LC), gas chromatography (GC), and supercritical fluid chromatography (SFC) techniques. The introduction by Bartle is general to MDC and covers the topics of peak capacity, statistical overlap, resolution, and column compatibility. Multidimensional and electrodriven separations are described by Degen and Remcho. However, the emphasis in Mondello et al.’s book is undoubtedly on multidimensional gas chromatography. In the present book, we exclusively deal with liquid-phase techniques, mostly liquid chromatography with a brief focus on capillary electrophoresis (CE). Other reviews of multidimensional separations have been published. These include a book on polymer characterization by hyphenated and multidimensional techniques (Provder et al., 1995), a review on polymer analysis by 2DLC (van der Horst and Schoenmakers, 2003), and two reviews on two-dimensional techniques in peptide and protein separations (Issaq et al., 2005; Stroink et al., 2005). Reviews on multidimensional separations in biomedical and pharmaceutical analysis (Dixon et al. 2006) and multidimensional column selectivity (Jandera, 2006) were recently published. Suggested nomenclature and conventions for comprehensive multidimensional chromatography were published in 2003 (Schoenmakers et al., 2003), and a book chapter in the Advances in Chromatography series on MDLC was published in 2006 (Shalliker and Gray 2006).

1.2 HOW THIS BOOK IS ORGANIZED This book is organized into five sections: (1) Theory, (2) Columns, Instrumentation, and Methods, (3) Life Science Applications, (4) Multidimensional Separations Using Capillary Electrophoresis, and (5) Industrial Applications. The first section covers theoretical topics including a theory overview chapter (Chapter 2), which deals with peak capacity, resolution, sampling, peak overlap, and other issues that have evolved the present level of understanding of multidimensional separation science. Two issues, however, are presented in more detail, and these are the effects of correlation on peak capacity (Chapter 3) and the use of sophisticated Fourier analysis methods for component estimation (Chapter 4). Chapter 11 also discusses a new approach to evaluating correlation and peak capacity.

6

INTRODUCTION

The columns, instrumentation, and methods chapters, Chapters 5–8, include presenting the necessary background information for the reader to be brought to the modern literature on the instrumentation used in 2D methods. Specifically, Chapter 5 gives a general overview, Chapter 6 discusses the method development needed besides the usual 1D optimizations for chromatographic operation, Chapter 7 discusses monolithic columns used in 2DLC, and Chapter 8 discusses ultrahigh pressure multidimensional LC. Perhaps the biggest increase in the application and development of the MDLC technique since Cortes’s book is in life sciences, which accounts for approximately half of this book. One reason for this may be due to the high level of interest in studying the human proteome (proteomics). Proteomics is such a demanding application that the separating power needed to resolve even the normal proteins in the body is so demanding that maximum separation power is needed to provide this capability. Many aspects of separations in proteomics are discussed in Chapters 9–13, 15 and 16. Chapter 14 discusses enantiomeric compound separations by MDLC. Industrial applications of MDLC are discussed in Chapters 17 and 18. These chapters embody the types of applications used in polymer and surfactant analysis that have become mainstream in the MDLC literature. Most life science applications are currently centered on proteomics. However, other systems biology approaches are also likely to need the resolving power of MDLC. These approaches include metabolomics, the small-molecule metabolite profiles of cellular processes and glycomics, the study of oligosaccharides or chains of sugars. The developments that are given in the life sciences applications in this book set the stage for studying these other areas. In most cases, the mixtures of molecules in metabolomics and glycomics are too complex to study by any single chromatographic column methods. MDLC is well suited for these types of studies, especially when the detection is by mass spectrometry or some multidimensional version of mass spectrometry such as MS–MS. The concepts in multidimensional separations are also well suited for other biological system separations such as cell separations. Cell separations are generally done by techniques such as flow cytometry (cell sorting) magnetic encoding, sedimentation, and isopycnic (density gradient) separation. However, techniques such as field-flow fractionation (FFF) and dielectrophoresis (separation by nonuniform AC electric fields) may augment the main techniques in the context of the second dimension. Having learned a number of experimental and theoretical aspects of multidimensional techniques from 2DLC, forming new, more advanced systems that separate cells may be expedited through the application of present MDLC research.

REFERENCES Bushey, M.M., Jorgenson, J.W. (1990). Automated instrumentation for comprehensive two-dimensional high-performance liquid chromatography of proteins. Anal. Chem. 62, 161–167.

REFERENCES

7

Celis, J.E., Bravo, R. (1984). Two-Dimensional Gel Electrophoresis of Proteins. Academic Press, New York. Consden, R., Gordon, A.H., Martin, A.J.P. (1944). Qualitative analysis of proteins: a partition chromatographic method using paper. Biochemical J 38, 224–232. Cortes, H.J. (1990). Multidimensional Chromatography, Techniques and Applications, Chromatographic Series Vol. 50. Marcel Dekker Publishing, New York. Dixon, S.P., Pitfield, I.D., Perrett, D. (2006). Comprehensive multi-dimensional liquid chromatographic separation in biomedical and pharmaceutical analysis: a review. Biomed. Chromatogr. 20, 508–529. Erni, F., Frei, R.W. (1978). Two-dimensional column liquid chromatographic technique for resolution of complex mixtures. J. Chromatogr. 149, 561–569. Issaq, H.J., Chan, K.C., Janini, G.M., Conrads, T.P., Veenstra, T.D. (2005). Multidimensional separations of peptides for effective proteomics analysis. J. Chromatogr. B 817, 35–47. Jandera, P. (2006). Review: column selectivity or two-dimensional liquid chromatography. J. Sep. Sci. 29, 1763–1783. Mondello, L., Lewis, A.C., Bartle, K.D. (2002). Multidimensional Chromatography. John Wiley & Sons, Inc., New York. Moore, A.W., Jorgenson, J.W. (1995). Comprehensive three-dimensional separation of peptides using size exclusion chromatography/reversed-phase liquid chromatography/optically gated capillary zone electrophoresis. Anal. Chem. 67, 3456–3463. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250, 4007–4021. Provder, T., Barth, H.G., Urban, M.W., editors (1995). Chromatographic Characterization of Polymers: Hyphenated and Multidimensional Techniques, Advance in Chemistry Series 247. American Chemical Society,Washington, DC. Shalliker, R.A., Gray, M.J. (2006). In: Grushka, E., Grinberg, N., editors. Concepts and Practice of Multidimensional High-Performance Liquid Chromatography Advances in Chromatography, Vol. 44. Taylor and Francis Group, New York. Schoenmakers, P.J., Marriott, P., Beens, J. (2003). Nomenclature and Conventions in Comprehensive Multidimensional Chromatography. LCGC Europe, June 2003, 1–4. Stroink, T., Ortiz, N.C., Bult, A., Lingeman, H., de Jong, G., Underberg, W.J.M. (2005). On-line multidimensional liquid chromatography and capillary electrophoresis for peptides and proteins. J. Chromatogr. B 817, 49–66. Van der Horst, A., Schoenmakers, P.J. (2003). Comprehensive two-dimensional liquid chromatography of polymers. J. Chromatogr. A 1000, 693–709. Yan, F., Subramanian, B., Nakeff, A., Barder, T.J., Parus, S.J., Lubman, D.M. (2003). A comparison of drug-treated and untreated HCT-116 human colon adenocarcinoma cells using a 2-D liquid separation mapping method based on chromatofocusing pI fractionation. Anal. Chem. 75, 2299–2308.

PART I THEORY

2 ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA

2.1 INTRODUCTION As discussed in Chapter 1 of this book, multidimensional separations have their origin in some of the earliest works in analytical bioseparations where the separations were carried out in the planar mode of operation using paper-like media (Consden et al., 1944). One of the main innovations in these studies was the realization that using more than one simultaneous separation mechanism had enormous potential for complex separations, especially those of biological origin. Planar techniques evolved to become the mainstay of protein analysis where proteins are separated in the first dimension by isoelectric focusing according to pI and in the second dimension by gel electrophoresis, which separates on the basis of molecular weight (O’Farrell, 1975). The theoretical work that exploited the advantages of the multidimensional separation format appears to have been developed much later than the original experimental work. One of the earliest studies was conducted by Connors (1974), who assumed that the distribution of spots on a two-dimensional thin-layer chromatography (2DTLC) plate could be modeled using a Poisson distribution of data on each retention axis. He then constructed equations that related the number of chromatographic systems needed to resolve a specific number of compounds. One Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

11

12

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

5

4

3 m 2

1

0

50

100

150

200

T

FIGURE 2.1 Plot of the number of chromatographic systems, m, required to resolve T compounds under a number of specific chromatographic conditions as discussed in Connors (1974). Figure reprinted with permission from the American Chemical Society.

of the results of these equations is shown in Fig. 2.1 under a number of assumptions explained in the original paper. This research implied that the challenge was to find separation systems that are uncorrelated. The correlation problem haunts multidimensional separation systems and ultimately determines their success or failure. Connors’ research was limited to different solvent systems used in each dimension of 2DTLC; however, the concepts are similar to finding uncorrelated chromatographic column systems that can be used in multidimensional chromatography (MDC), so that the separation mechanisms are different (essentially uncorrelated) among the various columns. When columns are uncorrelated we will refer to this phenomenon as an orthogonal separation. The quantification of orthogonality and correlation among the dimensions are discussed in detail here and also in the Chapters 3 and 12. The use of the Poisson distribution for this purpose predates the statistical overlap theory of Davis and Giddings (1983), which also utilized this approach, by 9 years. Connors’ work seems to be largely forgotten because it is based on 2DTLC that doesn’t have the resolving power (i.e., efficiency or the number of theoretical plates) needed for complex bioseparations. However, Martin et al. (1986) offered a more modern and rigorous theoretical approach to this problem that was further clarified recently (Davis and Blumberg, 2005) with computer simulation techniques. Clearly, the concept and mathematical approach used by Connors were established ahead of its time. Many examples of planar systems used for two-dimensional liquid chromatography (2DLC) abound in the literature. In the quest for higher performance

PEAK CAPACITY

13

separation systems, a number of investigations, specifically addressing the performance of each dimension in the context of MDC were conducted. In this regard, theoretical studies by Guiochon et al. (1983a,b) and Giddings (1984, 1987, 1990) accelerated the need to examine column-based systems to increase the performance and attempt to resolve zones at the single component level. Following these studies, the experimental realization of a comprehensive MDC column system (Bushey and Jorgenson, 1990) showed results for protein separations that demonstrated the importance of MDC, specifically 2DLC. Although relatively unknown, the instrumentation for 2DLC was conceived and implemented by Erni and Frei (1978). They reported the valve configuration presently used in most comprehensive 2DLC systems. However, they automated neither the valve nor the data conversion process to obtain a contour map or 2D peak display. They used a gel permeation chromatography (GPC) column in the first dimension and a reversedphase liquid chromatography (RPLC) column in the second dimension and studied complex plant extracts. The papers by Guiochon and Giddings emphasized the study of multidimensional resolution and peak capacity. It is these insights that have largely fueled serious attempts to exploit the potential rewards of coupling columns together. In addition, these papers provided the basis for further theory-based investigations into the mechanics of MDC that have largely superseded the original papers. In this chapter, many of the results of these older studies will be discussed along with research that provided more detailed insights into factors that control the potential of MDC. 2.2 PEAK CAPACITY One of the most common reasons for using MDC techniques is to increase the peak capacity. Peak capacity, as defined by Giddings (1967, 1991), is the number of peaks that could be put side by side at some stated resolution between two retention times or at a start and finish length, in the case of a planar separation system. The peak capacity concept was originally derived as a metric for studying the possible number of peaks in a separation system when peaks are ordered; that is, when peak retention times are some multiple so that peak spacing is a constant. In fact, peak capacity was never advocated by Giddings as something that was measurable; rather, it was a concept based on how many peaks would fit into a separation space on the basis of the width of a peak. The peak width was related to the number of plates and a fixed resolution. It is this ordering that gave the concept a theoretical bent as real separations are not ordered; the retention times in most separation techniques appear almost random across a range of separation time. The mathematical definition of peak capacity, nc, for an isocratic separation is given as (Grushka, 1970) pﬃﬃﬃﬃ N tf nc ¼ 1þ ln ð2:1Þ t1 4 where N is the number of plates and tf and t1 are the times of the final peak, and the void peak, respectively. The peak width is not constant in isocratic separations, the same is

14

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

true of isothermal separations in gas chromatography. Under gradient elution conditions (and temperature programmed gas chromatography), the peak width is assumed to be constant (Horvath and Lipsky, 1967), and the peak capacity can be approximated for a planar system as nc ¼

L 4sRs

ð2:2Þ

where s is the standard deviation of a Gaussian zone (in length units) and Rs is the stated resolution, that is, the resolution level that a peak will be recognized as being resolved from its neighbor. For a time-based column system undergoing gradient elution where peak width is constant (Horvath and Lipsky, 1967; Grushka, 1970), the definition of peak capacity is pﬃﬃﬃﬃ N tf nc ¼ 1 ð2:3Þ 4 t1 Constant peak width is recognized to give more peak capacity in LC and the same is true in 2DLC. The use of this equation is interesting in that the number of theoretical plates is not constant across a chromatogram of constant peak width. This is easily shown by recalling the definition of the number of plates as N¼

t2 s2

ð2:4Þ

where the denominator is a constant, that is, constant peak variance or width. A better and simpler equation for peak capacity can be derived by dividing the retention time range by some peak width, which is defined independently so that nc ¼ ðtf t1 Þ=W

ð2:5Þ

where W is the peak width. This definition is also useful for discussing the so-called “sample peak capacity’’ (Dolan et al., 1999) when one crosses over to the peak capacity of real samples with regard to the number of peaks that actually appear on the chromatogram, irrespective of whether single- or multidimensional techniques are utilized. The point cannot be overemphasized, however, that the calculated peak capacity will always overestimate the number of peaks that appear on an experimental chromatogram due to the apparent random placement of peaks. Guiochon et al. (1983a,b) gave detailed calculations of 2D and 3D peak capacities for zones in a unique chromatographic column-like device. They clearly recognized the enormous peak capacity advantage in using the multidimensional approach and how this could help resolve peak fusion. Giddings (1984) emphasized that the peak capacity for a multidimensional chromatographic system could be approximated by the simple product of individual peak capacities in each dimension: nT ¼ n 1 n 2 n 3

ð2:6Þ

where nT is the total peak capacity of the separation system. Interestingly, this point was emphasized in a review by Freeman (1981) where he attributes the origin of this

PEAK CAPACITY

z

15

nz adjacent Gaussian profiles

y

ny adjacent Gaussian profiles

FIGURE 2.2 The peak capacity of a 2D system (Giddings, 1987) represented by the number of boxes is approximately equal to the product of the peak capacities nx and ny generated along two individual axes, as represented by the number of adjacent Gaussian profiles. Reprinted with permission from J. High Resolut. Chromatogr., Alfred Huethig Publishers.

“product rule’’ to a popular separation science textbook (Karger et al., 1973). However, the “product rule’’ seems to be largely missed in the MDC literature prior to Giddings’ work in 1984. Many researchers in the field recognize Fig. 2.2 as a radical but effective depiction of this concept. The numbers that resulted from multiplying individual column peak capacities showed the tremendous promise for this style of separation. It also fueled speculation in bringing column chromatography methods to bear on the multidimensional system instead of planar separation systems and was a large driving force in the construction of comprehensive 2DLC instrumentation that fully utilized column chromatography (Bushey and Jorgenson, 1990). As discussed below, if the two media used for separation in 2DLC are correlated with respect to the retention mechanism, the peak capacity will be lower than expected from the product approximation. The dependence of the peak capacity product on the correlation between retention mechanisms is covered in Chapter 3. Furthermore, as pointed out by Carr and coworkers (Stoll et al., 2006), if the second dimension sampling of the first dimension is undersampled, the potential peak capacity will not be able to be utilized. This is discussed in the theory of zone sampling section below. Further implications of peak capacity limitations due to sampling have been recently given by Tanaka and coworkers (Horie et al., 2007). There has been an attempt to measure the peak capacity in 1DLC and 2DLC by assigning a range of useful retention time between the unretained marker that elutes at t1 and some stated value of the retention factor k0 leading to a zone at tf and plugging in a value for the peak width W. This number is useful but will never be equal to the number

16

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

of experimentally accessible peaks, because the peaks are not ordered. This was the whole basis for the statistical peak overlap theory of Davis and Giddings (1983); peaks were found to be randomly placed in a complex chromatogram. However, including experimental parameters such as the gradient time, as demonstrated by Carr and coworkers (Stoll et al., 2006), in maximizing the peak capacity in 2DLC with high temperature gradient elution chromatography, allows the functional dependencies to be understood and is very useful for technique optimization. The peak capacity value does not imply the time it takes to produce these peaks. As is often the case in the chromatographic theory, one must also discuss the rate of peak capacity production as a function of time for understanding the technique efficacy, and this line of development was recognized early on by Grushka (1984). It has been noted that peak capacities of 900 in 25 min are achievable by 2DLC (Stoll et al., 2006); that is roughly one peak every 2 s. A recent detailed look at the peak capacity production rate was undertaken by Carr and coworkers (Wang et al., 2006) in the context of gradient elution chromatography for use in 2DLC. The use of the so-called “Poppe plot’’ was extended for gradient elution by these authors and this may lead to better optimization schemes, especially when gradient elution is utilized. An example of a Poppe plot is shown in Chapter 6. Since gradient elution brings such higher performance in LC, it is safe to say that this approach will become a standard operating procedure in future 2DLC experiments, especially when fast second dimension elution is needed. The concept of peak capacity is rather universal in instrumental analytical chemistry. For example, one can resolve components in time as in column chromatography or space, similar to the planar separation systems; however, the concept transcends chromatography. Mass spectrometry, for example, a powerful detection method, which is often the detector of choice for complex samples after separation by chromatography, is a separation system itself. Mass spectrometry can separate samples in time when the mass filter is scanned, for example, when the mass-to-charge ratio is scanned in a quadrupole detector. The sample can also be separated in time with a time-of-flight (TOF) mass detector so that the arrival time is related to the mass-to-charge ratio. It is not so surprising that we can talk about the peak capacity of the detector itself, as if the detector were a column or planar separator. Russell and coworkers (2002) have examinedthepeakcapacityofpeptideseparationsusingionmobility–massspectrometry (IM–MS). They have estimated that the peak capacity of IM–MS is approximately 2600 for peptides. If this were orthogonal and peak capacities multiplicative between dimensions, this would suggest that the combination of 2DLC (with n in the hundreds) and IM–MS would result in a total peak capacity somewhat below one million for peptide separations. Merenbloom et al. (2007) examined the coupling of IM–MS in two dimensions, which they call two-dimensional IMS (2D-IMS). Peak capacities ranging from480–1360are quoted for this technique that are farless than those quoted byIM–MS in a single dimension, as given above. Frahm et al. (2006) estimated the peak capacity of Fourier Transform–Ion Cyclotron Resonance (FT–ICR) and TOF mass spectrometry, specifically for proteomics applications. The concept of peak capacity is applicable to all mass spectrometry applications independent of chromatography. However, mass spectrometry may be orthogonal to the chromatographic dimensions and this offers a chance to get the multiplicative advantage of MDC peak capacity and mass spectrometry peak

RESOLUTION

17

capacity. Other works that have mentioned the peak capacity of mass spectrometry in the context of LC–MS and 2DLC–MS but have not rigorously measured it include those of Lewis et al. (1997), Shen et al. (2001), and Valentine et al. (2001). Peak capacity can also be determined for the diode array detector (DAD) often used as a multiwavelength UV detector for liquid chromatography; these detectors allow fused component zones to be resolved through chemometric techniques and do not have the large peak capacity that mass spectrometry brings. However, even doubling the peak capacity through the use of a DAD detector can provide an advantage when coupled with chemometric techniques, discussed below.

2.3 RESOLUTION Giddings (1990) presented a derivation applicable to both the planar format such as TLC that is distance-based and the comprehensive multidimensional separations that are time-based. The resolution was shown to be equal to the Euclidean norm of zone resolution components. This can be summarized as qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ð2:7Þ Rs ¼ Rs21 þ Rs22 þ where the subscripted resolution is that of the corresponding resolution of zone pairs in each dimension. This one-dimensional resolution for time-based systems is Rsx ¼ Dtx =4sx

ð2:8Þ

where Rsx is the resolution in dimension x, Dtx is the time difference between two peaks in dimension x, and s is the zone standard deviation in dimension x. It is assumed here that all peaks are Gaussian and the standard deviations of two adjacent peaks in a single dimension are approximately equal. In this formulation, the resolution is assumed to be independent of angle between zones. This assumption was checked by Schure (1997) as the equation for resolution as a function of angle between the two zones was previously available (Shi and Davis, 1993). This analysis shows that for a number of cases, the error is approximately less than 10% when using the Euclidean norm of resolutions, as shown in Fig. 2.3. Another way to measure resolution from experimental 2DLC data is to use a computer method to calculate the first and second moments of the zones. For highly fused zones this must be done with a parameter estimation algorithm based on some minimization criteria; usually, some form of least-squares method can be utilized to fit the zone shapes with a zone model. However, another method used to measure resolution, which is less accurate but less demanding, is to take the peak and valley measurements f and g along the line of zone intersection, shown schematically in Fig. 2.4, so as to calculate P ¼ f/g. Then Equation 2.9 is used to get the resolution rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 1 1P ð2:9Þ Rs ¼ ln 2 2

18

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

FIGURE 2.3 The error inherent in using the Euclidean norm to calculate resolution as a function of the angle made between the two zones. The conditions used for curves A, B, C, and D are described in the original paper (Schure, 1997). Reprinted by permission from J. Micro. Sep.

f g hy

3

t2,y

y

2 t1,y 1

t2, y

t1, x

0 0.00

0.50

1.20

x

1.80

2.40

3.00

FIGURE 2.4 Determining resolution based on a peak-valley measurement for twodimensional chromatography. The f and g values are measured and used to calculate P ¼ f/g giving the resolution through Equation 2.9. Reprinted with permission from Murphy et al. (1998) by courtesy of the American Chemical Society.

ORTHOGONALITY

19

This approach is reviewed in full detail for 1D chromatography by Schoenmakers (1986) and extended by Schure (1999) for dimensions of two and larger. The peak amplitudes f and g are determined either through programmed search or through inspection in a spreadsheet program. Both quantities are corrected for a finite baseline amplitude. It is interesting that this approach works for 1D, 2D, 3D and so on. — it is dimensionally invariant. Since resolution is rarely needed with great accuracy, this gives a convenient method for determining resolution in 2DLC. This approach was extended for non-Gaussian peaks (Peters et al., 2007).

2.4 ORTHOGONALITY The challenge in effectively utilizing the multidimensional peak capacity is to find different types of columns that can uniformly spread the component peaks across the separation space. This challenge means that the separation mechanism of the two columns should be as dissimilar as possible or uncorrelated. A number of experimental studies have been undertaken to examine this effect (Liu et al., 1995; Slonecker et al., 1996; Gray et al., 2002). Chapter 3 examines the effect of correlation on peak capacity in detail using simulation techniques. The mathematical development of estimating correlation is taken from Liu’s work and is a simple treatment. In 2DLC, we can form a retention matrix composed of the scaled k0 values for each zone so that each dimension (i.e., each unique column) has a k0 row vector for the compounds that were separated on it. In two dimensions, the retention representation becomes a matrix, k0 , so that k0 takes the form for a fourcomponent mixture: 0

k 11 0 k 21

0

k 12 0 k 22

0

k 13 0 k 23

0

k 14 0 k 24

ð2:10Þ

where the first index i is the column dimension number and the second index j is the component number. This matrix is scaled so that 0

k ij ¼ ðkij mi Þ=si

ð2:11Þ

where kij is the original (unscaled) retention measurement, which is equal to (tr/t0) 1. Note that the retention time tr and the void time t0 have their usual meanings and mi is the mean of the ki row vector and si is the standard deviation of the ki row vector values. Using standard statistical notation (Malinowski, 2003) the correlation matrix C is given by 1 0T 0 k k ð2:12Þ C¼ N1 where N is the number of components in each dimension and the T refers to the matrix transpose operator. The correlation matrix C is an N by N matrix with unity value diagonal elements and is symmetrical (Cij ¼ Cji), so only the upper or lower triangular

20

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

A Np

N2

Effective area

g b a¢

C

a Nt

FIGURE 2.5 Effective nonorthogonal 2D retention space from Liu (1995). Reprinted with permission from the American Chemical Society.

terms need to be discussed. The diagonal elements of this matrix are equal to 1 because these values are completely correlated with themselves. Experimental values of the correlation matrix (Liu et al., 1995), which were derived from gas chromatographic data, demonstrated that, at least for gas chromatography, quite dissimilar columns must be used to minimize correlation. Since retention in gas chromatography is strongly affected by the solute vapor pressure, this makes finding dissimilar columns a very important part of the method development. Similar concerns exist in MDLC, but the retention mechanisms tend to vary more among different stationary phases than in MDGC. Liu also discusses how to geometrically determine the peak spreading angle for the two-dimensional chromatogram shown in Fig. 2.5. Using the derivation in Liu’s paper, and referring to Fig. 2.5, the effective or “practical’’ peak capacity, Np, is shown to be equal to the product of peak capacities per each dimension (which gives an area) minus the areas A and C. This treatment is practical for many separation systems where there is correlation in the retention mechanisms. However, as pointed out by Gilar et al. (2005), many 2DLC systems don’t have clear demarcated regions where all of the zones occur. For these cases and as a general method, Gilar proposed to use a binning method to identify the effective area utilized over a nonuniform separation space for estimating the orthogonality and consequently the potential peak capacity for peptides in a complex proteomics characterization. This approach has many interesting applications and may see increased use in the future as a separation diagnostics. Another approach to examine orthogonality is to use information theory (IT). Slonecker et al. (1996) used a specific set of target analytes and single column separations on different types of columns to see which combinations lead to minimum correlation and maximal orthogonality. These methods were employed in a farreaching study where techniques like RPLC, supercritical fluid chromatography (SFC), gas–liquid chromatography (GLC), and micellar electrokinetic chromatography (MEKC) with a host of different column materials per technique were examined.

TWO-DIMENSIONAL THEORY OF PEAK OVERLAP

21

The idea here was to examine which pair of techniques and individual columns could lead to the best separations in 2DLC. This is achievable by using 1D separations and then comparing how the retention of each component varies across the separation space. Another innovation here was the use of IT-derived metrics such as information entropy, informational similarity, and the synentropy. As stated in this paper, “The informational similarity of 2D chromatographic systems, H(k,l), is a measure of global solute crowding or dimensional saturation.’’ In addition, this study quantitates the synentropy whereby “a synentropy of 0% describes a 2D chromatographic system whose dimensional retention mechanisms are very different. A synentropy of 100% describes a 2D chromatographic system whose dimensional retention mechanisms are identical.’’ This study showed that similar mechanisms of separation, such as those derived primarily from hydrophobicity, resulted in high values of synentropy. One of the most interesting findings of this study is that the combination of MEKC using an SDS retentive phase and an SFC system with a C1 phase showed an informational similarity of 0 and the 2D chromatogram showed a distinct lack of grouping for the solutes selected. Another study of this type was recently published by Gray et al. (2002) where the same IT techniques were used in a study of RPLC in the first dimension and a carbonaceous phase in the second dimension. This study also incorporated the measures of correlation and spreading angle from Liu’s study. No matter how sophisticated the individual columns are, the columns will be useless for separation in MDLC if their retention mechanisms have a high degree of correlation. Note that some aspects of using IT to describe multidimensional chromatographic separations were shown many years ago by Huber et al. (1979).

2.5 TWO-DIMENSIONAL THEORY OF PEAK OVERLAP The statistical theory of peak overlap (Davis and Giddings, 1983) is a great advance in understanding the true resolving power of chromatography. The theory is simple yet powerful and predicts that the probability of finding a truly resolved single component peak was approximately 13–37% for a complex chromatogram at various levels of saturation. It was well recognized that improvements in this probability of finding single peaks with single component purity would require huge increases in peak capacity. Davis and coworkers (1991, 1993) examined the ramifications of random zones placed in two-dimensional separation spaces. This work discovered that the peak capacity was not as efficiently utilized in two dimensions as opposed to onedimensional separations. Davis (1991) said ‘‘Although more than 10 times the needed peak capacity is available, approximately one-third of the zones are lost in overlap.’’

Davis goes on to say that ‘‘The loss is comparable to that found in 1D gas chromatography of complex mixtures, in which only 2 times or so peak capacity (as defined for maxima) is available than is

22

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

needed. One really does lose resolving power, per unit peak capacity, when one moves from one dimension to two dimensions.’’

Certainly two-dimensional techniques have far greater peak capacity than onedimensional techniques. However, the two-dimensional techniques don’t utilize the separation space as efficiently as one-dimensional techniques do. These theories and simulations utilized circles as the basis function for a two-dimensional zone. This was later relaxed to an elliptical zone shape for a more realistic zone shape (Davis, 2005) with better understanding of the surrounding boundary effects. In addition, Oros and Davis (1992) showed how to use the two-dimensional statistical theory of spot overlap to estimate the number of component zones in a complex two-dimensional chromatogram. Davis (1993) extended the statistical overlap theory to generalized n-dimensional separations with the consistent result that the separations get much better, but as dimensionality increases the efficiency of using that separation space decreases. For n-dimensional separations, Davis says ‘‘The theory shows that the maximum number of spots per unit capacity and the maximum number of any kind of multiplet per unit capacity both decrease geometrically with increasing n.’’

These are most important realizations that will guide the evolution of multiple dimension chromatographic systems and detectors for years to come. The exact quantitative nature of specific predictions is difficult because the implementation details of dimensions higher than 2DLC are largely unknown and may introduce chemical and physical constraints. Liu and Davis (2006) have recently extended the statistical overlap theory in two dimensions to highly saturated separations where more severe overlap is found. This paper also lists most of the papers that have been written on the statistical theory of multidimensional separations. Other methods based on signal processing algorithms can be used to estimate the number of component zones in 2D complex chromatograms. These are typically based on autocovariance functions and other signal processing algorithms associated with Fourier methods that are applied to experimental data. A good primer on these types of methods in chromatography are discussed by Felinger (1998). Chapter 4 by Dondi, Pietrogrande, Marchetti, and Felinger elaborates on this approach, which has been previously shown to be quite valuable in proteomics applications (Pietrogrande, 2005, 2006). The mathematical and separation mechanics of this method have been previously discussed in detail (Marchetti et al., 2004). These approaches, which originated in the Fourier-space description of complex chromatograms, were used to study the statistical overlap of peaks in one dimension. These techniques can locate order within peaks (in n-dimensional chromatography) and are additionally useful because one can also study the signal and noise properties of the chromatogram as these techniques are firmly based on signal processing. It is expected that these methods may benefit in the future from additional signal processing approaches that utilize nonlinear operators, such as wavelet transforms (Percival and Walden, 2006).

DIMENSIONALITY, PEAK ORDERING, AND CLUSTERING

23

These techniques offer even more interesting noise-filtering properties as they are capable of localizing time- and frequency-varying noise and signal components.

2.6 DIMENSIONALITY, PEAK ORDERING, AND CLUSTERING Giddings (1995) suggested that there was another way to look at what was needed for complex molecule separations and suggested the idea of sample dimensionality. Essentially this metric defines “the number of independent variables that must be specified to identify the components of the sample.’’ The concept is very useful for industrial polymers and some biopolymers. If we take a molecule that has distributions in two separate parts, for example, an alcohol ethoxylate surfactant that has alkyl chain length and polyethylene oxide length distributions, then there are two characteristics that may be resolved chromatographically. In this case, the sample dimensionality, s, is equal to two. This example is well-illustrated in Chapter 18, where the two groups comprising alcohol ethoxylates are in fact well resolved, each in its own chromatographic dimension. Giddings further discussed cases in which order in the chromatographic dimension can allow resolution where disorder clearly creates more stringent requirements for higher peak capacity and higher efficiency. Although polymeric samples may be advantageously separated this way within the confines of a small number of dimensions, extremely complex samples, such as protein tryptic digests that contain peptides, may have too large an s number or may not clearly express higher s in a chemical sense. Hence, the ordering will not occur because the chromatographic response cannot be isolated at the amino acid level. For peptides this might be possible if amino acid sequences were well defined such as 10 cysteines followed by 20 glycines, followed by 6 prolines, and so on. However, the amino acid sequences found in peptides use an almost non-repeating sequence of the amino acids so that the sample dimensionality here is 20 (for the 20 standard amino acids found in peptides), but the resolution of each type may be impossible because of the nonrepeating sequence. Standard chromatographic systems such as RPLC or ion-exchange chromatography would not see the individual amino acid functionality, but would rather discriminate over an average conformational free energy. Even affinity systems of higher dimensionality would be overwhelmed for this style of separation. When retention ordering can be established, the theoretical peak capacity could be effectively utilized in a multidimensional separation system in a far more efficient manner. However, one is reminded that with the exception of synthetic polymers and a few other special cases of small molecules, real samples have almost random retention time distributions. It is rare when the free energy, enthalpy, and entropy of interaction are determined in LC for molecules utilized in retention mechanism studies. However, the retention energetics have been determined in GC studies by Davis et al. (2000) who found that many complex samples will exhibit Poisson distributions of retention times due to a Poisson distribution in enthalpy and a compensating distribution in entropy.

24

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

2.7 THEORY OF ZONE SAMPLING Unlike the continuous zone development mechanism utilized in a planar separation experiment, comprehensive MDLC is a sequential operation in which finite volumes of eluant are injected into the next dimension column. Because of this finite volume aspect, the mechanism and consequences of sampling eluant from one column with subsequent injection into the next column must be understood. Undersampling would lead to a loss in two-dimensional resolution and oversampling would lead to excessively long run times as the second dimension column would be used in a very inefficient way. The first attempt at understanding the sampling relationship was made by Murphy et al. (1998). Their study examined how segmenting the continuous elution from the first dimension column would affect the resolution of the resulting two-dimensional zone shape. The main assumption made in their study was that there would be complete mixing in the sample loop as each first dimension eluent volume was collected in the sample loop. The continuous nature of the first dimension is truncated into finite volume elements as shown in Fig. 2.6. This truncation operation can be viewed as a mixing or “deseparation’’ process. The simple way to deal with this truncation mathematically is to calculate the width or standard deviation of the truncated zone as a function of the number of segments or “samples’’ across the peak. The clearest way to do this is to use the definition of the standard deviation as the square root of the second moment of the distribution. The explicit definition of the second moment is given in Equation 2.13 under the square root sign and is the integral across the peak of the truncated concentration times the difference squared between

1.00

Concentration

0.75

1 3

0.50

10 0.25 30 0.00 –0.50

–0.25

0.00 0.25 Nondimensional time

0.50

FIGURE 2.6 Segmenting the concentration signal over a Gaussian zone with 1, 3, 10, and 20 samples taken in phase to the main peak. The area under these segmented curves is equal. This figure is taken from Murphy (1998). Reprinted with permission from the American Chemical Society.

THEORY OF ZONE SAMPLING

25

time and the mean time.

vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u þ¥ ð pﬃﬃﬃﬃﬃﬃ u u t ðttÞ2 CS ðtÞdt sS ¼ M2 ¼

ð2:13Þ

¥

where CS is the normalized (unity area) sampled concentration across the first dimension column that lies in the sample loop. In Fig. 2.6, the sampling is shown to be “in-phase.’’ This refers to the zone sampling exactly coinciding with the start and end of the zone. In real applications, this will almost never be the case. Hence, we need to investigate the effect of phase in addition to how the segmentation of the zone leads to different values of sS as a function of the sampling phase. Toward this goal we write the average concentration of solute in the sample loop, hCi, accumulated over a time interval ts which is between ti and ti1 as 1 hCðti1 ; ti Þi ¼ ts

ðti

0

Cðt Þdt

0

ð2:14Þ

ti1

where t0 is the dummy variable of integration. For Gaussian zones the functional form of the Gaussian component is " # 1 ðttÞ2 CðtÞ ¼ pﬃﬃﬃﬃﬃﬃ exp ð2:15Þ 2s2 s 2p Finally, we obtain CS as the sample loop concentration by summing up the individual loop solute concentrations across all time segments as CS ðtÞ ¼

N0 X

fuðtti1 Þuðtti ÞghCðti1 ; ti Þi

ð2:16Þ

i¼1

In Equation 2.16, u(t) is the unit function such that u(t) ¼ 0 when t < 0 and u(t) ¼ 1 when t 0 and N0 is the number of samples across a peak. Equation 2.16 is then substituted into Equation 2.13 to get sS in Equation 2.13, given s in Equation 2.15. It is possible to write Equation 2.13 explicitly but the expression is very complex and reveals nothing in analytical form when written as a function of phase. Therefore, it is very easy to program the integral evaluation of Equation 2.13, as a function of phase, in a programming language such as FORTRAN using numerical techniques to integrate the functions. This has been the approach taken in the sampling study (Murphy et al., 1998). Furthermore, integrals of functions that contain Gaussian functions invariably lead to terms with the error function, erf(x). This function has known approximations (Abamowitz and Stegun, 1965) yet has no analytical form; it is usually evaluated with computer numerical methods so this approach is justified. It is well known that the second moment of a uniform density function (a function of width x and constant height) is simply x2/12 (Abamowitz, 1965). The standard deviation of this function is then the square root of the second moment that is x/3.464. If the number of samples across the peak is 1, as shown in one of the curves

26

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

Standard deviation relative to gaussian

5 A

4

B

3

C D 2

1 0

12 4 8 Number of samples across peak width

16

FIGURE 2.7 The ratio of ss/s, as a function of the number of samples and as a function of sampling phase, from Murphy (1998). The four phases are explained in the original paper. Reprinted with permission from the American Chemical Society.

given in Fig. 2.6, the width x ¼ 0.8. The standard deviation is then equal to 0.2309. The original (i.e., unsegmented) Gaussian zone in Fig. 2.6 has a standard deviation of 0.1 that gives the ratio of ss /s ¼ 2.309. Hence for a sample rate of 1 with sampling being done completely “in-phase,’’ there will be over two times the zone width, as measured by the standard deviation. In the limit of a large number of samples, the ratio ss /s tends toward 1. This is shown in Fig. 2.7, curve D (in-phase sampling) for one sample across the peak. Figure 2.7 shows the ratio ss /s for four different sampling phases using the numerical procedure described above. It is predicted that the loss in multidimensional resolution would be small as long as approximately four or more collection “samples’’ occur over the peak. Furthermore, the effect of phase between the four phase evaluations in Fig. 2.7 tends to diminish greatly when the number of samples is four or more. The effect of phase on the ratio ss /s for the number of samples across the peak equal to 1, 2, 3, 4, and 5 is given in the original paper where it was shown that variation with phase is greatly diminished once one samples four or more times over a peak width. A number of experimental aspects of sampling are discussed in Chapter 6. Others have looked at the zone sampling problem. The theory of incomplete sampling of the first dimension was studied by Seeley (2002). Carr has also commented about the sampling (Stoll et al., 2006) that few users of 2DLC have sampled the first dimension adequately to minimize the 2D broadening effect from incomplete sampling.

2.8 DILUTION AND LIMIT OF DETECTION One of the disadvantages of the column format for MDC is that the zone is diluted in the first column, the flow is optionally split and then saved in the sample loop with subsequent dilution in the second column separation. The extent of dilution found with columns does

CHEMOMETRIC ANALYSIS

27

not happen in planar format because the whole zone is utilized in both dimensions. This dilution can cause severe, and in a number of cases has caused, insurmountable problems with detection in techniques such as chromatography-field flow fractionation (FFF). Schure (1999) has studied the effect of multidimensional dilution for column-based separations that incorporate chromatography, capillary electrophoresis (CE), and FFF. In all of these cases, the dilution factors are multiplicative; this gives the direct result that the limit of detection for MDC is T m0;inj ¼ 5FVdil

ð2:17Þ

where m*0,inj is the mass limit of detection, F is the ratio of noise amplitude to detector sensitivity, and VTdil is the total dilution volume of the sample. This last term is equal to the original volume of injection times the product of the split ratio per each dimension, the dilution factor in each dimension, and the reciprocal of a function of the number of samples taken across a peak (Schure, 1999). The dilution volume will vary with efficiency (i.e., the number of plates N) and the retention volume, VR, for a single column as pﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃ Vdil ¼ VR 2p= N ð2:18Þ For a 25-cm length column with 4.6 mm internal diameter, and other parameters taken to be typical of a modern column, the dilution factor was calculated (Schure, 1999) to be 25. This compares with the dilution factor for 2DLC of 2500 when the split ratio and finite peak sampling is factored in. Other techniques like LC/FFF give dilution factors of nearly half a million. While researchers are enthusiastic about the gain in peak capacity given by 2DLC, this comes at a price for detection limits. This is a critical aspect in applications like biomarker research in proteomics, where many biomarker molecules are thought to be present at trace concentration levels. For these cases extremely sensitive detectors are required.

2.9 CHEMOMETRIC ANALYSIS A number of aspects of chemometric approaches have been discussed by Synovec et al. (2003). This work also covers the topic of run-to-run retention time alignment where it was claimed that variations in temperature, flow rate, column degradation, and other factors cause a lack of reproducibility in 2D chromatographic data (Pierce et al., 2005). This may have been an issue in the past and in MDGC. The present situation, however, shows that tighter instrumental controls and better columns have effectively alleviated this problem as exemplified by Carr and coworkers (2006) who have measured peak retention times with reproducibility to 0.2 s. This number is even more impressive considering that a high-temperature column was utilized within the context of gradient elution. However, it has been noted that to effectively utilize single dimension LC data with MS and MS–MS strategies for proteomics database searching, it was necessary to align both LC and MS data (Jaitly et al., 2006). It is expected the same will be true when 2DLC is used as the separations front-end for database searching when the retention

28

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

time component is part of the search even with very exacting chromatographic reproducibility. Nonetheless, chemometric methods may be useful for the analysis of highly fused peaks. When multichannel detectors are utilized, this approach can be successful. However, with so many potential peaks occurring within a chromatographic space, the chemometric methods must be automated so as to defuse regions of a chromatogram without intensive operator intervention. This approach is difficult with 1D data and may be even more difficult to automate for higher dimensional data. Nonetheless, chemometric methods may become indispensable when 2DLC is more routinely applied in certain specific turn-key applications like proteomics research where pattern searching may be more important than a comprehensive quantitative analysis and identification of all peaks. A number of chemometric studies have used data obtained from 2DGC (Van Mispelaar et al., 2003; Sinha et al., 2004a, 2004b) but very few studies have utilized chemometric analysis of 2DLC data. One study (Fraga and Corley, 2005) utilized the Generalized Rank Annihilation method (GRAM) and parallel factor analysis (PARAFAC) methods to analyze 2DLC data. 2DLC data has been analyzed using chemometric methods by Rutan and coworkers (Porter et al., 2006) with application to metabolomics. These authors realized that the information content in 2DLC with a multichannel detector is extremely rich. Even though the second dimension separation can remove fusion from a first dimension peak, the multichannel detector can also remove peak fusion in the second dimension. The autocovariance methods discussed above in the “Two-Dimensional Theory of Peak Overlap’’section qualify as chemometric methods and these may become standard tools of MDC. If chemometric tools become routinely available, they may be utilized to classify the performance metrics of MDC systems. This is discussed in Chapter 4. Pattern recognition has not been actively applied to MDLC data but has been effectively utilized for classification of jet fuel mixtures with MDGC (Johnson and Synovec, 2002). For the analysis of zones not resolved well in 2DLC, for example, with higher molecular weight polymers that have wide distributions of both molecular weight and structural and compositional variation, analyzing the zone structure is no longer a matter of finding patterns of peaks. Here the individual zones are fused and their analysis is more directed at composite zone shapes. Many examples of this are shown in Chapter 17. For these types of zones, the “chemical variance’’ method (Vivo-Truyols and Schoenmakers, 2006) has been proposed. This transformation method, which reduces the data matrix rank by an axis transformation method, may be useful for visualizing and characterizing heavily fused zones. This method may also be useful for visualizing crowded 2D chromatograms where zones are resolved but the order is not apparent from direct visualization. Further discussion of chemometric methods used in 2DLC is given in a recent review of fast high-temperature 2DLC methods (Stoll et al., 2007).

2.10 FUTURE DIRECTIONS Many theoretical aspects of MDLC have been studied in the past 20 years and there is no question that future discussions and research along these lines will take place and

FUTURE DIRECTIONS

29

focus on optimizing the speed and resolution of the technique to serve specific applications. Microfluidics-based chromatography columns have been evolving for a number of years in chip format and the whole multidimensional system may be implemented in chip format in the future. There are advantages and disadvantages of chip-based MDLC systems. However, one clear advantage of chip-based systems is the ease with which the valves (i.e., fluidic switches), columns, sample loops, detector ports, flow system, and other components (perhaps even the detector) can be integrated at the chip level. Implementing multidimensional systems with dimension greater than two on chips may be feasible and one may find topology different from the “standard’’ comprehensive multidimensional liquid chromatograph. The term topology here refers to the connection map or graph of the columns. Presently, 2DLC just follows one column with another and turns two 1D linear spaces into a 2D area. There is no reason this connection map must be this simple and networks of separator systems with complex topology should offer unique separation abilities. This logic is somewhat analogous to what the “application specific integrated circuit’’ or ASIC has become in the electronics industry; one customizes the connection of the chip system to serve a specific purpose while the basic elements, be it transistors or in our case columns such as MEKC and CE systems, are present. Ramsey et al. (2003) have described a microfluidic device that incorporates two-dimensional separations of protein digests that incorporates MEKC as the first dimension separator and CE as the second dimension. The valve action is induced electrically, which frees one of the bulky mechanical valves used in larger scale column-based systems. The electrically-driven valve action will enable the additional complexity to be implemented with reliability. A chip-based multidimensional separation of proteins was described (Shadpour and Soper, 2006) where gel electrophoresis was utilized in the first dimension and MEKC was carried out in the second dimension. Additional examples of 2D chip systems are discussed in Chapter 5. From a theoretical point of view there is a great deal of work necessary to understand what this type of system could bring. For example, if a detector is fast and can be multiplexed, many different types of columns can be integrated into the chip system and utilized simultaneously. In addition, separations need not be completely comprehensive, for example, eluent from the second dimension column might be passed to a third column and fourth column at different times and single dimension chromatography be run on these — somewhat like heart cutting zones from the end of a 2DLC system. But the complexity that would be possible on the chip could be high and theory and simulation will be needed to evaluate the possible relationships required to run this sort of separation. It is also quite possible at that stage of separator complexity that even extremely complex samples of biological origin may be sufficiently separated to give single component separations. This would greatly help in the vast dynamic range problem that often makes the search for biomarkers extremely difficult. One example of a tree-based separator system is shown below in Fig. 2.8 where the Bethe lattice or Cayley tree is shown (Wilson, 1996). This graph can be expanded to any number of levels and can function with different types of columns and electrophoretic elements. This is not the only graph that can function as a complex multidimensional separator system. But it is an example of something with multiple

30

ELEMENTS OF THE THEORY OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY

2

3

4

1

0

FIGURE 2.8 Separator systems cascaded to form a Bethe lattice or Cayley tree where the point of introduction is the graph vertex 0 and solute can be sampled from any of the outward nodes at position 1, 2, 3, 4, and so on. The sample loops and valves are not shown.

columns and different types of stationary phases that could function for very complex separations. These graphs, in the case of separation science, are directed graphs or digraphs (Bang-Jensen and Gutin, 2002) as the flow is unidirectional and does not change direction. Graph theory provides a number of interesting mathematical properties that may be useful for complex separation models. Trees, graphs, and networks of separator systems can be envisioned to be highly tuned for specific applications. However, a system of this type may also be ideal for methods development, as a general system with different stationary phases could be utilized adaptively for iterative method development. Besides offering the complexity needed for complex separations, chip-based systems may also be conducted with an energy input lower than the traditional externally integrated column system. Some of the aspects to this type of chip system, which incorporate discrete chromatographic media, were recently discussed (Wirth, 2007). The practicality of this approach will become apparent in the next decade as applications will drive the demand for this type of system. REFERENCES Abamowitz, M., Stegun, I.A., editors. (1965). Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables. Dover Publications, New York. Bang-Jensen, J., Gutin, G. (2002). Digraphs: Theory, Algorithms and Applications. Springer, New York.

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Giddings, J.C. (1995). Sample dimensionality: a predictor of order–disorder in component peak distribution in multidimensional separation. J. Chromatogr. A 703, 3–15. Gilar, M., Olivova, P., Daly, A.E., Gebler, J.C. (2005). Orthogonality of separation in twodimensional liquid chromatography. Anal. Chem. 77, 6426–6434. Gray, M., Dennis, G.R., Wormell, P., Shalliker, R.A., Slonecker, P. (2002). Two-dimensional reversed phase separations: isomeric separations incorporating C18 and carbon clad zirconia stationary phases. J. Chromatogr. A 975, 285–297. Grushka, E. (1970). Chromatographic peak capacity and the factors influencing it. Anal. Chem. 42, 1142–1147. Grushka, E. (1984). Effect of high speed on peak capacity in liquid chromatography. J. Chromatogr. 316, 81–93. Guiochon, G., Beaver, L.A., Gonnord, M.F., Siouffi, A.M., Zakaria, M. (1983a). Theoretical investigation of the potentialities of the use of a multidimensional column in chromatography. J. Chromatogr. 255, 415–437. Guiochon, G., Gonnord, M.F., Zakaria, M., Beaver, L., Siouffi, A.M. (1983b). Chromatography with a two-dimensional column. Chromatographia 17, 121–124. Horie, K., Kimura, H., Ikegami, T., Iwatsuka, A., Saad, N., Fiehn, O., Tanaka, N. (2007). Calculating the optimal modulation periods to maximize the peak capacity in twodimensional HPLC. Anal. Chem. 79, 3764–3770. Horvath, C.G., Lipsky, S.R. (1967). Peak capacity in chromatography. Anal. Chem. 39, 1893– 1893. Huber, J.F.K., Kenndler, E., Reich, G. (1979). Quantification of the information content of multi-dimensional gas chromatography and low resolution mass spectrometry in the identification of doping drugs. J. Chromatogr. 172, 15–30. Karger, B.L., Snyder, L.R., Horvath, C. (1973). An Introduction to Separation Science. WileyInterscience, New York, Chapter 19, pp. 560–564. Jaitly, N., Monroe, M.E., Petyuk, V.A., Clauss, T.R.W., Adkins, J.N., Smith, R.D. (2006). Robust algorithm for alignment of liquid chromatography–mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. Anal. Chem. 78, 7397–7409. Johnson, K.J., Synovec, R.E. (2002). Pattern recognition of jet fuels: comprehensive GC GC with ANOVA-based feature selection and principal component analysis Chemom. Intell. Lab. Syst. 60, 225–237. Lewis, K.C., Opiteck, G.J., Jorgenson, J.W., Sheeley, D.M. (1997). Comprehensive on-line RPLC-CZE-MS of peptides. J. Am. Soc. Mass Spectrom. 8, 495–500. Liu, S., Davis, J.M. (2006). Dependence on saturation of average minimum resolution in twodimensional statistical–overlap theory: peak overlap in saturated two-dimensional separations. J. Chromatogr. A 1126, 244–256. Liu, Z., Patterson, D.G., Lee, M.L. (1995). Geometric approach to factor analysis for the estimation of orthogonality and practical peak capacity in comprehensive two-dimensional separations. Anal. Chem. 67, 3840–3845. Malinowski, E.R. (2003). Factor Analysis in Chemistry. 3rd edition. John Wiley & Sons, Inc., New York. Marchetti, N., Felinger, A., Pasti, L., Pitrogrande, M.C., Dondi, F. (2004). Decoding twodimensional complex multicomponent separations by autocovariance function. Anal. Chem. 76, 3055–3068.

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Q1

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Martin, M., Herman, D.P., Guiochon, G. (1986). Probability theory for number of mixture components resolved by n independent columns. Anal. Chem. 58, 2200–2207. Merenbloom, S.I., Bohrer, B.C., Koeninger, S.L., Clemmer, D.E. (2007). Accessing the peak capacity of IMS–IMS separations of tryptic peptide ions in He at 300 K. Anal. Chem. 79, 515–522. Murphy, R.E., Schure, M.R., Foley, J.P. (1998). Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70, 1585–1594. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250 (10), 4007–4021. Oros, F.J., Davis, J.M. (1992). Comparison of statistical theories of spot overlap in twodimensional separations and verification of means for estimating the number of zones. J. Chromatogr. 591, 1–18. Percival, D.B., Walden, A.T. (2006). Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, New York. Peters, S., Vivo-Truyols, G., Marriott, P.J., Schoenmakers, P.J. (2007). Development of a resolution metric for comprehensive two-dimensional chromatography. J. Chromatogr. A 1146, 232–241. Pierce, K.M., Wood, L.F., Wright, B.M., Synovec, R.E. (2005). A comprehensive twodimensional retention time alignment algorithm to enhance chemometric analysis of comprehensive two-dimensional separation data. Anal. Chem. 77, 7735–7743. Pietrogrande, M.C., Marchetti, N., Dondi, F., Righetti, P.G. (2006). Decoding 2D-PAGE complex maps: relevance to proteomics. J. Chromatogr. B 833, 51–62. Pietrogrande, M.C., Marchetti, N., Tosi, A., Dondi, F., Righetti, P.G. (2005). Decoding twodimensional polyacrylamide gel electrophoresis complex maps by autocovariance function: a simplified approach for proteomics. Electrophoresis 26, 2739–2748. Porter, S.E.G., Stoll, D.R., Rutan, S.C., Carr, P.W., Cohen, J.D. (2006). Analysis of four-way two-dimensional liquid chromatography-diode array data: application to metabolomics. Anal. Chem. 78, 5559–5569. Ramsey, J.D., Jacobson, S.C., Culbertson, C.T., Ramsey, J.M. (2003). High efficiency, twodimensional separations of protein digests on microfluidic devices. Anal. Chem. 75, 3758– 3764. Ruotolo B.T., Gillig, K.J., Stone, E.G., Russell, D.H. (2002). Peak capacity of ion mobility spectrometry: separation of peptides in helium buffer gas. J. Chromatogr. B 782, 385–392. Schoenmakers, P.J. (1986). Optimization of chromatographic selectivity. J. Chromatogr. Lib. 35, Elsevier Science Publishers, Amsterdam. Schure, M.R. (1997). Quantification of resolution for two-dimensional separations. J. Micro. Sep. 9, 169–176. Schure, M.R. (1999). Limit of detection, dilution factors, and technique compatibility in multidimensional chromatography, capillary electrophoresis, and field-flow fractionation. Anal. Chem. 71, 1645–1657. Seeley, J.V. (2002). Theoretical study of incomplete sampling of the first dimension in comprehensive two-dimensional chromatography. J. Chromgr. A 962, 21–27. Shadpour, H., Soper, S.A. (2006). Two-dimensional electrophoretic separation of proteins using poly(methyl methacrylate) microchips. Anal. Chem. 78, 3519–3527.

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Shen, Y., Tolic, N., Zhao, R., Pasa-Tolic, L., Li, L., Berger, S.J., Harkewicz, R., Anderson, G.A., Belov, M.E., Smith, R.D. (2001). High-throughput proteomics using high efficiency multiple-capillary liquid chromatography with on-line high performance ESI FTICR mass spectrometry. Anal. Chem. 73, 3011–3021. Shi, W., Davis, J.M. (1993). Test of theory of overlap for two-dimensional separations by computer simulations of three-dimensional concentration profiles. Anal. Chem. 65, 482–492. Sinha, A.E., Hope, J.L., Prazen, B.J., Fraga, C.G., Nilsson, E.J., Synovec, R.E. (2004a). Multivariate selectivity as a metric for evaluating comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry subjected to chemometric peak deconvolution. J. Chromatogr. A 1056, 145–154. Sinha, A.E., Fraga, C.G., Prazen, B.J., Synovec, R.E. (2004b). Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography–time-of-flight mass spectrometry data. J. Chromatogr. A 1027, 269–277. Slonecker, P.J., Li, X., Ridgway, T.H., Dorsey, J.G. (1996). Informational orthogonality of two-dimensional chromatographic separations. Anal. Chem. 68, 682–689. Stoll, D.R., Cohen, J.D., Carr, P.W. (2006). Fast, comprehensive online two-dimensional high performance liquid chromatography through the use of high temperature ultra-fast gradient elution reversed-phase liquid chromatography. J. Chromatogr. A 1122 (1–2), 123–137. Stoll, D. R., Li, X., Wang, X., Carr, P. W., Porter, S.E.G., Rutan, S.C. (2007). Fast comprehensive two-dimensional liquid chromatography. J. Chromatogr. A 1168 (1–2), 3–43. Synovec, R.E., Prazen, B.J., Johnson, K.J., Fraga, C.G., Bruckner, C.A. (2003). Chemometric analysis of comprehensive two-dimensional separations. In: Advances in Chromatography, Vol. 42. Grushka, E., Brown, P.R., editors. Marcel Dekker, New York, pp. 1–42. Valentine, S.J., Kulchania, M., Srebalus Barnes, C.A., Clemmer, D.E. (2001). Multidimensional separations of complex peptide mixtures: a combined high-performance liquid chromatography/ion mobility/time-of-flight mass spectrometry approach. Int. J. Mass Spectrom. 212, 97–109. Van Mispelaar, V.G., Tas, A.C., Milde, A.K., Schoenmakers, P.J., van Asten, A.C. (2003). Quantitative analysis of target components by comprehensive two-dimensional gas chromatography. J. Chromatogr. A 1019, 15–29. Vivo-Truyols, G., Schoenmakers, P.J. (2006). Chemical variance, a useful tool for the interpretation and analysis of two-dimensional chromatograms. J. Chromatogr. A 1120, 273–281. Wang, X., Stoll, D.R., Carr, P.W., Schoenmakers, P.J. (2006). A graphical method for understanding the kinetics of peak capacity production in gradient liquid chromatography. J. Chromatogr. A 1125, 177–181. Wilson, R.J. (1996). Introduction to Graph Theory. Addison Wesley, Essex, England. Wirth, M.J. (2007). Separation media for microchips. Anal. Chem. 79, 800–808.

3 PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY Joe M. Davis Department of Chemistry and Biochemistry, Southern Illinois University at Carbondale, Carbondale, IL 62901–4409, USA

3.1 INTRODUCTION The peak capacity of a one-dimensional separation was quantified first by Giddings (1967), who defined it as the maximum number of peaks separable in a specified region of space. The concept was generalized to two-dimensional (2D) separations by Guiochon et al. (1982, 1983) and also by Giddings (1984, 1987). In the generalizations, the peak capacity of a 2D separation is equal to, or slightly less than (Guiochon et al., 1982, 1983), the product of the one-dimensional peak capacities of the two dimensions. Peak capacities are reported routinely as figures of merit in 2D separations, including two-dimensional liquid chromatography or 2DLC although these concepts are general and apply to all two-dimensional separations including column mode and planar mode. The concept was extended to include mass spectrometry as a third dimension (Lewis et al., 1997; Shen et al., 2001; Valentine et al., 2001); see additional references in the previous theory chapter. A minor problem with 2D peak capacity is that it is based on a square or rectangular tessellation (Guiochon et al., 1982) instead of a cubic closest packed

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

35

36

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

arrangement, which is the most condensed packing possible (Cotton and Wilkinson, 1972). The major problem with 2D peak capacity as a figure of merit, however, is the correlation of separation mechanisms, which causes only a fraction of the 2D space to be used. The full use of the peak capacity is possible only if the mechanisms are orthogonal, that is, if the separative displacements in the two dimensions are independent. The means to attain orthogonality have been reported in two-dimensional gas chromatography (2DGC) (Venkatramani et al., 1996) but not in 2DLC, where current success is largely empirical. Correlation was attributed by Giddings to a sample dimensionality (i.e., the number of independent variables needed to identify sample components) that is smaller than the separation dimensionality (Giddings, 1995). A few means have been proposed to determine the amount of space actually used in a 2D separation and the degree of orthogonality. Giddings qualitatively addressed the effects of correlation on the used space (Giddings, 1984). The large number of orthogonal separative displacements suggested by him was reduced markedly by Slonecker et al. (1996), who examined 105 types of 2D separations with concepts from information theory (e.g., entropy, mutual information, synentropy, and informational similarity). They showed that orthogonality among mechanisms is rare in 2D separations and correlation is common. Liu et al. (1995) proposed the means based on factor analysis to calculate the effective angle spanned by retention-time coordinates in 2D separations. The angle, which can vary from 0 to 90 , measures the correlation and was used to define a practical peak capacity. Jandera et al. (2004) suggested that the peak capacity of a 2D separation should be calculated as a weighted sum of a purely orthogonal capacity, based on the multiplication of onedimensional capacities, and a purely serial capacity, based on the square root of the sum of squares of one-dimensional capacities. The weighting factor was the ratio of free-energy differences of repeat structural units in the two dimensions. Gilar et al. (2005a) qualitatively related orthogonality to the linear correlation coefficient of 2D retention-time coordinates and defined the practical peak capacity as the product of the 2D peak capacity and the fraction of peak-capacity units occupied by coordinates (Gilar et al., 2005b). They also introduced a measure of orthogonality. Only a few researchers have used these concepts, however (Gray et al., 2002, 2003, 2004, 2005), and most 2D separations that have been described as orthogonal have not been evaluated as such. This chapter examines another measure of the space used by 2D separations subject to correlation. Some researchers use the words, peak capacity, to express the maximum number of zones separable under specific experimental conditions, regardless of what fraction of the space is used. By definition, however, the peak capacity is the maximum number of separable zones in the entire space. No substantive reason exists to change this definition. The ability to use the space, however, depends on correlation. In deference to previous researchers (Liu et al., 1995; Gilar et al., 2005b), the author adopts the term, practical peak capacity, to describe the used space. The practical peak capacity is the peak capacity, when the separation mechanisms are orthogonal, but is less than the peak capacity when they are not. The subsequent discussion is based on practical peak capacity.

THEORY

37

3.2 THEORY The question posed here is: What is an appropriate measure of practical peak capacity in 2D separations, particularly in 2DLC? Figure 3.1a, c, and e shows graphs of retention-time coordinates in three model systems of comprehensive 2DLC, which were mimicked to answer this question. The coordinates are uniformly random but also correlated, that is, the second-dimension retention time tr,2 depends on its first-dimension counterpart, tr,1. In Figure 3.1a, the coordinates form a wedge, with tr,2 confined between two lines of nonzero slope that intersect the space’s right-hand side. In Figure 3.1c, the coordinates form a fan, with tr,2 confined between two lines of nonzero slope for small tr,1 and between the line of smaller slope and an upper limiting value for large tr,1. In Fig. 3.1e, the coordinates lie within a parallelogram, with the maximum tr,1 and tr,2 values associated with a single retention-time coordinate. While no graph exactly mimics coordinates in 2DLC, all exhibit the correlated increase of tr,2 with tr,1 that often is observed. Figure 3.1b, d, and f shows graphs similar to Figure 3.1a, c, and e, but the coordinates lie in a reduced square having unit dimensions. The reduced coordinates x and y are x ¼ ðtr;1 t0;1 Þ=ðtrmax ;1 t0;1 Þ

ð3:1aÞ

y ¼ ðtr;2 t0;2 Þ=ðtrmax ;2 t0;2 Þ

ð3:1bÞ

where t0,1 and t0,2 are the void times in the first and second dimensions, as shown in Fig. 3.1a, c, and e. Times trmax ;1 and trmax ;2 are the maximum retention times in the first and second dimensions, such that 0 x 1 and 0 y 1. These reduced graphs are similar to those of other researchers (Slonecker et al., 1996; Gilar et al., 2005b). The geometrical restrictions on the reduced times are imposed by various lines having slopes b1 and b2 (b1 5 b2) and by the square’s boundaries. The slopes themselves have restricted ranges, which are reported in the figures. The spaces defined by the coordinate patterns in Fig. 3.1b, d, and f are called WEG (wedge), FAN (fan), and PAR (parallelogram). Any measure of the coordinate correlation is arbitrary. Here, the linear correlation coefficient r is used, largely because it is familiar. It measures the quality of a leastsquares fit of a line to coordinates, with a magnitude that varies from 0 (for uncorrelated random coordinates) to 1 (for fully correlated coordinates lying on the line). For the correlated coordinates in Fig. 3.1b, d, and f, theoretical relationships for r, that is, r ¼ f (b1, b2) can be derived as shown in Appendix 3B. They show that r lies between the inclusive bounds of 1/2 and 1 for WEG, and between the inclusive bounds, 0 and 1, for FAN and PAR. The practical peak capacity of these spaces can be characterized by 2D statistical overlap theory. Consider a relatively simple problem of probability. If, on average, m circles of diameter d0 are distributed randomly in a large area A, then the average number p of clusters of isolated and overlapping circles approaches (Roach, 1968)

38

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

FIGURE 3.1 (a, c, and e) Mimics of correlated retention times tr,1 and tr,2 in comprehensive 2DLC. (b, d, and f) Reduced retention-time coordinates x and y. Equations of lines defining spatial boundaries are reported.

THEORY

p ¼ 4amexpð4aÞ=½1expð4aÞ

39

ð3:2Þ

where saturation a is 02 =ð4AÞ ¼ m=n P a ¼ mpd

ð3:3aÞ

and nP is the number of circles of area pd20 =4 that can fit in A without consideration of packing constraints. In other words, it is a geometric measure of the number of spatial nP decreases, with a resultant increase of units available. As a increases for constant m, clustering and reduction of p. In statistical-overlap theory, d0 represents the contour of single-component peaks (i.e., peaks produced by pure compounds), p represents the average number of observable peaks (with each circle cluster representing a group of overlapping single-component peaks and isolated circles representing isolated singlecomponent peaks), and nP represents one measure of peak capacity, when A is both rectangular and fully used. Equation 3.2 was proposed by Roach (1968) almost 40 years ago to model the overlap of coal particulates sampled from air onto a flat surface. The equation was verified by studying the clustering of randomly distributed circles in a square representing the reduced space of a 2D separation (Oros and Davis, 1992). It then was modified (Rowe and Davis, 1995) to study the clustering of inhomogeneous random distributions of circles (Rowe et al., 1995; Davis, 2004), in which more circles are found in parts of the reduced square than in others, and to address the clustering of ellipses and reduction of clustering that occurs near the reduced-square boundaries (Davis, 2005). For simplicity, only Equation 3.2 is used in this chapter. Obviously, zones in 2DLC are not circles; they are three-dimensional profiles (e.g., bi-Gaussians) having variable breadths and heights. With the identity, d0 ¼ 4sR*s , where R*s is the average minimum resolution between single-component peaks required for separation, Equation 3.2 can be used to model the average number p randomly of peak maxima in a rectangular area A containing (on average) m distributed bi-Gaussians having equal standard deviations s in both dimensions (Shi and Davis, 1993). The substitution of this identity into Equation 3.3a produces * 2 a ¼ 4mpðsR s Þ =A

ð3:3bÞ

Studies of both homogeneous (Shi and Davis, 1993) and inhomogeneous (Rowe and Davis, 1995) random distributions of bi-Gaussians were made to determine values of R*s at low a. These studies, however, are superceded by a recent development. Figure 3.2 is a graph of R*s versus a for 2D separations, as predicted by theory (Liu and Davis, 2006). The graph's signiﬁcance is discussed brieﬂy. Consider two neighboring peaks, each of which may contain more than one compound. Let B and C represent the two compounds closest to each other, with B in one peak and C in the other. Clearly, B and C are resolved. However, if efﬁciency is degraded and the two peaks overlap to form a single peak, then B and C are not resolved. Theory shows the minimum resolution of B and C decreases with increasing peak multiplicity (i.e., the number of compounds per peak). Since Rs is a weighted combination of all possible minimum resolutions of B and C, it also decreases with increasing multiplicity, that is, increasing a. At large a, it even is less than 0.5 (the smallest resolution separating two

40

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

0.8 Rs* 0.7

0.6

0.5

0.4

0

0.2

0.4

a

0.6

0.8

1

FIGURE 3.2 Graph of average minimum resolution Rs versus saturation a for 2D separations of randomly distributed bi-Gaussian single-component peaks. Reprinted from Liu and Davis, copyright (2006), with permission from Elsevier.

Gaussians or bi-Gaussians of equal standard deviation and height), since the likelihood of multiplicity is high. The value of Rs at a ¼ 0 is 0.726, the average minimum resolution required to separate two Gaussian or bi-Gaussian single-component peaks having equal standard deviations and exponentially distributed heights (Creten and Nagels, 1987; El Fallah and Martin, 1987; Schure, 1991; Felinger, 1997). For complex mixtures, this height distribution has a basis that is both theoretical (Pietrogrande et al., 2000) and experimental (Nagels et al., 1983; Nagels and Creten, 1985; Dondi et al., of bi-Gaussians in area A, 1986; Pietrogrande et al., 2000). For a speciﬁed number m with standard deviation s in both dimensions and exponentially distributed heights, Equation 3.3b and Fig. 3.2 uniquely determine a (i.e., for any m , A, and s, only one value of Rs determines a from Equation 3.3b, with the coordinate (a, Rs ) also lying on the graph in Fig. 3.2). A similar theory exists for Rs in one dimension (Davis, 1997a, b). It is important to realize that statistical-overlap theory is not constrained by the contour of area A, which does not have to be rectangular as in earlier studies (in addition to previous references, see Davis, 1991; Martin, 1991, 1992). In other words, Equations 3.2 and 3.3 should apply to the spaces WEG, FAN, and PAR. In this chapter, the number of clusters of randomly distributed circles in such areas is compared to the predictions of Equations 3.2 and 3.3a to assess the relationship between nP and practical peak capacity. Similarly, the number of peak maxima formed by randomly distributed bi-Gaussians in such areas is compared to the predictions of Equations 3.2 and 3.3b, and to Fig. 3.2, to make another assessment. In general, statistical-overlap theory has been developed and applied to several systems, mostly one-dimensional ones. Two reviews summarize many developments (Pietrogrande et al., 2000; Felinger and Pietrogrande, 2001). With regards to other multidimensional separations, theories have been reported for n-dimensional

PROCEDURES

41

separations (Martin, 1991, 1995; Davis, 1993) and column switching (Peters and Davis, 1998; Samuel and Davis, 2002). Dondi and coworkers have proposed means to interpret peak overlap in 2D electrophoretic gels by partitioning them into narrow strips, to which one-dimensional theory is applied (Pietrogrande et al., 2002, 2003; Campostrini et al., 2005). In addition, they derived a theory based on autocorrelation methods (Marchetti et al., 2004), which addresses the correlation of zone centers due to similarities of chemical structure. Such correlations are not considered here.

3.3 PROCEDURES The details of computing correlated coordinates (x, y) in spaces WEG, FAN, and PAR are of marginal interest to most of the readers. They are reported in Appendix 3A. Limiting expressions of r for large numbers of coordinates in WEG, FAN, and PAR were calculated from Equation 3.B4 and expressions described in Appendix 3B for a family of b1 and b2 values and were tested by simulation. Specifically, 500 (x, y) random correlated coordinates were distributed over WEG, FAN, and PAR for specific b1 and b2, and r was calculated from the usual statistics equation (Eq. 3.B1 in Appendix 3B). The simulation was repeated 1000 times, and the average r was compared to the limiting expressions. For specific b1, b2 and r chosen as described below, two types of simulations were carried out in WEG, FAN, and PAR. In the first, the number of clusters formed from ¼ 500 circles centered at random correlated coordinates was counted in 250 m simulations for a ¼ 0.05, 0.10, 0.15, . . . , and 1.0. The areas A are reported in Appendix 3A; these areas and Equation 3.3a determined circle diameter d0. Circles having centers lying within d0 were clustered. The average number of clusters then was compared to p, Equation 3.2. In the second type of simulation, the number of peak ¼ 500 bi-Gaussians, having equal standard deviations s in both maxima formed by m dimensions and exponentially distributed heights, was counted in 100 simulations having the same saturations and coordinates as before. At any a, s was chosen to satisfy both Equation 3.3b and Fig. 3.2. Single-component peaks were computed on square grids extending from the maximum height by 6 s and containing nodes spaced by s/5. The heights of single-component peaks were exponentially random. After summing all proﬁles, peak maxima were identiﬁed by nodes having greater intensities than their eight surrounding neighbors. The average number of maxima was compared to p, Equation 3.2. In both simulations, the actual number m of coordinates in a and standard simulationpwas ﬃﬃﬃﬃ chosen in accordance with Poisson statistics (i.e., mean m ), using the Box–Muller transform (Press et al., 1992) as an approximadeviation m tion to the Poisson distribution. For simulations in WEG, b2 was set to 1 and Equation 3.B4 in Appendix 3B was solved for b1 at r ¼ 0.55, 0.60, 0.65, . . . , and 0.95. For simulations in FAN, equations described in Appendix 3B were solved with b1 ¼ 1/b2 at r ¼ 0.1, 0.2, 0.3, . . . , and 0.9. For simulations in PAR, arbitrary b2 values were chosen and equations described in Appendix 3B were solved for b1 at r ¼ 0.1, 0.2, 0.3, . . . , and 0.9.

42

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

The random number generator in all simulations was RAN3 (Press et al., 1992). Computations were made in FORTRAN 90. To reduce cancellation errors, theoretical values of r for PAR were computed in double precision.

3.4 RESULTS AND DISCUSSION Figure 3.3a–c shows graphs of correlation coefficient r versus b2 for different b1 in WEG, FAN, and PAR. The curves are limiting values of r evaluated as described in Appendix 3B. The symbols are the average r’s from 1000 simulations of 500 random correlated coordinates for different b1 and b2. The agreement is excellent, except for r 5 0.1. The small deviation probably is caused by minor imperfections in the random number generator. Some trends merit comment. For all three spaces, r increases with increasing b1 for appropriate b2, because coordinates are distributed over increasingly confined spaces. Also, as b1 approaches b2, r approaches 1 because coordinates lie along a line. For WEG, r equals 1/2 for all positive b2, when b1 ¼ 0. For FAN, r approaches constant values as b2 approaches infinity because coordinates fill the entirety of the upper lefthand side of the space. This trend is also found in PAR. Also for PAR, r has a very shallow minimum for large b1 (e.g., 0.7 and 0.9), prior to increasing slightly with increasing b2 and then approaching an asymptote. The simulation results also show this trend, but the precisions do not establish significance. It is evident from Fig. 3.3 that the same r can be found in all three spaces for appropriate b1 and b2. Some judgment was required in choosing b1 and b2 for further study. In all cases, b2 was chosen and b1 was evaluated for periodically spaced r’s using equations reported or described in Appendix 3B. Specifically, for WEG, b2 was assigned the value 1, such that the line of greater slope intersected the upper right-hand corner of the space. For FAN, b2 was assigned the value 1/b1 to produce a symmetrical space (i.e., one having equal extents at the right vertical and upper horizontal boundaries). For PAR, b2 was selected arbitrarily to produce spaces somewhat resembling real 2DLC chromatograms. The b1 and b2 values for different r’s are reported in Tables 3.1, 3.2, and 3.3 for WEG, FAN, and PAR, respectively. Figures 3.4a-d, 3.5a-d and 3.6a-d show graphs of coordinates in WEG, FAN, and PAR, respectively, for different r, showing the effects of correlation on the coordinate distributions. Figures 3.4e and f, 3.5e and f, and 3.6e and f show contour images of biGaussians in these spaces for specific r and a. Although all bi-Gaussians have equal standard deviations in both dimensions, overlap causes observed spots to be oriented in differentdirections.Furthermore,thebi-Gaussianshavinglargeheightsformlargerspots. Figure 3.7a–f shows graphs of p versus a determined from simulations in WEG, FAN, and PAR, with p equal to the average numbers of circle clusters in Fig. 3.7a, c, and e, and equal to the average numbers of peak maxima in Fig. 3.7b, d, and f. The curve is the Roach equation, Equation 3.2. The symbols represent simulation results obtained for different r’s. In all cases, a good agreement exists between simulation and theory, with p values for simulations at different r almost superposing at the same a. The agreement is best at small r and a, simply because the spaces are larger and

(a)

WEG

b1 = 0.9

1 r

b1 = 0.7

b1 = 0.5

0.9 b1 = 0.3 0.8 b1 = 0.1

0.7 0.6 0.5

b1 = 0 0.4 0

0.2

0.4

1

0.6

b2

0.8

1

(b) FAN

r 0.8

b1 = 0.7

0.6

0.4

b1 = 0.9 b1 = 0.5 b1 = 0.3

0.2

b1 = 0.1

b1 = 0 0 0

5 (c)

10

b2

15

20

25

20

25

b1 = 0.9

PAR

1 r

b1 = 0.7 b1 = 0.5

0.8

0.6 b1 = 0.3 0.4 b1 = 0.1

0.2 b1 = 0

0 0

5

10

b2

15

FIGURE 3.3 Graph of linear correlation coefficient r versus b2 for various b1 in (a) WEG, (b) FAN, and (c) PAR. Curves are predictions; points are simulation averages. 43

44

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

FIGURE 3.4 (a–d) Graphs of 500 coordinates in WEG for different r’s. (e and f) Contour images of 500 bi-Gaussians in WEG for specified r and a.

RESULTS AND DISCUSSION

FIGURE 3.5

As in Fig. 3.4, but for FAN.

45

46

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

FIGURE 3.6 As in Fig. 3.4, but for PAR.

RESULTS AND DISCUSSION

47

FIGURE 3.7 Graphs of p versus a. Curves are Roach equation, Equation 3.2. Symbols are simulation averages for various r’s, with p equalling average numbers of circle clusters in (a), (c) and (e), and average numbers of maxima in (b), (d), and (f).

“boundary effects’’ are less pronounced (Davis, 2005). Also, the agreement is slightly better for circle clusters than for maxima at large a but the difference is small. The need to account for the variation of Rs with a is apparent in the insert to Fig. 3.7b , which shows the large deviation between simulation and theory that results when Rs is assigned its limiting value at a ¼ 0, 0.726. The agreement provides the answer to the question posed earlier in the chapter: a measure of practical peak capacity, as assessed by statistical-overlap theory, is nP

48

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

TABLE 3.1 Values of b1 for Different r’s in WEG Calculated from Equation 3.B4 r

b1

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95

0.06570 0.1301 0.1940 0.2586 0.3252 0.3957 0.4730 0.5630 0.6810

b2 ¼ 1.

TABLE 3.2 r’s in FAN r 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Values of b1 and b2 for Different b1

b2

0.09420 0.1792 0.2581 0.3333 0.4070 0.4811 0.5587 0.6444 0.7500

10.62 5.580 3.874 2.999 2.457 2.079 1.790 1.552 1.333

b2 ¼ 1/b2.

TABLE 3.3 r’s in PAR r 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Values of b1 and b2 for Different b1

b2

0.0324 0.1021 0.0929 0.2171 0.0206 0.2955 0.4419 0.5602 0.6831

15.0 10.0 5.0 5.0 2.75 2.75 2.75 2.75 2.75

b2's were chosen arbitrarily.

CONCLUSIONS

49

itself, which is the geometrical fraction of the peak capacity actually used for separation. It is not the product of the two independent one-dimensional peak capacities, unless the coordinates are uncorrelated. This should be true, even if coordinates are not random and the Roach equation does not apply. It also should be true, even though real 2DLC chromatograms are more complicated than the model systems studied here. It is instructive to compare nP to other geometrical definitions of practical peak capacity. The practical peak capacity of Liu et al. (1995) is related to the area between two lines passing through the origin and intersecting the upper- and righthand sides of the 2D space, with an angle d ¼ cos1r between the lines. Therefore, the space of Liu et al. resembles FAN. For FAN at r ¼ 0.7, d is 45.6 , which is larger than the actual angle, tan1 b2 tan1 b1 ¼ 31.6 . Therefore, the practical peak capacity of Liu et al. is larger than nP, and this trend is also found for other r’s. The practical peak capacity of Gilar et al. (2005b) is the number of peak-capacity units that are occupied by retention times. Unlike here, only the occupied units in the relevant geometry are counted, and their number depends on saturation. From their work, one can deduce that the fraction of area A containing homogeneous random retention times in area units of pd20 =4 (circular or otherwise) is 1 exp(a). Therefore, the practical peak capacity of Gilar et al. for homogeneous random retention times is nP[1 exp(a)]. These observations are not made as criticisms but to show the inconsistency among different geometrical approaches to defining practical peak capacity. Even the suggestion that it should be defined by nP is simplistic except in ideal cases. Suppose a few 2D coordinates lie outside an otherwise well-defined space containing random coordinates. What is the practical peak capacity here; that is, how does one define the area associated with the outlying, isolated coordinates? Similarly, suppose the distribution of 2D coordinates is inhomogeneously random, with many coordinates in part of the 2D space and only a few coordinates in another part of equal area. Are these two areas “used’’ equally by coordinates in defining practical peak capacity? Statistical-overlap theory suggests the answer is “yes,’’ but the author thinks this answer is not the best one.

3.5 CONCLUSIONS The author anticipates that many readers will find the results reported here to be commonplace. If so, then why do we so often report the individual peak capacities of the two dimensions and their product as the 2D peak capacity? One answer—the conservative one—is that the latter is indeed the maximum number of peaks that can be separated, in agreement with the definition. A more realistic answer is that it is easy to do and appears more impressive than it really is—especially to those who fund our work. In fact, as a practical metric it is often nonsense. Because orthogonality is so difficult to achieve, especially in 2DLC, the peak capacity is a measure of only instrumental potential, not of separation potential, and consideration of

50

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

the instrumentation alone is deceptive. In such a situation, peak capacity has no significance; only practical peak capacity does. We should distance ourselves from peak capacity in 2DLC, except as a gauge of instrumental performance, and seek meaningful assessments of practical peak capacity. Indeed, this chapter should be read as a “call to arms’’ of appropriately minded researchers to propose a good metric for practical peak capacity. On the basis of the results reported here, the metric should reflect the area used by the separation, in agreement with others (Liu et al., 1995; Gilar et al., 2005b). The inconsistency discussed above among the different ways to express this area, however, suggests a new approach. What is the best way to measure area, especially for isolated zones? One approach simply is to adapt the definition of Gilar et al. and count the number of units of peak capacity that are occupied by retention-time coordinates. Alternatively, these coordinates could be “fuzzied’’ by probability distributions allowing for the finite breadths of peaks, since peaks that are not centered within a capacity unit extend into other units, especially if they are multiplets. Another approach might be to generalize the automated measurements of Lan and Jorgenson from a one-dimensional space to a 2D one and measure the utilized space directly (Lan and Jorgenson, 1999). If this chapter causes the reader to reflect on the appropriate definition, it will have served its purpose.

APPENDIX 3A GENERATION OF RANDOM CORRELATED COORDINATES An internet search by the author found several algorithms for generating correlated coordinates having a specified linear correlation coefficient. However, none of the sites addressed the spaces considered here or discussed the theory of coordinate generation. A constant density of correlated random coordinates (x, y) was distributed over nonrectangular areas A by equating the cumulative distribution function of area A, expressed relative to x, to a random number R (0 R 5 1) and then solving the resultant equation for x (Press et al., 1992). Ordinate y was then uniformly distributed between its bounds at that x using another random number. The cumulative distribution functions F(x) are derived simply and in the same way for all spaces. For example, study of Fig. 3.1b shows the differential area dA of WEG lying between x and x þ dx is Db x dx, where Db ¼ b2 b1. The division of dA by the total area Aw of WEG Z1 AW ¼ Db

x dx ¼ Db=2

ð3:A1Þ

0

gives 2x dx ¼ f(x)dx, where f(x) is the probability density function (pdf) of the area. F(x) then is obtained by integrating f(z) dz from the lower area bound to x, where z is a dummy variable.

FIGURE 3.A1 Partitionings of (a) FAN and (b), and (c) PAR for calculation of cumulative distributions. 51

52

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

Consequently, for WEG, the coordinate equations are Zx z dz ¼ x2 ¼ R1

2

ð3:A2aÞ

0

where the domain of x is p 0ﬃﬃﬃﬃﬃ ﬃ x 5 1 and R1 is a random number. The solution to Equation 3.A2a (i.e., x ¼ R1 ) then is paired with ordinate y ¼ ðb1 þ DbR2 Þx

ð3:A2bÞ

where R2 is another random number, with the range of y equal to b1x y 5 b2x. Equation 3.A2b simply represents a uniform distribution of all ordinates between the lower bound, y ¼ b1x (for R2 ¼ 0), and the upper bound, y ¼ b2x (for R2 ¼ 1). In other words y ¼ lower bound þ ðupper boundlower boundÞ R2

ð3:A2cÞ

which also applies to all expressions for y below. Each evaluation of Equations 3.A2a and 3.A2b with new R1 and R2 produces a new coordinate pair (x, y) in WEG. The space FAN was partitioned into regions 1 and 2, as shown in Fig. 3.A1a, with the abscissa of the demarcation equal to b1 2 (the partition was not necessary but was made simply for convenience). Cumulative distribution functions were evaluated for both regions, and one was selected for coordinate generation by computing another random number R0. For R0 f, where f is the fractional area of region 1 (i.e., the area of region 1, divided by the summed areas of regions 1 and 2), the coordinate was computed from equations for region 1; otherwise, from equations for region 2. In region 1, the area AF,1 is Db=ð2b22 Þ and the pdf is 2b22 x, with x having the domain, 0 x 5 1/b2. Abscissa x is evaluated from Zx 2b22

z dz ¼ ðb2 xÞ2 ¼ R1

ð3:A3Þ

0

(The solutions to this and subsequent equations in x are not given here, although all are analytical.) Ordinate y again is computed from Equation 3.A2b. In region 2, the area AF,2 is ! b ð3:A4aÞ AF;2 ¼ 1b1 =2ðb2 Þ1 þ 12 2b2 and the pdf is (AF,2)1(1 b1x), with x having the domain, 1/b2 x 5 1. Coordinates x and y are calculated from

APPENDIX 3A GENERATION OF RANDOM CORRELATED COORDINATES

ðAF;2 Þ

1

Zx ð1b1 zÞdz ¼ ðAF;2 Þ

1

xb1 x =2ðb2 Þ 2

1

þ

1=b2

b1 2b22

53

! ¼ R1 ð3:A4bÞ

y ¼ b1 x þ ð1b1 xÞR2

ð3:A4cÞ

where the range of y equals b1x y 5 1. The space PAR was partitioned among regions 1, 2, and 3, as shown in Fig. 3.A1b and c. The abscissas demarking regions 1 and 2, and regions 2 and 3, are xL and xH, respectively. They have different values, depending on b1 and b2. Specifically, for b1 þ b2 > 2, xL and xH are the abscissas of vertices at the upper left and lower right, respectively, of PAR (Fig. 3.A1b); for b1 þ b2 5 2, they are the abscissas of vertices at the lower right and upper left (Fig. 3.A1c). Their values are determined by the intersection of the appropriate lines in Figs. 3.A1b and 3.A1c, For

b1 þ b2 > 2 :

xL ¼ ð1b1 Þ=Db; xH ¼ ðb2 1Þ=Db

ð3:A5aÞ

For

b1 þ b2 5 2 :

xL ¼ ðb2 1Þ=Db; xH ¼ ð1b1 Þ=Db

ð3:A5bÞ

One notes that the xL and xH values simply are reversed for the two cases. For b1 þ b2 ¼ 2, xL equals xH and region 2 vanishes. Different regions for coordinate generation were selected as for FAN, based on comparisons of fractional areas to a random number. For region 1, the area AP,1 is Dbx 2L =2 and the pdf is 2x=x 2L , with x having the domain, 0 x 5 xL. The abscissa x is calculated from 2 x2L

Zx z dz ¼ ðx=xL Þ2 ¼ R1

ð3:A6Þ

0

and ordinate y again is calculated from Equation 3.A2b. In region 2, the area AP,2 is (1 b1)(xH xL) for b1 þ b2 > 2, and is (b2 1)(xH xL) for b1 þ b2 5 2. In both cases, the pdf is (xH xL)1, with the domain of x equal to xL x 5 xH. Abscissa x is evaluated from 1

Zx

ðxH xL Þ

dz ¼ xL

For b1 þ b2 > 2, ordinate y is calculated from

xxL ¼ R1 xH xL

ð3:A7aÞ

54

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

y ¼ b1 x þ ð1b1 ÞR2

ð3:A7bÞ

with the range, b1x y 5 b1(x 1) þ 1. For b1 þ b2 5 2, ordinate y is calculated from y ¼ b2 ðx1Þ þ 1 þ ðb2 1ÞR2

ð3:A7cÞ

with the range, b2(x 1) þ 1 y 5 b2x. For region 3, the area Ap,3 is AP;3 ¼ Dbð1=2 þ x2H =2xH Þ

ð3:A8aÞ

the pdf is (AP,3)1 Db(1 x), and the domain of x is xH x 5 1. Abscissa x is calculated from

1

Zx

ðAP;3 Þ Db xH

x2 x2 ð1zÞ dz ¼ ðAP;3 Þ1 Db x xH þ H ¼ R1 2

ð3:A8bÞ

whereas y is y ¼ b2 ðx1Þþ1Dbðx1ÞR2

ð3:A8cÞ

with the range, b2(x 1) þ 1 y 5 b1(x 1) þ 1. Area A defining a in Equation 3.3 was calculated from Aw for WEG, the sum of AF,1 and AF,2 for FAN, and the sum of AP,1, AP,2, and AP,3 for PAR. Similar coordinate distributions can be made for any simple geometrical space of interest.

APPENDIX 3B DERIVATION OF LIMITING CORRELATION COEFFICIENT r The linear correlation coefficient r of N coordinates (x, y) is N N N P P P N xi yi xi yi i i si ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ r ¼ sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ N 2 2 N N N P P P P xi yi N x2i N y2i i

i

i

ð3:B1Þ

i

where i is an index. The limiting value of r as N approaches infinity is calculated for homogeneous random coordinates by approximating each sum in Equation 3.B1 by its equivalent integral

APPENDIX 3B DERIVATION OF LIMITING CORRELATION COEFFICIENT r

Zx2

N X

lim N! ¥

55

qi ¼ N

i

f ðxÞqðxÞdx

ð3:B2Þ

x1

where q is the continuous equivalent of coordinate or coordinate combination qi (e.g., xi becomes x, xiyi becomes xy, etc.), x1 and x2 are the minimum and maximum values of x in the region to which the summation applies, and f(x) and y are the relevant pdf and function, respectively, in Appendix 3A. In effect, Equation 3.B2 equates the discrete and continuous first moments of the distribution of random variable q; the pdf f(x) is needed to weight different q in accordance with their likelihood of occurrence. Comparison of Equations 3.B1 and 3.B2 shows that the limiting value of r is independent of N. For example, the pdf f(x) for WEG is 2x and for large N N X

Z1 xi ¼ 2N

i N X

x2 ðb1 þDbR2 Þdx ¼ Nðb1 þb2 Þ=3

ð3:B3bÞ

0

Z1 xi yi ¼ 2N

x3 ðb1 þDbR2 Þdx ¼ Nðb1 þb2 Þ=4

ð3:B3cÞ

0 N X i

i

0

yi ¼ 2N

i

N X

ð3:B3aÞ

Z1

i N X

x2 dx ¼ 2N=3

Z1 x2i

¼ 2N

x3 dx ¼ N=2

ð3:B3dÞ

0

Z1 x3 ðb1 þDbR2 Þ2 dx ¼ Nðb21 þb22 þb1 b2 Þ=6

y2i ¼ 2N

ð3:B3eÞ

0

where y is expressed by Equation 3.A2b, R2 is assigned its average value 1/2, and R22 is assigned its average value 1/3 (as determined by simulation). The final expression for r simplifies to 1 sþ1 r ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ; 2 1þs2 s

s ¼ b1 =b2 ;

1=2 r 1

ð3:B4Þ

and depends only on the ratio, b1/b2. Spaces FAN and PAR were addressed similarly, with Pthe integrals evaluated for different regions. The evaluations for any sum type (e.g., N i x i ) then were added, with weights equal to the fractional areas. Consequently, numerous algebraic equations were produced, which were not simplified in evaluating r, Equation 3.B1. FORTRAN programs for their evaluation are available on request. Unlike Equation 3.B4, r for FAN and PAR does not depend on b1/b2.

56

PEAK CAPACITY IN TWO-DIMENSIONAL LIQUID CHROMATOGRAPHY

These equations and Equation 3.B4 were used to evaluate r for specified b1 and b2 (see Fig. 3.3) and to evaluate b1 for specified b2 and r (see Tables 3.1–3.3).

REFERENCES Campostrini, N., Areces, L.B., Rappsilber, J., Pietrogrande, M.C., Dondi, F., Pastorino, F., Ponzoni, M., Righetti, P. (2005). Spot overlapping in two-dimensional maps: a serious problem ignored for much too long. Proteomics 5, 2385. Cotton, F.A., Wilkinson, G. (1972). Advanced Inorganic Chemistry, 3rd edition. Interscience Publishers, New York. Creten, W.L., Nagels, L.J. (1987). Computation of determination limits for multicomponent chromatograms. Anal. Chem. 59, 822. Davis, J.M. (1991). Statistical theory of spot overlap in two-dimensional separations. Anal. Chem. 63, 2141. Davis, J.M. (1993). Statistical theory of spot overlap for n-dimensional separations. Anal. Chem. 65, 2014. Davis, J.M. (1997a). Justification of probability density function for resolution in statistical models of overlap. Chromatographia 44, 81. Davis, J.M. (1997b). Extension of statistical overlap theory to poorly resolved separations. Anal. Chem. 69, 3796. Davis, J.M. (2004). Assessment by Monte Carlo simulation of thermodynamic correlation of retention times in dual-column temperature programmed comprehensive two-dimensional gas chromatography. J. Sep. Sci. 27, 417. Davis, J.M. (2005). Statistical-overlap theory for elliptical zones of high aspect ratio in comprehensive two-dimensional separations. J. Sep. Sci. 28, 347. Dondi, F., Kahie, Y.D., Lodi, G., Remelli, M., Reschiglian, P., Bighi, C. (1986). Evaluation of the number of components in multicomponent liquid chromatograms of plant extracts. Anal. Chim. Acta 191, 261. El Fallah, M.Z., Martin, M. (1987). Influence of the peak height distribution on separation performances: discrimination factor and effective peak capacity. Chromatographia 24, 115. Felinger, A. (1997). Critical peak resolution in multicomponent chromatograms. Anal. Chem. 69, 2976. Felinger, A., Pietrogrande, M.C. (2001). Decoding complex multicomponent chromatograms. Anal. Chem. 73, 619A. Giddings, J.C. (1967). Maximum number of components resolvable by gel filtration and other elution chromatographic methods. Anal. Chem. 39, 1027. Giddings, J.C. (1984). Two-dimensional separations: concept and promise. Anal. Chem. 56, 1258A. Giddings, J.C. (1987). Concepts and comparisons in multidimensional separation. J. High Resolut. Chromatogr. Chromatogr. Commun. 10, 319. Giddings, J.C. (1995). Sample dimensionality: a predictor of order-disorder in component peak distribution in multidimensional separation. J. Chromatogr. A 703, 3.

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Gilar, M., Olivova, P., Daly, A.E., Gebler, J.C. (2005a). Two-dimensional separation of peptides using RP-RP-HPLC system with different pH in first and second separation dimensions. J. Sep. Sci. 28, 1694. Gilar, M., Olivova, P., Daly, A.E., Gebler, J.C. (2005b). Orthogonality of separation in twodimensional liquid chromatography. Anal. Chem. 77, 6426. Gray, MJ., Dennis, G.R., Slonecker, P.J., Shalliker, R.A. (2003). Evaluation of the twodimensional reversed-phase–reversed-phase separations of low-molecular mass polystyrenes. J. Chromatogr. A 1015, 89. Gray, M.J., Dennis, G.R., Slonecker, P.J., Shalliker, R.A. (2004). Comprehensive two-dimensional separations of complex mixtures using reversed-phase liquid chromatography. J. Chromatogr. A 1041, 101. Gray, M.J., Dennis, G.R., Slonecker, P.J., Shalliker, R.A. (2005). Utilising retention correlation for the separation of oligostyrenes by coupled-column liquid chromatography. J. Chromatogr. A 1073, 3. Gray, M., Dennis, G.R., Wormell, P., Shalliker, R.A., Slonecker, P. (2002). Two dimensional reversed-phase–reversed-phased separations. Isomeric separations incorporating C18 and carbon clad zirconia stationary phases. J. Chromatogr. A 975, 285. Guiochon, G., Beaver, L.A., Gonnord, M.F., Siouffi, A.M., Zakaria, M. (1983). Theoretical investigation of the potentialities of the use of a multidimensional column in chromatography. J. Chromatogr. 255, 415. Guiochon, G., Gonnord, M.F., Siouffi, A., Zakaria, M. (1982). Study of the performances of thin-layer chromatography. VII. Spot capacity in two-dimensional thin-layer chromatography. J. Chromatogr. 250, 1. Jandera, P., Novotna, K., Kolarova, L., Fischer, J. (2004). Phase system selectivity and peak capacity in liquid column chromatography—the impact on two-dimensional separations. Chromatographia 60, S27. Lan, K., Jorgenson, J.W. (1999). Automated measurement of peak widths for the determination of peak capacity in complex chromatograms. Anal. Chem. 71, 709. Lewis, K.C., Opiteck, G.R., Jorgenson, J.W., Sheeley, D.M. (1997). Comprehensive on-line RPLC-CZE-MS of peptides. J. Am. Soc. Mass. Spectrom. 8, 495. Liu, S., Davis, J.M. (2006). Dependence on saturation of average minimum resolution in twodimensional statistical-overlap theory: peak overlap in saturated two-dimensional separations. J. Chromatogr. A 1126, 244. Liu, Z., Patterson, D.G., Jr., Lee, M.L. (1995). Geometric approach to factor analysis for the estimation of orthogonality and practical peak capacity in comprehensive two-dimensional separations. Anal. Chem. 67, 3840. Marchetti, N., Felinger, A., Pasti, L., Pietrogrande, M.C., Dondi, F. (2004). Decoding twodimensional complex multicomponent separations by autocovariance function. Anal. Chem. 76, 3055. Martin, M. (1991). Que Peut-on Esperer du Couplage Entre Methods de Separation? Approache Statistique du Probleme. Proc. Congre Mesucora’91 1,3. Martin, M. (1992). Quelles Performances Attendre des Couplages en Chromatographie? Spectra 2000, 169 (Suppl.), 5. Martin, M. (1995). On the potential of two- and multi-dimensional separation systems. Fresenius J. Anal. Chem. 352, 625.

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Nagels, L.J., Creten, W.L. (1985). Evaluation of the glassy carbon electrochemical detector selectivity in high-performance liquid chromatographic analysis of plant material. Anal. Chem. 57, 2706. Nagels, L.J., Creten, W.L., Vanpeperstraete, P.M. (1983). Determination limits and distribution function of ultraviolet absorbing substances in liquid chromatographic analysis of plant extracts. Anal. Chem. 55, 216. Oros, F.J., Davis, J.M. (1992). Comparison of statistical theories of spot overlap in twodimensional separations and verification of means for estimating the number of zones. J. Chromatogr. 591, 1. Peters, M., Davis, J.M. (1998). Statistical-overlap theory for column switching. Am. Lab. 30 (18), 32C. Pietrogrande, M.C., Cavazzini, A., Dondi, F. (2000). Quantitative theory of the statistical degree of peak overlapping in chromatography. Rev. Anal. Chem. 19, 123. Pietrogrande, M.C., Marchetti, N., Dondi, F., Righetti, P.G. (2002). Spot overlapping in twodimensional polyacrylamide gel electrophoresis separations: a statistical study of complex protein maps. Electrophoresis 23, 283. Pietrogrande, M.C., Marchetti, N., Dondi, F., Righetti, P.G. (2003). Spot overlapping in twodimensional polyacrylamide gel electrophoresis maps: relevance to proteomics. Electrophoresis 24, 217. Press, W.H., Teukolsky, S.A., Vetterling, W., Flannery, B.P. (1992). Numerical Recipes in FORTRAN, 2nd edition. Cambridge University Press, Cambridge, UK. Roach, S.A. (1968). The Theory of Random Clumping. Methuen, London. Rowe, K., Davis, J.M. (1995). Relaxation of randomness in two-dimensional statistical model of overlap: theory and verification. Anal. Chem. 67, 2981. Rowe, K., Bowlin, D., Zou, M., Davis, J.M. (1995). Application of 2-D statistical theory of overlap to three separation types: 2-D thin-layer chromatography, 2-D gas chromatography, and liquid chromatography/capillary electrophoresis. Anal. Chem. 67, 2994. Samuel, C., Davis, J.M. (2002). Statistical-overlap theory of column switching in gas chromatography: application to flavor and fragrance compounds. Anal. Chem. 74, 2293. Schure, M.R. (1991). Resolution enhancement of chromatographic data. Considerations in achieving super-resolution with the constrained iterative relaxation method. J. Chromatogr. 550, 51. Shen, Y., Tolic, N., Zhao, R., Pasa-Tolic, L., Li, L., Berger, S.J., Harkewicz, R., Anderson, G.A., Belov, M.E., Smith, R.D. (2001). High-throughput proteomics using high-efficiency multiple-capillary liquid chromatography with on-line high-performance ESI FTICR mass spectrometry. Anal. Chem. 73, 3011. Shi, W., Davis, J.M. (1993). Test of theory of overlap for two-dimensional separations by computer simulations of three-dimensional concentration profiles. Anal. Chem. 65, 482. Slonecker, P.J., Li, X., Ridgway, T.H., Dorsey, J.G. (1996). Informational orthogonality of twodimensional chromatographic separations. Anal. Chem. 68, 682. Valentine, S.J., Kulchania, M., Srebalus Barnes, C.A., Clemmer, D.E. (2001). Multidimensional separations of complex peptide mixtures: a combined high-performance liquid chromatography/ion mobility/time-of-flight mass spectrometry approach. Int. J. Mass. Spectrom. 212, 97. Venkatramani, C.J., Xu, J., Phillips, J.B. (1996). Separation orthogonality in temperatureprogrammed comprehensive two-dimensional gas chromatography. Anal. Chem. 68, 1486.

4 DECODING COMPLEX 2D SEPARATIONS Francesco Dondi, Maria C. Pietrogrande, and Nicola Marchetti Department of Chemistry, University of Ferrara, 1-44100 Ferrara, Italy

Attila Felinger Department of Analytical Chemistry, University of P ecs, P ecs, Hungary

4.1 INTRODUCTION Separation and identification of complex multicomponent mixtures is a challenging task for frontier research fields, such as proteomics or metabolomics, as well as for traditional research fields (polymer, natural product, food, and environmental chemistry). Mixtures involved in these kind of analysis, can easily contain up to 5000 single components (SCs) or even more, but their exact number (m) is usually unknown. The number of SCs that can be highlighted strongly depends on two closely related factors belonging to the sample complexity: the power of the separation method (complete separation of each SC from all of the others is quite difficult to achieve) and the selected detection threshold level. Additionally, mixtures are defined as complex not only in terms of the number of SCs, but also by looking at the similarity of their SCs, the number and abundance of SC classes, for example, homologous series. These points were discussed in one of the last papers by Giddings (1995) where the concept of sample dimensionality, as a quantitative index of sample complexity, was introduced for the first time.

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

59

60

DECODING COMPLEX 2D SEPARATIONS

High separation capability is an increasingly requested feature in analyzing complex multicomponent mixtures: two-dimensional (2D) separation techniques are much preferred over the traditional one-dimensional separation methods such as gas chromatography (GC), liquid chromatography (LC), or electrophoresis because of the tremendous increase in peak capacity and selectivity (Schoenmakers et al., 2003). As a consequence, these separation methods produce bidimensional maps containing an elevated number of overcrowded spots. A clear example of that is displayed in Figure 4.1a, where a 2D polyacrylamide gel electrophoresis (2D PAGE) analysis1 is reported. Among the 2D separation techniques, LC LC — which constitutes the subject of this book—is the most suitable because it can combine quite different selectivity features belonging to different LC elution modes. The LC LC map of complex multicomponent mixtures contains an elevated number of SC spots: some of them are randomly spread over the 2D space, while others are organized into the so-called spot trains, as it can be seen in the several examples reported in the present book. Mastering the complexity of multicomponent 2D maps in terms of SCs and SC classes requires a specific theoretical support. Mixture identity is, in fact, exhausted not only by SC identity but also by the relationships—qualitative and quantitative—among the different SCs: such a richness of features is just revealed by the separation process and must be evaluated and managed in order to reach optimum separation results. Because of the tremendous amount of analytical– chemical information produced, an appropriate data handling method is mandatory to fully exploit multidimensional techniques. For example, in the map reported in Fig. 4.1a, 2315 spots are detected while the number of identified proteins is 865, but the reality can be much more complex than that shown in the figure. This also indicates the seriousness of the degree of overlapping and its dependence on the concentration. On the contrary, Figure 4.1b (Demalte-Annessi et al., 1999) enlightens the existence of ordered structures of compounds (see red boxes in the ordered region) as examples of compound classes embedded in a random constellation of spots. The goal of this chapter is to review the recent significant advances achieved in the study of 2D maps (Marchetti et al., 2004; Pietrogrande et al., 2002, 2003, 2005, 2006 a; Campostrini et al., 2005). Fundamental aspects concerning the intimate structure of multicomponent mixtures and separations will be discussed in Section 4.2 . Description of the methods recently developed by the present authors for characterizing the separation pattern complexity of a 2D multicomponent map will be presented in Sections 4.3 and 4.4 . These methods allow one to describe complex 2D separations in terms of SC number (m), the detection of hidden homologous series, spot shape features and separation performance. For these reasons they are named as “decoding methods.’’ In Section 4.5, the most recent achievements derived from the application

1 Human 2D PAGE database of Danish Centre for Human Genome Research, http://www.biobase.dk/cgibin/celis.

FIGURE 4.1 2D polyacrylamide gel electrophoresis (2D-PAGE) maps of protein mixtures. (a) Experimental 2D-PAGE map of keratinocytes. (Reproduced from Human 2D-PAGE database of Danish Center for Human Genome Research, www.biobase.dk/cgi-bin/celis, with kind permission.) The windows report two enlarged zones of the map showing different overcrowding conditions. (b) Example of digitized map of identified spots from colorectal adenocarcinoma cell line 2D PAGE map. Two regions of the map are enlightened showing different pI interdistance distribution: ordered and disordered. Enlarged detail: Histogram of the pI interdistance values and the estimated distribution using Equation 4.33 (red line). Reproduced from Pietrogrande et al. (2002) with permission from Wiley-VCH Verlag GmbH. (See color plate.)

62

DECODING COMPLEX 2D SEPARATIONS

of these procedures will be reviewed as application examples of the present models and methods.

4.2 FUNDAMENTALS: THE STATISTICAL DESCRIPTION OF COMPLEX MULTICOMPONENT SEPARATIONS The multicomponent mixtures considered here (indicated as “complex’’) are made by more than approximately 50 SCs: the reasons of this borderline value will be clear in the following text. These mixtures, when subjected to separation processes of medium-to-high efficiency, often produce “random’’ separation patterns. For example, their 1D chromatograms are typically made up by a random sequence of both Gaussian-shaped peaks—likely corresponding to SC peaks (singlets)—and of cluster peaks—corresponding to more than one SC (multiplet peaks). This finding is also common to 2D separations (see e.g., Fig. 4.1a and b) where “randomness’’ is spread over the two dimensions. The appearance of “randomness’’ is however a “subjective’’ impression and it does not mean that regularities are absent in the retention pattern. On the contrary, regularities are often present; in particular, they are of “statistical’’ character and thus “objective.’’ However, they are somewhat difficult to catch at first sight, since they do not exhibit themselves as “deterministic’’. In fact, statistical regularities are not located in a specific point or restricted region of the separation space, but they become evident only when the whole separation space is systematically investigated and the statistics of local observed features is made. For these purposes, a specific theoretical background and handling process are requested. One can also understand that statistics will lead the separation process itself, and great benefit in the separation strategy will result once these features will be mastered. A separation scientist has nonetheless diffidence to accept the statistical behavior in multicomponent separations. Honestly, we shared such uncertainty with some colleagues, too (Guiochon, 2005): it seems that this way of thinking is deeply rooted in our minds, since it comes rightly from our chemical way of thinking. In fact, when we are handling complex mixtures, we are very often interested only in some specific and “relevant-for-us’’ compounds, having precise, that is, deterministic— and thus not random—retention time positions. Moreover, a given group of selected SCs have often structures related to each other: the separation process, obeying well-established retention–structure relationships, will produce “deterministic’’ and not random retention patterns. This point of view can be extended to the whole separation pattern, with the result that its character should be deterministic. Randomness and its statistical description seem thus to conflict with both the practice and the “first principles’’ of separation science. These apparent paradoxes will be discussed below. All these uncertainties about the “randomness’’ in multicomponent retention patterns are overcome once its features are precisely stated. This will allow the subjective nature of the randomness to be made objective through proper analysis. We start with discussing the fact that the “pure random’’ pattern does not exist, but full

FUNDAMENTALS: THE STATISTICAL DESCRIPTION

(a)

63

S1 S2

SN–1 SN

Stot 0

6

5

m

10

3

5

m

(b)

3. f (µ) 2.

1.

0.

5.

m x 10

FIGURE 4.2 (a) Example of Poissonian sequence generation, Stot, by superimposition of elementary subsequences Si having uncorrelated phases. (b) Interdistance frequency function of the Poissonian sequence. Reproduced from Felinger et al. (1990) with permission from the American Chemical Society.

randomness is a limit condition when the multicomponent mixture is complex enough: with the help of the graphical scheme reported in Fig. 4.2 (Felinger et al., 1990) we will show how the “randomness’’ degree onsets and increases. Let us consider one homologous series—generally indicated as i-series—and characterized by a given structural increment, for example, a CH2 group. At the same time, let us separate this series through a 1D system where the separation coordinate is labeled as x. The partition free energy of the ni component of the i-series is Dmi;x ðni Þ ¼ ai;x þ bi;x ni ;

ni ¼ 1; ; nmax;i : i ¼ 1; ; imax

ð4:1Þ

64

DECODING COMPLEX 2D SEPARATIONS

where Dmi,x represents the partition free energy of the ni term of this i-series, while ai,x and bi,x are the intercepts and the slopes, respectively, here called the “phases’’ and the “frequencies’’ according to the nomenclature introduced by (Giddings 1995). i is ranging from 1 to imax (see Eq. 4.1), where imax is the number of homologous series (compound classes). In a 1D separation under good elution programming conditions, any i-series produces a retention sequence Si related to the same Equation 4.1 through a constant factor since, under these conditions, retention is proportional to partition free energy. Partition free energy in Equation 4.1 can be equally replaced by any other property proportional to the retention (e.g., electrophoretic mobility, isoelectric point, etc.). Fig. 4.2a (Felinger et al., 1990) reports the effect of superimposing several ordered sequences Si of retention points: the individual ordered patterns are lost and the overall SC sequence is seen as “random.’’ This property appears evident when three windows of equal length are separately considered (see Fig. 4.2a): they contain six, three or five points, respectively, which are clearly randomly varying numbers. We also understand that when the number of sequences, imax, increases, the randomness degree will be greater and greater, but our “subjective’’ feeling proves unable to precisely define the nature and the degree of such an increasing randomness. Randomness can be made “objective’’ by measuring some random variables over it and then performing an appropriate statistics. Let us consider the interdistances between subsequent time positions (Dx) in a superimposed pattern: they are clearly not regular and can be employed to characterize randomness (see Fig. 4.2a). In fact, when their frequency function is computed (Fig. 4.2b) a distinct and familiar regularity singles out the exponential function. f ðDxÞ ¼ l0 expðDxl0 Þ

ð4:2Þ

where l0 ¼

1 Dx

ð4:3Þ

is the frequency and Dx is the average interdistance. The distribution function F(m ¼ r) of the number of points m falling into a given interval DX, see the boxes marked in Fig. 4.2a—once computed over several intervals, will exhibit a distinct feature, which in this case is close to Poisson law (Feller, 1971), defined as Fðm ¼ rÞ ¼ m ¼ l0 DX

expðmÞmr r!

ð4:4Þ ð4:5Þ

where m is the average—or the expected—number of points in this windows. It is known that the Poisson pattern described by Equation 4.3 has also the unique property that interdistances between subsequent SC positions, Dx, are exponentially distributed (Feller, 1971) according to Equations 4.2 and 4.3. Accordingly, the feature enlightened

FUNDAMENTALS: THE STATISTICAL DESCRIPTION

65

in Fig. 4.2b proves that the randomness determined by the overlapping process of different sequences has a Poissonian character. It can be theoretically proven that the overall sequence approaches the Poisson law when imax increases (Feller, 1971). Moreover l0 is the inverse of the harmonic average value b given by l0 ¼

imax X 1 1 b bx i¼1 i;x

ð4:6Þ

Therefore, the actual randomness degree of a finite number of overlapping sequences is not yet perfectly Poissonian, as shown by the scattered point around the exponential decay in Fig. 4.2b. In this sense, both the Poisson law and the Exponential law (Eqs. 4.4 and 4.2) are thus “approximating’’ functions. In general, provided that the number of sequences (imax) are high enough, the approximation degree is good and one can replace the sample frequencies by those computed either by the Poisson law (Eq. 4.4) or the exponential law (Eq. 4.2) without having significant errors. This approximating condition means, in practice, that if one uses a statistical test (e.g., the c2 statistics) to verify the H0 hypothesis there is no statistical difference between the “actual’’ sequence of retention time positions and that one given by the Poisson law with the same parameters (Eqs. 4.2 and 4.3). Furthermore, the H0 hypothesis is not rejected (Dondi et al., 1986) and the SC retention time distribution is uniformly random (Davis and Giddings, 1985). Note that this way of proceeding does not mean that the actual sequence has features mathematically equal to those of the Poisson law, but that the Poisson law serves as an approximation, that is, a substitute most useful as will be shown below. Consequently, we can also affirm that a given multicomponent retention pattern, produced by a specific complex mixture, is “deterministic’’ in itself, but “random’’ when it is analyzed by the instrument of statistics. This clarifies the kind of information one can get by statistical investigation: only collective properties are obtained, and this is the character of information obtained by the present approach when applied to multicomponent separations. The Poissonian character of a multicomponent chromatogram assumed by Davis and Giddings (1983) as the fundamental hypothesis of their so-called Statistical Model of Overlapping (SMO) has thus well-founded grounds and it cannot be simply considered as one among the many possible models. Moreover, the SMO allowed deriving a method of estimating an important collective property of the mixture, the number of SCs, as well as other collective properties, such as the number of singlets present in the retention pattern. The SMO approach was also applied to describe 2D and 3D multicomponent separations (Davis, 1991; Oros and Davis, 1992). Mixing SCs in a mixture and overlapping many ordered sequences over a retention axis are two faces of the same coin: they are both entropy-creating processes and can equally be interpreted by using the entropy function concept (Dondi et al., 1998). Moreover, it is also worthwhile to compare similarities and differences between two processes producing randomness, the above-described Poisson type (Fig. 4.2a and Eq. 4.4), and the Gaussian one that is so often evocated in many fundamental branches of natural sciences (Feller, 1971). The latter refers to the addition of independent

66

DECODING COMPLEX 2D SEPARATIONS

random variables and is related to several important physical phenomena, for example, the diffusion process and the law of error distribution. Both these processes have the ultimate effect of destroying the order: the Poissonian one occurs by a superimpositionmixing mechanism, while the Gaussian one by an addition mechanism. Moreover both these distributions are maximum entropy type distributions (Davis, 1991; Dondi et al., 1998). Thus, both Gauss and Poisson laws play a fundamental role in separation science: they should constitute a natural way of thinking for scientists. One can consider the effect of varying either ai,x or bi,x when many ordered retention sequences are superimposed in 1D retention patterns. The four sequences considered in Fig. 4.2a differ, in fact, both in ai,x and bi,x. In Fig. 4.3a and b these two effects are

FIGURE 4.3 Example of 1D separations as the result of five superimposed homologous series having (a) constant phase (red arrow in inset) and (b) constant frequency (blue arrow in inset). (See color plate.)

FUNDAMENTALS: THE STATISTICAL DESCRIPTION

67

considered separately. Moreover, the band-broadening effect is also introduced. In particular, Fig. 4.3a represents a 1D chromatogram of a mixture having five homologous series with the same constant functional group (ai,x constant) but different bi;x increments (e.g. due to different branching, saturation degree, etc.). The second case (Fig. 4.3b) is representative of five linear homologous series having the same , OH, NO, etc.). It increment (e.g., CH2) but different functional groups (e.g., C can be seen that the result seems apparently the same, since both of them exhibit random character. Randomness degree with ai,x or bi,x in 2D retention patterns is instead quite different when one goes from 1D to 2D separations and this is clearly seen in Fig. 4.4a and b. In particular the randomness, and hence the spot overlapping degree,

FIGURE 4.4 Example of 2D separations of five homologous series having (a) constant phase (red vector in inset) and uncorrelated frequencies and (b) constant frequency (blue vector in inset) and uncorrelated phases. (See color plate.)

68

DECODING COMPLEX 2D SEPARATIONS

is greater in the case of randomness of the frequency values (bi,x; bi,y) than in the case of the only randomness of the phases values (ai;x ; ai;y ) as one can see by comparing Fig. 4.4a and b, respectively. By increasing the dimensionality of the separation system, the observed general effect is that the degree of separation increases, as one can see by comparing Figs. 4.3 and 4.4. We can say that this acts as a “demixing’’ or “decoding’’ process allowing singling out the hidden features of the complex multicomponent mixture. However this “decoding’’ effect acts quite differently according to the specific randomness in either ai,x or bi,x parameters: in the case when only the phase values of the homologous series are randomly distributed, almost complete resolution can be achieved when going from 1D to 2D (cf. Figs. 4.3b and 4.4b). The conclusion is thus that one should have alternative statistical methods to estimate the randomness degree. For example, the interdistance statistics presented in Fig. 4.2b, does not exhaust the richness of information hidden in complex retention patterns. In fact, when the Dx statistics are computed (see histograms in Fig. 4.1b), only interdistances between subsequent positions are accounted for and their order is lost. We will see that the autocovariance function discussed in Section 4.4 is able to single out the features lost by the simple interdistance statistics. Obviously, when going from 2D to 1D, we observe an inverse behavior, that is, a randomness is created. However, such a feature does not have only virtual interest but is of surprisingly practical value, as will be discussed in the subsequent paragraph. In fact the SMO approach, based on a 1D random pattern, was successfully applied to multicomponent 2D separations (Pietrogrande et al., 2002, 2003; Campostrini et al., 2005).

4.3 DECODING 1D AND 2D MULTICOMPONENT SEPARATIONS BY USING THE SMO POISSON STATISTICS The SMO developed by Davis and Giddings thoroughly defines the statistical regularities of Poissonian complex multicomponent chromatograms. Let us first consider the existence of statistical regularities in a heuristic way. This is illustrated in Fig. 4.5 by reporting three different hypothetical windows (Fig. 4.5b–d) of a multicomponent chromatogram obtained by computer simulation: the retention times obey the Poisson law and the SC peak height was assumed constant for the sake of simplicity. All these windows contain 10 SCs and each chromatographic pattern appears quite different from the others, but some statistical properties are similar. In order to explain this, we need to recall some basic separation parameters. The resolution between two SC peaks of the same height is defined as RS ¼

Dx0 4s

ð4:7Þ

where Dx0 is the interdistance between subsequent retention times and s is the peak standard deviation. In Fig. 4.5a, the well-known case of RS ¼ 1 is reported. When the well-known number of separated bands at RS ¼ 1 is estimated for Fig. 4.5b–d, by

DECODING 1D AND 2D MULTICOMPONENT SEPARATIONS

69

roughly assuming the resolution criterion shown in Fig. 4.5a, we always observe a value of 4. The number of separated bands in a given retention pattern window is obviously deterministic, but its value exhibits nonetheless regularity of a statistical type if the retention pattern is of a Poisson type. The point regarding a precise method of peak counting will be discussed below.

FIGURE 4.5 Clustering in 1D separations. (a) Resolution between adjacent SCs defines the critical distance Dx 0 . (b) interdistances between adjacent SCs (fourth through seventh SC) are considered. I > Dx0, II < Dx0, and III > Dx0. Thus fifth and sixth SC are merged in the same band (doublet) that is completely separated from the previous and subsequent bands.

70

DECODING COMPLEX 2D SEPARATIONS

The separation extent is defined as p g¼ m

ð4:8Þ

where p is the number of separated zones at a given RS value. Therefore, constant values (0.4 at RS ¼ 1) are obtained for the cases in Fig. 4.5b–d. Since g and p are related to the ensemble of SCs or the whole chromatographic pattern, they are examples of statistical parameters. Several other statistical parameters can be defined. If only one SC originates the peak, it will be defined as a singlet and its number will be called the number of singlets (s); when two SCs are overlapped together within the same separated zone, one has a doublet and its number will be d; likewise one has triplet peak number (t), and so on. Thus, the parameter p is given by p ¼ sþdþtþ

ð4:9Þ

and m ¼ s þ 2d þ 3t þ

ð4:10Þ

Not all of the above-described statistical quantities are chromatographically “observable.’’ For example, s, d, t, and m are not directly observable unless selective detectors as a mass spectrometer is employed (Campostrini et al., 2005) and thus they are “hidden’’ quantities. The point will be discussed in the third section of this chapter. A precise criterion should be established concerning the SC clustering mode that produces a peak. The criterion put forward (Davis and Giddings, 1983) is that only when interdistances between a SC peak and the other two nearby ones are greater than the critical distance Dx0 ¼ 4sRS (see Fig. 4.5a), the SC is completely separated and can be regarded as a singlet. Otherwise, SC peak clusters including two or more SCs having subsequent interdistances lower than Dx0 will generate a multiplet. In Fig. 4.5b, a doublet peak is described. Extension to the 2D case was done by Marchetti et al. (2004) and Marengo et al. (2005). Such a definition is however only for theoretical purposes and is not applicable since it implies that one knows the location of every SC inside the bands. Peaks and their number are instead currently detected and measured by an integrator or scanning devices and thus are instead “observable’’ quantities. The main drawback is that they are evaluated on the basis of established threshold and resolution criteria that very often are not precise. Nonetheless, the response values, even if poorly defined, can be used to estimate m—a “hidden’’ quantity, see above—in a random retention pattern, as described below. Fig. 4.6a reports a “line’’ chromatogram as a series of vertical lines whose location and heights correspond to the retention times and areas, respectively, of all of the peaks detected by the integrator according to a given experimental setup. There are 19 detected peaks, p0 , represented as Yi, i ¼ 1, . . ., p0 . The prime symbol (0 ) signifies the particular way of counting peaks as performed by the integrator.

DECODING 1D AND 2D MULTICOMPONENT SEPARATIONS

71

y

∆x0 = 0

(a) 2

4

6

8

10

12

14

Time

y 3

∆ x 0 = 0.14 2

2 4

6

2 8

10

12

(b)

14

Time

5 y

∆ x 0 = 0.34 2

4

6

3

2

2

(c) 8

10

12

14

Time

FIGURE 4.6 Scheme of the SMO procedure to estimate the number of SC. (a) Original line 0 chromatogram. (b, c) Multiplets are formed by applying increasing critical distance values, Dx 0 .

The following quantities can be calculated: 0

Ytot ¼

p X

Yi

ð4:11Þ

i

yobs ¼

Ytot p0

ð4:12Þ

Again we remember that each pulse of the “line chromatogram’’ (Fig. 4.6a), may be either a singlet or a muItiplet peak, and the recorded peak height is proportional to the area value of the detected cluster. can be obtained by using a procedure originally developed The number of SCs, m, by Davis and Giddings (1983, 1985) and further extended by Dondi et al. (1998) and Pietrogrande et al. (1995). It consists of generating other chromatograms, as shown by the procedure described in Fig. 4.6b and c, by increasing Dx0 0 and by merging together those peaks whose interdistance falls within Dx0 0. In Fig. 4.6b the number of detected peaks decreases from 19 to 16, since clusters were merged in three cases. The procedure is repeated with increasing Dx0 0 values: by this way, a set of values 0 ðy obs ; Dx 0 Þj is obtained, which is then employed to fit the following equation: ln yobs ¼ ln y þ m

Dx 0 DX

ð4:13Þ

where Ytot ð4:14Þ m is the estimate of the true SC number m. The method based on In Equation 4.13 m Equation 4.13 is an example of “statistical inference,’’ which allows one to obtain an yobs ¼

72

DECODING COMPLEX 2D SEPARATIONS

estimate of a “population’’ quantity. The bar over m just shows the character of average value estimate of the random variable m described by the Poisson law (see Eq. 4.2 ). In fact, the real sequence of SCs hidden in the line chromatogram should be considered as only one of the infinitely possible Poisson sequences of points pﬃﬃﬃﬃdescribed by Equation 4.2. The m value estimate has a standard deviation equal to m, which is the standard pﬃﬃﬃﬃ deviation of the Poisson variable m: for m > 50, the relative percent error (¼ 100= m) is lower than about 15%, and this justifies the above criterion put forward for defining “complex’’ multicomponent mixtures. Once m has been calculated, the so-called saturation factor in 1D separation can be obtained from a1D ¼

m n1D

ð4:15Þ

where n1D is the peak capacity of 1D separations. It is defined as n1D ¼

DX Dx0

ð4:16Þ

The SMO theories (Davis and Giddings, 1983; Pietrogrande et al., 1995) give estimate of s, d, t, . . . , and p in a given multicomponent retention pattern: s ¼ m expð2a1D Þ

ð4:17Þ

d ¼ m expð2a1D Þ½1expða1D Þ

ð4:18Þ

t ¼ m expð2a1D Þ½1expða1D Þ2

ð4:19Þ

p ¼ m expða1D Þ

ð4:20Þ

Consequently, several “hidden’’ quantities can be estimated on the basis of the SMO approach. The procedure based on Equation 4.13 can be simply extended even to 2D separations as described in Fig. 4.7. In practice, the 2D pattern, in terms of spot positions and abundances, is divided into several strips. Each strip is transformed into a 1D “line chromatogram’’ and the procedure described in Fig. 4.7 is then applied. Equation 4.13 is employed to calculate the m value of each strip from which the total m value is obtained. Applications to this procedure will be reported in Section 4.5. At this point, the reader’s attention is drawn to the fact that the procedure of transforming 2D strips into 1D chromatograms (see Fig. 4.7) once more corresponds to the overlapping mechanisms described in Fig. 4.2 and has been evocated in comparing Fig. 4.4 with Fig. 4.3. In this way, if random structures (e.g., such as those marked in Fig. 4.1b) are present, their “memory’’ is lost and the 2D pattern is reduced to a Poissonian 1D one. Therefore, the number of SCs can be correctly estimated, even if the 2D pattern was not Poissonian.

DECODING 1D AND 2D MULTICOMPONENT SEPARATIONS

0

2

4

Second separation axis, a.u.

0

6

2

73

8

4

8

6

8 6 4 2 0 0.0

0.2

0.4

0.6

0.8

1.0

First separation axis, a.u.

FIGURE 4.7 Procedure developed to reduce a 2D separation into several 1D chromatograms suitable for applying SMO method on 2D maps. As an example, only the first two strips are reported.

The SMO can be extended to the overlap probabilities of 2D spots. Davis and coworkers (Davis, 1991; Oros and Davis, 1992) determined the number of singlets, doublets, triplets, and total number of spots as s ¼ m expð4a2D Þ

ð4:21Þ

d¼

m expð4a2D Þ½1expð4a2D Þ 2

ð4:22Þ

t¼

m expð4a2D Þ½1expð4a2D Þ2 3

ð4:23Þ

p¼m

4a2D expð4a2D Þ 1expð4a2D Þ

ð4:24Þ

74

DECODING COMPLEX 2D SEPARATIONS

Equations 4.22–4.24 are the 2D equivalents of Equations 4.17–4.20. The comparison of the two sets of equations shows a surprising consequence. If the peak capacities of the 1D and 2D separation systems were identical, the 2D separation would lead to more severe overlap. In order to have the same number of components isolated as singlets with a 1D and a 2D separation system, the peak capacity of the 2D system (n2D) should be double of that of the 1D system (n1D). Ideally, in an orthogonal system n2D ¼ n21D , but part of the gain in peak capacity is lost due to the increased probability of peak overlap provided the 2D chromatogram is disordered.

4.4 DECODING MULTICOMPONENT SEPARATIONS BY THE AUTOCOVARIANCE FUNCTION Multicomponent signals often contain hidden structure or periodicities that are difficult to spot. Due to structural relationships that exist among the different sample components, the retention pattern—the distribution of peaks or spots—is characteristic, as discussed in Section 4.2. The ordered or structured distribution of the spots in a 2D separation space is often hidden to the eye but can be recognized by a proper mathematical tool. Fourier analysis has been long used to identify periodicity and order deeply buried in signals. Therefore, by calculating the power spectrum of the chromatogram, one can obtain information not directly available from the chromatogram. The statistical model of peak overlap clearly explains that the number of observed peaks is much smaller than the number of components present in the sample. The Fourier analysis of multicomponent chromatograms can not only identify the ordered or disordered retention pattern but also estimate the average spot size, the number of detectable components present in the sample, the spot capacity, and the saturation factor (Felinger et al., 1990). Fourier analysis has been applied to estimate the number of detectable components in several complex mixtures. An adaptation of Fourier analysis to 2D separations can be established by calculating the autocovariance function (Marchetti et al., 2004). The theoretical background of that approach is that the power spectrum and the autocovariance function of a signal constitute a Fourier pair, that is, the power spectrum is obtained as the Fourier transform of the autocovariance function. A multicomponent 2D chromatogram is considered as a series of 2D peaks with random position and height. For the sake of simplicity, here we assume that the peaks are modeled with bidimensional Gaussian peaks, thus the signal is expressed as ¥ X

"

ðxx0;i Þ2 ðyy0;i Þ2 hi exp f ðx; yÞ ¼ 2s2x 2s2y i¼¥

# ð4:25Þ

where hi is the random height and (x0,i, y0,i) the random position of peak i. The x and y widths of the elliptic 2D peaks are sx and sy, respectively. The autocovariance function of a 2D signal is calculated as

DECODING MULTICOMPONENT SEPARATIONS

s2 þ a2h cðx; yÞ ¼ h Tx Ty

75

Z¥ Z¥ uðv; wÞuðv þ x; v þ yÞdvdw

ð4:26Þ

¥ ¥

where ah is the mean value and s2h is the variance of the peak amplitudes; Tx and Ty are the mean x and y distances between adjacent peaks. Therefore, for f(x,y), we get the following autocovariance function: " # VT2 ðs2h =a2h þ 1Þ x2 y2 exp 2 2 cðx; yÞ ¼ 4sx 4sy 4psx sy mXY

ð4:27Þ

where m is the number of detectable components and X and Y are the lengths of the rectangular separation space. In the derivation of the above equation, we introduced the volume under a bidimensional Gaussian peak as Vi ¼ 2psxsyhi and the total volume under the entire chromatogram as VT ¼ 2psxsymah, where sx and sy are the SC spot standard deviation along the two separation axes. The above equation gives the autocovariance function of a disordered 2D chromatogram and serves as a basis to estimate the number of detectable components and the average widths of the peaks (i.e., spot sizes) along the x and y axes. In Fig. 4.8a a section of a disordered 2D separation map and its autocovariance function is plotted. The main peak is a bidimensional Gaussian peak centered at the origin (Fig. 4.9). Besides this peak in the center, some minor fluctuations of the autocovariance function are observed (Fig. 4.8b). The fluctuations can be considered as noise due to the fact that in a finite 2D chromatogram, we have a small number of spots and a finite section of the stochastic ensemble. The 2D autocovariance function can also be calculated from the 2D chromatogram acquired in digitized form, Ck;l ¼

y l x k N X k ¼ Mx ; ; 1; 0; 1; ; Mx 1 NX fi;j f Þ fiþk; jþl f l ¼ My ; ; 1; 0; 1; ; My Nx Ny i¼1 j¼1

ð4:28Þ

where Mx and My are the maximum number of lags over which the 2D autocovariance function is calculated. A nonlinear curve fitting procedure of the experimental (Eq. 4.28) to the theoretical (Eq. 4.27) 2D autocovariance function can serve to perform some fundamental characterization of the 2D separation. The total volume (VT) and the peak height dispersion (s2h =a2h ) can be readily measured in the chromatogram, thus the number of components (m) and the peak widths (sx and sy) can be estimated (Marchetti et al., 2004). The 2D autocovariance function can be utilized in a rather simple manner to get a quick estimation of the number of detectable components and the average peak widths. For that purpose only the maximum amplitude of the autocovariance function and the width at half-height should be measured (Pietrogrande et al., 2005, 2006a). The

76

DECODING COMPLEX 2D SEPARATIONS

FIGURE 4.8 Autocovariance function method computed on computer-generated 2D map. (a) Simulated disordered 2D maps containing 100 components. (b) Autocovariance function of the 2D map. Reproduced from Marchetti et al., (2004) with permission from the American Chemical Society.

autocovariance function at the origin equals cð0; 0Þ ¼

VT2 ðs2h =a2h þ 1Þ 4psx sy mXY

ð4:29Þ

Furthermore, since the xﬃ and y half-widths of the bidimensional Gaussian at halfpﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃ height are hx;1=2 ¼ 2 ln2sx ¼ 1:665sx and hy;1=2 ¼ 2 ln2sy ¼ 1:665sy , the number of components is estimated as m¼

VT2 ðs2h =a2h þ 1Þln 2 0:22 VT2 ðs2h =a2h þ 1Þ ¼ phx;1=2 hy;1=2 C0;0 XY hx;1=2 hy;1=2 C0;0 XY

ð4:30Þ

Only the central section of the autocovariance function has to be calculated (Fig. 4.9) and simple computations are required to estimate the sample complexity, that is, the

DECODING MULTICOMPONENT SEPARATIONS

77

FIGURE 4.9 The autocovariance function close to the origin of a 2D separation. The maximum amplitude and the characteristic half-widths at half-heights are used in the simplified analysis. Reproduced from Marchetti et al., (2004) with permission from the American Chemical Society.

number of SCs, m, and the separation performance represented by the average spot dimensions sx and sy. These computations, based on Equations 4.27–4.30, require the detection of the spots and the evaluation of their intensities (i.e., volumes) so that the peak height dispersion s2h =a2h is obtained from the 2D map. This problem is discussed in detail by Marchetti et al. (2004). The authors analyzed several 2D maps with the number of components up to m ¼ 1000 and saturation factors between a ¼ 0.05 and 0.3. They could estimate with accuracy the number of components and the average spot dimensions by fitting Equation 4.27 to the 2D autocovariance function calculated from the 2D separation map. Also, the simplified approach based on Equation 4.30 was successfully applied to computergenerated maps (Pietrogrande et al., 2005). The estimation of the number of components is essential for the calculation of the separation extent (see Eq. 4.8), which characterizes the completeness of the separation process. The estimation of the average spot size may be an important means to optimize the efficiency of the 2D separation and to detect overloading effects. When the positions of the spots reveal an ordered pattern on the separation map, the long-term correlations in the autocovariance function can be used to decode the ordered structure of the retention pattern. We can use a simple linear relationship to estimate the position of the nth spot (see Eq. 4.1) ðxn ; yn Þ ¼ ðDmx ; Dmy Þ ¼ ðax þ bx n; ay þ by nÞ

ð4:31Þ

The above relationship can be related to the change of partition free energies of members of homologous series due to the addition of a given structural increment or to any physical–chemical property that affects the retention.

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DECODING COMPLEX 2D SEPARATIONS

The 2D autocovariance function of the ordered chromatogram is " # nmax VT2 ðs2h =a2h þ 1Þ X 1 ðxbx kÞ2 ðyby kÞ2 exp cðx; yÞ ¼ 4s2x 4s2y 4psx sy mXY k¼0 ðnmax k1Þ

ð4:32Þ

where nmax þ 1 is the number of elements of the ordered series. The above autocovariance function shows a regular repetitive pattern of peaks. Since the distances between adjacent spots in the chromatogram are bx and by in the x and y directions, respectively, these distances and their harmonics will appear in the autocovariance function, yielding an ordered 2D spot series. Most often, real 2D chromatograms exhibit a composite ordered and disordered characteristic, that is, a series of disordered spots are superimposed over ordered spot sequences. When the chromatogram is derived from a mixture of several chemical families, a superficial look at the 2D separation map may give the impression of randomness. In that case, the autocovariance function, however, can resolve and help identify the hidden structured nature of the map. In Fig. 4.10, the superposition of 10 different ordered spot sequences is shown. All of them have different ax, ay phase values but the same ax, by frequencies. The symmetry of the spot series in the autocovariance function is obvious and the common frequency of the spot trains can be easily identified through the main spot train of the autocovariance function, which runs through the origin. The most intense, major spot train in the autocovariance function follows the direction of the spot trains in the separation map. The spot trains running along the x and y axes give the horizontal and vertical characteristic spot interdistances in the separation map. As an X-ray diffraction image map helps identify the lattice structure of a crystal, the autocovariance function of a 2D separation map may help recognize the chemical structure of complex mixtures. Another case where the spot trains of constant phases and random frequency values are superimposed is illustrated in Fig. 4.11. In the neighborhood of the origin, the frequency values can be easily identified. Symbol þ indicates the 10 frequency values for n ¼ 1 in Equation 4.27. When lines are drawn from the origin through these points, the spots corresponding to n ¼ 2 and n ¼ 3 can also be identified. Now the autocovariance function looks less ordered than before and the repetitive pattern is more difficult to recognize. Nevertheless, the directions along which the spots are repeated with constant distances can be identified and the superposition of chemical families is recognized. The applicability of the 2D autocovariance function method and the most relevant results obtained will be discussed in the next section.

4.5 APPLICATION TO 2D SEPARATIONS This section reviews the most recent results obtained by applying the abovedescribed methods to complex 2D separations. In particular, the case of 2D-PAGE

APPLICATION TO 2D SEPARATIONS

79

FIGURE 4.10 The autocovariance function of a complex map. (a) Density plot of 10 2D sequences (represented by Eq. 4.31). The structured map was obtained by assuming constant frequency values (bx ¼ 0.1 and by ¼ 0.1) and by changing the phase values (ax, ay)i to the following values: 1 (0.0445, 0.8182), 2 (0.0435, 0.7416), 3 (0.0232, 0.4456), 4 (0.0398, 0.6897), 5 (0.0554, 0.2403), 6 (0.0600, 0.0291), 7 (0.2585, 0.0411), 8 (0.4686, 0.0460), 9 (0.5264, 0.0301), 10 (0.8635, 0.0395). (b) 2D autocovariance function of map in (a) Reproduced from Marchetti et al. (2004) with permission from the American Chemical Society.

(2D-polyacrylamide gel electrophoresis) maps of protein mixtures is discussed. 2D PAGE is considered the classical and principal tool for protein separation—prior to mass spectrometry—to achieve the main goal of proteomics, that is, a comprehensive identification and quantification of every protein present in a complex biological sample that would allow analysis of an entire intact proteome (Wilkins et al., 1997; Righetti et al., 2001; Hamdan and Righetti, 2005). The major advantage of 2D-PAGE is that it enables simultaneous separation of thousands of unknown proteins, first by charge using isoelectric focusing (IEF) and

80

DECODING COMPLEX 2D SEPARATIONS

FIGURE 4.11 The autocovariance function of a structured map. (a) Density plot of 10 2D sequences (see Eq. 4.31). The structured map was simulated by assuming constant phase values (ax ¼ 0.05 and ay ¼ 0.05) and by changing the frequency values (bx, by)i to the following: 1 (0.1395, 0.2001), 2 (0.1932, 0.0542), 3 (0.1135, 0.0843), 4 (0.3568, 0.0549), 5 (0.0123, 0.2319), 6 (0.2873, 0.0102), 7 (0.0332, 0.1474), 8 (0.1512, 0.2576), 9 (0.3501, 0.1504), and 10 (0.2212, 0.2543). (b) 2D autocovariance function of map in (a); frequency position (bx, by)i; see (a) corresponding to correlation at n ¼ 1 (þ) (see Eq. 4.31); correlations at n ¼ 2 (~), correlation at n ¼ 3 (^); correlations belonging to the same series (dashed line). Reproduced from Marchetti et al. (2004) with permission from the American Chemical Society.

then by size using SDS–polyacrylamide gel electrophoresis. Since each cell or biological fluid can be formed by thousands of proteins present in a wide range of relative abundance and displaying great differences in structure and size, a comprehensive separation of all the proteins present in the sample is still far from being achieved and comigrating proteins in the same spot are in general present. At present, increasing accuracy and precision are achieved in image acquisition of the complex 2D gel and spot detection due to the development of computer-assisted analysis as well as in protein spot identification and quantitation due to the developing

APPLICATION TO 2D SEPARATIONS

81

of specific software programs (Melanie; PD Quest; Orengo et al., 2003). The explosion in proteomics research in recent years has brought a very large amount of generated information and data analysis algorithms to efficiently use the large amounts of data produced by each analytical run and solve the complexity of the proteome sample (Orengo et al., 2003; Farriol-Mathis et al., 2004; Stanislaus et al., 2004; Marengo et al., 2005). The statistical degree of overlapping (SDO) and 2D autocovariance function (ACVF) methods have been applied to 2D-PAGE maps (Marchetti et al., 2004; Pietrogrande et al., 2002, 2003, 2005, 2006a; Campostrini et al., 2005): the means for extracting information from the experimental data and their relevance to proteomics are discussed in the following. The procedures were validated on computersimulated maps. Their applicability to real samples was tested on reference maps obtained from literature sources. Application to experimental maps is also discussed. 2D maps with known separation properties were generated by computer calculations: each spot is considered as a point described by two position coordinates (pI and Mr of the center of the spot) and by a third coordinate describing spot intensity. The rejection algorithm (Pietrogrande et al., 2002; Marengo et al., 2005) was applied for independently generating the pI and Mr coordinates displaying the same distribution along the pI and Mr axes present in experimental maps: it was computed on the basis of the pI and Mr coordinates of 1956 identified spots in reference maps of human tissues retrieved from the SWISS-2D-PAGE database (Expasy). Intensity values were generated according to a random distribution, since it has been demonstrated to be the most probable distribution for a high number of components (Marchetti et al., 2004). Moreover, experimental reference maps of human tissues were studied: pI and Mr coordinates of identified spots were retrieved from the SWISS-2D-PAGE database, the values sx ¼ 0.009 pH and sx ¼ 0.0002 log Mr were assumed for spot dimension since they represent the standard case for experimental 2D-PAGE maps—normal sample loading of a tissue homogenate (ca. 1 mg total protein) and standard gel sizes (18 20 cm, IEF SDS-PAGE). As an example, the 2D-PAGE map of colorectal adenocarcinoma cell line (DLD1_HUMAN) (Demalte-Annessi et al., 1999) is reported in Fig. 4.1a. Looking into the details of the colorectal adenocarcinoma map, some regions exhibit the random pattern (disordered region in Fig. 4.1b, bottom histogram), while others show an order in spot position, since trains of protein spots are evident (ordered region in Fig. 4.1b, top histogram). The final result is that spot positions in the whole map are completely disordered, as the result of superimposition of many ordered and disordered sequences: by applying a c2 statistical test, the H0 hypothesis was accepted to verify that the SC positions in the real map display a uniformly random distribution (Pietrogrande et al., 2003, 2005). 4.5.1

Results from SMO Method

4.5.1.1 Estimation of the Separation Parameters The extension of the SDO procedure to 2D separations implies that the 2D map is divided into many strips considered as 1D separations on which computations are performed. Different

82

DECODING COMPLEX 2D SEPARATIONS

procedures can be used for division into strips: it can be performed along the pI or the Mr axis to obtain a fixed (10, second column in the table reported below) or a variable number of strips, by fixing the minimum number of SCs present in each strip (20, second column in the Table reported below) (Pietrogrande et al., 2002). On the basis of spot position and intensity, by assuming different critical inter0 distance Dx 0 values, experimental points can be obtained and fitted by a straight line the estimated number of (Eq. 4.13) whose slope represents a statistical estimation of m, single components. The values estimated for each 1D strip were added to obtain the total number of proteins (Pietrogrande et al., 2002). As an example, the data computed on colorectal adenocarcinoma cell line map (DLD1_HUMAN) (Demalte-Annessi et al., 1999; Pietrogrande et al., 2002). (Fig. 4.1a) are reported as follows. m 108 108

Strip number 10 3(20)

pﬃﬃﬃﬃ m

e%

CV%

101 10 105 10

5.6 2.8

2.0 1.9

m

Even if the number of proteins present in the map is low, the method provides a values with good accuracy—expressed as the relative helpful tool to estimate m vs. real m values, e%—and precision—as relative standard error of the estimated m deviation CV% on 50 runs—in particular, if the division procedure into variable number of strips is used (Pietrogrande et al., 2002). values estimated 4.5.1.2 Estimation of Spot Overlapping Degree Using the m from the SMO and 2D autocovariance function methods, the original SDO approach (Davis and Giddings, 1983, 1985) can be used to statistically estimate the overlapping degree present in the map, that is, number of spots formed by one, two, and three proteins (Pietrogrande et al., 2003). This information is useful for estimating the influence of different experimental conditions (strip dimension, detection system performance, pI range) on spot overlapping. The method (based on Eqs. 17–29) was applied to computer-generated 2D separations describing experimental 2D-PAGE gels. It was verified that the common situation of 2D-PAGE maps—ca. 1 mg total protein loaded and 18 20 cm, IEF SDS-PAGE gel sizes—is an overcrowded condition, where the singlets are the least abundant species. For example, in the case of 1500 proteins loaded, the singlets would be only 27% of the observed spots and this value falls to 14% for a total of 3000 polypeptide chains in the sample (Pietrogrande et al., 2003). Moreover, the method makes possible the prediction of the influence of different experimental conditions (strip dimension, detector system performance, pI range) on the overlapping degree. This information is useful for estimating the degree of error associated with identification and quantitation of each protein and for setting up experimental strategies, which will increase the resolution and separation performance (Pietrogrande et al., 2003). For example, the most overcrowded case

APPLICATION TO 2D SEPARATIONS

83

of a 3000 protein mixture, the dramatic situation of broad-range immobilized pH gradient (IPG) (pH 3–10 gradient), in 7 cm long strips—in which only 5% of the proteins will appear on the map as pure spots—can be significantly improved using longer strips—39 cm long to obtain 29% of pure spots—or detector devices with higher resolution—s ¼ 43%. An extraordinary improvement in spot purity is obtained if narrow-range strips—1 pH unit is covered between acid and basic zones—are used, that is, from 33% of singlets for 7 cm strips to 69% and 75% for 18 and 39 cm strips, respectively. Recently, the SMO and SDO models were, for the first time, applied to an experimental 2D-PAGE map and validated by data independently obtained by mass spectrometry (MS) analysis (Campostrini et al., 2005). The studied sample was a neuroblastoma xenograft implanted in mice, submitted to 2D-PAGE separation and analyzed by MS in a quadrupole—time-of-flight (TOF) mass spectrometer (2D map in Fig. 4.12). The spot purity was experimentally measured by MS analysis on 74 studied spots: 52 spots were found to be singlets (71% of the spots), 14 (19%) were doublets, 6 (8%) were triplets, and 1 each were quadruplets and quintuplets. The overlapping degree was also estimated using the SDO model: both the methods based on addition of 1D strips (Eqs 4.17–4.20) and on the whole 2D map (Eqs. 4.21–4.24) yielded the same results, since a low saturation is present in the map. The estimated values show an excellent agreement with experimental MS data

FIGURE 4.12 Experimental 2D-PAGE map of a neuroblastoma xenograft implanted in mice. The circles indicate the spots identified by MS analysis. Reproduced from Campostrini et al. (2005) with permission from Wiley-VCH Verlag GmbH.

84

DECODING COMPLEX 2D SEPARATIONS

suggesting that the SDO model is a powerful and robust method to accurately predict the overlapping degree present in a map. 4.5.1.3 Identification of the Ordered Pattern The SDO method can also be a powerful tool for identifying the presence of an ordered structure in the spot positions, even if it is hidden in the spot overcrowding and might escape detection. In fact, the specific feature of the experimental points, that is, deviation from linearity in Equation 4.13, can be related to properties of the separation pattern. A general relationship has been derived, showing that the function describing the interdistance distribution f(x) can be obtained by computing the first derivative of the relationship between the quantity 1=yobs and the critical interdistance values 0 Dx 0 (Pietrogrande et al., 2002):

f ðxÞ ¼ y

d

1 yobs

Dx0 0

ð4:33Þ

As an example, the case of an ordered region of the colorectal adenocarcinoma map (DLD1_HUMAN; Fig. 4.1b) is reported. Spot positions in an ordered region were projected into the pI separation axis and the histogram of pI interdistance distribution was computed (top inset in Fig. 4.1b). The order is singled out by the presence of some maxima in the histogram, corresponding to the interdistance values repeated in the map, for example, 0.04 and 0.06 pI values. This ordered separation pattern (deviation from linearity in Eq. 4.13) can also be correctly estimated by computation of Equation 4.33 (plot reported in the top inset in Fig. 4.1b). The obtained results show a good correspondence between the real (histogram in the inset in Fig. 4.1b) and the estimated distribution (continuous line in the inset in Fig. 4.1b). These results are particularly relevant, since such constant interdistances may be related to the chemical properties of the proteins present in the sample. For example, they may be consistent with protein isoforms differing in a constant variation of the number of ionogenic groups in the molecule suggesting the presence of co- and posttranslational modifications (Wilkins et al., 1997; Righetti et al., 2001; Mann and Jensen, 2003; Farriol-Mathis et al., 2004; Hamdan and Righetti, 2005). 4.5.2

Results from 2D Autocovariance Function Method

4.5.2.1 Estimation of the Separation Parameters The 2D autocovariance function method was validated on computer-generated maps describing experimental 2D-PAGE maps and applied to some reference 2D maps retrieved from the SWISS-2D-PAGE database (Marchetti et al., 2004; Pietrogrande et al., 2005): a hepatoblastoma-derived cell line (HEPG2_HUMAN) (Sanchez et al., 1995, 1997), a colorectal adenocarcinoma cell line (DL-1) (DLD1_HUMAN) (Demalte-Annessi et al., 1999) and a human plasma (PLASMA_HUMAN) (Blatter et al., 1993; Eckerskorn et al., 1997). The elliptical spot shape represents experimental conditions, that is,

APPLICATION TO 2D SEPARATIONS

85

sx ¼ 0.009 pH and sx ¼ 0.0002 logMr , correspond to the standard case of a 18-cm strip of broad pH range (pH 3–7) with standard (1 mm) scanner resolution (Pietrogrande et al., 2003, 2005). Spot abundance (AM) was described by an exponential (E) distribution (first column in the following table) yielding s2h =a2h ¼ 1:0 (fifth column). The 2D autocovariance function was computed on the digitized map signal using Equation 4.28 and the separation parameters were estimated according to Equation 4.30. The results obtained are reported in the following table (Pietrogrande et al., 2005). AM E E E

m

sx

HEPG2 0.009 99 DL-1 0.009 108 PLASMA 0.009 626

sy

s2h =a2h

mest

sx,est

sy,est

s2m =a2m

0.0002

1

100 10 0.009

0.0002

0.99

0.0002

1

104 10 0.009

0.0002

0.99

0.0002

1

601 24 0.009

0.0002

0.97

A correct estimation of the number of proteins, m, present in the sample can be obtained within a bias of 4% (compare second and sixth columns), though the number of proteins considered is low and the theoretical model describing the distribution of SC positions is far from being thoroughly known. The most critical parameter in Equation 4.30 is the SC abundance dispersion ratio (s2h =a2h , fifth column in the table). As a consequence of SC spot overlapping, the experimentally accessible parameter is the maximum spot dispersion ratio (s2m =a2m , ninth column), which is introduced in Equation 4.30 to approximate the s2h =a2h value (compare fifth and ninth columns in the table). The results in the table also show that the 2D autocovariance function method gives a correct estimation of the mean spot shape for all the simulated maps (sx and sy in third to fourth and seventh to eighth columns) (Pietrogrande et al., 2005). It must be emphasized that the availability of the SMO and 2D autocovariance function methods as two independent statistical procedures to estimate the same parameter, m, the number of proteins, is a helpful tool to verify the reliability of the results obtained. In the case of the 2D PAGE map of colorectal adenocarcinoma cell line (DL-1) an excellent agreement was found between the values obtained from the SMO method—m ¼ 101 10 and m ¼ 105 10—and the 2D autocovariance function procedure—m ¼ 104 10 (Pietrogrande et al., 2006a). 4.5.2.2 Identification of Ordered Structures The great strength of the 2D autocovariance function in identifying ordered sequences was tested in the case of spot trains in 2D-PAGE maps. They are consistent with protein isoforms differing in a constant variation of the number of ionogenic groups in the molecule, as a consequence of co- and post-translational modifications (PTMs), such as glycosylation, phosphorylation and deamidation (Wilkins et al., 1997; Righetti et al., 2001; Hamdan and Righetti, 2005). Identification of protein PTM’s is very important information for proteomics, since it has been well established that PTMs

86

DECODING COMPLEX 2D SEPARATIONS

APPLICATION TO 2D SEPARATIONS

87

occur on almost all proteins and are of extreme biological importance, that is, they can regulate a variety of protein activities, such as enzymatic activity, ability to interact with other proteins, subcellular localization, and targeted degradation (Mann and Jensen, 2003; Farriol-Mathis et al., 2004). As an example, the case of spot trains showing a one-dimensional shift parallel to the pI axis is reported. This is a common feature in 2D gels due to protein isoforms submitted to PTMs yielding a change in amino acid charges with a consequent alteration in pI, while not necessarily in Mr (Wilkins et al., 1997; Righetti et al., 2001; Hamdan and Righetti, 2005). For studying this effect, a computer-simulated map was generated (Fig. 4.13a), where a train of eight spots in the pI range 5–6.4 pH, with a constant DpI of 0.2 pH at a constant log Mr value of 0.67 was superimposed onto a disordered map containing 200 SCs. The 2D autocovariance function was computed on the selected map region that contains the spot train (eight proteins) in addition to 53 proteins randomly located (4.5–7 pI and 0.65–0.68 log Mr values, enlarged inset in Fig. 4.13a). From the 2D autocovariance function, the number of proteins present in this map region, m, can be correctly estimated: m ¼ 53 7 for the original computer-generated map containing 53 SCs (blue line in inset in Fig. 4.13b) and m ¼ 62 8 for the map where the spot train was added (red line in inset in Fig. 4.13b). In the 2D autocovariance function plot (Fig. 4.13b) well defined deterministic cones are evident along the DpI axis at values DpH 0.2, 0.4, 0.6 pH: they are related to the constant interdistances repeated in the spot trains. This behavior is more clearly shown by the intersection of the 2D autocovariance function with the DpI separation axis. The inset in Fig. 4.13b reports the 2D autocovariance function plots computed on the same map with (red line) and without (blue line) the spot train. A comparison between the two lines shows that the 2D autocovariance function peaks at 0.2, 0.4, 0.6 DpH (red line) clearly identifying the presence of the spot train singling out this ordered pattern from the random complexity of the map (blue line, from map without the spot train). The difference between the two lines identifies the contribution of the two components to the complex separation: the blue line corresponds to the random separation pattern present in the map; the red line describes the order in the 2D map due to the superimposed spot train. The high sensitivity of the 2D autocovariance function method in detecting order is noted; in fact it is able to detect the presence of only sevenfold repetitiveness hidden in a random pattern of 200 proteins (Pietrogrande et al., 2005).

3 FIGURE 4.13 Identification of a train of spots by the 2D autocovariance function method. (a) Simulated map formed by a train of eight spots in the pI range 5–6.4, with a constant DpI of 0.2 pH and at a constant log Mr value of 0.67, in addition to 200 SCs randomly located. Enlarged detail: selected 0.65–0.68 log Mr region of the map containing a train of eight spots. (b) Plot of the 2D autocovariance function computed on the selected map region containing a train of eight spots. Enlarged detail: 2D-EACVF values over the pI separation axis: comparison between the 2D EACVF computed on the map with the train of spots (red line) and the map without the spot train (blue line). Reproduced from Pietrogrande et al. (2005) with permission from Wiley-VCH Verlag GmbH. (See color plate.)

88

DECODING COMPLEX 2D SEPARATIONS

4.6 CONCLUDING REMARKS At present, increasing accuracy and precision are achieved in separation performance and signal acquisition for complex 2D separations. However, the complexity of the plethora of data obtained requires proper signal processing procedures for a complete extraction of all of the analytical information. The mathematical–statistical methods reviewed here have proven to be powerful tools for the extraction of the most relevant information on the separation: sample complexity, separation performance, overlapping extent, and identification of ordered patternspresentinspotpositionsrelatedtochemicalcompositionofthecomplexsample. Moreover, the two procedures display different and complementary properties so that each of them is the method of choice to obtain specific information on the 2D separations. The SMO procedure is an unique tool to quantitatively estimate the degree of peak overlapping present in a map as well as to predict the influence of different experimental conditions on peak overlapping. The strength of the 2D autocovariance function method lies in its ability to simply single out ordered retention pattern hidden in the complex separation, which can be related to information on the chemical composition of the complex mixture. These statistical methods give a comprehensive description of the whole separation and therefore can also be used as tools to investigate separation properties for quality assurance procedures. Further developments of this topic are in progress, both in developing theoretical models and in applying the procedures to real experimental cases. In particular, the methods can be extended to hyphenated techniques to investigate the complex signals obtained from mass spectrometry detection (Pietrogrande et al., 2006b).

ACKNOWLEDGMENTS This work was supported by the Italian University and Scientific Research Ministry (grants 2003039537_005 and 2005032388_004) and by the University of Ferrara, Italy; it was also supported by grant T 048887 from the Hungarian National Science Foundation (OTKA).

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Davis, J.M., Giddings, J.C. (1983). Statistical theory of component overlap in multicomponent chromatograms. Anal. Chem. 55, 418. Davis, J.M., Giddings, J.C. (1985). Statistical method for estimation of number of components from single complex chromatograms: application to experimental chromatograms. Anal. Chem. 57, 2178. Demalte-Annessi, I., Sanchez, J.-C., Hoogland, C., Rouge, V., Binz, P.-A., Appel, R.D., Hochstrasser, D.F. (1999). Submitted to the SWISS-2D-PAGE database. Dondi, F., DuhaleKahie, Y., Lodi, G., Remelli, M., Reschiglian, P., Bighi, C. (1986). Evaluation of the number of components in multicomponent liquid chromatograms of plant extracts. Anal. Chim. Acta. 191, 261. Dondi, F., Bassi, A., Cavazzini, A., Pietrogrande, M.C. (1998). A quantitative theory of the statistical degree of peak overlapping in chromatography. Anal. Chem. 70, 766. Eckerskorn, C., Strupat, K., Schleuder, D., Hochstrasser, D.F., Sanchez, J.-C., Lottspeich, F., Hillenkamp, F. (1997). Analysis of proteins by direct-scanning infrared-MALDI mass spectrometry after 2D-PAGE separation and electroblotting. Anal. Chem. 69, 2888. Expasy,http://www.expasy.ch. Farriol-Mathis, N., Garavelli, J.S., Boeckmann, B., Duvaud, S., Gasteiger, S.E., Gateau, E., Veuthey, A.-L., Bairoch, A. (2004). Annotation of post-translational modifications in the Swiss-Prot knowledge base. Proteomics 4, 1537. Felinger, A., Pasti, L., Dondi, F. (1990). Fourier analysis of multicomponent chromatograms. Theory and models. Anal. Chem. 62, 1846. Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. II, 2nd edition. John Wiley & Sons Ltd, New York. Giddings, J.C. (1995). Sample dimensionality: a predictor of order–disorder in component peak distribution in multidimensional separation. J. Chromatogr. A 703, 3. Guiochon, G. (2005). Private communication. Hamdan, H., Righetti, P.G. Proteomics Today. Wiley, Hoboken, 2005. Mann, M., Jensen, O.L. (2003). Proteomic analysis of post-translational modifications. Nature Biotech. 21, 255. Marchetti, N., Felinger, A., Pasti, L., Pietrogrande, M.C., Dondi, F. (2004). Decoding twodimensional complex multicomponent separations by autocovariance function. Anal Chem 76, 3055. Marengo, E., Robotti, E., Antonucci, F., Cecconi, D., Campostrini, N., Righetti, P.G. (2005). Numerical approaches for quantitative analysis of two-dimensional maps: A review of commercial software and home-made systems. Proteomics 5, 654. Melanie II, Geneva BioInformatics, GeneBio S.A., http://www.genebio.com. Orengo, C.A., Jones, D.T., Thorton, J.M. (2003). Bioinformatics. Genes, Proteins and Computers. BIOSOxford, UK pp. 245–271. Oros, F.J., Davis, J.M. (1992). Comparison of statistical theories of spot overlap in twodimensional separations and verification of means for estimating the number of zones. J. Chromatogr. A 591, 1. PD Quest, Bio-Rad Laboratories Inc., http://www.biorad.com. Pietrogrande, M.C., Dondi, F., Felinger, A., Davis, J.M. (1995). Statistical study of peak overlapping in multicomponent chromatograms: importance of the retention pattern. Chemometr. Intell. Lab. Syst. 28, 239.

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Pietrogrande, M.C., Marchetti, N., Dondi, F., Righetti, P.G. (2002). Spot overlapping in two-dimensional polyacrylamide gel electrophoresis separations: a statistical study of complex protein maps. Electrophoresis 23, 283. Pietrogrande, M.C., Marchetti, N., Dondi, F., Righetti, P.G. (2003). Spot overlapping in twodimensional polyacrylamide gel electrophoresis maps: relevance to proteomics. Electrophoresis 24, 217. Pietrogrande, M.C., Marchetti, N., Dondi, F. Righetti, P.G. (2006a). Decoding 2D-PAGE complex maps: relevance to proteomics. J. Chromatogr. B 833, 51. Pietrogrande, M.C., Marchetti, N., Tosi, A., Dondi, F., Righetti, P.G. (2005). Decoding twodimensional polyacrylamide gel electrophoresis complex maps by autocovariance function: a simplified approach useful for proteomics. Electrophoresis 26, 2739. Pietrogrande, M.C., Zampolli, M.G., Dondi, F. (2006b). Identification and quantification of homologous series of compound in complex mixtures: autocovariance study of GC/MS chromatograms. Anal Chem. 78, 2579. Righetti, P.G., Stoyanov, A., Zhukov, M.Y. (2007). The Proteome revisited: Theory and Practice of All Relevant Electrophoretic Steps. Elsevier, Amsterdam. pp. 275-378. Sanchez, J.-C., Appel, R.D., Golaz, O., Pasquali, C., Ravier, F., Bairoch, A. Hochstrasser, D.F. (1995). Inside SWISS-2D PAGE database. Electrophoresis 16, 1131. Sanchez, J-C., Wirth, P., Jaccoud, S., Appel, R.D., Sarto, C.Wilkins, M.R., Hochstrasser, D.F. (1997). Simultaneous analysis of cyclin and oncogene expression using multiple monoclonal antibody immunoblots. Electrophoresis 18, 638. Schoenmakers, P., Marriott, P, Beens, J. (2003). Nomenclature and conventions in comprehensive multidimensional chromatography. LC-GC Europe 16, 335. Stanislaus, R., Jiang, L.H., Swartz, M., Arthur, J. Almeida, J.S. (2004). An XML standard for the dissemination of annotated 2D gel electrophoresis data complemented with mass spectrometry results. BMC Bioinformatics 5, 9. Wilkins, M.R., Williams, K.L., Hochstrasser, D.F. editors. (1997). Proteome Research: New Frontiers in Functional Genomics. Springer, New York.

PART II COLUMNS, INSTRUMENTATION AND METHODS DEVELOPMENT

5 INSTRUMENTATION FOR COMPREHENSIVE MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Robert E. Murphy Kroungold Analytical, Inc., Encinitas, CA 92024, USA

Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Co., Springhouse, PA 19477-0904, USA

5.1 INTRODUCTION Multidimensional liquid chromatography (MDLC) is a high performance separation system that is gaining popularity for the separation of complex samples including synthetic polymers and biomolecules. The two-dimensional version of MDLC, 2DLC, has gained popularity in recent years as a column-based separation method with much higher resolution, selectivity, and peak capacity than single-column separation methods. In these systems, sequential finite-volume aliquots from the first column (i.e., first dimension) are sampled by the second column (i.e., second dimension) typically by using sampling loops and valves. These multidimensional chromatographic systems, which operate primarily with liquid-phase samples, have been reviewed previously and these references are listed in Chapter 1 of this book. In this chapter, we will review the instrumentation requirements for comprehensive 2DLC systems and for the simpler systems that make deliberate cuts of the first dimension Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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FIGURE 5.1 Heart-cut 2DLC where a zone from the first-dimension separation is reinjected onto a second-dimension column for improved resolution of three analytes coeluting on the first-dimension column.

and divert the zone to another column—the so-called “heart cutting’’ mode of operation. In the heart-cutting mode of operation, one or several discrete zones are collected from the first-dimension column and reinjected into the second-dimension separation system. The resulting data are one or more individual one-dimensional datasets and are useful for resolving fused peaks from specific region(s) of the first-dimension separation system. An example of zone reinjection is shown in Fig. 5.1; clearly, the second column provides the selectivity for the three peaks that the first column did not have. Heart cutting can be implemented in the simplest of cases with a fraction collector (or even just a bottle) where the zone of interest or a segment of the zone is collected. The collected sample is then injected into an LC system with different column selectivity. This mode of operation has a long history starting with the isolation of plant chlorophyll extracts using adsorption columns (Manning and Strain, 1943). These early developments of the heart-cutting methodology are summarized by Dixon et al. (2006) in their review of MDLC. However, the modernversion of heart cutting appears to be described using high performance liquid chromatography (HPLC) equipment by Huber et al. (1973) where a valve is used to divert the column effluent into another column system with separate pump and detector(s). This configuration is shown in Fig. 5.2 utilizing a six-port valve. The simplest way to conceptualize the comprehensive 2DLC experiment is to collect fractions from a single column (in this case the first dimension or first column). All of the collected fractions are then injected individually into a second column (the second dimension). The resulting chromatograms are then put into the rows of a matrix. An eight-port or a ten-port valve with dual matching sample loops is generally used for automated comprehensive 2DLC so that solute is injected repetitively into the

HEART-CUTTING VERSUS COMPREHENSIVE MODE

Pump 1

Pump 1

Column 1

Column 1

Position A

95

Position B Waste

Waste

Loop

Loop

Pump 2

Pump 2 Column 2

Column 2

Detectors

Detectors

FIGURE 5.2 Heart-cut 2DLC with a six-port valve. In position A, the loop is being loaded with a sample from column 1, and column 2 is being equilibrated. In position B, the loop is injected onto column 2 where separation takes place with subsequent detection.

second-dimension separation system. Alternatively, columns can be used in place of sample loops. The specific choice of parameters used for the 2DLC experiment is discussed in the next chapter along with method development procedures. The detector signals from the second dimension column are formed into a matrix A and the matrix is visualized and analyzed. The Aij matrix is the amplitude of a singlechannel detector where i is the row number and represents the sequential injection number for the second-dimension analysis. The subscript j is the column number and designates the detector amplitude at time tj. It is assumed that the detector sampling rate is constant from injection to injection so that tj is a constant in each row. Furthermore tjþ1 tj is a constant throughout the chromatogram. The matrix is visualized through contours of constant amplitude or concentration or through a two-dimensional peak presentation. This presentation mode will be discussed in detail below.

5.2 HEART-CUTTING VERSUS COMPREHENSIVE MODE The number of samples or heart cuts injected into the second-dimension column defines whether the technique is referred to as heart cutting or comprehensive. We arbitrarily define heart-cut 2DLC as sampling less than 10 injections into the second dimension, and comprehensive 2DLC as sampling more than 10 injections into the second dimension with repetitive sampling over the length of the first-dimension experiment. Moreover, heart-cut 2DLC generally samples certain portions of interest

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TABLE 5.1 Classification and Examples of Two-Dimensional Liquid-Phase Separation Techniques Number of fractions sampled into second dimension Technique example Typically < 10 (heart cut)

Heart-cut LC Cross-fractionation LC/LC (mixed mode)

Typically > 10 and repetitive (comprehensive)

LC/LC (six-port valve) LC/CE Thermal FFF/GPC LC/LC (eight-port valve) Gel electrophoresis/LC CE/GE TLC/TLC IEF/PAGE

References Majors (1980) Augenstein and Stickler (1990) Pasch et al. (1992) Balke and Patel (1980) Chapter 11 Holland (1995) Lemmo and Jorgenson (1993) Venema et al. (1997) Erni and Frei (1978) Bushey and Jorgenson (1990) Rose and Opiteck (1994) Liu and Sweedler (1996) Rezanka (1996) Celis and Bravo (1984)

in the first dimension separation, whereas comprehensive 2DLC samples the entire first dimension into the second dimension. The difference in sampling also results in different data presentations. Heart-cut 2DLC is presented as stacked plots of each injection into the second dimension, and comprehensive 2DLC can be represented as a contour or projection. Table 5.1 lists several heart-cut and comprehensive techniques. Heart-cut 2DLC is very common and has great application for the increased resolution of one or several components from the first dimension (Augenstein and Stickler, 1990; Majors, 1980; Pasch et al., 1992; and Dixon et al., 2006). Heart-cut 2DLC for the analysis of polymers is often referred to as “cross-fractionation’’ (Balke and Patel, 1980). Protein digest analysis with MS/MS identification has been called “multidimensional protein identification technology’’ or “MUDPIT.’’This is described in detail in Chapter 11. In comprehensive 2DLC, partial sampling is used when the entire volume cannot be injected into the second dimension (Holland and Jorgenson, 1995; Venema et al., 1997) or when capillary electrophoresis (CE) is utilized in the second dimension (Lemmo and Jorgenson, 1993). 2DLC utilizing an eight-port or ten-port valve is commonly used for sampling the entire volume of the first dimension (Erni and Frei, 1978; Bushey and Jorgenson, 1990a). Gel electrophoresis (GE) was coupled to HPLC in a comprehensive mode (Rose and Opiteck, 1994), as well as CE and GE (Liu and Sweedler, 1996). Two-dimensional thin layer chromatography (2DTLC) (Rezanka, 1996) and polyacrylamide gel electrophoresis (PAGE)/isoelectric focusing (IEF) (Celis and Bravo, 1984) are also considered comprehensive since the entire sample is subjected to both dimensions noting that all two-dimensional planar techniques are comprehensive by definition.

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Far more information can be obtained by using comprehensive sampling as opposed to the heart-cutting method as higher resolution is typically obtained across the whole range of first-dimension elution (Erni and Frei, 1978; Bushey and Jorgenson, 1990b; Kilz et al., 1995; Murphy et al., 1998a). In some respect, heart cutting is useful when it is known where the zone is overlapped a priori but this case is rarely known for samples where composition is variable. The instrumentation used to implement this comprehensive sampling is the main topic of this chapter. The most common comprehensive mode uses a sampling valve so that second dimension elution can begin as soon as a sampling loop has stored the necessary amount of first column solute. In this case, there is no need for storing the effluent from the first column; it is continuously allocated to a sampling loop with subsequent injection into the second-dimension column. Schoenmakers et al. (2003) define a two-dimensional separation as comprehensive if 1. every part of the sample is subjected to two different separations; 2. equal percentages (either 100% or lower) of all sample components pass through both columns and eventually reach the detector; 3. the separation (resolution) obtained in the first dimension is essentially maintained. These authors clarify these criteria but the essential operation is that the comprehensive separation takes a one-dimensional data representation and through the use of a second separation mechanism converts this to a two-dimensional presentation of the data, as seen in most of the chapters of this book. Topics which will be presented in this chapter include the hardware, software, automation, valve and column configurations, and integration used in comprehensive 2DLC. Aspects of the 2DLC experiment in conjunction with multichannel detectors such as UV diode array optical detectors and mass spectrometers are discussed along with the handling of the data, which is expected to expand in scope in the future as chemometric methods are more widely used for data analysis. 5.3 CHROMATOGRAPHIC HARDWARE 5.3.1

Valves

Many different types of valves are used to control the collection and injection of stored column effluent. We will review most of the valve systems that have been used in 2DLC and will highlight the advantages of these systems where possible. Note that there is an endless combination of these configurations. We note one excellent review (Shalliker and Gray, 2006) that contains details of valve configurations with a good level of detail. When elution chromatography is used in both dimensions, the valve configurations are similar for the different column combinations. However, when CE is utilized as the second dimension, other types of interfaces not based on valves have been implemented with unique advantages. These and the microfluidic implementation of sampling systems for chip-based two-dimensional separations will be discussed below.

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5.3.1.1 2DLC with Partial Sampling or Heart-Cut Mode A six-port valve is generally used for the partial sampling of the first dimension separation. Fig. 5.2 shows the valve configuration for heart cutting parts of zones for further analysis in a second column. This setup can be used for sampling the first dimension one or several times depending on the run time in the second dimension and the problem being solved. The most common application of this valve is for analyzing one fraction that is unresolved in the first dimension, shown in Fig. 5.1. Operation with heart-cut 2DLC is not as complex as comprehensive 2DLC since the sampling is less frequent and most often done manually. Fig. 5.3 displays a schematic for the HPLC configuration using two six-port valves along with the steps employed for the quantitative analysis of antibodies in serum

FIGURE 5.3 2DLC configuration and sequence utilizing a protein A affinity column in the first dimension and an SEC column in the second dimension. In step 1, the sample is injected onto the affinity column and the first-dimension separation takes place while the SEC column is being equilibrated. In step 2, valve 1 moves to position 2 and a fraction of the affinity separation is collected into the loop. In step 3, valve 1 moves back to position 1 and the collected sample is injected onto the SEC column for MS analysis. In step 4, after the protein elutes from the SEC column valve 2 is switchedtoposition2andtheSECcolumneffluentissenttowastetoavoidsaltsfromenteringtheMS.

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FIGURE 5.4 Chromatograms of 2DLC (affinity/SEC/MS). Bottom trace is affinity separation with UV detection and 2 min fraction specified. Middle trace is MS total ion chromatogram showing protein elution and salts diverted to waste. Top trace in MS extracted ion chromatogram of protein of interest.

(Murphy et al., 2005). The system utilizes affinity chromatography in the first dimension for the retention of antibodies, and the six-port valve is used to capture the antibody fraction in the loop during elution, then transferred to a size exclusion chromatography (SEC) column. The SEC column separates the proteins from the lower mass salts so that mass spectrometry (MS) can be used as a detector without introducing deleterious effects due to the presence of salt. The resulting chromatograms are shown in Fig. 5.4. This system allows for the automated analysis of serum and is used for both quantitative and qualitative analyses. There are many more applications of 2DLC with a six-port valve and a list of biomedical applications was previously reviewed (Somsen and deJong, 2002). 5.3.1.2 2DLC with Complete Sampling or Comprehensive Mode Table 5.2 lists several comprehensive 2DLC methods with the corresponding columns in each dimension, valves, and detectors utilized. Many 2DLC systems use 8-port valves, 10-port valves, or fraction collectors to retain the entire sample or increase the concentration for the second dimension detection. An eight-port valve with matching sample loops is typically used for the coupling and repetitive sampling of the first-dimension separation system when the comprehensive mode of operation is utilized, as shown in Fig. 5.5. This valve configuration can be used for the heart-cut mode where only a portion of the sample from column 1 enters column 2, or it can be used in the comprehensive mode where the total sample from column 1 enters column 2 depending on the sampling rate. This was the valve configuration used by Erni and Frei (1978) in the first comprehensive 2DLC

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TABLE 5.2 Sampling First dimension

Two-Dimensional Liquid Separation Examples with Comprehensive Second dimension

Detection

Separation

Valve

References Bushey and Jorgenson (1990a) Isobe et al. (1991) Holland and Jorgenson (1995) Kilz et al. (1995) Opiteck et al. (1997)

CEC

SEC

UV

Protein mix

8-port

AEC

RPLC

UV

Brain extracts

6-port

AEC

RPLC

LIF

Protein digest

6-port

NPLC

SEC

UV, RI

6-port

CEC

RPLC

UV, MS

SEC

RPLC

UV

Copolymer blends Protein mix and E.coli lysate E.coli lysate

RPLC

SEC

ELS

Polymer mix

NPLC

RPLC

ELS

AEC/CEC

RPLC

UV

Surfactant oligomers Protein mix

HILIC

RPLC

UV, MS

CF

RPLC

MS

CEC

RPLC

UV

RPLC

RPLC

UV

Protein mix, cell lysates Breast cancer cell lysates Protein digest Maize metabolites

8-port

2 4-port

Opiteck et al. (1998) 8-port Murphy et al. (1998a) 8-port Murphy et al. (1998b) 8-port Unger et al. (2000) 8-port Murphy (2001) Fraction Chong et al. collector (2001) 10-port Stoll and Carr (2005) 2 6-port Stoll et al. (2006)

AEC: anion-exchange chromatography; CEC: cation-exchange chromatography; CF: chromatofocusing; HILIC: hydrophilic interaction chromatography; NPLC: normal-phase liquid chromatography; SEC: sizeexclusion chromatography; RPLC: reversed-phase liquid chromatography; ELS: evaporative light scattering; LIF: laser-induced fluorescence; MS: mass spectrometry; RI: refractive index; UV: ultraviolet.

instrument and in the first automated comprehensive 2DLC instrument (Bushey and Jorgenson, 1990a). Instead of using sample loops in the eight-port valve, several groups utilize matching columns to alternate sampling in the second dimension (Opiteck et al., 1997; Wagner et al., 2002). This can be difficult, however, because the elution volume of the columns must be exactly matched or adjacent rows in the 2D matrix will be offset and scaled due to variation between columns. This can be partially compensated for in software but it places severe demands on matching columns. Figure 5.6 shows the configuration of an eight-port valve with dual columns in the

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FIGURE 5.5 2DLC with eight-port valve and two sample loops. In position A, loop 1 (L1) is being loaded with sample from column 1, and loop 2 (L2) is being injected onto column 2. In position B, L2 is being loaded with sample from column 1, and L1 is being injected onto column 2.

second dimension. Sample loops are not necessary for 2DLC under a variety of conditions as highly aqueous solvent conditions can be used to load hydrophobic solutes into reversed-phase columns with subsequent elution under gradient conditions (Unger et al., 2000). The most common case of this is where components elute from an ion-exchange column into a reversed-phase column in the second dimension and these systems are commonly utilized in protein and peptide separations (Unger et al., 2000; Liu et al., 2002) and in other applications, for example, the separation of

Pump 1

Pump 1

Position A

Position B Column 1

Column 1

C2-B C2-A

Pump 2

Detectors Waste

Pump 2

Detectors Waste

FIGURE 5.6 2DLC with eight-port valve and two columns in second dimension. In position A, column 2-A is being loaded with sample from column 1, and column 2-B is being analyzed utilizing pump 2. In position B, column 2-B is being loaded with sample from column 1, and column 2-A is being analyzed.

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complex surfactant mixtures (Haefliger, 2003). In these cases, a 10-port valve is most commonly used, as discussed below. All of the valves used in 2DLC are two-position valves as they alternate between two positions (A and B in Figs. 5.2, 5.5 and 5.6). Two-position 10- and 12-port valves may also be used similarly to six- and eight-port valves and the differences in application with these are now discussed. It is commonly assumed in 2DLC instruments that employ sample loops that there is complete mixing within the sample loop prior to flushing the loop volume to the second column. However, it was discovered (Van der Horst and Schoenmakers, 2003) that if the fluids in the two sample loops are pumped into the second column in different order (e.g., in the forward direction in loop 1 followed by the reverse direction in loop 2) the resulting 2D chromatograms show irregularities. This is typically the case with an eight-port valve and will depend on loop volume and the inherent broadness of the component zones. This can be corrected by allowing the fluid in the loop to be pumped in the same direction for both sample loops by using a 10-port valve. Figure 5.7 shows the 10-port valve configuration that allows the flow to enter and exit the sample loops in the same direction as they were filled. In fact, 10-port valves are becoming more popular for 2DLC. One commercial 2DLC system, the Perceptive Biosystems Integral 100Q workstation, utilized multiple 10-port pneumatically-driven twoposition valves for column switching and other functions. This unit is no longer manufactured. Higher performance systems where gradient elution is conducted at high speed (Stoll and Carr, 2005; Stoll et al., 2006) utilize 10-port valves. It is also common with 10-port valves to dispense with the use of the sample loops and put two Pump 1 Position A

Pump 1 Position B

Column 1

Pump 2

Column 1

Pump 2

L1

L2

Column 2

Detectors

L1

L2

Waste Column 2

Waste

Detectors

FIGURE 5.7 2DLC with 10-port valve and two sample loops. In position A, loop 1 (L1) is being loaded with sample from column 1, and loop 2 (L2) is being injected onto column 2. In position B, L2 is being loaded with sample from column 1, and L1 is being injected onto column 2.

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second-dimension columns in place of the sample loops. This is a much more difficult experiment to balance but does have advantages while the columns are new and remain balanced. This configuration typically requires very narrow bore firstdimension columns to be used to minimize the volume of effluent that will be flushed into the next dimension. A 12-port valve was used for the periodic sampling of the first column onto multiple second-dimension columns for the 2DLC analysis of aromatic amines and other species (Venkatramani and Zelechonok, 2003). The utility of the 12-port valve is that two columns can be utilized in the second dimension and flow is kept constant through both columns. This configuration requires three sample loops for implementation. The output of the second-dimension columns are connected so that both columns continuously feed the detector. Multiposition valves direct the flow to multiple ports that may be useful for multiple columns in the second dimension. This approach eliminates or greatly reduces the constraints of slowing down the first dimension to get an adequate sampling rate. A potential configuration of using multiple columns in the second dimension is shown in Fig. 5.8. This system would need eight pumps and eight detectors to match the eight columns and would allow for every eighth sample to enter the same column, thus increasing the sampling rate over using one column in the second dimension. As with the dual-column arrangement shown in Fig. 5.6, the adjacent rows may not match perfectly, resulting in increased variability and compensation may be necessary via software. However, this approach may be useful if configured with parallel-column chromatography systems that are now being offered commercially.

First Dimension

Second Dimension

Detectors

FIGURE 5.8 2DLC with multipositional valve. Effluent for the first dimension column can be directed to one of eight columns in the second dimension for increased sampling rate.

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5.4 CE INTERFACES 5.4.1

Gated Interface for HPLC–CE

Because of the small volumes encountered in CE, implementing CE as a second dimension is difficult if a valve is used. More efficient, lower volume unions have been utilized in a number of cases. The main types of these interfaces include optical gating and flow gating, which are discussed below. Electrical gating is described in detail in Chapter 15. Fraction collection is also used, as discussed in Chapter 16, although this takes longer and is a less efficient method than the other comprehensive 2D schemes. Chip-based separation systems typically use some form of electrical gating and these systems will be discussed below. The first instrument to couple LC and CE in a comprehensive multidimensional separation system was described by Bushey and Jorgenson (1990b). This system used a six-port valve and utilized a unique timing diagram for applying the voltage to the CE system. It used a valve that was synchronized so as to utilize electroinjection of the zone into the CE system. Further applications of the electromigration method with valves include protein separations by SEC/CE (Lemmo and Jorgenson, 1993a). Optically gated interfaces (OGI) for diverting solute into a CE column from a firstdimension chromatography column were pioneered by Monnig et al. (1991a and 1991b). The principle of this type of interface is as follows. The components in the mixture to be analyzed are tagged with a fluorescent molecule and continuously introduced into the capillary inlet. Near the capillary inlet, a relatively high powered laser is utilized to photodegrade the fluorescent tag. When the tag is degraded, the solute becomes undetectable to a fluorescence detector, which is placed near the capillary outlet. The sample zone is generated by a transient blocking of the laser beam that opens a small width “fluorescent enabled’’ region in the solute mixture, which is separated within the CE column. In this way, the second-dimension column can be just an extension of the first-dimension column; no special tee or valve is needed for this dimension with the possible exception of having a splitting union so that column volumes may be matched to a smaller diameter capillary used for CE. This mode of operation is known to yield very narrow injection widths. However, this form of sample introduction to the CE capillary is limited to fluorescence detection and does not work for detection by mass spectrometry. Applications of OGI for the reversed-phase liquid chromatography (RPLC)/CE analysis of peptides have been described (Moore and Jorgenson, 1995a) as has the SEC/RPLC/CE separation of peptides (Moore and Jorgenson, 1995b)—a threedimensional separation system. The zone broadening mechanics of the OGI system have been studied (Moore and Jorgenson, 1993). Because of the sampling requirements for comprehensive MDLC, as discussed in Chapter 2, CE with an OGI in the third dimension is ideal because the total run time of the third-dimension separator must be very fast. Otherwise, the preceding dimension separator must be slowed down to allow proper sampling. OGI techniques have been used extensively in single capillary systems where a narrow injection width is needed along with a simplified interface. These techniques are summarized by Hapuarachchi et al. (2006). There are many advantages to this

CE INTERFACES

105

Stainless steel tubing

µA 1/16” channel

RPLC capillary

CZE capillary

1/16” o.d. teflon sleeve

Lexan

PEEK tubing

FIGURE 5.9 The flow gating interface from Hooker and Jorgenson (1997). The cross-flow of buffer prevents LC effluent from electromigrating onto the CE capillary until an injection is desired. This figure is used by permission of the American Chemical Society.

configuration including measuring certain chemicals within complex mixtures in real time in a small volume measurement system. Microdialysis interfaces that employ OGI have been described (Tao et al., 1998; Thompson et al., 1999). This type of interface is also very useful for 1D CE systems that are implemented on-chip through microfluidic systems, as described below. The flow gating interface (FGI) is another type of interface that couples columns to CE shown in Fig. 5.9. This interface is especially useful for very small volumes of column effluent, which would be impractical to store in a sample loop, as in the normal valve configuration. In the FGI, a cross flow of buffer is used in a unique interface to divert solute into the capillary for CE analysis. This system was originally described by Lemmo and Jorgenson (1993b). A revised design was published by Hooker and Jorgenson (1997). The FGI has been interfaced to other separation systems, for example, microdialysis, with subsequent CE detection; see Lada and coworkers (Lada and Kennedy, 1996, 1997; Lada et al., 1997). Multidimensional LC–LC and LC–CE separations were recently reviewed for biological molecules (Evans and Jorgenson, 2004). 5.4.2

Microfluidic Valves for On-Chip Multidimensional Analysis

Chip-based systems that employ microfluidics have become a popular research area and a number of systems are now available commercially that utilize electrophoresis-

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based separations. Implementing MDLC systems on a chip can be difficult. However, 1D chip-based systems that employ electrophoresis for biochemical analysis have been used, which employ optical gating (Pittman et al., 2003; Roddy et al., 2003), flow-through sampling (Chen et al., 2002), and microdialysis sampling (Huynh et al., 2004). For implementing 2D systems on-chip, a number of approaches can be followed. One approach to implementing multidimensional separation systems on-chip is to use micellar electrokinetic chromatography (MEKC) as the first dimension, followed by CE in the second dimension (Ramsey et al., 2003). This allows some degree of orthogonality as the MEKC separation mechanism is sensitive to the degree of hydrophobicity of the solute. The second-dimension separation mechanism is sensitive to the charge and size of the solute. Gating of the solute into the electrophoresis channel is done by electrical switching. Detection is on-chip by laser-induced fluorescence. An image of this chip system is shown in Fig. 5.10. Note that MEKC is a popular alternative to a packed-column RPLC system due to the difficulty in packing columns on the chip. However, it is not unreasonable to think that monolithic columns will soon be integrated into the chip. Regardless, the MEKC system has a reversed-phase like behavior with respect to retention mechanism but is somewhat limited to samples that are soluble in MEKC buffer solutions that tend to be aqueous. For most samples of biological origin, this is not typically a problem. The use of electrically-gated solute injection into the electrophoresis system simplifies the chip design as electrical connections are easy to implement as compared to the microfluidics part of the chip. Voltage waveform manipulation via hardware and software are relatively easy to control and implement. Other combinations of first and second dimensions on chip-based separation systems are becoming more common. A particularly interesting combination is IEF/PAGE. This is an analog of the planar system used by biochemists for decades. However, IEF does not typically allow for fluorescent tags that are essential for laserinduced fluorescent detection; one of the most common on-chip detection systems. These and other first- and second-dimension combinations are given in a recent paper that discusses the implementation of MEKC in the first dimension and GE in the second (Shadpour and Sopor, 2006). This is a very powerful system. Again, sampling of the first dimension is by electrical switching, which is the most convenient method for on-chip systems.

5.5 COLUMNS AND COMBINATIONS There are many combinations of separations techniques and methods of coupling these techniques currently employed in MDLC systems. Giddings (1984) has discussed a number of the possible combinations of techniques that can be coupled to form twodimensional systems in matrix form. This matrix includes column chromatography, field-flow fractionation (FFF), various types of electrophoresis experiments, and more. However, many of these matrix elements would be difficult if not impossible to reduce to practice.

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107

FIGURE 5.10 The image of a microchip used in 2D MEKC-CE described in Ramsey et al. (2003). Note that injections are made at valve 1 (V1) for the first-dimension MEKC separation. Solute is sampled into the CE system at V2. Other designations are provided in the original reference. This figure is used by permission of the American Chemical Society.

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We have categorized the various two-dimensional separation systems by the number of fractions and amount of effluent sampled into the second dimension (Table 5.1). This discussion will be restricted to two-dimensional systems, although most of the principles will apply to more than two coupled separation systems. Furthermore, we limit our discussion here to dimensions that utilize column chromatography. Our discussion of 2DGE is mentioned in passing although we recognize that a great deal of the methodology and analytical outcomes apply jointly to the planar techniques that have been available for years prior to coupling columns with automated valves. The choice of columns used for 2DLC is based upon experience with the sample and resolution required. The HPLC column descriptors of selectivity, resolution, peak capacity, sample capacity, degree of sample recovery, and speed of separation have been discussed previously (Unger et al., 2000). Columns with higher peak capacity and sample capacity (IEC, HIC, NPLC, and RPLC) are preferred in the first dimension, and higher speed columns (SEC and RPLC) are better in the second dimension. This is discussed in detail in Chapters 2 and 6. For optimum resolution in two dimensions, the columns selected need to be orthogonal. Orthogonality is discussed in Chapters 2, 3, and 12. If two particular chemical functionalities are to be separated, then the ease of interpretation of the resulting data is more important than the optimal resolution. For some separations, a mixed mode of separation is observed, and having two dimensions facilitates the study of the differences in separation mechanisms. Overall, there are many combinations of 2DLC separation systems, and as long as the two mobile phase solvents are miscible, they can be coupled for improved resolution. Temperature is another variable to be considered to speed up the separation in the second dimension and will be discussed below. The variety of columns that are utilized in 2DLC to separate complex mixtures is exemplified in Table 5.2. Column selectivity in 2DLC was recently reviewed by Jandera (2006). 5.5.1

Column Systems, Dilution, and Splitting

The scale (capillary or standard bore) of chromatography systems used in 2DLC can vary widely and is dependent on the amount of sample available, detection system, and HPLC availability in the laboratory. Since the chromatography is taking place in two dimensions, zone broadening and dilution are taking place on both columns. Zone broadening leads to sample dilution. The dilution effect has been studied (Schure, 1999) in the context of MDLC and certain combinations of columns are more beneficial in minimizing dilution. This dilution effect is also discussed in Chapter 2. In industrial laboratories, where samples are not typically volume limited, analytical-scale HPLC systems are routinely used. To combine two analytical HPLC systems would involve standard valves with loop volumes from 10 to 1000 mL, and control of each system. If a six-port valve is utilized, then sampling a portion of the effluent from the first to second dimension would allow lower volumes to be injected into the second dimension, but with a loss in sensitivity. A system employing an eight-port valve in the comprehensive mode with analytical flow rates has been used to separate polymers and surfactants (Murphy et al., 1998a, 1998b). A fast second-dimension column flow rate is generally used to accommodate the higher sampling rate. When a narrow range of

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analyte properties are examined, then isocratic techniques can be used in the second dimension. In Chapter 18, isocratic reversed-phase HPLC is used to separate the alkyl portion of a complex surfactant mixture at 1-min intervals during a normal phase gradient experiment. In samples where there is a broad range of analyte properties, such as biological samples, gradient HPLC is needed in both dimensions to improve resolution of the entire sample. Chong et al. (2001) have used gradient chromatofocusing in combination with gradient reversed-phase HPLC for the mapping of protein samples from breast cancer cell lysates. Holland and Jorgenson (1995) coupled capillary anion-exchange and reversed-phased HPLC columns for the analysis of tryptic digests of the contents of a single cell. Recent work by Carr and coworkers (Stoll et al., 2006) has shown the utility in using fast gradient separations in 2DLC that have been facilitated by working at elevated temperature. The use of elevated temperatures in 2DLC is important, especially in the second dimension because of the importance in speeding this dimension up. Thus, the column diameters chosen for the two dimensions are determined by the amount of sample available and will dictate the flow rate ranges available to use. In split-flow systems, where only a portion of the first-dimension effluent is injected into the second dimension, the choice of column size is unlimited and the two methods can be developed independently. In comprehensive systems where the entire sample from the first dimension is injected into the second dimension, the flow rates are generally lower in the first dimension to accommodate the lower injection volumes into the second dimension. For example, for a 1-mm ID column in the first dimension with a flow rate of 50 mL/min and a sampling rate of 1 min, 50 mL could be injected onto the second dimension. A 50-mL injection onto a 4.6-mm ID column flowing at 1 mL/min should be accommodated fairly well based upon its composition. In Chapter 6, the first dimension column diameters are estimated based upon the injection volume and sampling rate into the second dimension. 5.6 DETECTION Detection in 2DLC is the same as encountered in one-dimensional HPLC. Avariety of detectors are presented in Table 5.2. The choice of detector is dependent on the molecule being detected, the problem being solved, and the separation mode used for the second dimension. If MS detection is utilized, then volatile buffers are typically used in the second-dimension separation. Ultraviolet detection is used for peptides, proteins, and any molecules that contain an appropriate chromophore. Evaporative light scattering detection has become popular for the analysis of polymers and surfactants that do not contain UV chromophores. Refractive index (RI) detection is generally used with size exclusion chromatography for the analysis of polymers. 5.7 COMPUTER HARDWARE AND SOFTWARE The equipment used in 2DLC is the same as in HPLC with the addition of valves and integrated control of both HPLC systems. Most chromatography data systems allow

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control over one HPLC system, but few can control two HPLC systems and the repetitive sampling for a 2DLC system. Thus, for 2DLC the control of each HPLC system is done by selecting one system as the main system and the second one as a slave to the first. This allows initiation control of the second HPLC system by contact closure such as during valve actuation. If mass spectrometry detection is used, this method of control is utilized since MS systems can currently control one HPLC. There are some integrated 2DLC systems on the market that can control two HPLC systems and valves. Computer hardware and software used in 2DLC generally take care of three critical operations. These include real-time control of valves and sequencing functions such as autosampler control, formatting the time series data into a 2D data matrix, and analyzing the data. These will be described in some detail. The control of valves and sequencing of autosamplers and injectors is typically a slow speed operation for 2DLC. For example, valves must be controlled on the chromatographic timescale so that switching times on the order of tens of seconds to minutes is not uncommon. However, the sequencing time control must be highly accurate to achieve reproducible chromatograms. Data formatting is accomplished by taking one or more detector signals, for example ultraviolet and refractive index detector signals, and formatting the data to be in matrix format. The computer program must manipulate 2D chromatograms including scaling, chromatogram subtraction, signal processing, and moments analysis. For multiwavelength detectors and mass spectrometer detectors, the software should also allow individual spectral analysis functions such as selection of wavelengths or mass to charge ratios for display. More details of these aspects will be covered below. 5.7.1

Software Development

There are a few commercially available systems that are loosely or tightly integrated for 2DLC. There are commercial systems for multidimensional gas chromatography as the interface between columns can be accomplished without valves using thermal modulation (Marriott, 2002) and the thermal modulator is tightly coupled to the software. Most scientists who work on developing 2DLC systems write their own software and valve sequencing systems. The software for these systems can be developed in higher level languages with graphical icon-oriented systems; one example of this is National Instrument’s LabVIEW system (National Instruments, 2007), which is popular for real-time instrument control on both Microsoft Windows-based operating systems and on Apple’s Mac OS X-based computers. Other popular languages include Microsoft’s Visual Basic and Visual Cþþ programming languages that feature good integration and graphical user interfaces (GUI’s) on Windows-based systems. For the examples used in this chapter, we utilize software that is available commercially (Kroungold, 2007) and is an “add-on’’ for existing chromatography systems. This is one of a few commercially available packages that allow scientists to add columns, pumps, valves, and sample loops to make their own 2DLC systems.

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FIGURE 5.11 Timing diagram for comprehensive 2DLC with either a two-position valve (bottom) or four-position valve (top). Repetitive sampling of the first dimension at each time (T1, T2, T3, T4, . . .,) results in an injection onto the second-dimension column.

5.7.2

Valve Sequencing

The sequence control of valves, autosamplers, fraction collectors, and other devices is typically performed under program control although dedicated hardware controllers can be used here. Valves can be controlled by standard signals such as RS-232 serial lines, a USB (universal serial bus) port, digital signals such as those from TTL voltages (transistor–transistor logic where signal levels are less than 5 V) and relay closures. A good reference to these terms and details are available in a well-known electronics book (Horowitz and Hill, 1989). The program must utilize a clock for an accurate time base and the clock is usually the internal clock of the computer that can be accurate to better than a millisecond. The timing diagrams for 2DLC are shown in Fig. 5.11 for two-position and multiposition valves. A two-position valve has the option of positions A or B, whereas the four-position valve has positions A, B, C, and D available. For twoposition valves, the valve is initially in position A at the beginning of the experiment and the duration interval of the first segment is denoted as T1. During T1, effluent from the first dimension column fills the first sample loop. At the end of the T1 interval, the position of the valve is changed to position B and the solute contained in sample loop 1 is separated on the second-dimension column. Sample loop 2 is now filled with the effluent from the first column as the elution of the second column takes place into the detector. This procedure is repeated until the entire first-column effluent is sampled (i.e., all of the injected sample components have eluted from the first-dimension column) or the experiment is stopped. This timing plan is simple but alternative schemes can be easily accommodated. Two common modifications include provision for a predelay period occurring prior to commencement of the experiment and letting a second valve shunt a portion of the

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second-dimension effluent that may contain salt to waste. Both of these will now be explained. Elution in the second dimension need not be executed until solute is present in the first sample loop. This predelay period allows the first-dimension column void solvent to be dumped to waste collection prior to the arrival of the first retained component. After some initial time, the regular sampling interval T1 ¼ T2 ¼ T3 . . . is started. Mass spectrometers that use electrospray ionization (ESI) do not function well if the eluent contains low volatility salts. This is a major concern when an ion-exchange column is used as a first-dimension column and the salt concentration is used to modulate the retention in this column. In this case, another valve can be connected between the second-dimension column and the detector so that any salt from the second-dimension elution process that is either unretained or weakly retained can be diverted prior to feeding zones to the mass spectrometer. These parameters are typically entered into a program that accepts the parameters as shown in Fig. 5.12. As shown here, the user enters the total run time, seconddimension time (equal to T1 ¼ T2 ¼ T3 . . .), any initial delay time, and operation information regarding triggering and valve states. Once the unit is triggered through a user’s start signal (keyboard initiated, contact sense initiated from an autosampler, data acquisition module, etc.) the software presents a real-time view of the current valve position as shown in Fig. 5.13. The sequence of repetitive valve switching continues until the data acquisition cycles are completed or the user interrupts the sequence. At that time data analysis is initiated by the user. In automatic operation mode, which is one of the user-selected modes in the software, the autosampler triggers another data acquisition and valve switching cycle so that a tray of samples can be readily analyzed via 2DLC.

FIGURE 5.12 User interface for the simplest of the 2DLC modes with no fraction collection or salt diversion. Figure courtesy of Kroungold Analytical (2007).

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FIGURE 5.13 Real-time display of valve sequencing. Figure courtesy of Kroungold Analytical (2007).

5.7.3

Data Format and Storage

If the data system is tightly integrated with the 2DLC system, the native data format can be used to convert the successive one-dimensional data vectors into a matrix form for visualization and analysis. However, there are currently few commercially available 2D systems that have tight integration of data acquisition and analysis. Consequently, the data are stored as raw data that can be easily imported into external software in a number of ways. An especially easy way to do this is to export the raw data into a spreadsheet program like Microsoft Excel using the comma-separated variable (CSV) data format and then use a graphics program that can read CSV files. This has its limits though because data files can get very large in certain cases, which we will now discuss. Chromatography equipment manufacturers and users have embraced a series of exchange formats and data standards that expedite this process. The standard for this was formerly known as the AIA (Analytical Instrument Association) format. Approximately 20 years ago, the AIA developed a set of standards for chromatography and mass spectrometry data. The standard is now called the ANDI/netCDF Chromatography Data Interchange format and is available as standards documents from ASTM International (formerly the American Society for Testing and Materials) as the E 1948-98 standard guide for analytical data interchange protocol for chromatographic data and the E 1947-98 standard specification of analytical data interchange protocol for chromatographic data. A similar set of standards exists for mass spectrometry data. These include the E 2077-00 standard specification for analytical data interchange protocol for mass spectrometric data and the E 2078-00 standard

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guide for analytical data interchange protocol for mass spectrometric data. These systems use the NetCDF packaging standard (NetCDF, 2007) software that can package (encapsulate) or unpack data. This system has been utilized extensively for both chromatography and mass spectrometry data exchange and is a de facto standard for getting data between third party software and an instrument vendor’s proprietary data formats. One packaging format that lacks the efficiency of storage that NetCDF offers but is gaining universal acceptance as a data structure encapsulation standard is the eXtensible Markup Language or XML (Benz and Durant, 2003). Currently, there are activities for both chromatography and mass spectrometry in this area in the project called AnIML (Analytical Information Markup Language), which is now driven by the ASTM subcommittee E13.15. In the case of mass spectrometry data, there is an XML data standard, mzXML, which is used for proteomics data. Furthermore, the mzData format has been touted among equipment manufacturers for proteomics-related data. Information about these formats is easily found on the World Wide Web. However, there does not appear to be a standard yet for multidimensional chromatographic data. The size of the datasets for 2DLC depends on the detector. For a single wavelength detector such as an evaporative light scattering detector (ELSD), we assume the following: there are 80 samples of the first-dimension effluent with 3 min runs in the second dimension. With the signal sampled 10 times a second, the resulting number of data values is 144,000. If the data are sampled by a 20-bit analog-to-digital converter this could be stored in 8 bytes of character data and this would give files that were on the order of 1.2 megabytes in length. If a UV–vis multiwavelength detector, for example, a photodiode array detector, is utilized with a range of 200–450 nm and with a resolution of 0.1 nm, this dataset would comprise 2500 amplitude values at each time slice. If sampled twice a second, the entire 2DLC dataset would contain 2500 28,800 ¼ 72,000,000 or 72 million numbers. If this is kept as a binary file with 4 bytes per data storage value, the file is of the order of 288 megabytes in length. Others have reported file sizes of approximately 100 megabytes for multiwavelength detectors when 100 wavelengths are stored with sampling rates of 80 samples per second and with run times on the order of an hour. In this case, some file compression may also be utilized to reduce file size. Higher sampling rates and higher spectral resolution will increase the file size into the gigabyte range. If the file is to be stored in character format instead of binary, the file size would double or triple. The problem here is not as critical for data storage length, as desktop computers often have mass storage capabilities larger than 300 gigabytes. However, the time needed to read these large datasets becomes a critical parameter when routine handling and analysis of these datasets is to take place. Furthermore, these data cannot be completely memory resident when large datasets greater than perhaps 500 megabytes are present. This is often the case for 2DLC when using mass spectrometry and the data must be partitioned into memory, which leads to far more complex analysis software. In many cases, data can be obtained offline and analyzed in a comprehensive manner from a fraction collector. For example, LC and gas chromatography (GC) can be utilized to form a 2D experiment — LC/GC. This is quite useful as the retention

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FIGURE 5.14 Software to read individual files from an offline GC instrument into the 2D software. Figure courtesy of Kroungold Analytical (2007).

mechanism of LC is different than the GC step. This is a powerful combination when components are sufficiently volatile to be analyzed by GC. The GC detector is typically a flame ionization detector or a mass spectrometer. The GC experiment is run on every LC collected fraction. After data acquisition, the data are converted to the AIA format and then a program is used to collimate the individual data files back into a two-dimensional format. Such a program for doing this is shown in Fig. 5.14. Note that the GC step here need not be fast; in the case of off-line techniques, the seconddimension speed requirement can often be relaxed and run times up to 15 min can be accommodated for an overnight analysis of 60 collected fractions.

5.8 ZONE VISUALIZATION 5.8.1

Contour Visualization

There are two common means of visualizing comprehensive 2DLC data. One is to draw the data as contours with component zones showing as spots, much like 2DGE, and the other is to show zones as a two-dimensional amplitude plot where peaks are shown. A small variation on contour plotting is to map the data value to a color or gray scale and present the color or gray scale as a pixel. The contour plot and pixel mapping are more convenient as these modes of plotting do not tend to hide or exclude features due to high amplitude peaks in the vicinity of low amplitude peaks, although visualizing any low amplitude contour or pixel map is difficult when high amplitude and low amplitude regions are adjacent. The contour drawing capabilities of most graphics software finds the regions of constant peak amplitude through interpolation. Typically, 10–20 contour amplitudes are picked from maximum to minimum. This interpolation can be sophisticated and may include a noise minimizing basis function. However, the use of this filtering may sometimes distort the data presentation. Because 2DLC data are typically performed

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on matrices with sizes larger than 60 rows (first dimension) and 100 columns (second dimension), another approach is to simply map the peak amplitude into a color map and deposit the color as a pixel or in a small rectangular region in the plot. The user typically has control on the color mapping function that gives the most esthetically pleasing plot. This technique is commonly used and provides a fast computational approach to data presentation. However, interpolation is often used and the interpolation function, while appearing to reduce noise, can also artificially broaden the 2D chromatogram and must be used with care. Plotting the logarithm of the amplitude with contours, mapped pixels, or peak plotting can help distinguish large and small adjacent peaks. Taking the logarithm of the amplitude is essentially a compression operation. However, small values of noise in the data tend to become amplified in this approach and more sophisticated techniques may then be necessary for noise suppression, such as gating the noise floor. Gray-scale contour plots are useful and one is shown in Fig. 5.15. These data are quite typical of the zone profiles from a 2DLC experiment and appear similar to the data from 2DGE when digitized in a densitometer after the proper staining treatment. Color contours and pixel maps are useful as they help display more of the zone shape. Other examples of gray and color contouring and pixel mapping of data are plentiful throughout this text.

FIGURE 5.15 A typical gray-scale contour plot of the 2DLC separation of tryptic digest peptides from BSA. From Stoll and Carr (2005). Reprinted with permission of the American Chemical Society.

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2D Peak Presentation

A good example of a 2D peak plot where the amplitude is directly visualized showing peaks is given in Fig. 15.7 of Chapter 15. In this plot, peaks are shown at an angle projection of the two independent variable axes. One problem, besides obscuring data with this approach, is that it is difficult to determine the retention time on each axis; the scientist must be creative in the interpolation of the peak maxima back to the axes. However, aside from esthetic preferences, the data are essentially the same as the contour or direct pixel representation. An important point to note is that since the data are contained in digital form, it is easy to obtain peak maxima information through programmed analysis. Hence, the visualization step is largely a cosmetic step that should be viewed as an information-conveying process, rather than as a substitution for experimental parameter analysis. 5.8.3

Zone Visualization in Specific Chemical (pI) Regions

A number of variations to contour, pixel mapping and peak plotting exist and these tend to depend on the type of column used in the first or second dimension. For example, as shown in Fig. 1.2 in Chapter 1, chromatofocusing can be used to exploit the pI dependence of proteomics samples as one of the dimensions. Hence, one dimension shows a pI range and the other shows the temporal zone production in gradient elution RPLC. By plotting the peak amplitude as a function of color, it is easy to see the individual zones and these can be compared by examination of neighboring pI regions as well as the evolution as a function of gradient modifier or of time although the two do not need to be linearly related. This approach is extremely powerful and can be further elaborated by substituting specific mass-to-charge (m/z) ratio amplitudes from a mass spectrometer. Visualizing analytical chemical concentration data from multivariate instrumentation has for many years been a fruitful area for more research and 2DLC fuels this need. Chemometric analysis may provide a better handle on where (i.e., what regions of multidimensional space) to visualize the data. 5.8.4

External Plotting Programs

Plotting data with two independent variables can be very subjective as to the most pleasing form of information display. Therefore, it is common, once the data are assembled into matrix form, to use an external plotting and graphics program to provide customization to the data plot. In addition, these types of packages can be used to distinguish and label individual peak features. Many PC graphics packages, including the plotting software inside Microsoft Excel, can be used for this purpose. As mentioned previously, for spreadsheet-based plotting systems, data can be imported as a CSV data file where data are separated by commas on a row-by-row basis. Other packages can be used that will add color coding to the data amplitude on the contours and/or peaks and some packages will accept more sophisticated data formats in binary. One example of these software packages was Transform that was sold as a general data analysis package for data packaged in matrix form or as a linear dataset

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that could be converted to matrix form. It was used by Bushey and Jorgenson (1990a) in their original publication on 2DLC. These features are now standard in high performance visualization software. Other plotting software is contained within more elaborate mathematics and analysis integrated systems including MATLAB (2007) and Mathematica (2007). The software used to create Fig. 5.15 was done by programming within the MATLAB package. 5.8.5

Difference Plots

One useful visualization technique is to show the difference between two 2DLC chromatograms. The peak shape reproducibility must be high as difference measurements are inherently noisy operations, as opposed to integration measurements that tend to smooth data irregularities. An interesting paper entitled “Comparative visualization for comprehensive two-dimensional gas chromatography’’ (Hollingsworth et al., 2006) shows difference plots from two-dimensional gas chromatographic data. Difference plots have been used routinely in 2DLC polymer analysis and now are used in proteomics applications. 5.8.6

Multi-channel Data

Interesting data presentations can be made with UV–visible multi-wavelength datasets and mass spectral datasets. In these cases, there are many interesting possibilities. For example, one can show the integrated signal (e.g., the total ion current in the case of mass spectrometry) as a function of the 2D retention. Alternatively, one can show the signal at a particular wavelength or m/z ratio in the 2D space. Or one can show the selective signals as a function of the highest masses or the most intense wavelength or m/z ratio. These presentations are typically dependent on the application, for example, top-down or bottom-up proteomics (see Chapter 13) or polymer analysis (Chapter 17). The presentation mode of the dataset can be utilized to explore the data and see interesting features. In the case of mass spectrometry, we show the data in Fig. 5.16 of an analysis of a protein sample mixture. The user clicks on a peak of interest in the 2D chromatogram and the mass spectrum appears in the graph below the colored pixel map plot. This is the amplitude, as a function of the m/z ratio, of the mass spectrometer at that location in the 2D chromatogram. If the user clicks on a part of the mass spectrum of interest or enters a mass range to view, the 2DLC contours are then shown as in Fig. 5.17. In this way one can check if other peaks with a specific m/z ratio are present. This software also has an automated browser mode that displays the 2D chromatogram repeatedly at different m/z ratios within a specified range. Much like 1D LC/MS, the data presentation has many possibilities because the data are part of a multivariate dataset. This means that there are multiple independent variables; besides the two retention time variables in 2DLC, one also has the m/z ratio so that 2DLC/MS has three independent variables. This can be challenging to visualize; the problem increases dramatically when MS/MS systems are utilized, for example, as in proteomics studies.

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FIGURE 5.16 Mass spectrometry data and the corresponding mass spectrum of a selected spot. Figure courtesy of Kroungold Analytical (2007). (See color plate.)

5.9 DATA ANALYSIS AND SIGNAL PROCESSING 2DLC in many ways parallels 2DGE when it comes to signal processing and data analysis. Many of the same operations, from zone subtraction to the determination of adjacent resolution, are quite similar and software is available for these operations in 2DGE. One of the data operations researched for 2DGE is the similarity in electropherograms. This can also be determined in 2DLC using the same type of methodology. One important difference, however, is that in 2DGE or any type of planar detection system, the signal quality and dynamic range of optical densitometry detectors are much smaller than with the types of detectors found in column chromatography and in 2DLC. Advanced processing methods using Fourier-based techniques such as autocovariance methods, described in Chapter 4, have not found their way into commercially available systems. There is no reason that these cannot be developed further for measuring the similarity between 2DLC chromatograms and estimating the number of peaks present in the 2DLC chromatograms. Chemometric analysis of 2DLC data is further discussed in Chapter 2.

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FIGURE 5.17 The 2DLC contours for m/z ¼ 1301 with a window width of 2 m/z units. Figure courtesy of Kroungold Analytical (2007).

Moment analysis is one of the simplest types of analysis and is useful for measuring the performance of the chromatography. Moments can be used to measure the same things that are measured in 1D chromatographic systems; these include the first, second, and third moments, which are more accurate than the related peak maximum, peak width, and peak asymmetry. In 2D, however, these values each have a component in each dimension and this can be easily determined in software-based measurement systems.

5.10 FUTURE PROSPECTS 2DLC has great potential for the analysis of complex samples from industrial or biological origin. It provides a powerful tool for the analysis of membrane proteins (Lohaus et al., 2007) that cannot be detected in 2D gels. When combined with high resolution mass spectrometry, 2DLC has the resolution to analyze and identify approximately 1300 proteins in human plasma (Jin et al., 2005). There is no doubt that the instrumentation will evolve and will provide an excellent chromatographic platform for in-depth analysis that single-column methods cannot provide. Although

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2DLC may be the method of choice for specific applications, these applications will need to be turnkey from the viewpoint of instrumentation and this has not yet been achieved in a wide context. When this desired goal is achieved, 2DLC will evolve to be a standard instrumental approach to the qualitative and quantitative analyses of complex materials.

REFERENCES Augenstein, M., Stickler, M. (1990). Gradient high-performance liquid chromatography of polymers. 1. Characterization of the products obtained by grafting methyl methacrylate onto ethylene–propene–diene rubber by SEC-HPLC cross fractionation using evaporative light scattering detection. Makromole. Chem. 191(2), 415–428. Balke, S.T., Patel, R.D. (1980). Coupled GPC/HPLC: copolymer composition and axial dispersion characterization. J. Polym. Sci., Polym. Lett. Ed. 18, 453–456. Benz, B., Durant, J. (2003). XML Programming Bible. John Wiley & Sons, Inc. New York. Bushey, M.M., Jorgenson, J.W. (1990a). Automated instrumentation for comprehensive two-dimensional high-performance liquid chromatography of proteins. Anal. Chem. 62, 161–167. Bushey, M.W., Jorgenson, J.W. (1990b). Automated instrumentation for comprehensive twodimensional high performance liquid chromatography/capillary zone electrophoresis. Anal. Chem. 62, 978–984. Celis, J.E., Bravo, R. (1984). Two-Dimensional Gel Electrophoresis of Proteins. Academic Press, New York. Chen, S.-H., Lin, Y.-H., Wang, L.-Y., Lin, C.-C., Lee, G.-B. (2002). Flow-through sampling for electrophoresis-based microchips and their applications for protein analysis. Anal. Chem. 74, 5146–5153. Chong, B.E., Yan, F., Lubman, D.M., Miller, F.R. (2001). Chromatofocusing nonporous reversed-phase high-performance liquid chromatography/electrospray ionization timeof-flight mass spectrometry of proteins from human breast cancer whole cell lysates: a novel two-dimensional liquid chromatography/mass spectrometry method. Rapid Commun. Mass Spectrom. 15, 291–296. Dixon, S.P., Pitfield, I.D., Perrett, D. (2006). Comprehensive multidimensional liquid chromatographic separation in biomedical and pharmaceutical analysis: a review. Biomed Chromatogr. 20, 508–529. Erni, F., Frei, R.W. (1978). Two-dimensional column liquid chromatographic technique for resolution of complex mixtures. J. Chromatogr. 149, 561–569. Evans, C.R., Jorgenson, J.W. (2004). Multidimensional LC–LC and LC–CE for high-resolution separations of biological molecules. Anal. Bioanal. Chem. 378(8), 1952–1961. Giddings, J.C. (1984). Two-dimensional separations: concept and promise. Anal. Chem. 56, 1258A–1264A. Haefliger, O.P. (2003). Universal two-dimensional HPLC technique for the chemical analysis of complex surfactant mixtures. Anal. Chem. 75, 371–378. Hapuarachchi, S., Premeau, S.P., Aspinwall, C.A. (2006). High speed capillary zone electrophoresis with online photolytic optical injection. Anal. Chem. 78, 3674–3680.

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Holland, L.A., Jorgenson, J.W. (1995). Separation of nanoliter samples of biological amines by a comprehensive two-dimensional microcolumn liquid chromatography system. Anal. Chem. 67, 3275–3283. Hollingsworth,B.V., Reichenbach,S.E.,Tao,Q., Visvanathan,A.(2006).Comparativevisualization for comprehensive two-dimensional gas chromatography. J. Chromatogr. A 1105, 51–58. Hooker, T.F., Jorgenson, J.W. (1997). A transparent flow gating interface for the coupling of microcolumn LC with CZE in a comprehensive two-dimensional system. Anal. Chem. 69, 4134–4142. Horowitz, P., Hill, W. (1989). The Art of Electronics. Cambridge University Press, New York. Huber, J.F.K., Van der Linden, R., Ecker, E., Oreans, M. (1973). Column switching in highpressure liquid chromatography. J. Chromatogr. 83, 267–279. Huynh, B.H., Fogarty, B.A., Martin, R.S., Lunte, S.M. (2004). On-line coupling of microdialysis sampling with microchip-based capillary electrophoresis. Anal. Chem. 76, 6440– 6447. Isobe, T., Uchida, K., Taoka, M., Shinkai, F., Manabe, T., Okuyama, T. (1991). Automated twodimensional liquid chromatographic system for mapping proteins in highly complex mixtures. J. Chromatogr. 588, 115–123. Jandera, P. (2006). Review: column selectivity for two-dimensional liquid chromatography. J. Sep. Sci. 29, 1763–1783. Jin, W.-H., Dai, J., Li, S-J., Xia, Q-C.Zou, H.-F., Rong, Z. (2005). Human plasma proteome analysis by multidimensional chromatography prefractionation and linear ion trap mass spectrometry identification. J. Proteome Res. 4(2), 613–619. Kilz, P., Kruger, R.P., Much, H., Schulz, G. (1995). Chromatographic Characterization of Polymers: Hyphenated and Multidimensional Techniques, Advances in Chemistry Series 247. American Chemical Society, Washington, DC. Chapter 17. Kroungold Analytical (2007). http://www.kroungold.com/ Lada, M.W., Kennedy, R.T. (1996). Quantitative, in vivo monitoring of primary amines in rat caudate nucleus using microdialysis coupled by a flow-gated interface to capillary electrophoresis with laser-induced fluorescence detection. Anal. Chem. 68, 2790–2797. Lada, M.W., Kennedy, R.T. (1997). In vivo monitoring of thiols in rat caudate nucleus using microdialysis coupled by a flow-gated interface with capillary electrophoresis. J. Neuroscience Methods 72, 153–159. Lada, M.W., Vickroy, T.W., Kennedy, R.T. (1997). High temporal resolution monitoring of glutamate with aspartate in vivo using microdialysis on-line with capillary electrophoresis with laser-induced fluorescence detection. Anal. Chem. 69, 4560–4565. Lemmo, A.V., Jorgenson, J.W. (1993a). Two-dimensional protein separation by microcolumn size-exclusion chromatography-capillary zone electrophoresis. J. Chromatogr. A 633(1–2), 213–220. Lemmo, A.V., Jorgenson, J.W. (1993b). Transverse flow gating interface for the coupling of microcolumn LC with CZE in a comprehensive two-dimensional system. Anal. Chem. 65, 1576–1581. Liu, Y., Sweedler, J.V. (1996). Two-dimensional separations: capillary electrophoresis coupled to channel gel electrophoresis. Anal. Chem. 68(22), 3928–3933. Liu, H., Berger, S.J., Chakraborty, A.B., Plumb, R., Cohen, S.A. (2002). Multidimensional chromatography coupled to electrospray ionization time-of-flight mass spectrometry as an

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6 METHOD DEVELOPMENT IN COMPREHENSIVE MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Robert E. Murphy Kroungold Analytical, Inc., Encinitas, CA 92024, USA

Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA

6.1 INTRODUCTION Q1

In one-dimensional chromatographic method development, an analyst is often faced with a complex task where the choice of column packing material, particle size, flow rate, detector(s), and sample preparation is intimately interlinked. In two-dimensional chromatographic method development, there are two columns for which individual method development needs to take place as well as the integration into one analysis scheme. In this chapter, we will review the present state of knowledge regarding method development in multidimensional liquid chromatography (MDLC). In addition, we will propose some rules that can guide the analyst with a recommended order of steps. Our discussion will be restricted to those methods that utilize two columns. Methods that utilize capillary electrophoresis (CE) as one of the dimensions will not be discussed here, although many of the principles remain the same.

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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We are guided by both literature and experience in this regard. Recognizing that there are many sources of information on single-column method development and optimization (Berridge, 1985; Schoenmakers, 1986; Glajch and Snyder, 1990; Snyder et al., 1997), we will not dwell on the method development and optimization step of any one-dimensional method, except for guiding the user of twodimensional liquid chromatography (2DLC) on issues specific to 2DLC. However, we will be pointing out many observations from previous method development experiences for biomolecules, biopolymers, and synthetic polymers we have learned by performing 2DLC. For 2DLC to gain wide acceptance, the two column methods must be separately optimized using the well-known 1D method development approaches. However, the main thrust here is to understand and control the variables that connect the two 1D method development processes into a successful 2D separation.

6.2 PREVIOUS WORK There have been very few method development processes proposed for 2DLC. One study (Schoenmakers et al., 2006) is titled “A protocol for designing comprehensive two-dimensional liquid chromatography separation systems.’’ This study advocates that one initially chooses the first-dimension maximum acceptable analysis time, the first-dimension maximum workable pressure drop, and the smallest first-dimension column diameter. The first two variables are then used to construct a “Poppe plot’’ (Poppe, 1997)—pronounced “Pop-puh’’ (Eksteen, 2007). The Poppe plot is a log–log plot of H/u0 ¼ t0/N versus the number of plates with different particle sizes and with lines drawn at constant void time, t0. H is the plate height, N is the number of plates, and u0 is the fluid velocity (assumed equal to the void velocity). The quantity H/u0 is called the “plate time,’’ which is the time for a theoretical plate to develop and is indicative of the speed of the separation, with units of seconds. In the Poppe plot, a number of parameters including the maximum allowable pressure drop, particle diameter, viscosity, flow resistance, and diffusion coefficient are held constant. A typical Poppe plot is shown in Fig. 6.1. For smaller particle diameters, the time to generate a plate is smaller than that for larger particles. Furthermore, the region above the line (longer time) is experimentally accessible with a pressure drop lower than the stated maximum, but the region below these lines is not accessible as the maximum pressure will be exceeded. There are vertical asymptotes to these curves; there is a limit where the column cannot deliver any higher number of plates. In addition to the curves for each particle diameter, there are also constant t0 lines on these plots, which are shown as dashed lines in Fig. 6.1. These dashed lines are determined by multiplying the number of plates N by H/u0, which gives units in seconds. The points where these dashed lines intersect the solid lines give the void time with a certain plate number and a certain rate of plate generation. In this regard, the Poppe plot gives a good indication of the overall performance of a column. However, in practice this type of plot cannot be expected to yield exact results because columns do not perform close to the theoretical

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–1

7.0 µm 5.0 µm –2

Log H/u

0

3.0 µm 2.0 µm 1.4 µm

–3

100 s 10 s

4

5

6

7

Log plate number

FIGURE 6.1 A Poppe plot for the required plate number in conventional HPLC. The parameters are taken from Poppe’s original paper (Poppe, 1997). The parameters are maximum pressure DP ¼ 4 107 Pa, viscosity h ¼ 0.001 Pa/s, flow resistance factor j ¼ 1000, diffusion coefficient D ¼ 1 109 m2/s, and reduced plate height parameters using Knox’s plate height model are A ¼ 1, B ¼ 1.5, C ¼ 0.05.

limit owing to instrumental limitations (Guiochon, 2006). However, we believe that these types of plots can give very useful ideas about column performance and they can be very helpful, at least, in a rough design of the first- and the second-dimension column. Given the construction of the Poppe plot, the number of plates, the column length, the peak capacity, and the particle diameter are determined in the Schoenmakers et al. (2006) scheme all for the first-dimension column. These are then used to determine the second-dimension parameters that include the particle diameter, the number of plates, column length, and peak capacity. Other variables are utilized and optimized from this scheme. Another study (Bedani et al., 2006) starts from the multidimensional sampling theory (Murphy et al., 1998a), which is discussed in Chapter 2. This sampling theory states that one needs to sample the first dimension separation system at least three to four times per peak width for maximum resolution. Bedani et al. then equate the second-dimension total analysis time to the first-dimension narrowest peak standard deviation. This defines the second-dimension operational parameters. All other parameters can be derived from this balance and Bedani’s study goes through this and discusses how the rest of these variables are obtained. There are two very important principles that appear in Bedani et al.’s study. First, the sampling theory dictates the constraints on speed between the dimensions. Second,

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unlike Schoenmaker’s approach, Bedani et al.’s approach does not establish limits to the columns or separation processes a priori, but rather assumes that the seconddimension parameters have been established and that the total system peak capacity is selected prior to determining everything else. The authors then establish from equations what the first dimension column length, efficiency, velocity, and other parameters should be. These authors also advocate the use of a stopped-flow method instead of running the first dimension column at low velocities to match the second column performance. Others have examined the necessary parameters that should be optimized to make the two-dimensional separation operate within the context of the columns that are chosen for the unique separation applications that are being developed. This is true for most of the applications shown in this book. However, one of the common themes here is that it is often necessary to slow down the first-dimension separation system in a 2DLC system. If one does not slow down the first dimension, another approach is to speed up the second dimension so that the whole analysis is not gated by the time of the second dimension. Recently, this has been the motivation behind the very fast seconddimension systems, such as Carr and coworker’s fast gradient reversed-phase liquid chromatography (RPLC) second dimension systems, which operate at elevated temperatures (Stoll et al., 2006, 2007). Having a fast second dimension makes CE an attractive technique, especially with fast gating methods, which are discussed in Chapter 5. However, these are specialized for specific applications and may require method development techniques specific to CE.

6.3 COLUMN VARIABLES Many of the possible column combinations that are useful in 2DLC are listed in Chapter 5. Besides the actual types of column stationary phases, for example, anionexchange chromatography (AEC), size exclusion chromatography (SEC), and RPLC, many other column variables must be determined for the successful operation of a 2DLC instrument. The attributes that comprise the basic 2DLC experiment are listed in Table 6.1. We will discuss these attributes individually and how they interact between dimensions. For example, a fast, high efficiency column in the first dimension places a huge burden on the second-dimension system to sample extremely fast so that typically four samples can be obtained across a peak. These interactions will become more apparent as we follow the proposed rules in the following section.

6.4 METHOD DEVELOPMENT Here we suggest the steps needed for developing a 2D method. These recommendations can result in either an application that far exceeds a one-column method or an application that fails and is replaced by a one-column method. In the case of a separation that can be adequately resolved with a one-dimensional method, the added

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TABLE 6.1

131

Typical Parameters Necessary for Consideration in 2DLC

Parameters for first dimension column Stationary phase Particle size or length scale Column inner diameter Column length Particle type (monolith, pelicular, nonporous, etc.) Temperature Isocratic or gradient, gradient parameters Solvent system Flow rate Maximum run time Parameters for second dimension column Stationary phase Particle size or length scale Column inner diameter Column length Particle type (monolith, pelicular, nonporous, etc.) Temperature Isocratic or gradient, gradient parameters Solvent system Flow rate Maximum run time Parameters for sampling interface Column configuration: sample loops or columns Loop or column volume

Typical value

Units

Reversed phase 3.5 1.0 25 Porous

mm mm cm

40 Gradient

C

Water:acetonitrile 50 120

mL/min min

Typical value

Units

Size exclusion 5.0 4.6 5 Porous

mm mm cm

40 Isocratic

C

Water:methanol 1.0 1

mL/min min

Typical value

Units

Sample loops 50

mL

complexity of 2DLC is not worth the effort. We assume that the methods described here apply to reasonably complex separations that require a multidimensional approach. Many of these choices have come with experience and with further applications development from the 2DLC literature. The order of presentation below is intended to explain theprocess of method developmentasa rule or learning.We haveattempted to do this in a hierarchical manner so that each aspect must be satisfied, or else the method will perform poorly even if lower ranking considerations are closely adhered to and highly

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optimized. We call these rules “The cardinal rules of 2DLC method development’’ because they have been utilized repeatedly and although developed heuristically, have appeared to be necessary for achieving good method performance. 6.4.1

The Cardinal Rules of 2DLC Method Development

1. Column selection: Column selection must lead to a minimum correlation between retention mechanisms (orthogonality must be maximized). 2. Sampling: The second column method must be as fast as possible to allow for optimal sampling of the first dimension. The method development of the second dimension should be done first. The sampling rate for the second-dimension column should maintain three to four samples across the narrowest peak in the first dimension for optimum 2DLC resolution. Less than three samples across the narrowest peak in the first dimension allow for faster analyses with lower 2DLC resolution. 3. Solvent systems and gradient elution: Solvent systems used in the first dimension must be “compatible’’ with the second-dimension solvent system. Gradient elution is highly desirable when RPLC is used in either the first or the second dimension as it can help limit the elution range and can be used to sharpen zones through its focusing effect. Salt gradients can be run for ion exchange in either dimension. However, these introduce additional complications when mass spectrometry is used as a detector. 4. Second-dimension elution time range: The second-dimension elution time range must be determined. The flow rate needs to be optimized for maximum resolution and speed. This will establish the performance of the second dimension. The elution time range can be tuned with either gradient elution and/or by flow rate to determine the sampling rate. 5. Sample loop volumes: Thevolumes of the sample loops that store eluent from the first dimension and inject eluent into the second-dimension column system must be determined. The loop volume divided by the second-dimension elution time range determines the first-dimension flow rate in comprehensive 2DLC. If the dilution factor is small in the second column, a flow splitter can maintain a small loop volume even with a substantial flow rate from the first-dimension column. 6. First-dimension optimization: The flow rate, elution time range, and the efficiency of the first-dimension column must be carefully controlled and matched to the second-dimension column, the sample loop volume, and the sampling rate. 6.4.1.1 Column Selection The selection of the two types of columns to be used is perhaps the most important consideration in 2DLC method development. This is driven by the need to have orthogonal dimensions for the solutes under investigation, otherwise the solutes will elute along the diagonal of the separation space, as discussed in Chapter 2. We have observed a number of 2DLC applications in the literature,

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especially those for small molecules, where peaks are crowded in the diagonal space that indicates a “less than optimal’’ retention mechanism for solutes between the two columns. This must be avoided for a successful application of high resolution 2DLC. Additionally, the choice of columns and solvents must spread the peaks across the retention dimensions as uniformly as possible. In this way, “cross-column’’ selectivity maximizes the resolution within the two-dimensional space. Some examples of the pairs of columns that have been previously utilized in 2DLC are given in Chapter 5 and in Stoll et al.’s (2007) study. The choice of these column types is critical and one should consider trying different columns in, at least, one dimension to see if the orthogonality and the spreading across the retention space work better with one column as opposed to another. If the 2DLC separation application has been described in the literature previously, those column types can be accepted if the separation was satisfactory. However, a little chemical knowledge can be extremely useful in the choice of columns. For example, if a class of molecules has two outstanding attributes for separation (e.g., there is charge and hydrophobicity differences between molecules), then the two candidates might include ion exchange and RPLC. This combination is well established for peptide and protein separations. Additionally, normal-phase liquid chromatography (NPLC) and RPLC may be appropriate when there are two distinct groups that are unique within the molecule. In any case, RPLC, the “workhorse’’ of liquid chromatography, is an ideal dimension for either the first or the second dimension, but is better suited to the first dimension when run times are long because of highly retained compounds. When run times are shorter in RPLC, either because of fast gradients that can be utilized or a chosen solvent system that limits the elution range, RPLC can be utilized as a fast second-dimension column system (Murphy et al., 1998b). For synthetic and naturally occurring polymers, a few well-established techniques have proven useful. The first column pair to try is RPLC, followed by SEC. As SEC has a limited elution range, it can be used as a very fast second-dimension technique with run times on the order of 1–2 min. There are many examples of fast second-dimension SEC columns in the literature (Murphy et al., 1998a; van der Horst and Schoenmakers, 2003). If molecules are small and polar and if the number of different solutes is large, RPLC and NPLC can be combined into a very powerful 2DLC separation system (Murphy et al., 1998b); see Chapter 18. Other applications that utilize different types of reversed-phase columns in both dimensions have been advocated by Carr (Stoll et al., 2006) for metabolomics work in small-molecule separations. These stationary phases include a pentafluorophenylpropyl stationary phase in the first dimension and a carbon-coated zirconia material stationary phase in the second dimension. A common mistake in 2D method development is to mismatch the solvent system; the two solvent systems must be miscible as discussed below. 6.4.1.2 Sampling One of the limitations of comprehensive 2DLC, as presently practiced, is that one must sample the eluent of the first column with the second column for a sufficient number of times so that peaks in the first dimension are not

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“deresolved.’’ This problem is discussed in Chapter 2, under Section 2.7. The essence of this problem is that undersampling the first dimension would allow a zone that has been separated in the first dimension to be mixed with other resolved zones within the confines of the sample loop. This can often result in a need to either speed up the second dimension or slow down the first dimension. As discussed in Chapter 2, we make the point here that there is a suggested sampling rate for the second column of three or four samples per minimum peak width of the first column. In other words, over the duration of a first-dimension peak, the second dimension should sample a peak three or four times. If the sampling rate is reduced to less than 1.5 times of the first-dimension peak width, the quantitative precision of total peak area and retention time is rapidly reduced as compared to higher sampling rates (Seeley, 2002). We now present some 2DLC chromatograms that suggest this point and the consequences of undersampling, from the viewpoint of chromatogram visualization. Fig. 6.2 shows the result of varying the sampling rate and Fig. 6.3 shows the result of varying the sampling phase. As shown clearly in Fig. 6.2, the sampling rate has a great influence on the resolution of neighboring zones. The trade off of resolution and sampling rate is evident from Fig. 6.2; a fast sampling rate will allow the first dimension to be resolved; however, the second dimension will exhibit a small decrease in resolution. But overall, the faster second-dimension analysis is desirable compared to the less sampled first-dimension analysis. The sampling phase is defined as the start of sampling relative to an eluting peak. The sampling phase is important because one may think that three samples per peak are adequate. However, if a small part of the peak, either at the beginning or at the end of the peak, is included in the sample, the phase effect would lead to a distinct undersampling of the first-dimension peak. It has been demonstrated that for more than four samples per peak, the phase of sampling has a minor effect (Murphy et al., 1998a). A lower sampling rate is more affected by variations in the phase since not all peaks elute at regular intervals. Thus, 2DLC chromatograms at higher sampling rates give better reproducibility because of the sampling phase not being a factor but above four samples per peak width this slows down the first dimension and becomes a deleterious effect. Because frequent sampling is necessary, the second dimension must be fast. Hence, the second-dimension technique must be developed first because it sets the stage for the type of performance that can be driven by the first dimension. This process has worked out well for a number of systems that we have studied. But the philosophy is simple: Why develop a high efficiency first dimension separation if this efficiency is distorted by the sampling process? Hence, the first dimension is matched in performance to the second-dimension system and related through the sampling criterion. 6.4.1.3 Solvent Systems and Gradient Elution Picking a compatible solvent system is extremely important. Strange effects happen when the solvents are not totally miscible in both dimensions and over all of the gradient ranges if one or more dimensions use gradient elution techniques. If gradient elution is feasible, it should be used, as is the case for a standard chromatographic analysis, because gradient elution

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FIGURE 6.2 The sampling time effect on two-dimensional resolution from Murphy (1998a). Amplitude changes because of peak overlap cause a corresponding change in gray scale between chromatograms. The sampling time is labeled on the 2D chromatograms. Reprinted with permission of the American Chemical Society.

separations generally produce higher resolution separations per unit time than isocratic separations. However, gradient elution in the second dimension requires fast gradients and column reequilibration time must be included in the second-dimension time. A number of 2DLC applications have attempted to use liquid chromatography at critical conditions (LCCC) and are discussed in Chapter 17. This mode of operation is useful for copolymer analysis when one of the functional groups has no retention in a very narrow range of the solvent mixture. However, determining the critical solvent composition is problematic as it is very sensitive to small changes in composition. This technique has advantages, but the method development is extremely difficult and is very much dependent on the exact nature of the sample as to whether it will work at all.

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FIGURE 6.3 The sampling phase effect on two-dimensional resolution for 3.8 samples per first-dimension peak (top 4) and 1.9 samples per first-dimension peak (bottom 4). A sampling time of 1 min was used for the 3.8 samples study and a sampling time of 2.0 min was used for the 1.9 samples study. The sampling phase is expressed as a delay time and noted on each chromatogram. Taken from Murphy (1998a) and reprinted with permission of the American Chemical Society.

Protein applications are extremely sensitive to solvent pH, salt concentration, and small molecular weight additives such as trifluoroacetic acid (TFA), which affect solute equilibria. These effects are known and depending on the specific application, proteins are often run under denaturing conditions, which offer vastly different retention conditions than nondenaturing conditions.

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137

6.4.1.4 Second-Dimension Elution Time Range We define the elution time range as the time between the start of the first peak and the end of the last peak for complete elution in the second dimension. In isocratic LC for the second dimension, the elution time range can be the same as the minimum sampling time. Alternatively, the sampling time can be run out to the end of the last peak of the second dimension. These two cases will be discussed in detail later. In gradient elution for the second dimension, the elution time range plus the equilibration time add up to the minimum sampling time. Thus, the sampling time must be greater than or equal to the elution time range plus the equilibration time for gradient elution operation; otherwise, peak overlay effects or peak wraparound will occur. For isocratic LC, the solute does not need to fully elute from the second-dimension column prior to the next sampling period. This is illustrated in Fig. 6.4, where it is shown that more than one sample can be resident in the column at one time. Using the chromatograms shown in Fig. 6.5, which show the effect of various injection volumes that will be discussed later, it is not necessary to wait for the full 2 min of sampling time. This significantly helps to speed up the sampling process and is most useful for SEC, where short elution time ranges are typically found and short times between the injection and nonretained peaks are typical of column operation. Figure. 6.4 shows that after 1 min, the first sampled components are about to elute. However, the next solute sample is introduced into the column at 1 min; the zone

FIGURE 6.4 Zone evolution on the second column showing zones for two different times at each sampling number. Note that there is more than one injection of the sample loop on the second-dimension column after the first injection.

138

METHOD DEVELOPMENT IN COMPREHENSIVE

(a) 400,000

25 µL

uV 200,000

0.0

1.0

Time, min

2.0

3.0

(b) 400,000

50 µL

uV 200,000

0.0

(c)

1.0

Time, min

2.0

400,000

3.0

100 µL

uV 200,000

0.0

1.0

Time, min

2.0

3.0

(d) 400,000 150 µL

uV 200,000

0.0

1.0

Time, min

2.0

3.0

FIGURE 6.5 Size exclusion chromatograms of a mixture of PEG 8000, PEG 1000, and PEG 200 at different injection volumes. Sample concentrations of 800 ppm (a), 400 ppm (b), 200 ppm (c), and 133 ppm (d). Run conditions: Polymer Standards Service styrene–divinyl ˚ pores; tetrahydrofuran benzene linear mixed bed, 50 mm 8 mm, 3 mm particles and 100 A flowing at 1 mL/min; evaporative light scattering detection. Taken from Murphy (1998a) and reprinted with permission of the American Chemical Society.

METHOD DEVELOPMENT

139

appears visually at the half-minute intervals shown in Fig. 6.4. This sample is largely unresolved in Fig. 6.4. Hence, there can be more than one sample on the seconddimension column at the same time with the restriction that sampling cannot be faster than the elution time range. The initial data, which are composed of baseline, are usually removed from the 2D chromatogram prior to construction of the matrix. In complex samples, when the range of elution times may not be known beforehand, there is the possibility of wraparound where components from the previous run are still eluting on the next second-dimension elution (Micyus et al., 2005). This situation is of concern and should be eliminated in the method development process for all but the most exploratory of work. This may require collecting fractions and injecting these fractions into the second-dimension column to determine the most retained compound retention time as part of the method development process. Reducing the analysis time in the second dimension will allow faster sampling times and higher 2DLC resolution, at least in the first dimension. The run time in the second dimension can be reduced by employing steeper solvent gradients and/or by increasing the flow rate. Fig. 6.6 gives an example of the separation of several proteins at different flow rates and injection volumes using RPLC on a monolithic column. At 1 mL/min the elution range is 5 min, whereas at 2 mL/min the elution range is 2.5 min. The higher flow rates reduce the elution range, but decrease resolution. The use of monolithic columns in the second dimension may have advantages since they allow higher flow rates (i.e., higher velocities) with reduced back pressures without major losses in resolution. However, the use of short monolithic columns may not possess the necessary efficiency needed for fast operation in the second dimension. Elevated temperature can also be a very effective way to increase column performance and reduce the second-dimension elution time range as retention is generally reduced at higher temperatures. This has been utilized by Carr and coworkers (Stoll et al., 2006, 2007) to perform very fast second-dimension elution time ranges, and it should be considered for faster chromatographic analysis in general. 6.4.1.5 Sample Loop Volumes The maximum injection volume of the seconddimension column is determined using the strongest solvent in the first dimension to minimize effects on band broadening and improve 2DLC resolution. For isocratic separations in the first dimension, the maximum injection volume onto the seconddimension column should be determined attheisocraticsolventcomposition.In Fig.6.5, variousinjectionvolumesareshownforSECanalysesofpolyethyleneglycolselutingina 1 min elution time range. Good resolution is obtained with 25–50 mL injection volume; 100 mL is still tolerable, but a 150 mL injection results in a significant loss in resolution. If gradient elution high performance liquid chromatography (HPLC) is used in the first dimension, several solvent compositions should be injected into the seconddimension system along with a test solute to see if there is an effect of solvent composition. Typically, the highest solvent strength of the first dimension will show the largest effect if the effect is present. If an effect is seen, then the largest possible injectionvolume is used before deleterious effects become noticeable. As long as these two solvent systems are miscible, there are generally few problems, and the effect of

140

METHOD DEVELOPMENT IN COMPREHENSIVE 3.23

100 2.60 %

3.83

A

5 µL

0.90

-6

C

B

3.24

100 2.61 %

3.84

10 µL

0.91 -5

3.25

100 2.61 % -12

3.85

25 µL

0.90

Time

0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00

0–5 min 100

1.69

1.36

2.01 %

5 µL 0.47

-1 100

1.69

1.35

1.99

% 0.49

2.30

10 µL

-1 100 % -0

1.69

1.36

0.48 0.20

0.40

0.60

1.99

2.31

25 µL 0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

Time

0–2.5 min

FIGURE 6.6 Merck Chromolith monolithic RPLC column at 1 mL/min (top) and 2 mL/min with various injection volumes. Protein standards: A ¼ aprotinin, B ¼ cytochrome C, C ¼ carbonic anhydrase.

injection volume can be determined by monitoring peak resolution with various injection volumes at different compositions similar to Fig. 6.5. The first-dimension maximum flow rate can now be determined from the injection volume divided by the sampling time. In Fig. 6.5, if 100 mL injections are utilized, the

METHOD DEVELOPMENT

TABLE 6.2

Method Development Examples Second dimension

Diameter, mm 4.6 4.6 1.0 1.0 0.3 0.3

141

First dimension

Injection volume, mL

Minimum sampling time, min

Maximum flow rate, mL/min

Diameter, mm

100 100 10 10 1.0 1.0

1.0 5.0 1.0 5.0 1.0 5.0

100 20 10 2.0 1.0 0.2

2.0 0.5 0.5 0.3 0.1 0.05

first-dimension flow rate should be set equal to 100 mL/min. Table 6.2 examines three different column diameters in the second dimension and the effect that sampling time has on the first-dimension maximum flow rate. At a constant flow velocity, the largest flow rates from the first dimension are accommodated with larger column diameters in the second dimension. In addition, smaller sampling times allow larger first-dimension flow rates. As the sampling time increases (i.e., a decrease in sampling rate), the first-dimension column flow rate must be decreased to accommodate the longer time required to fill the loop. Thus, faster sampling times allow larger first-dimension column diameters (and higher loading capacity), which may be critical for trace analysis or high dynamic range samples such as the plasma proteome. 6.4.1.6 First Dimension Optimization After the second-dimension separation has been developed, the first-dimension flow rate is determined. This includes selecting a first-dimension column diameter to work at the flow rate selected. We illustrate the selection process with an application that addresses a column method for proteins that functions as a replacement for planar 2D gel electrophoresis (2DGE) within a narrow molecular weight and pI range. In the planar experiment, isoelectric focusing is performed in the first dimension and sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS/PAGE) in the second dimension. We use the second-dimension separation from Fig. 6.6 with a 25 mL injectionvolume and 2.5 min sampling time; the separation is an RPLC method that uses a monolithic column. Thus, 10 mL/min is the maximum flow rate in the first-dimension. Fig. 6.7 shows the development of the first-dimension column that utilizes a hydrophilic interaction (or HILIC) column for the separation of proteins at decreasing flow rates. The same proteins were separated in Fig. 6.6 (RPLC) and 6.7 (HILIC) and have a reversed elution order, which is known from the basics of HILIC (Alpert, 1990). It is believed that HILIC and RPLC separations are a good pair for 2DLC analysis of proteins as they appear to have dissimilar retention mechanisms, much like those of NPLC and RPLC; it has been suggested that HILIC is similar in retention to NPLC (Alpert, 1990). Because the HILIC column used in Fig. 6.7 gave good resolution at 0.1 mL/min and no smaller diameter column was available, the flow was split 10-fold to match the second-dimension requirement.

142

METHOD DEVELOPMENT IN COMPREHENSIVE 32.83

100

0.1 mL/min

%

C B

85.73 89.56

A

101.80

40.11

-9 100

38.91

13.21

%

12.80

40.53 45.83

0.25 mL/min

33.63

-24 100 %

6.69

19.19 22.67

0.5 mL/min 5.60

-30 100 % -31

3.34

9.98 11.28

1.0 mL/min 10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00 110.00 120.00

Time

FIGURE 6.7 A HILIC column (Tosoh Amide-80, 25 cm 4.6 mm, 5 mm particles) at different flow rates with a solvent of ACN/water/0.1%TFA. The gradient is 80% to 15% ACN. Protein standards: A ¼ aprotinin, B ¼ cytochrome C, C ¼ carbonic anhydrase.

The resulting 2DLC chromatogram combining HILIC and RPLC is shown in Fig. 6.8 and compared to the electropherogram produced by 2DGE. The 2DLC result shown in Fig. 6.8 gives similar resolution to the 2DGE result in a 2 hour analysis time. The analysis could have been optimized further by decreasing the flow rate and

FIGURE 6.8 Right: 2DLC of Sigma M3411 proteins standards. The standards are A ¼ amyloglucosidase (pI 3.8, MW 89,000), B ¼ ovalbumin (pI 5.1, MW 45,000), C ¼ carbonic anhydrase (pI 6.6, MW 29,000), and D ¼ myoglobin (pI 7.6, MW 17,000). Left: 2DGE from Sigma Product Information sheet M3411: markers for two-dimensional electrophoresis.

PLANNING THE EXPERIMENT

143

slowing down the first dimension separation. The chromatographic peaks in Fig. 6.7 are about 5 min wide, which allows sampling twice of the peak width. Overall, increasing 2DLC resolution to four samples per chromatographic peak would double the analysis time. Depending on the equipment available and the analytical problem, a reduction in resolution is a trade-off for increased speed, a situation that is similar to one-dimensional chromatography.

6.5 PLANNING THE EXPERIMENT It is worthwhile to plan the 2D experiment and understand the type of 2D separation one is planning to conduct before going through the cardinal rules. For example, is the goal of the separation to try to resolve all of the peaks possible? This scheme has often been the goal for complex samples of biological origin where the increased resolution is the purpose of performing 2DLC. If the desired goal is to utilize the two dimensions to sort out specific interactions with each phase, as is the case for multifunctional surfactants and other polymers (see Chapter 18), then the goals of the separation are very much different. In this case, speed can be more important than the resolution of most zones. If one is willing to wait, much higher resolution of fused zones is possible with 2DLC, as is the case with one-dimensional LC. Again, the types of trade-offs between speed, resolution, and selectivity must be sorted out before one goes through the cardinal rule choices and designs.

6.6 GENERAL COMMENTS ON OPTIMIZING THE 2DLC EXPERIMENT: SPEED–RESOLUTION TRADE-OFF As with any separation technique, the desired goal is to maximize peak resolution at the fastest speed. Higher resolution in 2DLC is easier to achieve than when using onedimensional chromatography because selectivity differences between the two different columns can give a resolution enhancement. This is easily seen through the simplified resolution equation, discussed in Chapter 2, qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ Rs ¼ Rs21 þRs22 þ ð6:1Þ where the individual subscripted Rsi are the resolution in the first and second dimensions and these lead to the total resolution. This is the same formula as the distance d between two points x1,y1 and x2,y2 on a plane, also referred to as the ‘2 norm or Euclidean norm: d ¼ ½ðx1 x2 Þ2 þðy1 y2 Þ2 1=2

ð6:2Þ

Equation 6.1 demonstrates that if resolution is low in one of the dimensions and high in the other dimension, the result is at least as large as the higher resolution. However, limited sampling of the first dimension can make the standard deviation of the zone in

144

METHOD DEVELOPMENT IN COMPREHENSIVE

the first dimension appear from 1.5 to 3 times larger than if it were continuously sampled, as given in Fig. 2.7 of Chapter 2. Since the resolution is inversely proportional to the standard deviation of one of the zones to be resolved (assuming the neighbors have equal width), this implies that Rs1 is one third (2 samples per zone width) to two thirds (3 samples per zone width) that of a continuously sampled zone. For two zones with resolution of 1.0 in each dimension, the total resolution is 1.414 showing the increase in resolution because of the use of two dimensions; we assume here that this is approximate and that the two zones in question lie at 45 angles. For the case where the two zones are sampled with two or three samples per zone width, the value of Rs1 is reduced by 0.33 and 0.66, respectively, and the results after plugging into Equation 6.1 give a total resolution of, respectively, 1.05 and 1.20, a reduction of approximately 25% and 15% from the “ideal’’ continuously sampled zones. This is not a drastic amount of resolution loss, but it highlights one of the major difficulties in utilizing 2DLC or any multidimensional technique based on zone sampling. Faster sampling times result in higher resolution and decrease the total analysis time. Thus, the speed and efficiency of the second dimension is critical in maintaining the higher resolution advantage of 2DLC. We feel that the method development of fast second dimension column methods is the major challenge to the effective utilization of 2DLC.

ACKNOWLEDGMENT We thank Dwight Stoll, of the University of Minnesota, for helpful discussions.

REFERENCES Alpert, A.J. (1990). Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. J. Chromatogr. A 499, 177–196. Bedani, F., Kok, W.Th., Janssen, H.-G. (2006). A theoretical basis for parameter selection and instrument design in comprehensive size-exclusion chromatography liquid chromatography. J. Chromatogr. A 1133, 126–134. Berridge, J.C. (1985). Techniques for the Automated Optimization of HPLC Separations. John Wiley & Sons, Inc., New York. Eksteen, R. (2007). Personal communication. Glajch, J.L., Snyder, L.R., editors (1990). Computer-Assisted Method Development for HighPerformance Liquid Chromatography. Elsevier, Amsterdam. Guiochon, G. (2006). The limits of the separation power of unidimensional column liquid chromatography. J. Chromatogr. A 1126, 6–49. Micyus, N.J., Seeley, S.K., Seeley, J.V. (2005). Method for reducing the ambiguity of comprehensive two-dimensional chromatography retention times. J. Chromatogr. A 1086(1–2), 171–174. Murphy, R.E., Schure, M.R., Foley, J.P. (1998a). Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70(8), 1585–1594.

REFERENCES

145

Murphy, R.E., Schure, M.R., Foley, J.P. (1998b). One and two-dimensional chromatographic analysis of alcohol ethoxylates. Anal. Chem. 70, 4353–4360. Poppe, H. (1997). Some reflections on speed and efficiency of modern chromatographic methods. J. Chromatogr. A 778, 3–21. Schoenmakers, P.J. (1986). Optimization of Chromatographic Selectivity. Elsevier, Amsterdam. Schoenmakers, P.J. Vivo-Truyols, G., Decrop, W.M.C. (2006). A protocol for designing comprehensive two-dimensional liquid chromatography separation systems. J. Chromatogr. A 1120, 282–290. Seeley, J.V. (2002). Theoretical study of incomplete sampling of the first dimension in comprehensive two-dimensional chromatography. J. Chromatogr. A 962(1–2), 21–27. Snyder, L.R., Kirkland, J.J., Glajch, J.L. (1997). Practical HPLC Method Development. 2nd edition. Wiley Interscience, New York. Stoll, D.R., Cohen, J.D., Carr, P.W. (2006). Fast, comprehensive online two-dimensional high performance liquid chromatography through the use of high temperature ultra-fast gradient elution reversed-phase liquid chromatography. J. Chromatogr. A 1122, 123–137. Stoll, D.R., Li, X., Wang, X., Carr, P.W., Porter, S.E.G., Rutan, S.C. (2007). Fast, comprehensive two-dimensional liquid chromatography. J. Chromatogr. A 1168 (1–2), 3–43. Van der Horst, A., Schoenmakers, P.J. (2003). Comprehensive two-dimensional liquid chromatography of polymers. J. Chromatogr. A 1000, 693–709.

7 MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS Tohru Ikegami, Hiroshi Aoki, Hiroshi Kimura, Ken Hosoya, and Nobuo Tanaka Department of Polymer Science and Engineering, Kyoto Institute of Technology, Matsugaski, Sakyo-Ku, Kyoto 606-8585, Japan

7.1 INTRODUCTION As chromatography is going to be used for the separation of very complex mixtures, new methods and materials aiming at much higher efficiency have been examined in liquid-phase separations. Higher chromatographic performance than obtainable by using particle-packed columns with common high performance liquid chromatography (HPLC) instrumentation has been achieved by employing capillary electrochromatography (CEC) (Rozing et al., 1996), ultrahigh pressure HPLC (UPLC) (MacNair et al., 1997), and monolithic columns (Minakuchi et al., 1996). The reason why monolithic columns attract attention in spite of limited availability at present is that they can potentially provide higher performance than conventional particle-packed columns under similar operating conditions. In this chapter, current performance of monolithic columns, made of organic polymers or silica, will be briefly reviewed before describing examples of two dimensional – high performance liquid chromatography (2D-HPLC) methods using these monolithic columns.

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

147

148

MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

7.2 MONOLITHIC POLYMER COLUMNS This section provides an overview of properties of polymer monolith columns related to 2D-HPLC. Monolithic organic polymer columns, having longer history than silica monoliths, have been reviewed in detail recently by Svec and by Eeltink including their preparation methods and performance (Eeltink et al., 2004; Svec, 2004a). Polymer monolith columns commercially available include poly(styrene-co-divinylbenzene) (PSDVB) columns and poly(alkyl methacrylate) columns. Polymeric packing materials have been used in HPLC and have shown to be particularly suitable for applications for the separation of biological substances. They are free from silanol effects associated with silica-C18 phases and showed higher performance and recovery for proteins than silica-based materials in reversed-phase mode. They are also more chemically stable in aqueous, buffered mobile phases for ion-exchange (IE) or size-exclusion (SEC) mode applications, leading to frequent use in proteome studies. The efficiency and the mechanical stability of columns packed with organic polymer particles were somewhat lower than those of silica-based materials. Similar tendencies observed with monolithic materials seem to be the reason for their 2D applications in off-line mode so far reported. Polymer monolithic columns, however, will find a greater market share in monolithic columns when compared with polymer particulate columns in whole particle-packed columns. Polymer monolithic columns are also attractive for the application in CEC (Zou et al., 2002; Svec, 2004b), especially for chip-based separation systems, because of their flexibility in surface chemistry and higher chemical stability than silica materials. 7.2.1

Structural Properties of Polymer Monoliths

The major design concept of polymer monoliths for separation media is the realization of the hierarchical porous structure of mesopores (2–50 nm in diameter) and macropores (larger than 50 nm in diameter). The mesopores provide retentive sites and macropores flow-through channels for effective mobile-phase transport and solute transfer between the mobile phase and the stationary phase. Preparation methods of such monolithic polymers with bimodal pore sizes were disclosed in a US patent (Frechet and Svec, 1994). The two modes of pore-size distribution were characterized with the smaller sized pores ranging less than 200 nm and the larger sized pores greater than 600 nm. In the case of silica monoliths, the concept of hierarchy of pore structures is more clearly realized in the preparation by sol–gel processes followed by mesopore formation (Minakuchi et al., 1996). The monomers commonly used for the preparation of polymer monoliths are either hydrophobic, for example, styrene/divinylbenzene and alkyl methacrylates, or hydrophilic, for example, acrylamides. The polymerization is usually accomplished by radical chain mechanisms with thermal or photochemical initiation, as detailed in the reviews (Eeltink et al., 2004; Svec, 2004a and b). Internal structures of polymer monoliths are described to be corpuscular rather than spongy; this means throughpores were found to be interstices of agglomerated globular skeletons as shown in Fig. 7.1 (Ivanov et al., 2003). Porosity is presumably predetermined by the preparation

MONOLITHIC POLYMER COLUMNS

149

FIGURE 7.1 Scanning electron micrographs of a polystyrene–divinylbenzene monolithic column prepared in a 20-mm fused silica capillary tube (reproduced from the reference, Ivanov et al. (2003), with permission from American Chemical Society).

feed as the volume of porogen in the total mixture and the extent of shrinkage of skeletons during polymerization. The porosity predicted by the feed composition, or slightly smaller, has been found in many cases. It is of much interest to compare polymer monoliths with monolithic silica columns for practical purposes of column selection. Methacrylate-based polymer monoliths have been evaluated extensively in comparison with silica monoliths (Moravcova et al., 2004). The methacrylate-based capillary columns were prepared from butyl methacrylate, ethylene dimethacrylate, in a porogenic mixture of water, 1-propanol, and 1,4-butanediol, and compared with commercial silica particulate and monolithic columns (Chromolith Performance). Table 7.1 shows the pore properties of several polymer monolithic columns prepared from styrene/DVB, methacrylates, and acrylamides along with the feed porosity and column efficiency, summarized from several recent publications. Some important points seem to be clearly shown in Table 7.1, especially by the comparison of the properties between methacrylate-based polymer monoliths and silica monoliths. A rather limited range of mesopores in terms of size and volume were observed in the skeletons of polymer monoliths. The porosity of the polymer monolith seems to be lower than that of silica monolith. The total porosity of these monoliths is in the range of 0.61–0.73, whereas interstitial (through-pore) porosity and mesopore porosity are 0.28–0.70 and 0.03–0.24, respectively. In the case of poly(butyl methacrylate-co-ethylene dimethacrylate), the observed porosity is around 0.61– 0.71, resulting in permeability 0.15–8.43 1014 m2, whereas the observed porosity of silica monoliths prepared in a capillary is 0.86–0.96 and the permeability is 7–120 1014 m2. Higher permeability will be advantageous for 2D applications, as mentioned later. High performance monolithic columns were prepared from styrene and divinylbenzene (PSDVB, 200 mm i.d.) (Oberacher et al., 2004). The monoliths possess 5– 300 nm pores with porosity of ca. 50% and 20% for external and internal pores, respectively, with specific surface areas of 30–40 m2/g. The column showed permeability K ¼ 3.5 1015 m2 in water and slightly less in acetonitrile. The pore size

150

MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

distribution was not necessarily bimodal. Small-sized pores and skeletons seem to be the origin of high performance and high back pressure. A short column can be used for a high speed gradient separation of biological molecules. 7.2.2

Chromatographic Properties of Polymer Monolithic Columns

The monolithic PSDVB polymer columns mentioned above showed relatively low permeability but exceptionally high efficiency with plate heights of about 10 mm at optimum mobile phase linear velocity. The columns were particularly useful for gradient separation of peptides. Not many other examples, however, were found for characterization of polymer monoliths with respect to fundamental chromatographic properties, including column efficiency in an isocratic mode, because the primary application areas of polymer monoliths are gradient separations of biological macromolecules. According to the comparative evaluation cited above (Moravcova et al., 2004), the methacrylate-based monolithic columns showed retention behavior similar to that of the silica columns by HPLC in 70% ACN, although methylene selectivity (aCH2) was lower. The results were attributed to the lower surface hydrophobicity of the polymeric columns having polar ester groups. The van Deemter curves showed that the efficiency of the columns for homologous alkylbenzenes was lower than that of silica columns. In a review on the comparison between microparticulate and monolithic capillary columns (Eeltink et al., 2004), the efficiency of a wide range of polymer monoliths, including acrylamides, styrene/divinylbenzene, methacrylate, and acrylate, were discussed in detail. It was shown that better efficiency has been achieved with CEC mode, where flow is electrodriven, than with a pressure-driven mode. Acrylamide monoliths showed plate heights somewhere around 9–40 mm in CEC mode, though some better results were observed, whereas plate heights ranging 9–20 mm were reported for PSDVB, or methacrylate-based monoliths in CEC mode. Recently, methacrylamide-based C16 columns, prepared from methacrylamide (monomer), N,N0 -methylenebisacrylamide (crosslinking agent), 1-octadecene (functional monomer) in 1-propanol as porogen, have been proposed for CEC application (Zhang et al., 2005). The separation efficiency of 25,000/m (plate height 40 mm) was reported for neutral PAH compounds like fluorene. As shown in Table 7.1 listing the comparison among the polymer monoliths prepared from three major monomers, styrene/DVB, methacrylates, and acrylamides, the column efficiency of polymer monoliths (in terms of plate height) at optimum linear velocity of mobile phase seems to be a little lower (in HPLC mode, H ¼ 22–25 mm for methacrylate-based), compared to that of silica monolith (in HPLC, H ¼ 8–16 mm) and packed particles (in HPLC, H ¼ 7–19 mm with 5 mm particles), although the polymer monoliths performed relatively well in CEC mode, compared to the monolithic and particulate silica. The agglomerated globular structure of polymer monoliths may cause the lower permeability and efficiency of polymer monoliths, presumably because of the slow mass transfer in stationary phase and the irregularity in the structure, skeleton size, and channel size. The formation of agglomerated globules may be attributed to the choice of a

151

1.90

1.92

2.0–8.0

NA

NA

Skeleton 1.0–2.0 Particulate, 5 mm Particulate 3 mm

NA

NA

4.35

NA

9

13.1–25.2

NA

NA

NA

NA

0.84–1.3

NA

3–4

0.69–3.87

Globule 0.15–1.2 Granule 5

NA

340

140–340

2.0

2.5

0.3

NA

5.4–41.3

32–43

7–183

Surface Area, m2/g

Reference: aEeltink et al. (2004). bVirklund et al. (2001). cGusev et al. (1999). (2001). gJung and Hahn (2004). hJanc et al. (2002). iTanaka et al. (2002).

Silica particlea

Silica particlea

Styrene/ divinylbenzeneh Glycidyl MA/EDMAh 2,3-Dihydroxypropyl MA/EDMAh Silica monolitha, i

100

0.5

Globule

Styrene/ divinylbenzened Methacrylates (BMA/EDMA)a,e Acrylamidea,f,g

Meso, nm 3–4

Globule 2–5

Styrene/ divinylbenzenea–c

Macro, mm

0.6–1

Skeleton size, mm

Pore size

Properties of Polymer Monoliths for HPLC

Column materials (reference)

TABLE 7.1

3–13

5–20

5

NA

NA

NA

9–40

13–25

-

9–20

CEC

NA

NA

0.96

NA

NA

NA

NA

0.65

0.86–0.96

1.08 mL/g

1.05 mL/g

0.68 mL/g

NA

0.61–0.71

0.71

0.4–0.65

Observed Porosity

NA

4

7–120

NA

NA

NA

NA

Alkylbenzenes, PAHs Drugs, proteins (Commercial product) (Commercial product) (Commercial product) Alkylbenzenes, PAH Alkylbenzenes, PAHs Alkylbenzenes, PAHs

PAHs, pharmaceuticals, peptides Peptides

Analytes

Hoegger and Freitag

f

0.15–8.4

0.35

5–18

Permeabili ty (K), [m2] 1014

Moravcova et al. (2004).

e

0.71–0.85

0.55–0.65

0.6

0.6–0.65

Feed Porosity

Oberacher et al. (2004).

d

4–19

7–19

8–16

NA

NA

NA

6–330

22–25

8

15–85

HPLC

Plate Height, nm

152

MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

poor solvent for the monomers as porogen for the formation of through-pores, though solvent mixtures of poor and good solvents, binary or ternary, are occasionally used for pore size control. Karger and coworkers used tetrahydrofuran (THF) and 1-decanol as porogen for the preparation of PSDVB capillaries of bimodal pore distribution (Ivanov et al., 2003). The higher porosity greater than 0.7 is not easily achieved in the case of brittle polymer monoliths. 7.2.3

Two-Dimensional HPLC Using Polymer Monoliths

Polymer monolithic columns with small diameter have been successfully employed for proteome analysis. Karger and coworkers reported MALDI-TOF of separated fractions spotted on a plate from a polymeric reversed-phase column that showed high peak capacity (Chen et al., 2005). Huber and coworkers reported separation and detection of about 200 peaks within 5 min by using a PSDVB column (Premstaller et al., 2001). As to the application to 2D analysis, there seem to be still few examples of polymer monolith application. It is presently our understanding that polymer monoliths are used either in off-line approach, or in slow 2D separations that are slow elution at the first dimension with fractionation and storage, and subsequent slow elution of each stored fraction at the second dimension. The usefulness of polymer monolithic columns in 2D HPLC is nicely shown in the recent publication on 2D separation of protein digests (Toll et al., 2005; Dragan et al., 2005). The use of PSDVB columns for proteome analyses was demonstrated to achieve comprehensive 2D-HPLC separation by using PSDVB columns in an off-line mode. In one case PSDVB columns were operated in acidic and basic mobile phases of different pH values (Fig. 7.2, Toll et al., 2005) (also see Chapter 12; by Gilar et al. for examples of hybrid silica columns using different pH mobile phases for 2D peptide separations). PSDVB-based monolithic columns were also shown to be effective for characterization of posttranslational modification of proteins (Tholey et al., 2005). As mentioned earlier, high-speed separation is necessary to carry out fast, comprehensive 2D HPLC. The polymer monoliths have not been employed in such 2D HPLC, probably because permeability of polymer monoliths is not high enough to allow fast elution of the second dimension (2nd-D) in simple 2D operation, and the gradient cycle at the 2nd-D cannot be so fast to allow online 2D operation without reducing peak capacity at first dimension (1st-D). The application of polymer monoliths in 2D separations, however, is very attractive in that polymer-based packing materials can provide a high performance, chemically stable stationary phase, and better recovery of biological molecules, namely proteins and peptides, even in comparison with C18 phases on silica particles with wide mesopores (Tanaka et al., 1990). Microchip fabrication for 2D HPLC has been disclosed in a recent patent, based on polymer monoliths (Corso et al., 2003). This separation system consists of stacked separation blocks, namely, the first block for ion exchange (strong cation exchange) and the second block for reversed-phase separation. This layered separation chip device also contains an electrospray interface microfabricated on chip (a polymer monolith/

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FIGURE 7.2 Two-dimensional separation of tryptic peptides from a mixture of 10 proteins by reversed-phase separation at high pH followed by reversed-phase separation at low pH. 1stD: 50 mm 530 mm i.d., PSDVB monolith, linear gradient 0–30% acetonitrile in 72 mmol/L triethylamine-65 mmol/L acetic acid, pH 10.0, flow rate 18 mL/min, temperature 50 C, detection negative-mode ESI-MS. 2nd-D: 50 mm 100 mm i.d. PSDVB monolith, linear gradient 0%–35% acetonitrile in 6.5 mmol/L trifluoroacetic acid, pH 2.1 in 20 min, flow rate 500 nL/min, temperature 50 C, detection: positive mode ESI-MS. One microliter injection of 10 fractions from 1st-D, (reproduced from the reference, Toll et al. 2005, with permission from Elsevier).

multiple nozzle electrospray device) and is hyphenated to a mass spectrometer. The development of polymer monoliths of high permeability will enable their wide use in 2D HPLC.

7.3 MONOLITHIC SILICA COLUMNS Chromatographic use of monolithic silica columns has been attracting considerable attention because they can potentially provide higher overall performance than particle-packed columns based on the variable external porosity and through-pore size/skeleton size ratios. These subjects have been recently reviewed with particular interests in fundamental properties, applications, or chemical modifications (Tanaka et al., 2001; Siouffi, 2003; Cabrera, 2004; Eeltink et al., 2004; Rieux et al., 2005). Commercially available monolithic silica columns at this time include conventional size columns (4.6 mm i.d., 1–10 cm), capillary columns (50–200 mm i.d., 15–30 cm), and preparative scale columns (25 mm i.d., 10 cm).

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7.3.1

Preparation

Monolithic silica columns possess three-dimensional network structures consisting of silica skeletons of 0.5–2 mm diameter and through-pores of 1–8 mm (Ishizuka et al., 2002; Motokawa et al., 2002). They are prepared by sol–gel reactions starting from tetraalkoxysilanes, namely, tetramethoxysilane (TMOS) or tetraethoxysilane (TEOS). The characteristic cocontinuous structures of monolithic silica columns are produced by spinodal decomposition of initially homogeneous solutions of tetraalkoxysilanes. They undergo hydrolysis and polymerization in the presence of acetic acid to form silica gel. By controlling the rates of hydrolysis and polymerization causing gelation and phase separation between the silica-rich phase and the water-rich phase, the sizes of skeletons and through-pores can be varied. Domain size (combined size of through-pore and skeleton) can be controlled by the concentration of water-soluble polymer, namely, poly(ethylene glycol) (PEG). Mesopores are formed in silica skeletons by treatment with ammonia introduced after the formation of the network structure of silica skeletons. Ammonia can be generated by the hydrolysis of urea charged in an initial reaction mixture. The preparation procedure was developed, and discussed in detail, by Nakanishi and coworkers (Nakanishi, 1997). Monolithic silica columns can be prepared either in a test tube or in a fused silica capillary tube. In the case of preparation in a test tube, silica network structures undergo shrinkage during reaction and subsequent aging process, typically to 70% of the mold size (Minakuchi et al., 1996, 1997, 1998 a,b), whereas in a capillary the silica skeletons must be covalently attached to the wall so that no void can be formed. The monolithic silica structure can be formed in a capillary tube of up to 200 mm i.d. starting from TMOS, whereas a successful preparation of up to 530 mm ID capillary columns was reported starting from a mixture of TMOS and methyltrimethoxysilane (MTMS) (Motokawa, 2006). For the preparation in a test tube, the resulting silica monoliths are covered by PEEK resin to fabricate a column to be used under pressure of up to 200 bar. High-temperature treatment is commonly carried out at above 600 C for preparation in a test tube, and at 330 C for those in a capillary to prevent damage to the polyimide coating of the tube. Chemical modification of silica is carried out by oncolumn reaction with octadecyldiethylaminosilane in a capillary. In the case of monolithic silica prepared in a test tube, batch modification prior to the cladding process is possible.

7.3.2

Structural Properties of Monolithic Silica Columns

Monolithic silica columns currently available consist of silica skeletons of 0.5–2 mm diameter and through-pores of 1–8 mm. Figure 7.3 shows scanning electron micrographs (SEM) of monolithic silica prepared in a mold (a) and those prepared in a capillary (b–d) (Motokawa et al., 2002). Through-pores of a monolithic silica column are relatively large compared to those of a column packed with particles with (throughpore size)/(skeleton size) ratio in the range 1–4, much larger than that in a particlepacked column, 0.25–0.4 (Unger, 1979). Mesopores of 10–30 nm can be formed.

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FIGURE 7.3 Scanning electron micrographs of monolithic silica prepared from sol–gel methods. (a) monolithic silica prepared from TMOS in a test tube, and monolithic silica columns prepared from a mixture of TMOS and MTMS, (b) in a 50-mm fused silica capillary, (c) in a 100-mm fused silica capillary, and (d) in a 200-mm fused silica capillary tube (reproduced from the reference, Motokawa et al. (2002), with permission from Elsevier).

External porosity is above 60% for conventional size columns prepared in a test tube (Al-Bokari et al., 2002), and above 80% for those prepared in a capillary. The high external porosity and large (through-pore size)/(skeleton size) ratios can lead to much higher permeability of monolithic silica columns than that of a column packed with particles of similar column efficiency (Leinweber et al., 2002; Leinweber and Tallarek, 2003). For example, a Chromolith column provided by Merck shows permeability, K ¼ 7 1014 m2, twice as high as a column packed with 5 mm particles. Capillary columns with large through-pores show up to 30 times higher permeability, K ¼ 1.2 1012 m2 (Eq. 1, u: linear velocity of mobile phase, h: solvent viscosity, L: column length, and DP: column pressure drop) (Ishizuka et al., 2002). High permeability is an important feature of monolithic silica columns, particularly for 2D-HPLC applications. Higher flow rates generally afford a greater peak capacity for a short separation time at 2nd-D. A faster 2nd-D analysis is also advantageous as it permits more frequent sampling of the first dimension. An additional advantage of a monolithic silica column is the increased mechanical stability provided by the integrated network structure. Although columns with smaller domain size show high column efficiency and high pressure drop, those with larger domain size show low pressure drop and suitable for fast operation. K ¼ uhL=DP

ð7:1Þ

Disadvantages of monolithic silica columns include the labor-intensive preparation of individual columns with possible reproducibility problems, limited availability, and relatively short retention caused by the smaller amount of silica existing in a column

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MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

than in a particle-packed column. The k values found with monolithic silica-C18 columns were smaller than those with particle-packed columns by a factor of 2–5 depending on the total porosity, 80–85% for conventional size and 90–95% for capillary type. This can be a limitation for a 2nd-D column. Large volume injections frequently needed in 2D HPLC need highly retentive columns for maintaining the column efficiency. 7.3.3

Chromatographic Properties of Monolithic Silica Columns

50

180 150

Plate height, H / µm

Pressure drop, ∆P / kg cm–2

Correlation was found between domain size and attainable column efficiency. Column efficiency increases with the decrease in domain size, just like the efficiency of a particle-packed column is determined by particle size. Chromolith columns having ca. 2 mm through-pores and ca. 1 mm skeletons show H ¼ 10 (N ¼ 10,000 for 10 cm column) at around optimum linear velocity of 1 mm/s, whereas a 15-cm column packed with 5 mm particles commonly shows 10,000– 15,000 theoretical plates (H ¼10–15) (Ikegami et al., 2004). The pressure drop of a Chromolith column is typically half of the column packed with 5 mm particles. The performance of a Chromolith column was described to be similar to 7–15 mm particles in terms of pressure drop and to 3.5–4 mm particles in terms of column efficiency (Leinweber and Tallarek, 2003; Miyabe et al., 2003). Figure 7.4 shows the pressure drop and column efficiency of monolithic silica columns. A short column produces 500 (1 cm column) to 2500 plates (5 cm) at high linear velocity of 10 mm/s. Small columns, especially capillary type, are sensitive to extra-column band

120 90 60 30 0

0

1 2 3 4 5 Linear velocity, µ /mm s–1

40 30 20 10 0 0

1 2 3 4 Linear velocity, µ /mm s–1

5

FIGURE 7.4 (a) Plots of column back pressure against linear velocity of mobile phase (29, 32, 37). Mobile phase: 80% methanol. The pressures were normalized to the column length of 15 cm. Columns: 5 mm silica-C18 particles, Mightysil RP18 (.), Inertsil ODS-3(~). Monolithic silica column prepared in a mold, MS-PTFE(B)S-C18 (), MS-PEEK(&). Monolithic silica column in capillary, MS-FS(50)-A (*), MS-FS(50)-B (~), MS-FS(50)-C (!). MS-FS(50); 50 mm i.d. (A) 33.5 cm (effective length 25 cm), (B) 53.5 cm(effective length 45 cm), (C) 138.5 cm (effective length 130 cm). The van Deemter plots obtained for C18 monolithic silica columns and silica-C18 packed columns with hexylbenzene as a solute. (b) For columns of 4.6–7 mm i.d. Mobile phase: 80 % methanol. Symbols as in Figure 7.4 for the columns. Solute: hexylbenzene (reproduced from the reference, Tanaka et al. (2002) with permission from Elsevier).

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157

broadening. Injection and detection as well as line connection must be carried out not to reduce the performance of small size columns by minimizing extra-column effects (Ikegami et al., 2004). E ¼ DPt0 =hN 2 ¼ ðDP=NÞðt0 =NÞð1=hÞ ¼ H 2 =K

ð7:2Þ

Separation impedance (E value, Eq. 7.2) is calculated by multiplying a reciprocal number of theoretical plates per unit pressure drop with a reciprocal number of theoretical plates per unit time. Separation impedance is a measure of total column performance in terms of permeability and column efficiency. The separation impedance for a monolithic silica column was as low as 200–300, about 10 times smaller than those of particle-packed columns. Although the performance of monolithic silica columns can be higher than that of particle-packed columns at similar pressure drop in a slow-elution range, the column efficiency for high-speed operation comes closer to that of a column packed with particles. High porosity or large through-pores can be responsible for the reduction of column efficiency at high speed due to the increased contribution of slow mass transfer in the mobile phase. According to a simulation study, monolithic columns can show advantage over particle-packed columns for the separations that require more than 50,000 theoretical plates at a pressure limit of 400 bar (Desmet et al., 2005). Monolithic columns with small domain size, especially those with high porosity prepared in capillary, could not produce performance expected from the reduction of their unit size (domain size). This is partly because of the increased inhomogeneity of the network structure of a monolithic silica column. It has been suggested that an increase in homogeneity of monolithic silica can increase the performance by several times (Gzil et al., 2004). The silica skeletons prepared in a test tube are smoother and more homogeneous than those prepared in a capillary (Fig. 7.3). The inhomogeneity and large-sized through-pores seem to explain the lower performance of monolithic silica than a particle-packed column, particularly for capillary columns operated at high speed. High column efficiency at high linear velocity is particularly important for a second-dimension column in 2D HPLC. The use of monolithic silica columns in 2D HPLC is, however, advantageous in terms of mechanical stability of the column. It is highly desirable to use short monolithic silica columns that can achieve high efficiency at high linear velocity. In a sense each monolithic column is unique, or produced as a product of a separate batch, because the columns are prepared one by one by a process including monolith formation, column fabrication, and chemical modification. Reproducibility of Chromolith columns has been examined, and found to be similar to particle-packed-silicabased columns of different batches (Kele and Guiochon, 2002). Surface coverage of a Chromolith reversed-phase (RP) column appears to be nearly maximum, but greater silanol effects were found for basic compounds and ionized amines in buffered and nonbuffered mobile phases than advanced particle-packed columns prepared from high purity silica (McCalley, 2002). Small differences were observed between monolithic silica columns derived from TMOS and those from silane mixtures for planarity in solute structure as well as polar interactions (Kobayashi et al., 2004).

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MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

Monolithic silica columns with various surface derivatization, including ion exchange (Xie et al., 2005), and chiral functionalities (Chen et al., 2002; Lubda et al., 2003; Chankvetadze et al., 2004), as well as protein-immobilized monoliths (Kato et al., 2005) have been reported. The latter will be an important part of an integrated multidimensional separation/identification system.

7.4 PEAK CAPACITY INCREASE BY USING MONOLITHIC SILICA COLUMNS IN GRADIENT ELUTION A typical HPLC separation using a 15-cm column of 15,000 theoretical plates produces peak capacity (Giddings, 1991) of about 80–100 under isocratic conditions and up to 150 under gradient conditions in 1 h (Eq. 7.3, n: peak capacity, N: number of theoretical plates of a column, and tR and t1: retention time of the last and the first peak of the chromatogram, respectively). An increase in the number of separated peaks per unit time can be achieved by increased separation speed made possible by monolithic silica columns (Deng et al., 2002; Volmer et al., 2002). This has also been shown for peptides and proteins (Minakuchi et al., 1998; Leinweber et al., 2003). pﬃﬃﬃﬃ ð7:3Þ n ¼ 1þð N =4ÞlnðtR =t1 Þ Shallow gradient elution using a long monolithic silica capillary column is an easy and a viable approach to increase the number of separated peaks, or peak capacity, per unit time. In a liquid chromatography-mass spectrometry (LC-MS) system, the use of a monolithic silica capillary column resulted in better resolution in LC and detection and identification of greater number of peaks in MS. Figure 7.5 shows comparison of chromatograms for a gradient elution of methanol extracts of arabidopsis thaliana using 30–90 cm monolithic silica columns in capillary (Tolstikov et al., 2003). The increase in the column length, a greater number of peaks has been resolved without much increase in gradient time. The major factor for the improved detection/resolution seems to be the reduction of ion suppression. It is known that less easily ionizable solutes in unresolved peaks tend to be suppressed from ionization. The increased efficiency of a long capillary column, ca. 60,000 theoretical plates by 90-cm column, compared to 30,000, resulted in a greater resolution and less ion suppression to produce higher sensitivity and a greater number of peaks detected (Tolstikov et al., 2003). The results indicate that some peaks showed consistent intensity in MS trace, whereas other peaks showed increase in peak intensity up to certain level, with the increase in resolution provided by the longer column indicating the elimination of ion suppression. Small sample size, small amount of mobile phase required, and compatibility with micro- and nano-ESI interface are the features of capillary HPLC that are most conveniently carried out by using a long monolithic silica column. This was shown very nicely in a proteomic study using a 20 mm i.d., 70 cm monolithic silica capillary column (Luo et al., 2005) providing a peak capacity of 420 in a single run in 4 h. The advantage of using long capillary columns was also shown when they were applied to

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159

FIGURE 7.5 Replicate injections of an Arabidopsis leaf methanol extract on capillary monolithic C18 columns in positive ionization fullscan MS, given as base peak chromatograms. (a) 0.2 300 mm, (b) 0.2 600 mm, (c) 0.2 900 mm column. Mobile phase A: 6.5 mM ammonium acetate (pH 5.5, adjusted by acetic acid), B: acetonitrile. (a) 5% B–20%B (15 min)–70%B (22 min) to 100%B (57 min), 2.6 mm/s, (b) 5%B–20%B (15 min)–70%B (23 min) to 100%B (75 min), 2.6 mm/s, (c) 5% B–20%B (16 min)–70%B (23 min) to 100%B (110 min), 1.8 mm/s (reproduced from the reference, Tolstikov et al. (2003), with permission from the American Chemical Society).

2D separations. A monolithic silica-C18 capillary column was used as a 1st-D column coupled off-line to capillary electrophoresis (CE) in a metabolome study (Jia et al., 2004). Although it is not a simple 2D chromatographic system, the example showed that high peak capacity obtained by gradient elution with a long monolithic silica capillary under shallow gradient can result in high peak capacity in 2D separation when coupled with fast 2nd-D separation.

7.5 2D HPLC USING MONOLITHIC SILICA COLUMNS Recent proteomic or metabolomic analyses often require the separation/identification of several hundreds to several thousands of species in a mixture using LC–MS. It is practically impossible to achieve complete separation for a sample with complexity of

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MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

this magnitude by single HPLC run. Such a separation needs a system able to produce a very high peak capacity. In UPLC, peak capacity of 300–500 has been achieved under gradient conditions (MacNair et al., 1999; Mellors and Jorgenson, 2004; Patel et al., 2004). As mentioned earlier, a monolithic silica capillary column can also provide separation of several hundred species (Tolstikov et al., 2003; Luo et al., 2005). Twodimensional HPLC can potentially provide high peak capacity, because the peak capacity of 2D HPLC is theoretically a product of the peak capacities of the two systems (Eq. 7.4) (Giddings, 1991). n2DHPLC ¼ n1stD n2ndD

ð7:4Þ

The two HPLC systems should have differences in retention mechanism preferably orthogonal to each other (see Chapters 3 and 12 by Davis and Gilar et al., respectively). Various combinations have been employed in the past, including IE- RP, RP— SEC, SEC—SEC, and RP—RP (Bushey and Jorgenson, 1990; Opiteck et al., 1997a; K€ohne and Welsch, 1999; Wagner et al., 2002). In proteome analysis, the ion-exchange mode using salt gradient elution for the first dimension is commonly followed by the reversed-phase mode gradient elution to separate a very complex mixture of digested peptides. In a so-called shot gun approach, a single column packed successively with ion-exchange and reversed-phase packing materials is commonly employed (Wolters et al., 2001). Tens of thousands of peaks per day are processed to detect various proteins. The use of a long monolithic silica capillary for the 2nd-D resulting in increased detection/identification of proteins was also reported (Wienkoop et al., 2004). In the examples mentioned above, both 1st-D and 2nd-D HPLC systems are operated relatively slowly, because every fraction from the 1st-D is separated by a relatively slow gradient elution in the 2nd-D to complete the comprehensive separation. Sample storage loops or trapping columns may be used after the 1st-D column to hold every fraction. In fast and comprehensive online 2D HPLC, the separation of the 2nd-D must be very fast, to be completed within each sampling interval of the 1st-D. Various approaches were taken in the past to alleviate the problem of slow elution, or a limited volume or frequency of injection, for the 2nd-D (Bushey and Jorgenson, 1990; Opiteck et al., 1997a; K€ ohne and Welsch, 1999; Wagner et al., 2002). These approaches included the following: (i) smaller diameter column was employed in the 1st-D than in the 2nd-D, (ii) the 1st-D column was eluted slowly or intermittently, or (iii) two or more sets of columns and chromatographs were used for the 2nd-D. Simple 2D HPLC can be carried out by connecting the outlet of the 1st-D to the column or an injector loop of the 2nd-D chromatograph. The effluent of the 1st-D is fractionated at certain intervals, and the fraction is injected into the 2nd-D. While the next 1st-D fraction is loaded onto the 2nd-D, the 2nd-D separation of the previous fraction must be completed. Such an operation scheme needs very high speed separation for the 2nd-D. Monolithic columns seem to be suited as a 2nd-D column, because they possess high permeability and relatively high efficiency at high linear velocity of mobile phase, as well as high mechanical stability against fast flow.

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Utilizing the difference in selectivity between a monolithic silica-C18 column (2nd-D) and another particle-packed column of C18 phase (1st-D), 2D HPLC separation was shown mainly for basic compounds and other species (Venkatramani and Zelechonok, 2003). The authors also reported other examples of reversed-phase 2D HPLC, using amino- and cyano-derivatized particle-packed columns for 2nd-D separation. The combination of normal-phase separation for the 1st-D and reversedphase separation on monolithic C18 column for the 2nd-D was reported (Dugo et al., 2004). The use of a microbore column and weak mobile phase for the 1st-D and a monolithic column for the 2nd-D was essential for successful operation. Improvement in the 2D separation of complex mixtures of Chinese medicines was also reported (Hu et al., 2005). Following are practical examples of comprehensive 2D HPLC using monolithic silica columns that have been reported. 7.5.1

RP-RP 2D HPLC Using Two Different Columns

Simple and comprehensive 2D HPLC was reported in a reversed-phase mode using monolithic silica columns for the 2nd-D separation (Tanaka et al., 2004). Every fraction from the 1st-D column, 15 cm long (4.6 mm i.d.), packed with fluoroalkylsilyl-bonded (FR) silica particles (5 mm), was subjected to the separation in the 2nd-D using one or two octadecylsilylated (C18) monolithic silica columns (4.6 mm i.d., 3 cm). Monolithic silica columns in the 2nd-D were eluted at a flow rate of up to 10 mL/min with separation time of 30 s that provides fractionation every 15–30 s for the 1st-D, which is operated near the optimum flow rate of 0.4–0.8 mL/min. The 2D-HPLC systems were assembled, as shown in Fig. 7.6, so that the sample loops of the 2nd-D injectors were back flushed to minimize band broadening. In the simplest scheme of 2D HPLC, effluent of the first dimension (1st-D) was directly loaded into an injector loop (500 mL) of the 2nd-D HPLC for 28 s, and 2 s were allowed for injection. This operation was accompanied by the loss of 1st-D effluent for 2 s out of 30 s in each cycle. The flow rate of 10 mL/min allowed the elution of solutes having retention factors (k values) up to 8 for the 2nd-D within the 30-s separation window, with t0 of 3.5 s. Figure 7.7 a and b shows the chromatograms for the 1st-D and the 2nd-D, respectively, obtained for a mixture of hydrocarbons and benzene derivatives. The 1st-D chromatogram showed many overlapping peaks. PAHs were eluted as mixtures from the FR column, and some are separated in the 2nd-D. The loss of about 7% of the 1st-D effluent caused by a 2-s injection in a 30-s operation cycle, which could cause up to 20% loss of a peak in the most unfavorable case, or the narrowest peak at the beginning, can be avoided by using two six-port valves each having a sample loop (Fig. 7.6b); an alternative system uses a 10-port valve with two holding loops. The loops hold the effluent of the 1st-D alternately for 30 s during a complete separation cycle on the 2nd-D column to effect comprehensive 2D HPLC. From the 2nd-D chromatograms in Fig. 7.7b, a contour plot was obtained, as shown in Fig. 7.8. The 2D plots indicate that several types of hydrocarbons and benzene

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MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

(a)

(b) 1st-D column

1st-D column

1st-D detector 2nd-D column

2nd-D column

2nd-D Pump

(c)

Waste

Injector

1st-D column

Loop A Load, Loop B Inject Loop A Inject, Loop B Load

1st-D UV detector

2nd-D column A

2nd-D column B .

Injector B

Loop A (500 µl)

Loop B (500 µl)

Pump A Mobile phase

UV detector B

.

Injector A Waste

2nd-D Pump

Switch

Mobile 1st-D phase UV detector

Loop B

Loop A Load, Loop B Inject Loop A Inject, Loop B Load

1st-D Pump

A

Loop A

Waste

Load Inject

1st-D detector

Waste

Pump B 2nd-D

FIGURE 7.6 (a) Tubing connection at 2nd-D injector of simple 2D-HPLC. (b) Tubing connection of two six-port valves used as 2nd-D injector in simple and comprehensive 2DHPLC. (c) Scheme of comprehensive 2D-HPLC using two 2nd-D columns (reproduced from the reference, Tanaka et al. 2004, with permission from American Chemical Society).

derivatives were clearly distinguished from each other. A group of compounds showed similar behavior determined by their relative affinity to the two stationary phases; thus 2D reversed-phase HPLC can afford structural information for the solutes, especially when the separation mechanism on each stationary phase is known and widely different. The three stationary phases, FR, C18, and (pentabromobenzyloxy)propylsilyl-bonded (PBB), represent stationary phases providing the smaller and the greater dispersion interactions for solute retention (Turowski et al., 2003) to show the widely different selectivity from each other, making 2D separations possible. They are effective for the separation and characterization of organic compounds as in 2D separations in normal-phase–reversed-phase combination (Dugo et al., 2004). When two monolithic silica columns were used for two sets of 2nd-D chromatographs (Fig. 7.6c) separating each fraction of the 1st-D effluent alternately,

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163

FIGURE 7.7 Two-dimensional separation of a mixture of hydrocarbons and benzene derivatives in simple 2D HPLC. (a) Chromatogram obtained in the 1st-D on FR column in 60% methanol/water. (b) Chromatograms obtained in the 2nd-D on C18 column in 80% methanol/water. The insets 3(a) and 3(b) are expanded views of Fig. 7.7(a) and (b) respectively. Sampling every 30 s at the 1st-D. Flow rate: 0.4 mL/min for 1st-D, and 10 mL/min for 2nd-D (reproduced from the reference, Tanaka et al. 2004, with permission from American Chemical Society).

fractionation every 15 s of the 1st-D and separation time of 30 s at the 2nd-D were possible. In this case, two columns of the same stationary phase (C18) or different phases, C18 and PBB, could be employed for the 2nd-D, although the latter needed two complementary runs. The systems produced peak capacity of about 1000 in ca. 60 min with one column used for the 2nd-D, and in about 30 min when two columns were used for the 2nd-D (Fig. 7.8). Injection of a large volume sample can cause significant band broadening in the 2nd-D. The use of a smaller 1st-D column with a larger 2nd-D column is a possible approach to avoid this problem, but it is associated with considerable dilution of solutes. When a mobile phase of lower elution strength is used for the 1st-D separation than for the 2nd-D, the band broadening can be minimized. This is the reason why a FR column with lower retention was used in 60/40 methanol/water in the 1st-D, and a C18 phase was used in the 2nd-D with 80/20 methanol/water mobile phase. The loss of column efficiency in the 2nd-D was avoided even with the injection of 200 mL-fractions into a 3-cm column containing ca. 400 mL of mobile phase as the analytes were concentrated at the head of the more strongly retentive 2nd-D column.

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MONOLITHIC COLUMNS AND THEIR 2D-HPLC APPLICATIONS

FIGURE 7.8 A contour plot obtained for 2D HPLC using two 2nd-D columns in a system shown in Figure 7.6c. Sampling every 15 s at the 1st-D. Flow rate: 0.8 mL/min for 1st-D, and 10 mL/min for 2nd-D (reproduced from the reference, Tanaka et al. (2004) with permission from American Chemical Society).

7.5.2

RP–RP 2D HPLC Using Two Similar Columns

A comprehensive 2D HPLC can be carried out with two very similar columns in reversed-phase liquid chromatography (Ikegami et al., 2005). A mixture of water and tetrahydrofuran was used as a mobile phase in the 1st-D separation, and a mixture of water and methanol (CH3OH) in the 2nd-D separation with a common C18 stationary phase. In a RPLC separation that is usually dominated by hydrophobic interactions, the interactions between the solute and the stationary phases include instantaneous dipole–induced dipole interactions along with contribution of polar interactions depending on the structure of stationary phases. As each organic modifier undergoes different solute–solvent interactions based on the difference in dipole moment, polarizability, and hydrogen bond basicity as well as acidity, RPLC using mixtures of water–THF, water–CH3CN, and water–CH3OH exhibits significantly different selectivities (Tanaka et al., 1978; Dzido et al., 2002). Two-dimensional HPLC was carried out by using two C18 silica monolithic columns, a 10-cm column for the 1st-D, and a 5-cm column for the 2nd-D (Ikegami et al., 2005). The flow rate of each dimension was 0.65 and 9.5 mL/min, respectively, covering a k value range of up to 8 for the 2nd-D with the sampling time for the 1st-D of 45 s. A contour plot for 2D HPLC is shown in Fig. 7.9b, whereas

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FIGURE 7.9 Comparison of spots in a 2D-chromatogram and plots of retention factors in individual measurement. (a) Plots of k (45% CH3OH) values against k (22% THF) values on a Chromolith C18 column. Each mark in the plots stands for a group of benzene derivatives of similar functionality. (b) Contour plot for 2D HPLC. Mobile phase: 1st-D: 22% THF (0.1% HCOOH). Flow rate 0.65 mL/min, 2nd-D: Mobile phase: 45% CH3OH (0.1% HCOOH), Flow rate 9.5 mL/min. Sampling every 45 s (reproduced from the reference, Ikegami et al. (2005) with permission from Elsevier).

Fig. 7.9a shows plots of k values of sample compounds in 45% MeOH against k values in 22% THF. These two sets of plots are very similar. Figure 7.9 indicates that the 2D HPLC based on the difference in selectivity provided by an organic modifier that could be used for 2D HPLC to provide nominally large PC, but the selectivity difference was not quite orthogonal. The practically usable area for the 2D separation will be about half the orthogonal plotting area, because there is some similarity between the selectivities obtained with the two mobile phases. Gradient elution was employed in both dimensions to increase effective PC. In the 1st-D, THF concentration was increased from 15% to 30% linearly with a gradient time (tG) of 60 min, whereas in the 2nd-D, CH3OH concentration was changed from 30% to 45% linearly from 10 to 30 min. In the 2nd-D, each fraction from the 1st-D was separated under nearly isocratic conditions, because the separation time in the 2nd-D was only 45 s, which corresponds to a 0.6% change in CH3OH concentration for each separation. The results shown in Fig. 7.10 indicate that compared with Fig. 7.9b, the total analysis time was reduced by the elution in gradient mode in the 1st-D (15%–30% THF), early-eluting polar solutes showed larger k values than the isocratic mode that increased resolution, and that late-eluting solutes showed smaller retention times that compressed the scattered spots, as shown in Fig. 7.9b. The gradient elution mode was found to provide a more orthogonal 2D chromatogram, where the peaks were more evenly spaced and the blank region was reduced in the 1st-D and in the 2nd-D. As a consequence, the peak capacity in the 2nd-D separation could be used more efficiently,

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FIGURE 7.10 2D-chromatogram in gradient mode. Columns: Chromolith performance, 4.6 mm, 10 cm for 1st-D, and Chromolith speed 4.6 mm, 5 cm for 2nd-D. Mobile phase: 1st-D: 15% ! 30% THF (0.1% HCOOH) linear gradient, 0–60 min, flow rate 0.50 mL/min, Mobile phase: 2nd-D 30% ! 45% CH3OH (0.1% HCOOH), linear gradient, 10–30 min, Flow rate 9.5 mL/min. Sampling every 45 s (reproduced from the reference, Ikegami et al. (2005) with permission from Elsevier).

resulting in a significant improvement of separation efficiency compared with the isocratic mode. THF and methanol employed as organic modifiers of mobile phase provided a considerable difference in selectivity based on the polar interactions between solutes and the organic solvent molecules in the stationary phase. Acidic compounds, phenols and nitroaromatics, were preferentially retained in the THF-based mobile phase, whereas esters and ketones were preferentially retained in the methanol (a hydrogenbond donor) containing mobile phase. The system presented here seems to be very practical because any laboratory possessing two sets of HPLC equipment and two C18 columns can attempt similar 2D HPLC by simply changing the mobile phase for the two dimensions. 7.5.3 Ion Exchange–Reversed-Phase 2D HPLC Using a Monolithic Column for the 2nd-D Fast and simple 2D HPLC was also shown to be effective for the separation of a tryptic digest of bovine serum albumin (BSA) (Kimura et al., 2004). Every

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fraction from the first column, 5 cm long (2.1 mm i.d.) packed with polymer-based cation exchange beads eluted at 50 mL/min, was subjected to separation in the 2nd-D using an octadecylsilylated (C18) monolithic silica column (4.6 mm i.d., 2.5 cm, Chromolith Flash). The salt gradient in the 1st-D was provided by changing the mixing ratio of the two eluents, A, an aqueous 5 mM ammonium formate solution buffered at pH 3.1, and B, an aqueous 5 mM ammonium formate/ 500 mM ammonium chloride solution buffered at pH 3.1, with a typical gradient run from 100% A to 85% B within 50 min. The flow-rate in the 2nd-D was 5.0 mL/min with a typical gradient starting with 100% A (water with 0.1% formic acid) for 0.5 min for sample enrichment, and then increased to 50% B (acetonitrile containing 0.1% formic acid) within 1.17 min followed by a 0.33 min column regeneration step with the initial eluent. The linear velocity in the column was 6.6 mm/s. The loop for the 2nd-D was loaded with the effluent of the 1st-D at 50 mL/min for 1 min 58 s, and then the injection valve was turned to inject the 100 mL fraction for 2 s onto the 2nd-D HPLC. The flow rate was 5 mL/min, and the valve was turned back for the next loading, resulting in fractionation of the 1st-D every 2 min. In this case less than 2% of the effluent from the 1st-D was wasted during sample injection. The 2nd-D effluent eluted at 5 mL/min from the 2nd-D column, passed through a UV detector, and then was split by using a T-joint at approximately a 1/140 split ratio, resulting in a flow rate of ca. 36 mL/min going into the spray capillary for ESI-TOFMS detection. Figure 7.11a shows the separation of a tryptic digest of BSA by the ion-exchange mode under gradient conditions in the 1st-D, showing apparently overlapping peaks within 40 min. The peak width found for unretained Tyr-Gly-Gly (retention time, 3.70 min) was 0.79 min in this system. Therefore, the maximum possible loss of a solute band from the 1st-D during injection time of 2 s at the 2nd-D was estimated to be ca. 14%, assuming Gaussian peaks of similar width for retained substances under gradient conditions. Figure 7.11b shows total ion chromatograms for gradient runs of the 2nd-D between 18 and 24 min. Figure 7.12 shows a 2D chromatogram for the tryptic digest of BSA obtained from total ion monitoring by ESI-TOF-MS. From the 1st-D, 18 fractions were injected at 2 min intervals onto the 2nd-D reversedphase system generating 18 chromatograms that were used to produce a 2D chromatogram. The fractionation interval of 2 min was longer than the minimum peak width at the 1st-D, indicating considerable loss of peak capacity obtained for the 1st-D. Two minute sampling for the 1st-D, 118 s loading, and 2 s injection for the 2ndD injection, allowed 1 min for gradient separation in the 2nd-D. This resulted in maximum peak capacity of about 700 within 40 min. Although this may be an overly optimistic estimate, the results obtained in this work can be compared favorably in terms of numbers of detectable peaks per unit time with the results obtained in other previous studies, including gradient elution using a long capillary column. Peak capacity of well over a few thousand can be expected by adding the separation capability of MS detection (Opiteck et al., 1997b; Wolters et al., 2001).

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FIGURE 7.11 Chromatograms of tryptic digest of BSA. (a) 1st-D separation with gradient elution on a cation exchange column. Column: MCI CQK-31S, 50 mm long, 2.1 mm i.d. Flow rate: 50 mL/min. Mobile phase: A, aqueous 5 mM ammonium formate solution buffered at pH 3.1, and B, an aqueous 5 mM ammonium formate/500 mM ammonium chloride solution buffered at pH 3.1. Gradient from 100% A to 85% B in 50 min. (b) 2nd-D chromatograms of simple 2D-HPLC separation of a tryptic digest of BSA. Monolithic silica-C18 column (4.6 mm i.d., 2.5 cm) as 2nd-D column. Mobile phase A: 0.1% formic acid, B: acetonitrile containing 0.1% formic acid. Gradient started with 0% B at 0.5 min, increased to 50% B at 1.67 min followed by 0.33 min column regeneration with the initial eluent, A. Flow rate: 5.0 mL/min. ESI-TOF-MS detection, total ion chromatogram for the mass range 400–2000. Monolithic silica-C18 column (4.6 mm i.d., 2.5 cm) as 2nd-D column (reproduced from the reference, Kimura et al. (2004) with permission from Wiley).

7.5.4 IEX-RP 2D HPLC Using a Monolithic RP Capillary Column for the 2nd-D In the examples described in Sections 7.5.1–7.5.3, an extremely high linear velocity was employed in the 2nd-D, with the resulting high flow rate leading to large solvent consumption. Thus, miniaturization of the system is highly desirable, especially for the 2nd-D. In another 2D-HPLC separation of BSA digest, a capillary-type monolithic silica-C18 column (100 mm i.d., 10 cm, 1.6 mm through-pore size and 0.8 mm skeleton size) was employed as a 2nd-D column with split flow/injection following the first column. The 1st-D column was 5 cm long (2.1 mm i.d.), packed with polymer-based cation exchange beads, and was eluted at 50 mL/min (Kimura et al., 2004). The split ratio before the 2nd-D column was controlled to be 3/2000 with the flow rate in the capillary column at 3.0 mL/min and solvent delivery by the 2nd-D pump at 2 mL/min. In this case, the exit of the capillary column was directly connected to the ESI spray capillary with a union. The linear velocity in the 2nd-D column was 7.7 mm/s. The gradient was started with 100% A (water with 0.1% formic acid) until 0.5 min, increased to 50% B (acetonitrile containing 0.1% formic acid) at 3.3 min, then further

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FIGURE 7.12 Two-dimensional separation of tryptic digest of BSA in simple 2D HPLC. Conditions as in Fig. 7.11 (reproduced from the reference, Kimura et al. (2004), with permission from Wiley).

increased to 100% B within 0.2 min to wash the column, then returned to the initial condition, and held for 0.5 min for reequilibration. As the eluent was split after the injector, a very high flow rate was employed at the pump compared to the flow in the column, resulting in very little delay in the gradient for the capillary 2nd-D column. The use of a capillary column for the 2nd-D led to better MS detectability compared to a larger-sized column. The loop of the 2nd-D HPLC was loaded with the effluent from the 1st-D HPLC at 50 mL/min for 3 min 53 s, then the injection valve was turned to inject the 200 mL fraction for 7 s onto the 2nd-D HPLC at 3 mL/min, and turned back for loading for the next 3 min 53 s, resulting in fractionation of the 1st-D every 4 min. Thus, ca. 300 nL or 0.15% of each fraction from 1st-D (200 mL) was introduced into the 2nd-D column having approximately 800 nL column volume. Fig. 7.13 shows a 2D chromatogram obtained by using a 10-cm capillary column for the 2nd-D. Portions (0.5–2.5 min) where peaks were observed during the gradient elution are shown for the 2nd-D. The longer gradient time (tG) needed than the case shown in Fig. 7.12 resulted in the longer sampling interval for the large 1st-D band width, ca. 4 min for most of the bands observed in the 2D

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FIGURE 7.13 Two-dimensional separation of tryptic digest of BSA in simple 2D-HPLC. Capillary monolithic silica-C18 column (0.1 mm i.d., 10 cm) was used as 2nd-D column. Mobile phase for 2nd-D: gradient started with 0% B at 0.5 min, increased to 50% B at 3.3 min, to 100% B at 3.5 min, then returned to the initial condition and held for the last 0.5 min. Flow rate: 3.0 mL/min in capillary, and 2 mL/min at the pump. Other conditions are similar to those for Figure 7.11 (reproduced from the reference, Kimura et al. (2004) with permission from Wiley).

chromatogram. Because a longer column and longer separation time were employed for the 1st-D, a greater number of solutes were separated in the 2nd-D based on the higher column efficiency and longer gradient time in the 2nd-D. This also provided greater MS detection sensitivity due to the nearly optimum flow rate (3 mL/min) on the capillary column, the greater amount of sample introduced to the 2nd-D column because of the longer sampling interval, and the smaller extent of dilution due to the use of small diameter column. The peak capacity, however, was much smaller than in Fig. 7.12 because of the longer sampling time in the 1st-D. It has been recommended to take at least 3–4 fractions from one peak in the 1st-D to preserve the separation (Murphy et al., 1998), whereas less frequent sampling of 2s–4s for the 1st-D peaks, where s is the standard deviation of a Gaussian peak in the 1st-D, was reported to give optimum peak capacity (Horie et al., 2007). Although a low flow rate was provided by splitting the flow after the injector, this rules out the injection of very small sample size. It is desirable to develop a simple 2D-HPLC system consisting of two capillary columns with a short column for the 2nd-D.

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7.6 SUMMARY AND FUTURE IMPROVEMENT OF 2D HPLC At present the monolithic columns used in 2D HPLC, described in Sections 7.5.1– 7.5.4, commonly provide plate height, H, 10–20 mm at a linear velocity of 1–10 mm/s. Improvement in monolithic column performance to 2500 theoretical plates with 2.5 s t0 will increase peak capacity by 40% to ca. 30 under similar conditions. The performance of monolithic silica columns available at present is much lower at high linear velocity than the most advanced particle-packed columns, especially UPLC columns. If we were able to use a 25-mm-long UPLC column for the 2nd-D generating 5000 theoretical plates at 10 mm/s mobile phase velocity, we can get PC of about 43 in 30 s to further increase PC by 40% (see Chapter 8 by Evans and Jorgenson for further discussion of UPLC). It has been predicted that a peak capacity of 15,000 can be obtained in 8 h by using a high efficiency 2nd-D column and gradient elution for both dimensions (Gilar et al., 2004). Reversed-phase-mode 2D HPLC is particularly facile, as shown in Section 7.5.2. Even if one cannot use a comprehensive 2D HPLC system, and a simple 2D HPLC system is accompanied by loss of several percent of the 1st-D effluent, short sampling intervals will prevent the loss of a peak to effect 2D separation of all solutes present. Development of various stationary phases showing selectivity difference (Jandera et al., 2005) and improvement in the performance of monolithic silica columns having mechanical stability, as well as the improvement in miniaturized column-switching device will promote 2D HPLC as a useful high-peak-capacity separation tool. Band broadening due to undersampling for 1st-D effluent will cause a decrease in peak capacity of 2D-HPLC (Davis et al., 2008; Horie et al., 2007; Murphy et al., 1998, Seely, 2002). When considering the effect of undersampling, peak capacity of ca. 3000 or ca. 5000 within one hour will be possible by using a 1-cm monolithic silica column or a 1-cm column packed with 2-mm particles at 2nd-D, respectively, in combination with 1st-D gradient elution producing peaks of 10-s band width by employing an optimum sampling period of 2.2-4 s of 1st-D peaks (Horie et al., 2007). REFERENCES Al-Bokari, M., Cherrak, D., Guiochon, G. (2002). Determination of the porosities of monolithic columns by inverse size-exclusion chromatography. J. Chromatogr. A 975, 275–284. Bushey, M.M., Jorgenson, J.W. (1990). Automated instrumentation for comprehensive twodimensional high-performance liquid chromatography of proteins. Anal. Chem. 62, 161–167. Cabrera, K. (2004). Applications of silica-based monolithic HPLC columns. J. Sep. Sci. 27, 843–852. Chankvetadze, B., Yamamoto, C., Tanaka, N., Nakanishi, K., Okamoto, Y. (2004). Highperformance liquid chromatographic enantioseparations on capillary columns containing monolithic silica modified with cellulose tris(3,5-dimethylphenylcarbamate). J. Sep. Sci. 27, 905–911. Chen, H.S., Rejtar, T., Andreev, V., Moskovets, E., Karger, B.L. (2005). High-speed, highresolution monolithic capillary LC-MALDI MS using an off-line continuous deposition interface for proteomic analysis. Anal. Chem. 77, 2323–2331.

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chromatography–electrospray ionization mass spectrometry at high and low pH. J. Chromatogr. A 1079, 274–286. Tolstikov, V.V., Lommen, A., Nakanishi, K., Tanaka, N., Fiehn, O. (2003). Monolithic silicabased capillary reversed-phase liquid chromatography/electrospray mass spectrometry for plant metabolomics. Anal. Chem. 75, 6737–6740. Turowski, M., Yamakawa, N., Meller, J., Kimata, K., Ikegami, T., Hosoya, K., Tanaka, N., Thornton, E.R. (2003). Deuterium isotope effects on hydrophobic interactions. the importance of dispersion interactions in the hydrophobic phase. J. Am. Chem. Soc. 125, 13836–13849. Unger, K.K. (editor)(1979). Silica columns: packing procedure and performance characteristics. Porous Silica: Journal of Chromatography Library, Volume 16. Elsevier,Amsterdam. pp.169–186. Venkatramani, C.J., Zelechonok, Y. (2003). An automated orthogonal two-dimensional liquid chromatograph. Anal. Chem. 75, 3484–3494. Virklund, C., Nordstrom, A., Irgum, K. (2001). Preparation of porous poly(styrene-codivinylbenzene) Monoliths with controlled pore size distributions initiated by stable free radicals and their pore surface functionalization by grafting. Macromolecules 34, 4361– 4369. Volmer, D.A., Brombacher, S., Whitehead, B. (2002). Studies on azaspiracid biotoxins. I. Ultrafast high-resolution liquid chromatography/mass spectrometry separations using monolithic columns. Rapid Commun. Mass Spectrom. 16, 2298–2305. Wagner, K., Miliotis, T., Marko-Varga, G., Bischoff, R., Unger, K.K. (2002). An automated online multidimensional HPLC system for protein and peptide mapping with integrated sample preparation. Anal. Chem. 74, 809–820. Wienkoop, S., Glinski, M., Tanaka, N., Tolstikov, V.V., Fiehn, O., Weckwerth, W. (2004). Linking protein fractionation with multidimensional monolithic reversed-phase peptide chromatography/mass spectrometry enhances protein identification from complex mixtures even in the presence of abundant proteins. Rapid Commun. Mass Spectrom. 18, 643–650. Wolters, D.A., Washburn, M.P., Yates, J.R., III (2001). An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73, 5683–5690. Xie, C.H., Hu, J.W., Xiao, H., Su, X.Y., Dong, J., Tian, R.J., He, Z.K., Zou, H.F. (2005). Preparation of monolithic silica column with strong cation-exchange stationary phase for capillary electrochromatography. J. Sep. Sci. 28, 751–756. Zhang, K., Yan, C., Yang, J.J., Zhang, Z.C., Wang, Q.S., Gao, R.Y. (2005). Preparation and characterization of C16 monolithic columns for capillary electrochromatography. J. Sep. Sci. 28, 217–224. Zou, H., Ye, M., Luo, Q. (2002). Monolithic stationary phases for liquid chromatography and capillary electrochromatography. J. Chromatogr. A 954, 5–32.

8 ULTRAHIGH PRESSURE MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Charles R. Evans and James W. Jorgenson Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

8.1 BACKGROUND: MDLC IN THE JORGENSON LAB By the year 1990, many aspects of the field of multidimensional separations were well developed. On the theoretical front, Giddings (1987) had already described the multiplicative rule of peak capacity for ideal multidimensional separations and had set forth two fundamental criteria to define “ideal’’: that all dimensions must be fully orthogonal and that no resolution gained by one dimension may be lost on any subsequent dimension. In terms of experimental science, O’Farrell (1975) had long since published on 2D gel electrophoresis, a technique that continues to set the standard for high resolution separations of proteins. MDLC, however, remained comparatively delayed in its development. Although numerous investigations into MDLC had already been performed, most of them involved some form of heart cutting, wherein only a certain portion of the effluent from the first dimension column was analyzed on a second column. The heart-cutting approach has been reviewed in detail elsewhere (Cortes, 1990). The only major pre-1990 example resembling comprehensive MDLC, or LC LC, was reported by Erni and Frei (1978). Their technique used a size exclusion column coupled to a reversed-phase column with an eight-port switching valve. This instrument was used to analyze a complex plant extract. In this report,

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only seven fractions were transferred from the first column to the second. Thus, the contribution of the first separation to the overall resolution of the technique was quite limited. No further reports of online LC LC were published for some time, perhaps due to the apparent difficulty of making two different separation methods compatible to operate in direct connection with each other and the complexity of the instrumentation necessary to do so. 8.1.1

Cation Exchange–Size Exclusion

It was in this context that the first true comprehensive online LC LC separation was reported (Bushey and Jorgenson, 1990). Mixtures of intact proteins were analyzed using cation-exchange chromatography (CEX) as the first dimension and size exclusion chromatography (SEC) as the second. This research demonstrated that the practical difficulties of coupling two dissimilar LC modes for a comprehensive 2D separation are relatively easy to overcome when instrumentation is properly configured. The instrumentation used by Bushey and Jorgenson is depicted in Fig. 8.1. The first separation is run on a cation-exchange column using a gradient of increasing salt concentration. The outlet of the cation exchange column is coupled to an eight-port valve, which directs the effluent from the column to one of the two storage loops. After a sufficient period of time passes to precisely fill the sample loop with effluent from the first column, the valve is switched, causing the contents of the loop to be injected onto the size exclusion column using mobile phase flow from a second LC pump operating in isocratic mode. Meanwhile, the effluent of the first column is directed to the second storage loop. Once the second storage loop fills, the valve is actuated again and the contents of the second loop are injected onto the size exclusion column. The process is repeated until the cation-exchange separation is complete and all fractions have been analyzed by the size exclusion column. Because it is desirable to sample the first column many times over the duration of the run (Murphy et al., 1998), the size exclusion separations must be

FIGURE 8.1 Schematic diagram of a 2D CEX SEC instrument. Reprinted from Evans, C. R. and Jorgenson, J. W. (2004) Anal. Bioanal. Chem. 378, 1952–1961, with kind permission of Springer Science and Business Media.

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very rapid compared to the time necessary to complete the run. This is accomplished by using a relatively long anion-exchange gradient to spread the first separation over about 6 h and by using a fast mobile phase flow rate through the size exclusion column. The result is that the cation-exchange column can be sampled every 6 min, which is the time necessary to complete each size exclusion run. An UV absorbance chromatogram for a 2D separation of a mixture of nine proteins is shown in Fig. 8.2. The proteins are all resolved into single peaks, their positions determined by their charge and size. It is notable that no peaks appear during the first half of all of the size exclusion runs. This is because the exclusion volume— that is, the volume of mobile phase required to elute a molecule too large to enter the pores of the size exclusion particles—is roughly equal to the inclusion volume—the additional volume of mobile phase needed to elute a molecule small enough to completely permeate the pores. To eliminate this “wasted space’’ in the chromatogram, the eight-port valve can be cycled twice as frequently to overlap the useful, peak-containing portion of one size exclusion run onto the nonuseful

FIGURE 8.2 2D chromatogram of a mixture of eight proteins separated using CEX SEC. Detection was performed using UVabsorbance at 215 nm. Peak identities: (A) glucose oxidase, (B) ovalbumin, (C) b-lactoglobulin, (D) trypsinogen, (E) a-lactoglobulin, (F) conalbumin, (G) ribonuclease A, (H) hemoglobin, and (M) exclusion volume “pressure’’ ridge, and (N) inclusion volume “salt’’ ridge. Reprinted with permission from Bushey and Jorgenson (1990), copyright 1990, American Chemical Society.

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portion of another. This allows the first dimension separation to be run twice as fast, thereby reducing the total analysis time by half. The total peak capacity for this method was estimated to be 130, based on the product of the peak capacities visually estimated for each dimension. 8.1.2

Anion Exchange–Reversed Phase

The next major development in MDLC from the Jorgenson lab was a method designed for the analysis of peptides (Holland and Jorgenson, 1995). This method used gradient anion-exchange (AEX) chromatography as the first dimension and gradient reversed-phase liquid chromatography (RPLC) as the second. A schematic diagram of the instrumentation is depicted in Fig. 8.3. Two capillary columns with an inner diameter of 100 mm are used instead of LC column of conventional diameter. The first column is a 90 cm long capillary packed with 5-mm diameter anion-exchange particles. The anion-exchange mobile phase consists of 50% water/ 50% acetonitrile, a buffering component 5 mM 3-(N-morphilino) propanesulfonic acid (MOPS) at pH 7.9, and a salt component guanidine thiocyanate whose concentration increases from 2.5 to 170 mM over the course of a 40-h gradient. The long gradient and a slow flow rate (33 nL/min) cause the peptides to be eluted over a period of up to 25 h. Because the first dimension mobile phase contains 50% acetonitrile, to reduce any reversed-phase contribution to retention of the peptides

FIGURE 8.3 Schematic diagram of an AEX RPLC instrument. Reprinted with permission from Holland and Jorgenson (1995), copyright 1995, American Chemical Society.

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on the anion-exchange column, the effluent from this column must be diluted approximately 10-fold with a makeup flow of water to allow the peptides to be effectively retained on the second dimension. The diluted first column effluent is loaded into a single storage loop through an eight-port valve. When the loop is full, the valve is actuated and a second pump loads the sample onto a 3 cm long capillary column packed with 5 mm reversed-phase particles. The loading of the sample onto the RPLC column takes approximately 30 s, during which time the effluent from the first column is directed to waste. After this period, the valve is switched back and the peptides are eluted from the column using a reversed-phase gradient. The gradient begins at 24% acetonitrile/76% water plus 0.1% triflouroacetic acid and increases to 50% acetonitrile/50% water plus 0.1% triflouroacetic acid over 1.6 min, followed by a 0.5-min wash at 100% acetonitrile and a 0.9-min period of reequilibration to the starting conditions. Detection is accomplished by monitoring fluorescence at the outlet of the reversed-phase column; therefore, all peptides must be derivatized with a fluorescent tag prior to the injection onto the first column. For this work, the fluorescent molecule tetramethylrhodamine 5-isothiocyanate (TRITC) was used as the derivatization reagent. A further improvement to the system was later reported in which the 5 mm reversed-phase media was replaced with equivalent perfusion-based reversedphase stationary phase media (Holland and Jorgenson, 2000). These particles suffer less band broadening when run at the high flow rates necessary to carry out a complete reversed-phase separation every 3 min. The resolving power of this system is relatively high. Fig. 8.4 shows a 2D chromatogram obtained for the separation of tryptic digest of porcine thyroglobulin. Approximately 150 spots can be seen, and the peak capacity is estimated to be about 2030. The detection limit of this system was determined to be approximately 0.8 attomoles for TRITC-tagged glycine. 8.1.3

Cation Exchange–Reversed Phase

The first report of the coupling of a mass spectrometer to a comprehensive online LC LC separation was published in 1997 as a collaboration between Jorgenson research group and Robert Anderegg of Glaxo Wellcome, Inc. (Opiteck et al., 1997b). The configuration of the instrumentation is essentially the same as that of the CEX SEC method previously described, using two columns and an eight-port valve with two storage loops. Instead of a size exclusion column for the second dimension, a reversed-phase column was used. Both the cation-exchange and reversed-phase columns are packed in-house, having inner diameters of 750 and 500 mm, respectively. The first- and second-dimension separations are both carried out using gradient elution; the cation-exchange gradient lasts 2 h and each reversed-phase gradient lasts 3 min. Direct coupling to a mass spectrometer is achieved by splitting the flow from the outlet of the UV detector such that 10% of the column’s effluent flows to the electrospray interface of the mass spectrometer. This instrument was used to analyze mixtures of intact proteins, including protein standards and a cell lysate of the bacterium Escherichia coli. A UV

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FIGURE 8.4 2D chromatogram of a AEX RPLC separation of reduced porcine thyroglobulin. Reprinted from Holland and Jorgenson (2000), by permission of John Wiley & Sons, Ltd.

absorbance 2D chromatogram for a separation of the E. coli lysate is shown in Fig. 8.5. The chromatographic peak capacity of this system was estimated to be 512, based on a cation-exchange peak capacity of 16 and a reversed-phase peak capacity of 32. The mass spectrometer can be effectively considered to be a third dimension for this 2D system as it can distinguish two or more coeluting components not resolved by chromatography. If the peak capacity of a mass spectrometer is taken to be 5, the total peak capacity of the system is over 2500. In addition to increasing the total peak capacity of the system, the mass spectrometer allows the determination of the molecular weight of all analytes in the 2D separation.

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FIGURE 8.5 2D chromatogram of a CEX RPLC separation of an E. coli lysate. Reprinted with permission from Opiteck et al. (1997), copyright 1997, American Chemical Society.

8.1.4

Size Exclusion–Reversed Phase

Another approach to MDLC, also developed in collaboration with Robert Anderegg and Glaxo Wellcome, Inc., used SEC coupled to RPLC for the analysis of peptides (Opiteck et al., 1997a). Although size exclusion chromatography is sometimes considered a low resolution separation technique, its resolving power can be improved substantially by simply increasing the column length. To this ˚ pores end, six 30 cm long, 7.8 mm diameter size exclusion columns with 125 A (G2000SWXL, Toso-Haas, Montgomeryville, PA) were connected in series to give 1.8 m of effective column length and were used as the first dimension in a 2D separation. The schematic diagram of the instrument, shown in Fig. 8.6, differs slightly from the dual-storage-loop approach used in the previous methods. Two four-port valves are used to interface the first dimension directly with two 3.3 cm

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FIGURE 8.6 Schematic diagram of a 2D SEC RPLC instrument. Reprinted from Evans, C. R. and Jorgenson, J. W. (2004) Anal. Bioanal. Chem. 378, 1952–1961, with kind permission of Springer Science and Business Media.

long, 4.6 mm diameter reversed-phase columns placed in parallel (RP18, Micra Scientific, Northbrook, IL). Instead of storing the effluent from the size exclusion column assembly in a storage loop prior to the injection onto the second dimension, it is loaded directly onto one of the two reversed-phase columns. The 100% aqueous mobile phase used for the size exclusion separation is a weak eluent for RPLC, so all peptides eluting from the size exclusion column are retained in a narrow band at the head of the reversed-phase column. After a set length of time, the valve is switched, resulting in the connection of the column to a LC pump at its inlet and a dual UV/MS detector setup at its outlet. A 4-min reversed-phase gradient is then run to separate the peptides by hydrophobicity and elute them from the reversed-phase column. Meanwhile, the effluent from the size exclusion column assembly is directed to the second reversed-phase column. This parallel column arrangement is advantageous over the use of storage loops because it eliminates the time required to load the contents of the loop onto the second column. Peak position reproducibility proved to be excellent. A 2D UVabsorbance chromatogram of a tryptic digest of ovalbumin

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FIGURE 8.7 2D chromatogram of a SEC RPLC separation of a tryptic digest of ovalbumin. Reproduced with permission from Opiteck et al. (1997), copyright 1997, American Chemical Society.

separated on this system is shown in Fig. 8.7. The estimated chromatographic peak capacity of the separation is nearly 500. Several variations of this SEC RPLC system were also implemented. In one such enhancement, two 4.6 mm diameter reversed-phase columns were replaced with 1.0 mm diameter columns (Opiteck et al., 1998a). As the amount of sample injected and the flow rate of the first dimension were the same as in the previous study, the analyte is trapped at a higher concentration at the front of the second column. Much slower second dimension flow rates can be used since the columns are narrower, which results in increased peak concentration and allows much better signal to be attained from the mass spectrometer. This enhanced signal made it possible to perform online partial peptide sequencing by increasing the orifice voltage of the mass spectrometer to fragment the peptides. The SEC RPLC approach was also used to separate intact proteins rather than peptides, in a collaborative effort with Arthur Moseley at Glaxo Wellcome, Inc. (Opiteck et al.1998b). To optimize the size exclusion separation for proteins, six ˚ pores were exchanged for a series of eight columns with 250 A ˚ columns with 125 A ˚ ˚ pores or a combination of six columns with 250 A pores and six columns with 450 A

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FIGURE 8.8 2D chromatogram of a SEC RPLC separation of a native E. coli lysate. Reprinted from Opiteck et al. (1998), by permission of Academic Press.

pores for a maximum column length of 3.6 m. The system was configured with a UV detector, and mass spectrometry was performed off-line by collecting fractions and analyzing those of interest using MALDI-TOF-MS and/or electrospray-TOF-MS. A 2D chromatogram of a native E. coli lysate separated on this instrument is shown in Fig. 8.8. The chromatographic peak capacity was determined to be 1500. The system was also used to isolate particular proteins for further analysis. Figure 8.9 shows a UV chromatogram of an E. coli lysate grown to overexpress b-lactamase. The fraction containing an intense peak suspected to be the overexpressed protein was examined using MALDI-TOF/MS and ESI/MS, and was confirmed to be within 0.4% of the expected mass of b-lactamase, 28,907 Da. The fraction was also analyzed by Edman sequencing, which confirmed that the N-terminus of the protein had the expected amino acid sequence. The relative ease of isolating proteins in free solution and the corresponding option to analyze them using other techniques clearly highlight the utility of MDLC for proteomics compared to techniques such as 2D gel electrophoresis.

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FIGURE 8.9 2D chromatogram of a SEC RPLC separation of an E. coli lysate grown to overexpress b-lactamase. Reprinted from Opiteck et al. (1998), by permission of Academic Press.

Table 8.1 summarizes the MDLC separations reported to date by the Jorgenson lab. Both size exclusion and ion-exchange chromatographies have been used with essentially equal frequency and success as the first separation dimension. The most commonly used separation mode for the second dimension was RPLC, due to its high peak capacity, its potential for short run times, and its compatibility with online coupling to mass spectrometry via ESI. Using RPLC for the second dimension also allows on-column concentration of fractions from the first dimension as the aqueous buffers typically used for ion exchange or size exclusion separations are weak eluents for reversed-phase columns. Peak capacities of these MDLC separations are varied; however, not even the best has a peak capacity approaching that of 2D gel electrophoresis. Clearly, there is ample motivation for continuing to develop more powerful MDLC separation techniques.

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TABLE 8.1

Summary of MDLC Separations in the Jorgenson Lab

Year

Sample

Peak Capacity

Separation Modes

Detection

Reference

1990 1995 1997 1997 1997 1998 2000

Proteins Peptides Proteins Peptides Peptides Proteins Peptides

126 1050 512 520 495 1500 2028

CEX SEC AEX RPLC CEX RPLC SEC RPLC SEC RPLC SEC RPLC AEX RPLC

UV FL UV þ MS UV þ MS MS UV þ MS FL

Bushey and Jorgenson (1990) Holland and Jorgenson (1995) Opiteck et al. (1997b) Opiteck et al. (1997a) Opiteck et al. (1998a) Opiteck et al. (1998b) Holland and Jorgenson (2000)

8.2 ONLINE VERSUS OFF-LINE MDLC The majority of recent research involving comprehensive multidimensional LC has employed online coupling of dimensions, in which fractions from the first column are transferred directly to the second column using automated switching valves. Indeed, all the MDLC research from the Jorgenson lab that has been summarized to this point in the chapter lab has used online coupling. An alternative is off-line MDLC, where the effluent of the first column is physically collected as fractions that are later individually injected onto the second column. Although off-line coupling may seem to be a less sophisticated approach, a more thorough discussion of the relative advantages and disadvantages of online and off-line MDLC is merited. The primary advantages of the online approach to MDLC are automation and speed. An entire 2D separation can be carried out with no user intervention beyond the initial injection of the sample. This is very advantageous for the purpose of routine, high throughput analyses. The entire run can be finished in essentially the time that it takes for the first dimension separation to complete; therefore, the technique can be made quite rapid, especially compared to 2D gel electrophoresis and related techniques. However, these advantages impose certain limitations. Most significantly, the second dimension separation must be configured such that it is very rapid compared to the first. This is necessary so that many second-dimension runs can be carried out during the time it takes to complete a single run on the first dimension in order to adequately sample the column. This usually means that the resolution contributed by the second dimension must be compromised to improve the analysis speed. Secondly, the online approach requires relatively complex instrumentation. Two LC pumps must be operated simultaneously, and the operation of the switching valves used to connect the two dimensions must be timed precisely so that all of the effluent from the first column is transferred to and analyzed by the second column. The relative complexity and the cost of the instrumentation may discourage the average user from adopting online MDLC as an alternative to proven techniques such as 2D gel electrophoresis. Off-line MDLC is a comparatively simple approach. When minimally configured, it requires only a single LC pump, two separation columns of different types, and

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suitable containers for collecting fractions. A greater degree of automation can be achieved if a fraction collector is used to collect the fractions from the first column and an autosampler is used to inject the fractions onto the second column. There is no limit to the number of fractions that can be collected, so off-line coupling can be just as thorough in sampling the first dimension as online coupling. Fraction collection also permits sample manipulation between separation dimensions – for example, fractions could be concentrated via lyophilization or reconstituted in a solvent more appropriate for the analysis on the second dimension. Another major advantage of the off-line approach is that there is no need for the second dimension separation to be run faster than the first because fractions can be stored and run when time permits. Therefore, both separations can be optimized to provide the highest possible resolution, which could allow the second dimension to contribute a much higher peak capacity to the overall 2D method. Off-line MDLC also allows flexibility in the amount of each fraction transferred to the second dimension. This amount could be increased to improve sensitivity, decreased to prevent second-dimension column overload, or a fraction could be skipped altogether if the detector monitoring the first-dimension column effluent indicates that no analytes are present. Finally, an off-line 2D separation allows the flexibility of analyzing any fraction multiple times on the second dimension without repeating the entire 2D separation, should the sample prove of particular interest or if an instrument failure occurs. Of course, off-line sampling also has numerous disadvantages. Foremost is speed—off-line sampling is certain to take longer than an online approach because the fractions are usually run after the first dimension separation is complete, rather than as it is in progress. An additional factor is that the operator of the instrument may need to spend more time on actively working to carry out the 2D separation, performing tasks such as setting up for fraction collection, collecting the fractions, and manipulating the fractions such that they are ready to be analyzed on the second dimension. Finally, as the liquid fractions come in contact with more tubes and surfaces when fraction collection is performed than when online coupling is used, there is a greater possibility of sample losses with off-line coupling. In summary, both off-line and online approaches have substantial advantages and disadvantages. In general, it is probably best to use online coupling as a first choice due to its advantages in speed and automation. If, however, some portion of the technique is not amenable to online coupling, or the extra flexibility of off-line coupling is particularly advantageous for the task at hand, then off-line MDLC is a highly suitable option.

8.3 MDLC USING ULTRAHIGH PRESSURE LIQUID CHROMATOGRAPHY: BENEFITS AND CHALLENGES In two-dimensional separations, as in any other form of chromatography, it is desirable to generate very high peak capacity in as short a time as possible. In reality, some compromise between speed and resolution must be made, the specifics of which depend on the nature of the sample to be analyzed. A relatively new technique known

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as ultrahigh pressure liquid chromatography offers improved chromatographic performance without long run times (MacNair et al., 1997, 1999; Jerkovich et al., 2003; Mellors and Jorgenson, 2004; Patel et al., 2004). Therefore, UHPLC has the potential to substantially enhance either the speed or the peak capacity of LC LC, or even both simultaneously to some extent. The basis for this potential improvement is briefly described below, in terms of standard one-dimensional chromatographic theory. 8.3.1

An Introduction to UHPLC

The efficiency of a chromatographic separation can be described by the height equivalent of a theoretical plate (H), where lower values of H correspond to more efficient separations. The Van Deemter equation describes the relationship between H and mobile phase flow velocity (u) as the sum of three major terms, A, B, and C, each of which represents a different contribution to band broadening in a chromatographic column. H ¼ Aþ

B þ Cu u

where A corresponds to Eddy diffusion, B to longitudinal diffusion, and C to resistance to mass transfer. For more efficient separations, it is advantageous to minimize the value of all these terms. The magnitude of two of the terms is known to be related to the diameter of the particles (dp) with which the column is packed: A is proportional to dp and C is proportional to d2p . Therefore, it is desirable to use the smallest possible particles in order to achieve the highest efficiency. An added benefit of smaller particles is that the optimum linear velocity – that is, the value of u that gives the most efficient separation – increases as particle diameter decreases. Therefore, it is possible to perform both faster and more efficient separations by decreasing particle diameter. The drawback to small particles is that they have higher flow resistance than conventional-sized particles when packed in a column, and therefore generate greater backpressures. Conventional LC pumps are limited to a maximum operating pressure of around 400 bar (6000 psi), which can quickly be exceeded with particles of 1–2 mm diameter. One option is to decrease the column length to a few centimeters or less; this results in faster separations (Majors, 2003). However, no gain in separation efficiency is achieved due to the loss of column length. An alternative is to use specialized pumps capable of producing substantially higher pressures to overcome the increased flow resistance. The resulting technology is termed ultrahigh pressure liquid chromatography. One initial concern with UHPLC was that frictional heating caused by forcing mobile phase through a packed bed of small particles at high flow rates might have negative effects on separation efficiency. For conventional 4.6 mm diameter columns this would indeed be problematic due to the poor heat dissipation of such columns. This problem is easily overcome by using packed fused-silica capillaries with internal diameters ranging 10–150 mm because their increased surface area-to-volume ratio allows any heat generated to be quickly dissipated in air (MacNair et al., 1997). Runs have been performed successfully at pressures up to 100,000 psi using packed capillary columns (Patel et al., 2004). UHPLC has demonstrated plate counts over

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350,000 for isocratic reversed-phase separations and peak capacities over 500 using gradient elution (Jerkovich et al., 2003). 8.3.2

UHPLC for LC LC: High Speed Versus High Peak Capacity

If the main goal for a 2D separation is a short total analysis time, UHPLC is potentially useful. The limiting factor for the speed of a 2D separation is typically the time it takes to complete each second-dimension run. If this time is decreased, the first column can be sampled at shorter intervals and therefore can be run faster, thus reducing the total analysis time. To this end, the high pressure capabilities of UHPLC pumps could be devoted to generating very fast runs in relatively short columns. With the small particles used in UHPLC, fast runs do not result in excessive compromise in terms of chromatographic efficiency, so relatively high performance can be maintained. Therefore, fast UHPLC is potentially well suited to use as the second dimension of an LC LC separation. At present, there remains a practical hurdle preventing UHPLC from being used to enhance the speed of an online LC LC separation. Suitable low dead volume automated switching valves capable of operating at ultrahigh pressures are not yet widely available, although preliminary versions of such valves have been reported (Wu et al., 2001). For the time being, this limitation precludes the online coupling of UHPLC to another separation method. There is no reason to expect that UHPLC-compatible valves cannot be manufactured on a wider scale; however, so as interest in UHPLC continues to grow and new instrumentation is developed, online LC LC using UHPLC may become a realistic and attractive option for high speed 2D analyses. Another very important benefit of UHPLC in the context of MDLC is its potential to generate very high resolution separations. As previously discussed, the peak capacity of a multidimensional separation is equal to the product of the peak capacities of each dimension as long as all separation dimensions are orthogonal and no resolution gained on one dimension is lost on any subsequent dimension (Giddings, 1987; also see Chapters 3 and 12 by Davis and Gilar et al., respectively). Since reversed-phase UHPLC can give peak capacities well into the hundreds as a 1D technique (Jerkovich et al., 2003), it is easy to conceive that extremely powerful comprehensive 2D separations could be performed if UHPLC were appropriately combined with a second, orthogonal separation method. As already discussed, off-line coupling is an excellent option for MDLC separations when the main goal is to maximize resolution. For this reason, as well as due to the lack of valves compatible with ultrahigh pressures, the research reported in this chapter uses off-line coupling to interface the first dimension to the second. 8.3.3

LC UHPLC for Separations of Intact Proteins

One of the primary challenges facing the field of separation science is the analysis of the entire complement of proteins produced by an organism—a field of research known as proteomics. 2D gel electrophoresis remains the gold standard in protein separations, with the ability to resolve as many as 5000 proteins in a single gel

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(O’Farrell, 1975). However, 2D gel electrophoresis has pronounced limitations in several respects. It is a labor-intensive analysis usually requiring multiple days to complete. It is not readily coupled to mass spectrometry. Additionally, it is biased against certain classes of proteins. Very large or hydrophobic proteins may not enter the gel at all, and resolution of very acidic or basic proteins is often poor (Wehr, 2002). There is, therefore, widespread interest in devising a liquid-phase protein separation technique to supplement or replace 2D gel electrophoresis. Since UHPLC offers the potential to generate very high peak capacities, especially if used as part of a multidimensional separation, it is a logical candidate for application to the challenge of intact protein separations. In this chapter, research is reported in which UHPLC was used as one dimension in an LC LC separation of the soluble proteins produced by the bacterium E. coli. A schematic diagram of the general procedure used is shown in Fig. 8.10. Conventional-pressure anion-exchange chromatography, which separates proteins based on their charge, is used for the first dimension. Interfacing between the two dimensions is accomplished by fraction collection after dimension 1, followed by lyophilization of the volatile mobile phase and reconstitution of the fractions in order to concentrate the proteins. All fractions are then analyzed on the second dimension, ultrahigh pressure reversed-phase liquid chromatography (UHPRPLC), which separates proteins based on hydrophobicity. The outlet of the reversed-phase capillary column is directly interfaced with a mass spectrometer to carry out online electrospray time-of-flight MS, which provides intact molecular weight information for all detectable proteins.

FIGURE 8.10 Basic configuration of instrumentation used for off-line AEX UHP-RPLC.

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8.4 EXPERIMENTAL DETAILS 8.4.1

Instrumentation

The instrumentation used in this study consists of two separate LC systems, a lyophilization apparatus, and a mass spectrometer. The first-dimension separation is performed on commercially available instrumentation. AWaters Corporation 600E quaternary gradient LC pump (Milford, MA) is used to generate a salt gradient. The pump is connected to a Valco (Houston, TX) six-port valve with a 100-mL sample loop used to inject a sample onto the column. A 7.5 cm long, 7.8 mm diameter Waters Biosuite Q, 10 mm strong anion-exchange column, which contains polymeric particles bonded with a quaternary amine functionality, is used to carry out the first-dimension separation. Detection is performed using an Applied Biosystems 785AUVabsorbance detector (Foster City, CA) set at 280 nm. Fractions with a volume of 1.5 mL each are collected in microcentrifuge tubes using a Waters Fraction Collector II. Fractions are flash frozen using liquid nitrogen and are then placed in a SpeedVac Concentrator (Thermo Electron, Bellefonte, PA), which is pumped down to pressures between 102 and 103 Torr using an Edwards high vacuum pump (Wilmington, MA). Once the fractions have been lyophilized to dryness, they are reconstituted in 100 ml of a solution of 10% acetonitrile and 90% water (v/v). The fractions are then ready for analysis on the second dimension. The second LC system, used to perform gradient reversed-phase separations at ultrahigh pressures, is of a more unusual design. A simplified schematic diagram of this instrument, described in greater detail elsewhere (Link, 2004), is shown in Fig. 8.11. The instrument consists of two separate pumps. A Waters CapLC pump is responsible for injecting the sample and generating the reversed-phase gradient under low pressure conditions. A custom-designed hydraulic-amplifier pump is used

FIGURE 8.11

Simplified diagram of the gradient ultrahigh pressure instrument.

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to produce the ultrahigh pressures needed to carry out the separation. The two pumps are interfaced with the separation column at a four-port stainless steel injector block. The UHP pump connects to the injector block through the gradient storage loop, which is a several meter long length of 250 mm diameter stainless steel tubing that stores the gradient generated by the CapLC pump and subsequently allows it to be pumped onto the column by the ultrahigh pressure pump. Valves 1 and 2 are air-actuated on/off valves, model ASFVO, from Valco (Houston, TX). The separation column is a 50 mm ID capillary, 45 cm in length, packed in-house with 1.5-mm ethyl-bridged hybrid C18 reversed-phase particles provided by Waters Corporation (Milford, MA). An open-tubular splitter capillary, 10 mm in internal diameter and 1.3 m in length, is also connected to the injection block. To perform a run, a sample vial is loaded into the autosampler of the CapLC instrument. With valves 1 and 2 in the open position, the CapLC first generates an acetonitrile/water gradient, which travels into the injection block. Since the column and the splitter are both capillaries with very high flow resistance, essentially all the flow from the CapLC enters the gradient storage loop. Since the gradient will later be pumped onto the column by the ultrahigh pressure pump, located on the opposite end of the loop, the gradient must be loaded in reverse – that is, beginning with the highest desired acetonitrile content and ending with the lowest. The loading of the gradient is usually performed at a flow rate of 40 mL/min, whereas the ultrahigh pressure pump operates at 2 mL/min when a run is in progress. Therefore, a gradient that will run for 60 min only takes 3 min to load onto the gradient storage loop. Once loading of the gradient is complete, the CapLC loads the sample, typically 1 mL in volume, onto the storage loop in the same manner. After sample has been loaded, valve 1 is closed to isolate the CapLC pump from ultrahigh pressure. Valve 2 is also closed, and then the ultrahigh pressure pump is activated to pressurize the system to the desired run pressure. Once this pressure is reached, the pump operates at a constant flow rate of 2 mL/min. The splitter capillary diverts most of the flow from the gradient storage loop and keeps the backpressure at approximately 23,000 psi. The flow rate through the separation capillary at this pressure is approximately 100 nL/min, which means the split ratio is approximately 20:1. The sample in the gradient storage loop is the first liquid forced onto the column. Next, the column experiences the gradient, which although loaded in reverse, is now “played back’’ in the normal manner of low to high acetonitrile concentration. The outlet of the separation column is coupled using a Teflon sleeve to a nanoelectrospray tip purchased from New Objective (Woburn, MA). Online positive ion mode electrospray time-of-flight MS is performed using a Micromass Q-TOF Micro instrument (Waters Corp., Milford, MA). Mass spectra were acquired at a frequency of 2 Hz for the duration of the run. All mass spectra were acquired using the software package MassLynx 4.0 (Waters Corp., Milford, MA). 8.4.2

Data Analysis

2D chromatograms were prepared by loading total ion current (TIC) data from each reversed-phase chromatogram from MassLynx onto the data analysis software

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program Igor Pro 4 (Wavemetrics, Lake Oswego, Oregon), and the data from all fractions in an anion-exchange run were combined into a two-dimensional dataset called a “wave.’’ Since peaks in the chromatogram vary in intensity over several orders of magnitude, the logarithm of the TIC intensity was taken for all points in the wave to more clearly show both the high and low intensity peaks. The wave was plotted as an image to generate a 2D chromatogram. The upper limit of the intensity scale was set by the most intense peak in the chromatogram, whereas the lower limit was set in a manner such that the majority of background noise that did not correspond to detectable proteins was kept to the lowest portions of the scale. To determine the molecular mass of the proteins in the sample from multiply charged ions in the electrospray mass spectra, all chromatograms were thoroughly surveyed for peaks that gave a detectable charge envelope. When such a peak was found, all MS scans under the peak were summed, and the resulting mass spectrum was background subtracted, smoothed, and centered according to MassLynx default parameters to convert the spectrum from continuum data to a line spectrum. Then an adjacent pair of ions from the same charge envelope in the mass spectrum was identified visually. Using the MassLynx “Find Manual’’ dialog, the ion pair’s m/z values were used to discover all remaining ions in the same series and calculate the actual molecular weight of the intact protein. If more than one charge envelope was present in the same mass spectrum, all other detectable protein masses were also measured. This procedure was repeated in the same manner for all peaks in all of the chromatograms, and protein mass data were recorded in tabular form. Identification of the proteins based on their molecular weight was not attempted. Instead, the data were used to determine the number of probable unique protein masses detected in each fraction and in each 2D run. If the same protein mass appeared in more than one fraction of a 2D run, it was counted as being found only in the fraction where its base peak intensity was the greatest. 8.4.3

Chromatographic Conditions

A volatile salt in the mobile phase of the anion-exchange separation was preferred so that it could be removed from the fractions by lyophilization. Ammonium acetate was used because it sublimes under vacuum and can serve as both the elution salt and a buffer at pH 8.5. All anion-exchange runs were performed using a 0.5 mL/min flow rate. Flow was held isocratic at 100% mobile phase A for 10 min following sample injection, after which a linear gradient from 0% to 50% mobile phase B was run over 30, 60, or 120 min. The mobile phase was then ramped to 75% B over 5 min and held at this composition for 15 min to maximize protein elution. The mobile phase was then returned to 100% A. Buffer A was 25 mM ammonium acetate, pH 8.5. Buffer B was 1 M ammonium acetate, pH 8.5. Fractions were collected every 3 min beginning immediately after the injection of the sample. For the 30-min gradient, 15 fractions were collected; for the 60-min gradient, 25 fractions were collected; and for the 120-min gradient, 40 fractions were collected. The reversed-phase gradient separation was run at a flow rate near 100 nL/min and pressure around 23,000 psi. The gradient used to elute the proteins from the

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reversed-phase column is as follows: the mobile phase composition was held isocratic at 90% mobile phase C/10% mobile phase D for the first 15 min of the run to allow the sample in the gradient storage loop to be fully transferred to the column. Then a linear gradient was run from 10% to 65% mobile phase D over 60 min. The mobile phase composition was then returned to 90% C/10% D. Mobile phase C was deionized water with 0.2% formic acid. Mobile phase D was acetonitrile with 0.2% formic acid. 8.4.4

Samples

An extract of the soluble proteins of the bacterium E. coli was provided by the Giddings lab in the Department of Microbiology and Immunology at the University of North Carolina at Chapel Hill. The details of the procedure for the preparation of this extract have been reported elsewhere (Link, 2004). 8.5 RESULTS AND DISCUSSION Figure 8.12 shows the UV absorbance chromatograms of three anion-exchange separations of an E. coli lysate carried out using gradients ranging 0.025–0.5 M ammonium acetate over 30, 60, and 120 min. Fraction collection was performed during each of these runs; fractions were changed at the intervals shown as vertical lines on the chromatograms. The sample is too complicated to be fully resolved by a single anion-exchange separation, as indicated by the presence of multiple overlapping peaks in all chromatograms. Some additional resolution is apparently gained by extending the gradient from 30 to 60 or 120 min. It is difficult to ascertain how much actual improvement in the separation of the proteins is achieved from the UV chromatograms because no peaks are fully resolved even with the longer gradients. Although it is difficult to assign a peak capacity to these separations without fully resolved peaks to examine, 20 would be a reasonable, conservative estimate for the 120-min gradient separation. Fractions from the anion-exchange separations were analyzed on the second dimension UHP-RPLC. For each anion exchange separation, a series of reversedphase chromatograms are produced. A representative chromatogram of a reversedphase separation of one anion-exchange fraction is shown in Fig. 8.13. The chromatogram is a plot of the total ion current measured by mass spectrometer as a function of retention time. Peaks typically do not appear until after 25–30 min in most of the reversed-phase chromatograms, mainly because of the delay associated with transferring the sample from the gradient storage loop onto the reversed-phase column. Further inspection of the chromatogram reveals that there is a great deal of variability in peak shape. Some peaks are sharp and symmetrical, having base widths as small as 10 s. Some proteins coelute, causing overlapping peaks and distorted peak shape, although more peaks are fully resolved in this chromatogram than in the anionexchange separation. Some nonoverlapping peaks are also broad and asymmetrical, having notable tailing and widths as large as 30 s. Although this inconsistency in peak shape is clearly not desirable, it is not atypical of reversed-phase protein separations. The high total resolving power of UHPLC helps to compensate somewhat for the fact

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197

FIGURE 8.12 UV absorbance chromatograms of three anion-exchange separations of an E. coli lysate. Vertical lines represent the times at which fractions were changed.

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FIGURE 8.13 Total ion current chromatogram for a UHP-RPLC–MS separation of one anion exchange fraction (fraction number 6 from the 30-min anion-exchange gradient).

that some peaks are wider than expected. Assuming an average peak width of 20 s and an elution window of 30 min, a typical reversed-phase peak capacity for this set of runs would be 120. All the reversed-phase chromatograms from the fractions of an anion-exchange separation can be combined and presented as a 2D chromatogram. In Fig. 8.14a–c, the 2D chromatograms for separations of the E. coli lysate using three different anion exchange gradients—30, 60, and 120 min in length—are shown. Reversed-phase retention time is plotted on the X-axis, anion-exchange retention time is plotted on the Y-axis, and the intensity scale (“Z-axis’’) represents the logarithm of the total ion current measured by the mass spectrometer. The data are essentially equivalent to an online comprehensive LC LC separation, even though off-line coupling was used. As such, it is possible to estimate a peak capacity for this 2D separation. For the longest anion-exchange separation, a total of 40 fractions were collected. Assuming that peaks may be split over two fractions, the peak capacity of the first dimension is taken to be half of this number, or 20, which is not unreasonable based on the visual estimate from the UV chromatogram. If the second-dimension peak capacity is 120 as previously estimated, the resulting total peak capacity of this 2D separation method is approximately 2400. In comparing the three chromatograms, it is apparent that the general shape of the elution profile is the same for all the three runs. Numerous peaks appear within the first 12 min of anion-exchange retention time in all three 2D chromatograms. These correspond to the proteins that were not-retained or very weakly retained on the anion-exchange column, which implies that they are the most basic proteins in the sample. There is then a gap of several minutes where few peaks appear; the gap is longer for the shallower anion-exchange gradient and shorter for the steep one. The time after this gap at which peaks reappear corresponds to the point on the gradient when the salt concentration is high enough to begin eluting proteins that were retained on the anion exchange column. Peaks are spread over a fairly wide portion of the

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199

FIGURE 8.14 2D chromatograms from AEX RPLC separations of an E. coli lysate using different anion-exchange gradient lengths. The scale at the right-hand side of the figure represents the signal intensity, as measured by the total ion current (TIC) from the mass spectrometer. Parts (a), (b), and (c) represent anion exchange gradient lengths of 30, 60 and 120 min, respectively, all plotted using the same intensity scale range. Part (d) is the same chromatogram as Part (c), except that the intensity scale range has been altered to enhance peak visibility (see the text for explanation). Chromatograms have been cropped to show only the separation space in which proteins were found to elute. (See color plate.)

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available separation space, although certain areas of the 2D plot clearly contain greater concentrations of protein than others. Additionally, there seems to be a weak correlation between retention in the two dimensions—proteins that elute early from the anion-exchange column (the most basic proteins) also tend to elute earlier in the reversed-phase runs. This correlation is expected, however, since many of the most basic proteins of E. coli are ribosomal proteins, which also tend to be fairly hydrophilic and thus are expected to be weakly retained on the reversed-phase column. In general, however, the two separation methods appear to be relatively orthogonal in that peaks are spread over a relatively wide range of the available 2D separation space. As the anion-exchange gradient is lengthened, more detail becomes visible in the 2D chromatograms. Regions that appear laden with many overlapping high intensity bands in the chromatogram from the steepest anion-exchange gradient begin to separate into resolved peaks with the longer gradients. Another trend is that the intensity of all peaks seems to diminish as the anion-exchange gradient is lengthened. This is most apparent in Fig. 8.14c, which is the 2D chromatogram generated from the longest anion-exchange gradient. To visually compensate for this decrease in sensitivity, Fig. 8.14d shows the same 2D chromatogram as Fig. 8.14c with the “Z-axis’’ scale adjusted in the manner such that many of the peaks hidden by the diminished signal intensity are revealed. The notable decrease in sensitivity with the shallower gradient is consistent with previous observations from gradient reversed-phase and ion-exchange separations of proteins. (Mal’tsev et al., 1990) Additionally, it has been shown that the peak capacity of a gradient separation will reach a maximum at certain gradient length and that making the gradient shallower beyond this point will result in no further improvement of peak capacity (Stout et al., 1986; Gilar et al., 2004). Both these factors imply that the point of diminishing returns must be considered when attempting to improve resolution by lengthening the gradient. Many of the trends that can be visually observed by examining the 2D chromatograms are also supported by the mass spectrometry data. Figure 8.15 presents graphs of the number of probable unique protein masses found in each fraction for the same three 2D runs. As is noted in the chromatograms, in all the runs there is a spike in the number of proteins detected in the second fraction that corresponds to proteins not retained by the anion-exchange column. This is followed by a gap of several fractions where few proteins are detected. After the gap formation, the majority of the proteins elute over in the next 10–25 fractions, depending on the gradient length. Several differences among the three runs are also apparent in the MS data. For one, lengthening the anion-exchange gradient does spread the proteins over a greater number of fractions, as anticipated. All proteins elute within 15 fractions for the 30-min gradient, whereas they are spread over 31 fractions for the 120-min gradient. Also notable is the fact that the number of proteins detected in each fraction is substantially less for the 120-min anion-exchange gradient run than for the 30- or 60-min gradients. As the gradient is lengthened, many of the proteins that elute in only one fraction in the shorter gradients are spread over two or more fractions in the longest gradient, and thus are more dilute. This reduces their MS signal intensity and causes

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201

FIGURE 8.15 Histograms representing the number of unique proteins found for three 2D separations of an E. coli lysate using anion-exchange gradients of different length (A ¼ 30-min gradient, B ¼ 60 min, C ¼ 120 min). The vertical bars represent the number of proteins found in each fraction. The upward-sloping line represents the cumulative number of unique proteins found up to and including the indicated fraction.

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TABLE 8.2 Summary of the Data from Three LC UHPLC Separations of an E. coli Lysate Anion-Exchange Gradient Length, min 30 60 120

# of probable unique proteins found

# of fractions with probable unique proteins

209 247 176

15 21 31

some proteins to fall below the detection limit, which prevents them from being counted. Just as is the case for the trend observed from visual inspection of the 2D chromatograms, the decrease in the number of proteins detected is consistent with expected drop-off in sensitivity as gradient length is increased (Mal’tsev et al., 1990). The data from the three 2D runs are summarized in Table 8.2. The total number of unique protein masses found in all fractions increased from 209 to 247 for the 30- and 60-min anion-exchange gradients, respectively. This suggests that the increased peak capacity contributed by lengthening the anion-exchange gradient allows more proteins to be resolved and detected. This trend did not continue when the gradient was lengthened further, however, as the number of proteins detected decreased to 176 for the 120-min anion-exchange gradient. As already discussed above, this drop-off probably results from the fact that the proteins become too dilute when spread over several fractions. The resulting diminished signal intensity offsets any gain in resolution achieved by lengthening the anion-exchange gradient beyond a certain point. Therefore, for the instrumentation used in this study, the 60-min anion-exchange gradient proved best. The chromatographic peak capacity of this separation method, estimated as 2400, represents one of the highest peak capacities for a comprehensive LC LC separation of intact proteins reported to date. Much potential still exists to further enhance the capabilities of this system through optimization of both the anion-exchange and the UHP-RPLC separations. Although the peak capacity of this system still falls substantially short of the capabilities of 2D gel electrophoresis, it is free from many of the cumbersome problems that plague gel-based separations. As time passes and further refinements are made, it is expected that ultrahigh pressure multidimensional liquid chromatography will become a practical technique for the separation of complex samples.

8.6 FUTURE DIRECTIONS FOR UHP-MDLC One of the major trends to be anticipated is greater availability of instrumentation that can be used to perform multidimensional separations at ultrahigh pressures. Some instrument makers have recently introduced LC pumps capable of operating at pressures up to 15,000 psi. As time progresses, ultrahigh pressure separations are likely to become more routine and the pressure limit of commercial instruments is likely to increase

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further. Given these trends, it is also reasonable to anticipate that valve technology will soon be available that will allow online coupling of dimensions compatible with ultrahigh pressures. Once online coupling becomes possible, UHPLC may find much more widespread applicability to MDLC separations due to its potential to enhance the speed of reversed-phase separations without excessive loss of peak capacity. Another potential application for multidimensional separations using UHPLC is the “bottom-up’’ approach to proteomics. This is an alternative to intact protein analysis, which involves enzymatically digesting the proteins to be analyzed to produce a mixture containing an extremely large number of peptides. The peptides are then separated using chromatography and analyzed via MS/MS. One advantage of this approach is that, unlike proteins, peptides are almost always well behaved on reversed-phase columns. The main disadvantage is that digesting a mixture of proteins further increases the complexity of an already complex sample, thereby making the separation of its components more challenging. UHPLC is well suited to this challenge—peak capacities of over 500 have been demonstrated for gradient UHPLC separations of peptides, while conventional reversed-phase HPLC typically gives peak capacities of 200 or less with similar samples (Jerkovich et al., 2003). If coupled to an appropriate orthogonal separation method, it is not difficult to envision that a MDLC technique using UHPLC could find use as part of a method for bottom-up proteomics. Another concept worthy of consideration is a MDLC separation in which both dimensions would be operated at ultrahigh pressures. The challenge on this front is that essentially all work using UHPLC to date has been performed using the reversed-phase separation mode. It remains to be shown whether UHPLC can give the same improvement in separation efficiency and peak capacity for other separation modes. If UHPLC does prove useful for separation modes, such as ion exchange or size exclusion, the coupling of one UHPLC separation to a second may still offer greater peak capacities and faster separation times than are presently possible with MDLC.

REFERENCES Bushey, M.M., Jorgenson, J.W. (1990). Automated instrumentation for comprehensive twodimensional liquid chromatography of proteins. Anal. Chem. 62, 161–167. Cortes, H.J. (1990). Multidimensional Chromatography: Techniques and Applications. Marcel Dekker, Inc., New York. Erni, F., Frei, R.W. (1978). Two-dimensional column liquid chromatographic technique for resolution of complex mixtures. J. Chromatogr. 149, 561–569. Giddings, J.C. (1987). Concepts and comparisons in multidimensional separation. J. High. Resolut. Chromatogr. Chromatogr. Commun. 10, 319–323. Gilar, M., Daly, A.E., Kele, M., Neue, U.D., Gebler, J.C. (2004). Implications of column peak capacity on the separation of complex peptide mixtures in single- and two-dimensional high-performance liquid chromatography. J. Chromatogr. A 1061, 183–192. Holland, L.A., Jorgenson, J.W. (1995). Separation of nanoliter samples of biological amines by a comprehensive two-dimensional microcolumn liquid chromatography system. Anal. Chem. 67, 3275–3283.

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Holland, L.A., Jorgenson, J.W. (2000). Characterization of a comprehensive two-dimensional anion exchange-perfusive reversed phase liquid chromatography system for improved separations of peptides. J. Microcolumn. Sep. 12, 371–377. Jerkovich, A.D., Mellors, J.S., Jorgenson, J.W. (2003). The use of micrometer-sized particles in ultrahigh pressure liquid chromatography. LCGC N. Am. 21, 600, 604, 606, 608, 610. Link, J.C. (2004). Development and application of gradient ultrahigh pressure liquid chromatography for separations of complex biological mixtures: Dissertation, University of North Carolina, Chapel Hill. Available from UMI ProQuest Digital Dissertations, Ann Arbor, MI, AAT 3156171. MacNair, J.E., Lewis, K.C., Jorgenson, J.W. (1997). Ultrahigh-pressure reversed-phase liquid chromatography in packed capillary columns. Anal. Chem. 69, 983–989. MacNair, J.E., Lewis, K.C., Jorgenson, J.W. (1999). Ultrahigh-pressure reversed-phase capillary liquid chromatography: isocratic and gradient elution using columns packed with 1.0-m m particles. Anal. Chem. 71, 700–708. Majors, R.E. (2003). HPLC column packing design. LC-GC Eur. 16(6a), 8–13. Mal’tsev, V.G., Nasledov, D.G., Trushin, T.B., Tennikova, L.V., Vinogradova, I.N., Volokitina, I.N., Zgonnik, V.N. (1990). High-performance liquid chromatography of proteins on short capillary columns. J. High Resolut. Chromatogr. 13, 185–192. Mellors, J.S., Jorgenson, J.W. (2004). Use of 1.5 mm porous ethyl-bridged hybrid particles as a stationary-phase support for reversed-phase ultrahigh-pressure liquid chromatography. Anal. Chem. 76, 5441–5450. Murphy, R.E., Schure, M.R., Foley, J.P. (1998). Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70, 1585–1594. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250, 4007–4021. Opiteck, G.J., Jorgenson, J.W., Anderegg, R.J. (1997). Two-dimensional SEC/RPLC coupled to mass spectrometry for the analysis of peptides. Anal. Chem. 69, 2283–2291. Opiteck, G.J., Lewis, K.C., Jorgenson, J.W., Anderegg, R.J. (1997). Comprehensive on-line LC/ LC/MS of proteins. Anal. Chem. 69, 1518–1524. Opiteck, G.J., Jorgenson, J.W., Moseley, M.A. III, Anderegg, R.J. (1998). Two-dimensional microcolumn HPLC coupled to a single quadrupole mass spectrometer for the elucidation of sequence tags and peptide mapping. J. Microcolumn. Sep. 10, 365–375. Opiteck, G.J., Ramirez, S.M., Jorgenson, J.W., Moseley, M.A. III (1998b). Comprehensive twodimensional high-performance liquid chromatography for the isolation of overexpressed proteins and proteome mapping. Anal. Biochem. 258, 349–361. Patel, K.D., Jerkovich, A.D., Link, J.C., Jorgenson, J.W. (2004). In-depth characterization of slurry packed capillary columns with 1.0-mm nonporous particles using reversed-phase isocratic ultrahigh-pressure liquid chromatography. Anal. Chem. 76, 5777–5786. Stout, R.W., Sivakoff, S.I., Ricker, R.D. (1986). Separation of proteins by gradient elution from ion-exchange columns. J. Chromatogr. 353, 439–463. Wehr, T. (2002). Multidimensional liquid chromatography in proteomic studies. LCGC N. Am. 20, 954, 956–958, 960–962. Wu, N., Lippert, J.A., Lee, M.L. (2001). Practical aspects of ultrahigh pressure capillary liquid chromatography. J. Chromatogr. A 911, 1–12.

PART III LIFE SCIENCE APPLICATIONS

9 PEPTIDOMICS Egidijus Machtejevas and Klaus K. Unger Institute of Inorganic Chemistry and Analytical Chemistry, Johannes Gutenberg-University, Duesbergweg 10-14, 55099 Mainz, Germany

9.1 STATE OF THE ART—WHY PEPTIDOMICS? Peptides often have very specific functions as mediators and indicators of biological processes. They play important roles as messengers, for example, as hormones, growth factors, and cytokines, and thus have a high impact on health and disease. Peptidomics comprises not only peptides, originally synthesized by an organism to perform a certain task, but also degradation products of proteins (degradome). Therefore, proteolytic cleavage of proteins leads to peptides as indicators of protease activity, degradation, and degeneration. The degradome is a very important part of protein metabolism, and thus also reflects the organism state. However, peptidomics is far more challenging compared with genomics and proteomics. The dynamic range of protein expression and posttranslational modification makes the identification of the entire proteome a far bigger and more complex challenge than the sequencing of the genome. The sensitivity of proteomics and peptidomics suffers from the lack of an amplification method, analogous to the polymerase chain reaction method, to reveal and quantify the presence of low abundance proteinaceous constituents. Proteome analysis usually includes the following strategies: native protein preseparation, then digestion followed by separation and identification (Figure 9.1a), or alternatively straight digestion, separation and identification by mass spectrometry (Figure 9.1b). Therefore, starting with one protein, after digestion we will end up with approximately 30 to 70 short peptide fragments. Identification of only very few of them will provide sufficient information as to which protein was present in the sample. Peptidomics does not possess such a feature: from Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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(a)

(b)

Sample prep

Sample prep

Peptides

Proteins

208

(c)

(d)

Sample prep AF-LC

LC

Digestion

Digestion

Sample prep LC

MD-LC

LC or MD-LC

LC or MD-LC

MS or MS/MS

MS or MS/MS

MS or MS/MS

MS or MS/MS

FIGURE 9.1 Liquid chromatography workflow strategy options in proteomics. (a ) “bottomup’’ approach; (b) “top-down’’ approach; (c) selective sample cleanup directly combined with chromatographic separation; (d) peptide capture with affinity restricted access material.

the beginning of the analysis to the end we have only one peptide at a certain concentration and we have to identify it. However, when peptides come from the degradome of proteins, then, naturally, peptidomics is in a similar situation as proteomics. The display level is difficult because of the wide range of peptide concentrations that spans over ten orders of magnitude. These challenges motivate researchers to develop reliable analytical platforms. Shortcomings in throughput are due to the absence of technologies that can deliver fast and parallel quantitative analysis of complex protein distributions in an automated fashion.

9.2 STRATEGIES AND SOLUTIONS Physiological and pathological changes are reflected in the production and the metabolism of proteins and peptides. Such changes are detectable in extracellular fluids, including blood plasma, cerebrospinal fluid, synovial fluid, and urine (Clynen et al., 2003). Protein samples of biological origin are by nature highly complex and require sophisticated analytical tools to provide reliable analysis of the components. Proteomics especially challenges the need for robust, automated, and sensitive high throughput technologies. Most single-dimension separations lack sufficient resolution capability to resolve complex biological matrixes. Separations employing multiple dimensions offer a better promise for such applications (Giddings, 1984; Wagner et al., 2002). Liquid chromatographic separation methods using different physical properties of peptides for molecular discrimination have been combined with varying degree of success. The ultimate goal is a rapid separation strategy that can be coupled with mass spectrometry, to provide a comprehensive monitoring of the changing concentration. Multidimensional liquid chromatography (LC) separation typically relies on utilizing

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two or more independent physical properties of the peptides to fractionate the mixture into individual components. Physical properties commonly exploited include size, shape, charge, hydrophobicity, and biomimetic or affinity interactions. These processes are the underlying phenomena for peptide/protein separations using different chromatographic modes, such as size exclusion, reversed phase, cation/anion exchange, and hydrophobic interaction columns. Liquid chromatographic techniques are fast, quantitative, easy to automate, and can be coupled more readily to mass spectrometry than two-dimensional gel electrophoresis (Premstaller et al., 2001). The drawback of LC is the limited peak capacity of a single column. Thus, multidimensional LC is the logical choice for increasing peak capacity, fractionating the eluent and transferring the fractions between different columns through automated valve switching (Cortes, 1990). Mass spectrometry has limitations with respect to sensitivity, therefore, a certain number of analyte molecules should be injected in order to be identified. Thus, detection is favored by applying higher amounts of the sample. Knowing the target analyte concentration in the sample and the mass spectrometer detection limits provides the answer to the question: how much we should inject? According to Geigy scientific tables (Lentner, 1984) human plasma contains only 0.03% peptides (dry mass). It might be estimated that in plasma there could be tens of thousands of different peptides with vast concentration differences. Therefore, huge injection volumes might be required. For example, Tatemoto (1982) extracted 0.6 mg of Peptide YY from four tons of porcine intestine. Dart et al. (1985) obtained 47 mg of transforming growth factor-b from 8.8 kg of human placenta. Another important prerequisite for the suitability of a separation system for proteomic analysis is the ability to handle very small amounts of biological material (Premstaller et al., (2001). These methods allow one to detect low concentrations of peptides from complex mixtures with a high degree of automation. Biological, individual, and variations between individuals (such as gender, age, and nutrition) affect peptidomes and require careful consideration in order to find valid biomarkers. A few, equally important factors for successful proteomic biomarker research are high sample quality, high sensitivity, and reproducibility that depend on proper selection of the high quality samples. Proteins are found in different cell compartments (cytoplasm, a range of intracellular organelles) or as secreted extracellular proteins (in various body fluids). Furthermore, proteins range from highly soluble hydrophilic proteins, to membrane associated and transmembrane proteins containing multiple hydrophobic transmembrane domains. Moreover, proteins often exist as multisubunit complexes or can form large macromolecular complexes with other proteins. No optimized conditions exist to suit the wide range of physical and chemical properties of proteins. When nucleic acids are purified from different samples it is assumed that all different DNAs or RNAs are extracted to the same or equivalent degree and such extraction is reproducible between samples. It would be naive to believe that all cellular proteins can be solubilized and efficiently extracted and that such extraction can be reproducibly repeated for many different samples. This means that the protein composition of two different tissues (e.g., liver and brain) cannot be quantitatively compared even if suitable affinity assays

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were available (Figure 9.1d), since inherent variability in the protein extraction step will make it impossible. A proteomic analysis of a sample usually consists of four steps. These are extraction of the proteins from the sample, their separation, detection and finally identification/ analysis of the individual separated peptides. It is of major importance to pay particular attention to the sample extraction, as any error or losses during this stage will strongly influence the results (see discussion above). Analyzing body fluids, sample collection protocols, and variations in sample treatment procedures play a major role in determining sample quality. Tammen et al. (2005) concluded that specimen collection is a crucial step for successful peptide biomarker discovery in human blood samples. The proteome of a blood sample throws light on the metabolic state of an individual at the moment of blood withdrawal. Furthermore, it represents a collection of information about physiological as well as patho-physiological processes occurring at the same time. Plasma samples are one of the major substances that could provide an adequate answer about the state of an organism in total. Initial sample treatment is the major step that ensures how representative the data are and to what degree component losses are acceptable. A large number of peptides, many of them in rather high abundance, are only present in serum and were not detected in plasma (Tammen et al., 2005). This is not surprising, because proteases are participating in clotting events, cleaving many proteins and releasing large quantities of peptides. In general, sample preparation protocols that limit the number of preparation steps, circumvent the loss or dilution of the sample, and concentrate the sample are preferred. Therefore, the most desirable sample pretreatment methods are those that are totally automated. Automation eliminates human type errors and also drastically increases throughput. Another important issue while working with patient biofluids is safety of the researcher who is at higher risk of exposure to an infectious illness. Fully automated sample treatment significantly reduces that risk. However, improvements in sample preparation, resolution, and data analysis are necessary before multidimensional liquid chromatography can be applied for the study of the peptidome. Several promising attempts have been made to analyze the peptidome (e.g., Richter et al., 1999). This group constructed a database of human circulating peptides. To establish a mass database, all 480 fractions of a peptide bank generated from human hemofiltrate were analyzed by MALDI-TOF mass spectrometry. Using this method, over 20,000 molecular masses representing native, circulating peptides were detected. Estimation of repeatedly detected masses suggests that approximately 5,000 different peptides were recorded. More than 95% of the detected masses were smaller than 15,000, indicating that the human hemofiltrate predominantly contains peptides (Richter et al., 1999). Silica-based restricted access materials (RAM) have been developed for cleanup in bioanalysis, first for low molecular weight compounds in biofluids (Rbeida et al., 2005) and subsequently for biopolymers such as peptides (Wagner et al., 2002). A classification of different types of RAM has been given by Boos and Rudolphi (1997). Novel RAMs with strong cation-exchange functionality have been synthesized and implemented in the sample cleanup of biofluids. Racaityt_e et al. (2000) have shown that this type of RAM is highly suitable for the online extraction and analysis of

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neuropeptides in plasma. Machtejevas et al. (2006) analyzed the pore structural parameters and size exclusion properties of LiChrospher strong cation- exchange and reverse-phase RAM. For peptide analysis out of the biofluids, the strong cationexchange functionality seems to be particularly suitable mainly because of the high mass capacity of the strong cation-exchange restricted access material (SCX-RAM) and the fact that one can work under nondenaturing conditions to perform effective chromatographic separations. Additionally, proper column operating conditions lead to a total effective working time for the RAM column of approximately 500 injections, depending on the type of sample, is comprehensively described. The restricted access principle is based on the concept of diffusion-based exclusion of matrix components and allows peptides, which are able to access the internal surface of the particle, to interact with a functionalized surface (Figure 9.2). The diffusion barrier can be accomplished in two ways: (i) the porous adsorbent particles have a topochemically different surface functionalization between the outer particle surface and the internal surface. The diffusion barrier is then determined by an entropy controlled size exclusion mechanism of the particle depending on the pore size of adsorbent (Pinkerton, 1991) and (ii) the diffusion barrier is accomplished by a dense hydrophilic polymer layer with a given network size over the essentially functionalized surface. In other words, the diffusion barrier is moved as a layer to the interfacial

FIGURE 9.2

Cartoon of a RAM particle.

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layer inside the adsorbent particles, and the exclusion properties are controlled by the size of the polymeric network protecting the internal surface that is no longer dependant on the average pore diameter of the adsorbent (Mazsaroff and Regnier, 1988). It should be emphasized that the restricted access principle is independent of the type and composition of the adsorbent, that is it can be applied to silica adsorbents as well as to polymeric packings. The size exclusion process is entropically driven, that is, proteins with decreasing shape and size penetrate an increasing volume of the porous particles. Size exclusion chromatography (SEC) of proteins is commonly carried out with buffer solutions containing a high salt concentration, for example, 0.1 M, at pH 5–7, which is needed to suppress electrostatic interactions between the solute and the charged surface of silicabased packings. In sample cleanup using a RAM-SCX column the concentration of salt is much lower, for example, less than 20 mM, and the pH is kept at approximately 3. Under these conditions, electrostatic attraction forces are dominant between the positively charged peptides and proteins whereas the negatively charged species are excluded from the pores of the RAM-SCX column through electrostatic repulsion forces (Figure 9.3a). After loading the RAM-SCX column an isocratic washing step elutes all the excluded compounds between the start and 15 min. After 15 min the trapped analytes are eluted from the RAM-SCX column with a strong eluent under gradient conditions during the period between 15 and 45 min (Figure 9.3b). Thus it is a charge and charge distribution selective process combined with SEC. Use of RAMSCX allows the direct application of biofluids onto the column. Small peptides are selectively trapped in the pores by the cationic functional groups while large molecular weight biopolymers (e.g., proteins) are directed to waste. This strategy performs the sample cleanup and selective peptide enrichment in one simultaneous step (Figure 9.1c). mAU (a)

300

(b)

250 200 150 100 50 0 0

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FIGURE 9.3 Typical SCX-RAM column separation profile: peaks (a) represent physical exclusion by pore size. Trapped retained biomolecules are separated by a gradient in the second step (b). Conditions: column—LiChrospher 60 XDS (SO3/Diol), 25 4 mm I.D., flow rate— 0.5 mL/min, gradient from 0 to 1 M NaCl in 20 mM KH2PO4 pH 2.5, containing 5% ACN in 30 min. Sample: 100 mL Human Hemofiltrate (3.7 mg/mL), UV detection at 214 nm.

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FIGURE 9.4 Schematic diagram of the on-line comprehensive two-dimensional HPLC system including an integrated sample preparation step.

Restricted access materials with a strong cation-exchange functionality have demonstrated a high potential for sample cleanup after online direct biofluid injections (Unger et al., 2004). For example, Wagner et al. (2002) used online 2D HPLC with sample cleanup (Figure 9.4), employing restricted access materials for mapping the small peptides and proteins in human hemofiltrate. The basic idea of the 2D-HPLC system is to employ an online sample cleanup strategy using two directly connected separation dimensions. In the first dimension, a strong cation-exchange restricted access media column is followed by an analytical cation-exchange column. In the second dimension, four parallel reversed-phase columns enable a fast separation. Enrichment of the fractions directly on top of the column does not require any sample storage, hence there is no vial contamination, wall adsorption or sample loss due to additional sample handling procedures such as fraction collection and reinjection. The procedure avoids sample dilutions and automatically desalts the analytes, thus preventing eluent incompatibilities. After sample loading, the cation-exchange RAM-column was placed in-line with the analytical cation-exchange column and analytes were eluted with a salt gradient. A total of 24 fractions of 4 min duration (2 mL of eluent) were transferred to the

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second dimension for subsequent reversed-phase chromatography on MICRA ODS I columns. These ion-exchange fractions were then separated on the four reversedphase columns running in parallel, resulting in six reversed-phase chromatograms for each RP column. The second dimension RP separations produced 60 or more resolved peaks in a single analysis, with little overlap between fractions, thus confirming the orthogonal nature of the two separation dimensions. This system reproducibly resolved a total of approximately 1000 peaks within a total analysis time of 96 min. Selected peaks from the RP step were sampled to analyze the molecular weights of the collected peptides by MALDI-TOF mass spectrometry and to determine their amino acid sequence by Edman degradation (Machtejevas et al., 2004). Though the potential for comprehensive peptide mapping and identification was demonstrated, the system complexity and the operation procedures were too complicated for routine analysis. An alternative system proved to be both simpler and more user friendly (Unger et al., 2004; Machtejevas et al., 2006). Thus far we have used this configuration to analyze human plasma, sputum, urine, cerebrospinal fluid, and rat plasma. For each particular analysis we set up an analytical system based on a simple but specific strategy (Figure 9.5). The analysis concept is based on an online sample preparation and a two-dimensional LC system: preseparating the majority of the matrix components from the analytes that are retained on a RAM-SCX column followed by a solvent switch and transfer of the trapped peptides. The SCX elution used five salt steps created by mixing 20 mM phosphate buffer (pH 2.5) (eluent A1) and 20 mM phosphate buffer with 1.5 M sodium chloride (eluent B1) in the following proportions: 85/15; 70/30; 65/45; 45/55; 0/100 with at the constant 0.1 mL/min flow rate. Desorption of the

FIGURE 9.5

2D-LC system set-up with integrated on-line sample preparation.

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adsorbed species from the RAM-SCX column could be accomplished by employing an eluent with a higher solvent strength or pH than the eluent used for loading the column. We preferred to use salt steps as pH elution required twice the time for reequilibration. The desorption step was repeated several times to eliminate memory effects. In order to avoid sample-to-sample cross contamination, two blank gradients were typically applied, although with specific analytes or higher loadings it could require up to five blank gradients. The transfer from the RAM-SCX column to the next (analytical cation-exchange column) is heavily dependant on the way this transfer is performed. Three different modes could be chosen to elute the trapped sample from the RAM-SCX column. 1. Isocratic elution with a strong solvent: After the elution of the unretained component from the RAM-SCX column a single step of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol was applied for 10 min using a constant 0.5 mL/min flow rate. This method is particularly attractive when relatively noncomplex samples are analyzed. In this case, RAM-SCX is used only as a sample precleaning column. Eluted components could then be separated, for example, according to hydrophobicity. This elution method is particularly suitable for column cleaning, eliminating carry over effects, and avoiding sample cross contamination. 2. Elutionwith alineargradient:Thisisperformed with a 20 mingradient from0% to 100% of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol at 0.5 mL/min. More complex mixtures require employing RAMSCX as an ion-exchange separation column. Linear gradients require more sophisticated switching techniques. The draw back is that in a multidimensional system, the next dimension should be very fast. A serious disadvantage is that a repeating series of the same component is observed in consecutively eluted fractions when the switching valve switches in the middle of the peak. This reduces MS sensitivity and complicates data analysis. 3. Elution with salt pulses: A multiple step elution is performed by the introduction of, for example, 5%, 10%, 25%, 50%, and 100% of 1.5 M sodium chloride in 19 mM phosphate buffer (pH 2.5) containing 5% methanol. Each step is for 10 min and run at 0.5 mL/min. This elution method compromises analytical system dimensionality, as the peak capacity of the ion-exchange chromatography (IEX) step is equal at most to the number of salt steps. However, in the second dimension only one or two columns are needed and there is no particular limitation in the second dimension separation time as peptides are eluted in portions in a controlled manner. However, the number of salt steps is limited by the total analysis time. In this case the multidimensional system is relatively simple. Each of these operations will lead to different results, because the desorption conditions of the target substances are not identical. As proteins trapped in a RAMSCX column will be subjected to different conditions (salt composition and molarity, pH), these changes might lead to conformational changes.

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Following elution from the IEX dimension, desalting and preconcentration of the fractions containing proteinaceous components were performed on two identical trap columns (Zorbax 300 SB-C18, 5 mm particles, 5 0.3 mm I.D., Agilent, Waldbron, Germany). As a final column a monolithic fused silica RP-18 endcapped capillary column of dimensions 150 0.1 mm I.D. (Chromolith CapRod, Merck KGaA, Darmstadt, Germany) was used. We preferred the monolithic type of column over a particulate capillary column for the following reasons: (a) the monolithic silica columns can be operated over a wide range of flow rates, which is particularly useful in the setup of multidimensional LC MS system to adjust for different column sizes; (b) in a MDLC-MS system the monolithic silica columns meet the requirement of high reproducibility comparable to particulate columns; (c) in terms of column robustness and usage flexibility monolithic silica columns are superior to packed particulate columns, for example, one could cut the top-end column when damaged, there is no change in the permeability as a result of pressure fluctuation and no frits are required at the end of the capillary directly connected to the MS. A 40 min acetonitrile gradient with 0.1% formic acid operating at constant 2 mL/min flow rate was employed to separate the trapped peptides. The end of the reversephase capillary column was directly inserted into a homemade robotic spotting apparatus so that the droplets were accumulated on a MALDI plate at 2 min intervals, filling a 100 spot MALDI plate per sample (5 fractions from the RAM-SCX column (salt steps), 20 fractions from the monolithic capillary RP 18e column). After the plate positions were filled the fraction were dried and 0.5 mL of matrix solution, consisting of a-cyano-4-hydroxycinamonic acid in 50% acetonitrile/4% formic acid/water (v/v/v) was spotted on the top. The MALDI plate was kept in the dark and analyzed within 12 h. Restricted access materials with a strong cation functionality and an average pore diameter of 6 nm were chosen to extract peptides from complex plasma samples. Ionexchange chromatography was employed in the second dimension as a mild separation technique that minimizes the risk of denaturation. Another important advantage of using strong cation adsorption compared to reverse-phase chromatography is the elimination of lipid-like plasma components, which preserves the column loading capacity, as the lipids are not bound, and allows an increase in sensitivity in mass spectrometry. Prefractionation can also be based on different physiochemical properties, such as net charge, mobility, size, hydrophobicity and affinity. By adjusting the average pore diameter of the RAM-SCX column the binding of highly abundant human serum albumin and other common large proteins in plasma can be minimized. Incorporating RAM-technology for sample preparation along with IEX and reversephase separations enables: (i) automation of the sample cleanup; (ii) adjusting the column mass loadability by choosing the appropriate column dimensions; (iii) combining sample cleanup with a selective prefractionation according to charge. With such sample pretreatment we obtained a peptide profile for the molecular weight range from 700 to 4500 Da. For the second dimension, reverse phase columns were chosen as they offer high capacity and efficient separation. As the sample is highly pretreated after the RAM-SCX column and separated into fractions on the analytical

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FIGURE 9.6 The peptide and small protein map from a 100 mL human plasma injection. Columns: sample preparation SCX RAM; analytical column: chromolith performance RP-18, 100 0.1 mm I.D. Minute fractions were analyzed using MALDI-TOF MS. Fraction numbers correspond to the time scale. Dot size is related to signal intensity.

SCX column, the final separation has the capability to resolve a high number of low concentration substances/peptides. In order to use MS as an elegant and effective detection technique there are some requirements to maximize sensitivity and resolution. For example, there is a need to avoid salts. Reverse phase chromatography is an effective means to desalt fractions from the RAM-SCX column. Two trap columns packed with reverse phase material perform this task. These also compensate for the flow-rate differences between the analytical and capillary columns. To make the last chromatographic step highly efficient and also to avoid dilution of the fractions, a reversed-phase capillary column was employed. Droplets/fractions from the reversed-phase capillary column were directly spotted onto the MALDI target plate thus avoiding dilution. Combined with sample volumes of 0.5 mL used in classical MALDI-MS sample spotting, sample concentrations can be so low that adhesion to tubes, tips and other surfaces might result in a substantial sample loss. As already mentioned the system can be used to analyze unfractionated human plasma, cerebrospinal fluid and urine samples in a fully automated way with high sensitivity. In all peptide displays (Figure 9.6), between 1000 and 4000 mass spectrometric signals were observed, which correspond to 500–2000 individual peptides. This number of additional observed peaks usually reflects redundancy (peptides that elute in more than one fraction), peptide species with and without oxidative states, or a small number of MS artifacts, such as fragment ions.

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9.3 SUMMARY AND CONCLUSIONS We predict that peptide analysis both for biomarker discovery and degradome studies will gain substantial interest in future diagnostic research. The presented 2D(3D)-LC technology platform with integrated sample cleanup provides a powerful tool and a sustainable platform for research in this field. The approach is highly flexible with respect to sample volume and loading capacity of the probes. System optimization enables the handling of a variety of biofluids and is highly flexible to choose appropriate LC modes for further separation.

REFERENCES Baggerman, G., Verleyen, P., Clynen, E., Huybrechts, J., De Loof, A., Schoofs, L. (2004). Peptidomics. J Chromatogr B 803, 3–16. Boos, K.S., Rudolphi, A. (1997). The use of restricted-access media in HPLC, Part I– Classification and review. LC-GC Int. 15602–611. Clynen, E., Baggerman, G., Veelaert, D., Cerstiaens, A., Van der Horst, D., Harthoorn, L., Derua, R., Waelkens, E., De Loof, A., Schoofs, L. (2001). Peptidomics of the pars intercerebralis-corpus cardiacum complex of the migratory locust, Locusta migratoria. Eur. J. Biochem. 268, 1929–1939. Clynen, E., De Loof, A., Schoofs, L. (2003). The use of peptidomics in endocrine research. Gen. Comp. Endocr. 132, 1–9. Cortes, H.J. (1990). Multidimensional Chromatography Techniques and Applications. Marcel Dekker, New York. Dart, L.L., Smith, D.M., Meyers, C.A., Sporn, M.B., Frolik, C.A. (1985). Transforming growth factors from a human tumor cell: characterization of transforming growth factor b and identification of high molecular weight transforming growth factor Ó. Biochem. 24, 5925–5931. Giddings, J.C. (1984). Two-dimensional separations: concept and promise. Anal. Chem. 56, 1258–1270. He, Q.Y., Chen, J., Kung, H.F., Yuen, A.P., Chiu, J.F. (2004). Identification of tumorassociated proteins in oral tongue squamous cell carcinoma by, proteomics. Proteomics 4, 271–278. Hunt, D.F. (2002). Personal commentary on proteomics. J. Proteome Res. 1, 15–19. Lentner, C. (1984). Human plasma composition (Physical chemistry, composition of the blood, haematology, sonatometric data). Geigy Scientific Tables, Vol. 3. Basle, Switzerland: Ciba Geigy. Machtejevas, E., Denoyel, R., Meneses, J.M., Kudirkaite, V., Grimes, B.A., Lubda, D., Unger, K.K. (2006). Sulphonic acid strong cation-exchange restricted access columns in sample cleanup for profiling of endogenous peptides in multidimensional liquid chromatography. Structure and function of strong cation-exchange restricted access materials. J. Chromatogr. A 1123, 38–46. Machtejevas, E., John, H., Wagner, K., St€andker, L., Marko-Varga, G., Forssmann, W.-G., Bischoff, R., Unger, K.K. (2004). Automated multi-dimensional liquid chromatography:

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sample preparation and identification of peptides from human blood filtrate. J. Chromatogr. B 803, 121–130. Machtejevas, E., Marko-Varga, G., Lindberg, C., Lubda, D., Hendriks, R., Unger, K.K. (2007). Sulphonic acid strong cation exchanger restricted access columns in sample cleanup for profiling of endogenous peptides in multidimensional liquid chromatography: on-line automated sample cleanup procedures for human urine. In preparation for J. Chromatogr. A. Mazsaroff, I., Regnier, F.E. (1988). Phase ratio determination in an ion-exchange column having pores partially accessible to proteins. J. Chromatogr. 442, 15–28. Pinkerton, T.C. (1991). High-performance liquid chromatography packing materials for the analysis of small molecules in biological matrices by direct injection. J. Chromatogr. 544, 13–23. Premstaller, A., Oberacher, H., Walcher, W., Timperio, A.M., Zolla, L., Chervet, J.-P., Cavusoglu, N., van Dorsselaer, A., Huber, Ch.G. (2001). High-performance liquid chromatography-electrospray ionization mass spectrometry using monolithic capillary columns for proteomic studies. Anal. Chem. 73, 2390–2396. Racaityt_e, K., Lutz, E.S.M., Unger, K.K., Lubda, D., Boos, K.S. (2000). Analysis of neuropeptide Y and its metabolites by high-performance liquid chromatography—electrospray ionization mass spectrometry and integrated sample cleanup with a novel restricted-access sulphonic acid cation exchanger. J. Chromatogr. A 890, 135–144. Rbeida, O., Christiaens, B., Hubert, Ph., Lubda, D., Boos, K.-S., Crommen, J., Chiap, P. (2005). Integrated online sample cleanup using cation exchange restricted access sorbent for the LC determination of atropine in human plasma coupled to UV detection. J. Pharm. Biomed. Anal. 36/5, 947–954. Richter, R., Schulz-Knappe, P., Schrader, M., Standker, L., Jurgens, M., Tammen, H., Forssmann, W.-G. (1999). Composition of the peptide fraction in human blood plasma: database of circulating human peptides. J. Chromatogr. B 726, 25–35. Tammen, H., Schulte, I., Hess, R., Menzel, C., Kellmann, M., Mohring, T., Schulz-Knappe, P. (2005). Peptidomic analysis of human blood specimens: comparison between plasma, specimens and serum by differential peptide display. Proteomics 5, 3414–3422. Tatemoto, K. (1982). Neuropeptide Y: complete amino acid sequence of the brain peptide. Proc. Natl. Acad. Sci. USA 79, 5485–5489. Unger, K.K., Machtejevas, E., Hennessy, T.P., Ditz, R. (2004). Multidimensionale LC/MS in der proteomanalyse – eine kritische Bestandsaufnahme. Laborwelt 5/4, 4–10. Verhaert, P., Uttenweiler-Joseph, S., de Vries, M., Loboda, A., Ens, W., Standing, K.G. (2001). Matrix-assisted laser desorption/ionization quadrupole time-of-flight mass spectrometry: an elegant tool for peptidomics. Proteomics 1, 118–131. Wagner, K., Miliotis, T., Marko-Varga, G., Bischof, R., Unger, K.K. (2002). An automated online multidimensional HPLC system for protein and peptide mapping with integrated sample preparation. Anal. Chem. 74, 809–820.

10 A TWO-DIMENSIONAL LIQUID MASS MAPPING TECHNIQUE FOR BIOMARKER DISCOVERY David M. Lubman Department of Chemistry, Department of Surgery, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI 48109, USA

Nathan S. Buchanan, Paweena Kreunin, and Yanfei Wang Department of Chemistry, The University of Michigan, Ann Arbor, MI 48109, USA

Fred R. Miller Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA

Kathleen Cho and Rong Wu Department of Pathology, The University of Michigan, Ann Arbor, MI 48109, USA

Steven Goodison Department of Surgery, University of Florida, Jacksonville, FL 32209, USA

Timothy J. Barder Eprogen, Inc., Darien, IL 60561, USA

10.1 INTRODUCTION Q1

Identification and validation of biomarkers predictive of disease, particularly cancer, is a significant and expanding area in clinical research. Quality cancer biomarkers should facilitate early detection and diagnosis of the disease, with more specific markers used for classification (such as grade/stage) or subtype determination of the Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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disease presenting in a patient. Moreover, each cancer subtype will have its own specific histopathology and prognosis and may be more benign or aggressive and consequently require a specific course of treatment. A method which can reliably identify biomarkers to distinguish specific types, subtypes and/or grades/stages of a cancer would ultimately lead to a more individualized targeted therapy, whereby each patient could be treated according to the specific presentation of the disease. Currently, several strategies are being used to search for biomarkers of cancer based upon either gene or protein expression. The advent of DNA microarrays has enabled the study of gene expression profiles for large numbers of tumor samples and has been used to characterize global and specific gene expression patterns of cancer (Golub et al., 1999; Schaner et al., 2003). This technology has enabled the study of gene expression profiles of large numbers of tumor samples, which can be used to classify different cancers based upon characteristic gene expression patterns. For example, Schaner, et. al., used DNA microarrays to identify groups of genes that could distinguish ovarian from breast carcinomas, clear cell subtype from other ovarian carcinomas, and grade I and II from grade III serous papillary carcinomas (Schaner et al., 2003). In other work, Giordano et al. (2001), were able to use gene expression profiles of adenocarcinomas of the lung, colon, and ovary to demonstrate the ability to classify tumors in an organ-specific manner. Numerous other studies have also used gene expression to classify various cancers and their subtypes and their relationship to one another (Ross et al., 2000; Korshunov et al., 2003; Tan et al., 2004). Classification of different types of cancers can also be accomplished by profiling protein expression in cells, biofluids or serum (Jones et al., 2002; Yanagisawa et al., 2003). The use of protein expression profiling may be essential for classification of cancers because many of the mRNAs are never expressed in the cell and it is ultimately the posttranslational protein expression that determines the function and structure of the cell. Moreover, protein expression can be profiled from either tissue, biofluids, or serum. The ability to profile protein expression in each of these media is essential to gaining insights into the systemic aspects of the disease. The analysis of serum is particularly important as it provides a noninvasive means for early detection of circulating biomarkers secreted into the bloodstream from invasive tumors. However, only a limited subset of potential markers may be secreted by the tumor and thus be useful for detection of a specific cancer. Often a biopsy of tissue is required to obtain a more complete picture of the protein expression of the cancer cell so that a more detailed diagnosis can be made. In other cases, analyses of bodily fluids such as those from cysts may be important for detecting early stages of cancer. 2D-polyacrylamide gel electrophoresis (2D-PAGE) is the classical method for profiling large numbers of proteins in cells (Gorg et al., 2000). 2D-PAGE involves an isoelectric separation of proteins based on pI in a first dimension followed by an electrophoretic MW separation in a second-dimension using a polyacrylamide gel. The result is a two-dimensional map of the proteins expressed in the cell. A number of studies using large numbers of quantitative 2D gels for tumor classification have been performed for bladder, breast, lung, prostate, and ovarian cancers (Alaiya et al., 2002; Jones et al., 2002) where, in general, benign and malignant

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tumors were identified by proteins that were differentially expressed and tumor stage classified by marker proteins that were upregulated or downregulated. For example, Alaiya et al. (2002), did extensive work on the classification of ovarian tumors using 2D gel electrophoresis. A total of 40 tumor samples were evaluated and hierarchal cluster analysis used to distinguish borderline ovarian tumors from malignant and benign tumors. Although 2D gel electrophoresis has remained the most powerful method for separating large numbers of proteins, the method has many limitations as a general tool for profiling large numbers of samples. The 2D gel method is generally a slow, manually intensive technique that can require several days to complete. Moreover, the irreproducibility of interlysate comparisons due to varying run conditions between gels makes “spot” comparison sometimes quite difficult and limits their quantitation. Run-to-run reproducibility can be a critical issue in such intralab comparisons and even more significant for interlab comparisons where run conditions can vary significantly. Identification of proteins in gel “spots” is also a key issue. Proteins embedded in the gel require manually intensive procedures to excise and purify the gel spots for further analysis by mass spectrometry. The limited amount of material that can be loaded onto 2D gels before streaking occurs and resolution ruined is also of concern to researchers. Low protein loading, although desirable for improving gel resolution, ultimately limits dynamic range and the amount of protein that can be recovered from each spot. This in turn limits the ability to perform accurate identification and structural analysis especially for low abundance proteins. The drawbacks of current gel-based separation methods have led to the development of new strategies using all liquid-based separations. Some of these methods have involved 2D liquid chromatography (Liu et al., 2002) while others have used batch liquid methods to separate proteins based upon some fundamental property, such as charge, size or hydrophobicity. Each of these methods have distinct advantages and disadvantages, but they all have the advantage of producing purified proteins in the liquid phase, which can readily be interfaced to mass spectrometry. In particular, the configuration described in this chapter (Figure 10.1) involves a 2D liquid separation based upon two phases of chromatography involving pI and hydrophobicity. It will be shown that this method generates a map analogous to 2D gel electrophoresis and can be readily interfaced to mass spectrometry to generate a highly reproducible map for interlysate comparisons for searching for markers of cancer.

10.2 METHODS FOR SEPARATING AND IDENTIFYING PROTEINS 10.2.1

pI-Based Methods of Separation

An important issue in 2D liquid separations is finding a first dimension, which can provide information on the pI of the protein. This is important as pI information has biological significance in proteomics where it is a physical property listed in the databases and can aid in protein identification. The use of pI

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FIGURE 10.1

Schematic overview of protein separation techniques.

also becomes essential for separation of various modified isoforms of proteins that might be poorly resolved, particularly with respect to phosphorylated and glycosylated isoforms. In recent work, there has been an effort to develop gel-free pI-based separations. These include liquid phase isoelectric focusing (IEF) using the Rotofor apparatus (Wall et al., 2001), which is a liquid-based analog of carrier ampholyte gels and the IsoPrime device (Zuo and Speicher, 2000; Zhu and Lubman, 2004) and other related units that employ isoelectric membranes to separate proteins in the liquid phase and are the analogs of IPG-based gels (Ek et al., 1983). Alternatively, continuous flow electrophoresis has been used to separate proteins based upon pI in the liquid phase (Hoffmann et al., 2001). These methods are batch liquid-phase separations and can be performed in either preparative or analytical scale separations. These methods allow higher sample loading capacity, which in turn increases the amount of protein available for subsequent analyses. However, these methods are not readily automated and in the Rotofor and continuous flow methods the fractionation suffers from protein overlap between proteins in adjacent fractions. Capillary isoelectric focusing has also been used to separate proteins based upon pI in one-dimensional and two-dimensional separation schemes. This method provides a means for microscale separation of proteins based upon pI (Tang et al., 1997; Zhou and Johnston, 2005). In current work chromatofocusing (CF), a column based method for separating complex mixtures of proteins according to pI, has been selected for the first separation dimension (Hutchens et al., 1984; Liu and Anderson, 1997). CF uses charge exchange on an ion exchange medium, where a pH gradient is generated by

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titration of a buffer against a start buffer, which sets the initial pH of the system. In the case of weak anion exchangers, proteins are loaded on the column at a high pH and as the titration proceeds, proteins with pI’s greater than the pH will elute down the column, with high pI proteins eluting from the column first sequentially followed by proteins of successively lower pI’s. Chromatofocusing has genuine advantages as a method for separating proteins based upon pH in that it can rapidly fractionate large numbers of proteins and achieve separation of proteins in narrow (0.1) pH fractions. The pH can be measured online and the fractions directly collected as the eluent of the liquid-based separation. Ultimately, it is a method that uses standard HPLC hardware and has been automated and sold under the commercial name Beckman–Coulter ProteomeLab PF2D. A pH gradient pI-based separation has also recently been applied to a microscale format (Andersen et al., 2004). 10.2.2

Chromatofocusing-A Column Based pH Separation

10.2.2.1 Tumor Tissues Lysate Preparation As with any analytical technique sample preparation is of vital importance in 2D liquid separation experiments. Most of the samples studied thus far have been either cell lines or tumor tissues. The tissues are often heterogeneous and require extensive sample preparation to properly lyse the sample and extract the proteins for chromatography. There are a number of procedures that have been used in our laboratory, but one of the most effective involves extensive bead blasting of the sample. The tumor sample preparation described herein has been used with ovarian tumor tissues. In this procedure the tumor tissue samples are cut into small pieces by using razor blades and tissue samples are subsequently sealed into 2 mL screw-cap microcentrifuge vials (BioSpec Products, OK), which also contain hundreds of minute glass beads (BioSpec Products, OK). Vials are subsequently filled full of lysis buffer (7.5 M urea, 2.5 M thiourea, 12.5% glycerol, 50 mM tris, 2.5% n-OG, 6.25 mM Tris-(2-Carboxyethyl) phosphine (TCEP), 1.25 mM protease inhibitor, pH adjusted to start pH-usually 8.5) with no air bubbles. The lysis buffer has been developed to solubilize and denature the proteins in a manner that is compatible with chromatography. In particular, n-octyl-bD-glucopyranoside (OG) is used as a nonionic detergent to solubilize the proteins, where SDS cannot be used. Other detergents such as Triton are incompatible with the mass spec analysis. TCEP or dithiothreitol (DTT) is important for breaking disulfide bonds, where denaturing the protein is important to obtain full interaction with the column in the hydrophobic separation. Tissue samples are then homogenized for 3 min in 10 s increments at 4800 rpm in the minibead beater cell disruptor and followed by centrifuging at 5000 rpm for 10 min at room temperature to pellet the bead mix. The supernatant containing proteins is collected and stored on ice. In order to avoid incomplete tissue disruption and protein extraction, the vials are filled with fresh lysis buffer again and homogenized in 2 min. These two lysis solutions are combined in 10 mL polycarbonate centrifuge tubes and insoluble material was precipitated by centrifugation at 35000 rpm for 1 h (80Ti Beckman Ultracentrifuge). Tissue lysates are stored at 80 C.

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10.2.2.2 Chromatofocusing Procedure CF and NPS RP-HPLC were continuously performed using an integrated protein fractionation system ProteomeLab PF 2D (Beckman Coulter, Inc., Fullerton, CA, USA). A High-performance Chromatofocusing (HPCF)-1D column (250 mm 2.1 mm) was used to perform chromatofocusing. Two buffers, a start buffer (SB) (Beckman Coulter, Inc., Fullerton, CA, USA) and an elution buffer (EB) (Beckman Coulter, Inc., Fullerton, CA, USA), were used to generate the pH gradient on the column. Both buffers were prepared in 6 M urea and 0.2% OG. Before running the CF, the pH of SB was adjusted to 8.5 þ/ 0.1 and EB was adjusted to 4.0 þ/ 0.1 using either a saturated solution (50 mg/mL) of iminodiacetic acid if the buffer was too basic or 1 M NH4OH if the buffer was too acidic. A PD-10 G-25 column (Amersham Pharmacia Biotech) was used to exchange the protein sample from the lysis buffer to the equilibration buffer used in the CF experiment. The HPCF-1D column was first flushed with 100% distilled water (filtered through a 0.45 mm filter) for 10 column volumes at 0.2 mL/min, then equilibrated with 100% SB for 30 column volumes. After equilibration with SB, the HPCF column was ready to start the ProteomeLab PF 2D default method where injection of the sample began the method. After the method had been started, the column was washed with 100% SB to remove material that did not bind to the column at pH 8.5. When the wash was complete, the UV absorbance returned to baseline. Once a stable baseline was achieved, the method was initiated at 100% EB. UV detection was performed at 280 nm and the pH was monitored online by a flow-through pH probe (Beckman Coulter, Inc., Fullerton, CA, USA). As the pH decreased, pH fractions were then collected in 0.2 pH intervals where 23 fractions in total were collected in the range of 8.5–4.0 pH. After the pH of the eluent reached 4.0, the HPCF column was washed with 10 columnvolumes of 1M NaCl and the fractions collected by time. After the salt wash, the HPCF column is washed with 10 column volumes of distilled or deionized water. The CF portion of the method for the ProteomeLab PF 2D required around 185 min including the salt wash. A typical chromatofocusing fractionation (including RPHPLC) of an E. coli sample as a function of pH is shown in Figure 10.2. 10.2.3

Nonporous Separation of Proteins

A unique feature of this multidimensional/liquid phase method is the use of nonporous silica (NPS) as a medium for the second dimension of separation (Nimura et al., 1991; Banks and Gulcicek, 1997; Barder et al., 1997). In any pI fraction obtained using CF of a whole cell lysate of a human cell line or tumor tissue sample, there may be from 50 to 150 proteins. In the mid-pH range 5.0–6.5, there are often large numbers of proteins in each fraction; whereas on the basic and acidic ends, there are generally fewer proteins. In order to separate large numbers of proteins with sufficient resolution, a 1.5 mm HPCF-2D (4.6 mm 33 mm) NPS C18 column (Beckman Coulter, Inc.) has been used. NPS RP-HPLC provides rapid and highly reproducible separations of proteins according to their hydrophobicities. The NPS packing material used in these RP separations eliminates problems associated with porous media where proteins adhere within the pores. In porous materials this often results in “smearing” of protein peaks

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FIGURE 10.2 2D map of a whole cell lysate (top) along with an illustration of the reproducibility of the pI versus hydrophobicity profiling technique (bottom) using the Beckman PF2D automated instrument and software. (See color plate.)

with a corresponding loss in resolution and protein recovery. The use of NPS media improves resolution and reduces separation times by as much as one-third compared to porous media. The fast separation times are due in part to the short columns used (4.6 mm 50 mm) and the elevated temperature that is typically around 60–65 C. This elevated temperature reduces solvent viscosity improving mass transport to/from

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the stationary phase improving resolution and also helps reduce column pressure. The separation efficiency remains high thanks to the use of 1.5 mm particles with highly uniform size distribution in these columns. Of greater significance is the reproducibility of the separation, which is essential for interlysate comparisons. The reproducibility in retention times for NPS separations has been found to be within 1 sec under the conditions used in our work. In addition, the recovery for proteins under 40 kDa may be as high as 90%. These columns have been used for separation of proteins of over 200 kDa MW in our experiments as shown by analysis using a 1D gel. In addition, columns with larger particle sizes have been used to separate proteins of over 400 kDa (55–56). The NPS RP-HPLC method provides a liquid phase method for separating large intact proteins for further analysis. More specifically, it provides a means of separating proteins for interfacing to mass spectrometric analysis. 10.2.4

Electrospray-Time of Flight-Mass Spectrometry

Electrospray-time of flight-mass spectrometry (ESI-TOF-MS) (Fenn et al., 1989) allows rapid determination of intact protein molecular weights from LC effluent or direct injection via syringe pump. Because ESI data are of far higher resolution and mass accuracy than traditional gel techniques not only does it support protein characterization but it can reveal isoforms and modifications in ways that would not be possible otherwise. The electrospray process produces a distribution of charges for each protein, so ESI is free from any inherent limits to the detectable protein molecular weights. The distribution of charges can also be quantitatively deconvoluted allowing determinations of relative protein expression levels as well as the original mass of the parent ion. Consequently, ESI-TOF-MS is an extremely powerful technique for protein mass mapping and further characterization of 2D liquid separations (Wall et al., 2001). Online LC-ESI-TOF-MS experiments are carried out in a very similar fashion to the off-line NPS-HPLC separations described above, with a few notable exceptions. Firstly, 0.3% (v/v) formic acid is added to each mobile phase to counteract the ionization suppression induced by TFA. Because of the formic acid UV detection must be carried out at 280 nm (as opposed to 214 nm). To aid in normalization between runs 1 mg of Bovine insulin (MW ¼ 5734 Da) is added to each chromatofocusing fraction prior to injection onto the column. Finally, the flow is split postcolumn directing 200 mL/min into the ion source and the remaining 300 mL/min through the UV detector and fraction collection. Because online separations provide such a wealth of information about target proteins, interpretation becomes of critical importance in order to make full use of the data. The first step in any analysis of LC-MS data involves integration and deconvolution of sample spectra to determine protein mass and intensity. In manual analysis (Hamler et al., 2004), users identify protein “umbrellas,” create a total ion chromatogram (TIC), integrate the protein peak, and deconvolute the resulting spectrum. Deconvolution of ESI spectra employs a maximum entropy deconvolution algorithm often referred to as MaxEnt (Ferrige et al., 1991). MaxEnt calculates

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a “mock” dataset that most closely resembles the “real” data and the intact mass and intensity mostly likely to result in such a spectrum. The process of selecting individual peaks and running deconvolutions is quite time intensive, both for processing computers and analysts. This has prompted the development of automated analysis tools such as Protein Trawler (Williams et al., 2002). Protein Trawler replaces the manual steps in data analysis by integrating fixed time segments, deconvoluting the spectra and reporting the resulting mass and intensities. The output from complex datasets is quite similar to that obtained manually and allows for far higher data throughput (Buchanan et al., 2005). Output from Trawler is then normalized against other “lanes” in a dataset using the deconvoluted bovine insulin intensity as an internal standard. Comparisons between pI separations or cell lines can take a variety of routes including normalization against the same insulin standard or normalization against a common protein found in the cell lines. Normalized peak lists are copied into plaintext files so data visualization can be carried out with two software tools, ProteoVue (Wall et al., 2001) and DeltaVue (Yan et al., 2003) (as in Figure 10.2). These tools display masses and intensities as banded mass maps or “virtual gels” allowing rapid visual interpretation that can reveal details and differences among samples. 10.2.5

MALDI Peptide Mass Fingerprinting

Proteomics ultimately hinges upon protein identification to reveal the meaning behind the masses, spots, or peaks detected by other means. Because fraction collection is a natural component of HPLC separations, intact proteins can be readily collected either for direct analysis or for proteolytic digestion and identification using peptide mass fingerprinting (PMF) in conjunction with matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Following collection, protein fractions are neutralized using ammonium bicarbonate (to counteract the FA/TFA) and digested overnight using modified sequencing grade porcine trypsin. Following digestion, samples are concentrated and desalted using a miniature pipette-based solid-phase extraction cartridge—one common brand is the Millipore ZipTip. Samples are then coprecipitated on an appropriate stainless steel MALDI target along with 1 mL of a standard and matrix solution containing 25% v/v saturated a-CHCA matrix in 60 : 40 acetonitrile: water, 0.1% TFA, 100 pg of angiotensin I (MW ¼ 1296 Da), and 250 pg of ACTH 1–16 and 17–38 (MW ¼ 2093 Da, and 2465 Da, respectively). These peptide standards act as an internal mass calibration over the range where most useful tryptic peptides are observed in MALDI. Samples are then introduced into the source region of the mass spectrometer where a pulsed laser (often N2) gently ablates the peptides from the target and measures the masses. Data analysis consists of summing and calibrating spectra, then recording sample peptide peaks (while excluding standards and noise). When working optimally with internal calibration, MALDI spectra can have a mass accuracy in excess of 20 ppm (0.002%). Mass accuracy is vital in the next step in which the sample peptide lists are searched against online databases (such as NCBI or SwissProt (Boeckmann

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et al., 2003)) using a utility such as Protein Prospector (Clauser et al., 1999) or Mascot (Perkins et al., 1999). Users can specify species, potential modifications, mass accuracy and a number of other parameters that can then be used to compare MALDI peak lists against extensive in-silico digests. The results returned from these searches are then assessed based on molecular weight search score (MOWSE score), percent coverage, potential modifications present, and detected species. Although individual experiments can differ in overall performance, “good” scores will likely have scores over 1000, coverage over 20%, and match the species exactly. 10.2.6

Data Analysis and Recombination

With the completion of these various analyses a final step must be undertaken to recombine the various datasets into a powerful, quantitative proteomic description of a given system. Databases, although an invaluable tool in protein identification, require significant interpretation by analysts in order to draw correlations between identities from MALDI-PMF and intact masses measured by ESI-TOF-MS. Any given database entry for a protein can include numerous isoforms, truncations, and modifications (both observed and theoretical) that contribute to the final molecular weight. With careful examination of database entries using online tools, such as ExPASy (Gasteiger et al., 2003), PMF identities can be annotated to indicate the range of possible molecular weights as well as special modifications, including phosphorylations and glycosylations, that may be of interest for future analysis. ID-mass correlation can also reveal previously unknown modifications provided other data match up. These are commonly revealed as shifts of þ16, þ42, and þ80 Da, for methionine oxidations, acetylations, and phosphorylations respectively. Not only does this suggest possible new avenues for research, but it allows for substantial improvements in correlation between PMF results and intact MW determination. Correlated mass-ID data can take several forms including annotated mass maps to catalog a cell system and tables of differential expression for potential biomarkers.

10.3 APPLICATIONS 10.3.1 Proteomic Mapping and Clustering of Multiple Samples—Application to Ovarian Cancer Cell Lines The two-dimensional liquid mass mapping method has grown to be a useful technique for classification and biomarkers identification. It can serve as a powerful tool for comparing the protein expression profiles of large numbers of samples. The use of protein expression is an informative means for classification to distinguish specific types, subtypes and/or grades/stages of a cancer according to the protein bands observed in their expression maps. The capability for automation of the method allows reproducible comparison of many samples and the use of differential analysis limits the number of proteins that might require further analysis by mass spec techniques.

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FIGURE 10.3 Fraction pI 6.05–6.20 gradient range from 30.0% to 78.0%. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption.

In this 2D liquid mass mapping technique, we can use ProteoVue software (Beckman-Coulter) to make comparisons between a large number of samples. Figure 10.3 shows peak patterns of 11 serous and ovarian surface epithelium (OSE) cell lines for fraction pI 6.05–6.20. The peak retention time and intensity can be obtained using the software. The average number of peaks for 11 samples is 74. After comparing protein expression maps, one finds that proteins can be clustered into three groups. One group is likely to be common to most cell lines. For this fraction, around 14 proteins have the same retention time and are likely to be identical for all cell lines. A second set of proteins are linked only to some group of cell lines. This set may provide the basis for detection and classification of serous carcinoma and have the potential to provide identifying biomarkers. A third group of proteins appears to be uniquely expressed on each individual cell line. It is possible to hypothesize that this third group of proteins is responsible for unique aspects of cell behavior. Figure 10.4 shows an example of specific types of serous carcinoma cell lines that clustered together using an automated 2D liquid fractionation system (Beckman PF2D) for the liquid phase separation and mapping of the protein expression for eight serous ovarian cancer and three OSE cell lines. Maps are produced using pI as the separation parameter in the first dimension and hydrophobicity based upon RP-HPLC separation in the second dimension. A dynamic programming method was used to correct for minor shifts in peaks during the HPLC gradient between sample runs. A correlation matrix was formed by calculating the Pearson correlation coefficient between each aligned pair of samples (Falk and Well, 1997). These correlation matrices were then visualized using hierarchical clustering techniques. Figure 10.4 is a hierarchical cluster analysis of “complete linkage,” dendrogram whose distance

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FIGURE 10.4 Cell lines are separately aligned and grouped based on similarities in their protein expression using a hierarchical clustering analysis technique. This technique produces a dendrogram in which pairs of points are joined sooner (i.e., closer to the ends of the dendrogram) if they have greater correlation.

between groups is defined as the distance between the most distant pair of objects, one from each group. Hierarchical clustering analysis was used to classify the different samples according to their corrected protein expression profiles. In the dendrograms, the length and the subdivision of the branches display the relatedness of the cell lines and the expression of the proteins. Several of the ovarian surface epithelial cell lines clustered together, while specific groups of serous carcinoma cell lines clustered with each other. Two sets of samples as IOSE-144-1 and IOSE-144-2 (Auersperg et al., 1994), which is life-extended OSE cells expressing SV40 large TAntigen were used to evaluate the method. It was found that OSE cell lines IOSE-144 and HOSE-A (Gregoire et al., 1998) clustered together, while the two IOSE-144 samples clustered together most closely as expected. In addition, several serous carcinoma cell lines clustered with each other, that is, DOV13, OVCA429, and OVCA433 clustered together. It is interesting that IOSE-80, which was derived from OSE, clusters with the highly invasive serous carcinoma lines HEY and PEO1 (Buick et al., 1985; Langdon et al., 1988). This is not surprising as the IOSE80 cell line has been cultured for many passages and may have obtained some of the characteristics of the carcinoma-derived lines. Although limited information is available on the cell lines, it is shown that the protein expression of certain cell lines is closely related to others and that these cluster together on the dendrogram. Identification of potential marker bands is an essential application of the 2D liquid mass mapping technique. Figure 10.5 shows an example of potential marker identification between two groups of serous carcinoma cell lines, where one group contains OVCA429, OVCA433, and DOV13 and the other group contains IOSE-80, PEO1, and HEY. Standardization and alignment of bands were performed and then comparisons were separately made at each hydrophobicity level between two groups of samples. A differentially expressed band is selected on the basis of having at least a four fold different mean level within the two groups of samples. In

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FIGURE 10.5 Differential analysis. Fraction pI 7.42–7.57, gradient range from 25.0% to 75.0%. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption.

addition, at least 25 consecutive hydrophobicity levels were required to meet these conditions in order for the band to be considered as a marker. Figure 10.5 shows peak patterns for these two cluster samples, group OVCA429, OVCA433, and DOV13 and group IOSE-80, PEO1, and HEY. The image is displayed in a format with each different sample on the x axis and hydrophobicity on the y axis. The relative intensities of the band are quantitatively proportional to the amount of corresponding protein detected by UV absorption. By comparing protein expression between these two groups, the four groups of bands marked in figure are only observed in the group IOSE-80, PEO1, and HEY but not in the group of OVCA433, OVCA429, and DOV13. The use of differential analysis allows us to identify proteins that may be common bands for classification and also reduces the potentially large amounts of protein expression data from a large number of samples into a manageable dataset. It thus reduces the number of significant bands that need to be identified by mass spec or other methods. 10.3.2 2D Liquid Mass Mapping of Tumor Cell Line Secreted Samples, Application to Metastasis-Associated Protein Profiles A 2D liquid mass mapping method has been developed in our laboratory for the analytical profiling of proteins in complex biological material. In the present study, we demonstrate the capability of this method for comparative protein mapping of isogenic

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breast tumor cell line clones. Liquid separation/mass mapping was applied to the extracellular component of the proteome of M4A4 and NM2C5 metastasis model cell lines (Urquidi et al., 2002; Goodison et al., 2003) in order to identify proteins, which are differentially expressed with respect to metastatic phenotype. Secreted and cell surface proteins are of substantial interest to disease progression as these secreted fractions are rich of therapeutic targets. Profiling the proteins expressed in these compartments could provide useful information on the molecular mechanism of tumor metastasis. In this study, serum-free conditioned media was collected from the cultured monoclonal cell lines and a mass mapping technique was applied in order to profile a component of each cell line proteome. After the serum-free conditioned media was thawed, a buffer (Kreunin et al., 2004) containing chaotropes, detergents, reducing reagents and protease inhibitors was immediately added. The buffer prevents protein degradation during the sample preparation step. The secreted protein sample was then exchanged from the serum-free conditioned media/buffer to the equilibrium buffer required for the chromatofocusing experiment using a gel filtration. Using the 2D liquid mass mapping approach, over 400 of the proteins were mapped and displayed as a 2D map of pI versus accurate Mr. This was performed over a pI range of 4.0–6.2, and a mass range of 6–80 kDa. An example of a differential display between CF fraction 5.6–5.4 from M4A4 and NM2C5 secreted samples is given in Figure 10.6, indicating the expression of several potential biomarkers as well as a number of common proteins. Confirmation of the identity

FIGURE 10.6 2D differential display of CF fractions from M4A4 and NM2C5 secreted samples. These fractions ranged in pH from 5.6 to 5.4. The differential display map was created using point-by-point subtraction of the areas of the deconvoluted peaks in the TIC. (See color plate.)

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of a number of differentially expressed proteins was achieved through trypsin digestion and analysis by MALDI-TOF MS peptide mapping. Eighty-eight unique proteins were identified and using a relative abundance threshold of more than twofold, 27 of the 88 proteins were confirmed as being differentially expressed with regard to metastatic phenotype. Proteins associated with the metastatic phenotype included osteopontin and extracellular matrix protein 1, whereas the matrix metalloproteinase-1 and annexin 1 proteins were associated with the nonmetastatic phenotype. 10.3.3 Identification Annotation and Data Correlation in MCF10 Human Breast Cancer Cell Lines The MCF10 model of proliferative breast disease (Miller, 2000) has been investigated as a model of human breast cancer. The model includes a number of cell lines encompassing the full range of disease states, from normal epithelial cells to malignant tumors. Several studies have been carried out on this cell line using the PF2D and related techniques. One such study detailed differential expression between MCF10A, MCF10CA1a.cl1 (CA1a), and MCF10CA1d.cl1 (CA1d) (Hamler et al., 2004). MCF10A is a “normal” immortalized epithelial cell line that was compared against two malignant lines (CA1a and CA1d). Work focused on two pI regions (8.0–7.6 and 6.0–5.6), identifying and quantitating all proteins detected therein. As seen in Figure 10.7 these proteins cover a wide range of molecular weights, from 5–75 kDa.

FIGURE 10.7 Annotated mass maps of MCF10A, CA1a, and CA1d. Normalized maps show identities and intensities of proteins from 3 MCF10 cell lines at pH 5.6–6.0 and 7.6–8.0.

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As described above all samples were separated online using LCT ESI-TOF-MS then normalized for relative quantitation using a bovine insulin internal standard. Fractions were then collected for MALDI-TOF-MS PMF, digested with modified porcine trypsin, and analyzed using the TofSpec2E. Following this analysis, three major classes of differentially expressed including proteins were revealed in these TABLE 10.1

Differentially Expressed Proteins in MCF10 Cell Lines Differential Expression

pH 5.6–6.0 Protein Thymosin-beta-10 Barrier to autointegration factor 60S acidic ribosomal P2 Galectin-1 Cyt c oxidase polypeptide Va Hsp 27 Peroxiredoxin 2 Nucleophosmin Inorganic pyrophosphate Actin, beta Keratin type I cytoskeletal 19 Keratin type I cytoskeletal 17 Keratin type II cytoskeletal 7 Keratin type I cytoskeletal 8 Heat shock protein 70 kDa (GRP78) Heat shock cognate 71 kDa protein (Hsp73)

pH 7.6–8.0 Protein ATP synthase coupling factor 6 NADH-ubiquinone oxidoreductase 13 kDa subunit Peptidyl-prolyl cis-trans isomerase A (Rotamase) Adenylate kinase isoenzyme 2 Annexin II Fructose biphosphate aldolase Phosphoglycerate kinase 1 Alpha enolase Elongation factor Tu, m. p. Serine hydroxymethylase, m.p. Pyruvate Kinase, M2 isoenzyme

Differential Expressiona Accession Number

CA1a

CA1d

P13472 O75531 P05387 P09382 P20674 P04792 P32119 P06748 Q15181 P60709 P08727 Q04695 P08729 P05787 P11021 P11142

56X 2.5X X 1.5X 0.2X 2Xb 0.07X 2X 7X 2X 0.3X 0.5X X X 90X 3Xb

30X 10X 10X 22X 2X Xb 0.2X n/d 3X 2.5X 0.5X n/d 3X 4X 35X Xb

Differential Expressiona Accession Number P18859 O75380 P05092 P54819 P07355 P04075 P00558 P06733 P49411 P34897 P14786

Differences are based on deconvoluted MaxEnt peak areas for each protein. a Expression level relative to normal MCF10A cells; nd ¼ not detected. b Proteins detected in CA1a and CA1d lines, but not observed in MCF10A cells.

CA1a

CA1d

6X 19X 3X X 4X 10Xb 3X Xb 0.7X n/d 1,500X

2X X 2X 2X X Xb X Xb 0.4X 0.4X 325X

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cell lines, including cytokeratins (CKs), annexins, and heat shock proteins (HSPs). Relative quantitation for these markers, along with other differentially expressed proteins, is shown in Table 10.1. Traditional proteomics studies, using gel techniques, have also singled out these markers, supporting the validity of 2D liquid separations for biomarker discovery. Cytokeratins are common structural components of the epithelial cells, but have been shown to change in type and distribution with cancer progression particularly, CK17 and 19 (under expressed in malignant lines) and CK7 and 8 (over expressed in CA1d). Upregulation of CK8 and down regulation of CK 19 was correlated with tumor progression and metastasis in breast cancer (Brotherick et al., 1998). Similarly upregulation of CK8 was correlated with poor prognosis (25% after 18 months) in patients with nonsmall cell lung cancer (Fukunaga et al., 2002). It has been suggested that CK changes of this type alter the cytoskeleton in such a way that cellular motility and invasiveness are enhanced. This leads to metastases and the resulting poor patient outcomes. Heat shock proteins are another broadly indicative class of markers in human cancers as they not only protect cells from environmental stressors found in typical tumor sites but also can confer resistance to some chemotherapeutic agents. HSPs, also referred to as molecular chaperones, accomplish this by playing an important role in protein folding, protecting nascent proteins from proteolytic digestion and inhibiting apoptotic pathways (Jaattela, 1999). As seen in Table 10.1 Heat shock proteins HSP70 and GRP78 were over expressed in malignant MCF10 cells, suggesting just such an adaptation to environmental stress. A recent study of hepatocarcinomas (Lim et al., 2005) indicates that HSP70 correlates with histological grade and GRP78 is correlated with grade, increased size, microvascular invasiveness, and tumor stage. Building on this knowledge a simple database of detected proteins has been developed for MCF10 cell lines compositing identified proteins along with their theoretical masses, detected experimental masses, and other critical information including links to their database entries and possible PTMs. This study also provided high expression (but not differentially expressed) “benchmark” proteins, such as a truncated 60 kDa Heat Shock Protein (P10809), which have helped to verify this methodology in subsequent experiments.

10.4 SUMMARY AND CONCLUSIONS The combination of mass spectrometry and liquid separations described here offers proteomic researchers the opportunity to move beyond the 2D gel paradigm by easily separating complex protein mixtures and readily identifying proteins by both pattern recognition and MS techniques. Because the system is flexible enough to allow online and off-line detection by spectrophotometry or mass spectrometry the right toolset can be brought to bear on a problem ensuring the most efficient use of instrumentation and time. Because it allows both rapid screening of cell lines and detailed studies of protein expression, this approach can be not only of diagnostic use but also reveal a wealth of

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knowledge about the underlying biology of cancers and other disease states. With coming advances in instrumentation and bioinformatics this information will continue to gain greater depth, detail, and accessibility.

ACKNOWLEDGMENTS This work was supported in part by the National Cancer Institute under grant R01CA10010 (DML, KRC), R01CA90503 (DML, FRM), R01CA108597 (SG) and the National Institutes of Health under grant R01GM49500 (DML). Support was also generously provided by Eprogen, Inc. and Beckman-Coulter, in particular for HPLC columns and use of the PF2D instrument. REFERENCES Alaiya, A. A., Franzen, B., Hagman, A., Dysvik, B., Roblick, U. J., Becker, S., Moberger, B., Auer, G., Linder, S. (2002). Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles. Int. J. Cancer 98(6), 895–899. Andersen, T., Pepaj, M., Trones, R., Lundanes, E., Greibrokk, T. (2004). Isoelectric point separation of proteins by capillary pH-gradient ionexchange chromatography. J. Chromatogr. A 1025(2), 217–226. Auersperg, N., Mainesbandiera, S. L., Dyck, H. G., Kruk, P. A. (1994). Characterization of cultured human ovarian surface epithelialcells- phenotypic plasticity and premalignant changes. Lab Invest 71(4), 510–518. Banks, J. F., Gulcicek, E. E. (1997). Rapid peptide mapping by reversed-phase liquid chromatography on nonporous silica with online electrospray time of flight mass spectrometry. Anal. Chem. 69(19), 3973–3978. Barder, T. J., Wohlman, P. J., Thrall, C., DuBois, P. D. (1997). Fast chromatography and nonporous silica. Lc Gc-Mag. Sep. Sci. 15(10), 918–926. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M. C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M. (2003). The SWISSPROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31(1), 365–370. Brotherick, I., Robson, C. N., Browell, D. A., Shenfine, J., White, M. D., Cunliffe, W. J., Shenton, B. K., Egan, M., Webb, L. A., Lunt, L. G., Young, J. R., Higgs, M. J. (1998). Cytokeratin expression in breast cancer: phenotypic changes associated with disease progression. Cytometry 32(4), 301–308. Buchanan, N. S., Hamler, R. L., Leopold, P. E., Miller, F. R., Lubman, D. M. (2005). Mass mapping of cancer cell lysates using two-dimensional liquid separations, electrospray-time of flight-mass spectrometry, and automated data processing. Electrophoresis 26(1), 248–256. Buick, R. N., Pullano, R., Trent, J. M. (1985). Comparative properties of 5 human ovarian adenocarcinoma celllines. Cancer Res. 45(8), 3668–3676. Clauser, K. R., Baker, P., Burlingame, A. L. (1999). Role of accurate mass measurement (þ/10 ppm) in protein identification strategies employing MS or MS MS and database searching. Anal. Chem. 71(14), 2871–2882.

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Ek, K., Bjellqvist, B., Righetti, P. G. (1983). preparative isoelectric-focusing in immobilized pH Gradients. 1. General principles and methodology. J. Biochem. Bioph. Meth. 8(2), 135–155. Falk, R., Well, A. D. (1997). J. Stat. Educ. 5(3). Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., Whitehouse, C. M. (1989). Electrospray ionization for mass-spectrometry of large biomolecules. Science 246(4926), 64–71. Ferrige, A. G., Seddon, M. J., Jarvis, S. (1991). Maximum-entropy deconvolution in electrospray mass-spectrometry. Rapid Commun Mass Sp. 5(8), 374–377. Fukunaga, Y., Bandoh, S., Fujita, J., Yang, Y., Ueda, Y., Hojo, S., Dohmoto, K., Tojo, Y., Takahara, J., Ishida, T. (2002). Expression of cytokeratin 8 in lung cancer cell lines and measurement of serum cytokeratin 8 in lung cancer patients. Lung Cancer 38(1), 31–38. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R. D., Bairoch, A. (2003). ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31(13), 3784–3788. Giordano, T. J., Shedden, K. A., Schwartz, D. R., Kuick, R., Taylor, J. M. G., Lee, N., Misek, D. E., Greenson, J. K., Kardia, S. L. R., Beer, D. G., Rennert, G., Cho, K. R., Gruber, S. B., Fearon, E. R., Hanash, S. (2001). Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles. Am. J. Pathol. 159(4), 1231–1238. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., Lander, E. S. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537. Goodison, S., Kawai, K., Hihara, J., Jiang, P., Yang, M., Urquidi, V., Hoffman, R. M., Tarin, D. (2003). Prolonged dormancy and site-specific growth potential of cancer cells spontaneously disseminated from nonmetastatic breast tumors as revealed by labeling with green fluorescent protein. Clin. Cancer Res. 9(10), 3808–3814. Gorg, A., Obermaier, C., Boguth, G., Harder, A., Scheibe, B., Wildgruber, R., Weiss, W. (2000). The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 21(6), 1037–1053. Gregoire, L., Munkarah, A., Rabah, R., Morris, R. T., Lancaster, W. D. (1998). Organotypic culture of human ovarian surface epithelial cells: a potential model for ovarian carcinogenesis. In vitro Cell Dev-An 34(8), 636–639. Hamler, R. L., Zhu, K., Buchanan, N. S., Kreunin, P., Kachman, M. T., Miller, F. R., Lubman, D. M. (2004). A two-dimensional liquid-phase separation method coupled with mass spectrometry for proteomic studies of breast cancer and biomarker identification. Proteomics 4(3), 562–577. Hoffmann, P., Ji, H., Moritz, R. L., Connolly, L. M., Frecklington, D. F., Layton, M. J., Eddes, J. S., Simpson, R. J. (2001). Continuous free-flow electrophoresis separation of cytosolic proteins from the human colon carcinoma cell line LIM 1215: a non two-dimensional gel electrophoresis–based proteome analysis strategy. Proteomics 1(7), 807. Hutchens, T. W., Gibbons, W. E., Besch, P. K. (1984). High-performance chromatofocusing and size-exclusion chromatography - separation of human uterine estrogen-binding proteins. J. Chromatogr. 297, 283–299. Jaattela, M. (1999). Escaping cell death: survival proteins in cancer. Exp. Cell. Res. 248(1), 30– 43.

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Jones, M. B., Krutzsch, H., Shu, H. J., Zhao, Y. M., Liotta, L. A., Kohn, E. C., Petricoin, E. F. (2002). Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer. Proteomics 2(1), 76–84. Korshunov, A., Neben, K., Wrobel, G., Tews, B., Benner, A., Hahn, M., Golanov, A., Lichter, P. (2003). Gene expression patterns in ependymomas correlate with tumor location, grade, and patient age. Am. J. Pathol. 163(5), 1721–1727. Kreunin, P., Urquidi, V., Lubman, D. M., Goodison, S. (2004). Identification of metastasisassociated proteins in a human tumor metastasis model using the mass-mapping technique. Proteomics 4(9), 2754–2765. Langdon, S. P., Hawkes, M. M., Hay, F. G., Lawrie, S. S., Schol, D. J., Hilgers, J., Leonard, R. C. F., Smyth, J. F. (1988). Effect of sodium–butyrate and other differentiation inducers on poorly differentiated human ovarian adenocarcinoma cell-lines. Cancer Res. 48(21), 6161– 6165. Lim, S. O., Park, S. G., Yoo, J. H., Park, Y. M., Kim, H. J., Jang, K. T., Cho, J. W., Yoo, B. C., Jung, G. H., Park, C. K. (2005). Expression of heat shock proteins (HSP27, HSP60, HSP70, HSP90, GRP78, GRP94) in hepatitis B virus-related hepatocellular carcinomas and dysplastic nodules. World J. Gastroenterol 11(4), 2072–2079. Liu, H. J., Berger, S. J., Chakraborty, A. B., Plumb, R. S., Cohen, S. A. (2002). Multidimensional chromatography coupled to electrospray ionization time-of-flight mass spectrometry as an alternative to two-dimensional gels for the identification and analysis of complex mixtures of intact proteins. J. Chromatogr. B 782(1–2), 267–289. Liu, Y. S., Anderson, D. J. (1997). Gradient chromatofocusing high-performance liquid chromatography. 1. Practical aspects. J. Chromatogr. A 762(1–2), 207–217. Miller, F. R. (2000). Xenograft models of premalignant human breast disease. J. Mammary Gland Biol. 5, 379–391. Nimura, N., Itoh, H., Kinoshita, T., Nagae, N., Nomura, M. (1991). Fast protein separation by reversed-phase high-performance liquid-chromatography on octadecylsilyl-bonded nonporous silica-gel — effect of particle-size of column packing on column efficiency. J. Chromatogr. 585(2), 207–211. Perkins, D. N., Pappin, D. J. C., Creasy, D. M., Cottrell, J. S. (1999). Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18), 3551–3567. Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J. C. E., Lashkari, D., Shalon, D., Myers, T. G., Weinstein, J. N., Botstein, D., Brown, P. O. (2000). Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24(3), 227–235. Schaner, M. E., Ross, D. T., Ciaravino, G., Sorlie, T., Troyanskaya, O., Diehn, M., Wang, Y. C., Duran, G. E., Sikic, T. L., Caldeira, S., Skomedal, H., Tu, I. P., Hernandez-Boussard, T., Johnson, S. W., O’Dwyer, P. J., Fero, M. J., Kristensen, G. B., Borresen-Dale, A. L., Hastie, T., Tibshirani, R., van de Rijn, M., Teng, N. N., Longacre, T. A., Botstein, D., Brown, P. O., Sikic, B. I. (2003). Gene expression patterns in ovarian carcinomas. Mol. Bio. Cell 14(11), 4376–4386. Tan, Y. X., Shi, L. B., Tong, W. D., Hwang, G. T. G., Wang, C. (2004). Multiclass tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Compu. Biol. Chemis. 28(3), 235–244.

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Tang, Q., Harrata, A. K., Lee, C. S. (1997). Two-dimensional analysis of recombinant E. coli proteins using capillary isoelectric focusing electrospray ionization mass spectrometry. Anal. Chem. 69(16), 3177–3182. Urquidi, V., Sloan, D., Kawai, K., Agarwal, D., Woodman, A. C., Tarin, D., Goodison, S. (2002). Contrasting expression of thrombospondin-1 and osteopontin correlates with absence or presence of metastatic phenotype in an isogenic model of spontaneous human breast cancer metastasis. Clin. Cancer Res. 8(1), 61–74. Wall, D. B., Kachman, M. T., Gong, S. Y. S., Parus, S. J., Long, M. W., Lubman, D. M. (2001). Isoelectric focusing nonporous silica reversed-phase high-performance liquid chromatography/electrospray ionization time-of-flight mass spectrometry: a three-dimensional liquidphase protein separation method as applied to the human erythroleukemia cell-line. Rapid Commun Mass Sp. 15(18), 1649–1661. Williams, T. L., Leopold, P., Musser, S. (2002). Automated postprocessing of electrospray LC/ MS data for profiling protein expression in bacteria. Anal. Chem. 74(22), 5807–5813. Yan, F., Subramanian, B., Nakeff, A., Barder, T. J., Parus, S. J., Lubman, D. M. (2003). A comparison of drug-treated and untreated HCT-116 human colon adenocarcinoma cells using a 2D liquid separation mapping method based upon chromatofocusing PI fractionation. Anal. Chem. 75(10), 2299–2308. Yanagisawa, K., Shyr, Y., Xu, B. G. J., Massion, P. P., Larsen, P. H., White, B. C., Roberts, J. R., Edgerton, M., Gonzalez, A., Nadaf, S., Moore, J. H., Caprioli, R. M., Carbone, D. P. (2003). Proteomic patterns of tumor subsets in non-small-cell lung cancer. Lancet 362(9382), 433– 439. Zhou, F., Johnston, M. V. (2005). Protein profiling by capillary isoelectric focusing, reversed phase liquid chromatography, and mass spectrometry. Electrophoresis 26(7–8), 1383–1388. Zhu, Y., Lubman, D. M. (2004). Narrow-band fractionation of proteins from whole cell lysates using isoelectric membrane focusing and nonporous reversed-phase separations. Electrophoresis 25(7–8), 949–958. Zuo, X., Speicher, D. W. (2000). A method for global analysis of complex proteomes using sample prefractionation by solution isoelectrofocusing prior to two-dimensional electrophoresis. Anal Biochem. 284(2), 266–278.

11 COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY AND TANDEM MASS SPECTROMETRY SYSTEMS FOR COMPLEX PEPTIDE MIXTURE ANALYSIS Michael P. Washburn Stowers Institute for Medical Research, 1000 E. 50th St., Kansas City, MO 64110, USA

In mass-spectrometry-based proteomics, the goal is to identify and characterize proteins from a protein complex, an organelle, a whole cell, or a biofluid. In each case, these are highly complex protein mixtures that will be made even more complex when digested into peptides for mass spectrometry analysis. This presents significant challenges to mass spectrometers because many peptides will have very similar mass to charge ratios, and different peptides will vary widely in abundance, making the detection and identification of low abundance proteins from a complex mixture challenging. A driving force in proteomics is the need to introduce into a mass spectrometer a few peptides at a time for identification by database searching algorithms. This requires powerful peptide separation methods. Increasingly, researchers refer to the approach where proteins are digested into peptides followed by peptide separation and tandem mass spectrometry as shotgun proteomics. Shotgun proteomics requires multidimensional separation of peptides to identify the majority of proteins in a complex mixture generated from a protein complex, organelle, whole cell, or biofluid. The multidimensional separations predominantly used include two-dimensional separations of peptides using strong cation

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

243

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COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

exchange (SCX) and reversed-phase (RP) chromatography coupled to tandem mass spectrometry (MS/MS). Because tandem mass spectra contain information based on peptide sequences, database searching algorithms that compare experimental tandem mass spectra to theoretical tandem mass spectra, such as SEQUEST (Eng et al., 1994), MASCOT (Perkins et al., 1999), and SONAR (Field et al., 2002) are used to determine the original protein content of a sample. Shotgun proteomics generates enormously complex datasets and presents complicated data analysis and organization challenges. This is beyond the scope of this review, however, it is the subject of recent reviews (Nesvizhskii and Aebersold, 2005; Sadygov et al., 2004). There exist essentially three categories of SCX/RP/MS/MS approaches. In one approach, SCX is run off-line followed by on-line RP/MS/MS (Fig. 11.1). In the offline SCX approach, fractions do not directly elute onto RP material but rather are collected. In one of the two in-line approaches, SCX is run in line with RP/MS/MS using different columns for SCX and RP (Fig. 11.2). In the multidimensional protein identification technology approach (MudPIT), SCX and RP are run in line in the same column, and this column serves as the ion source for a tandem mass spectrometer (Fig. 11.3). Both the in-line approaches are true SCX/RP/MS/MS approaches; the first approach could be abbreviated as SCX—RP/MS/MS where

SCX Injector

SCX Fractions HPLCautosampler Waste Flow splitter

RP trap

ESI into MS RP

HPLC

RP

200–300 nl/min flow rate

Waste kV

FIGURE 11.1 Off-line strong cation exchange and online RP/MS/MS configuration. Using an HPLC with a strong cation exchange column, a complex peptide mixture may be fractionated using a salt gradient into fractions. In this configuration, volatile salts such as ammonium acetate and ammonium formate, or salts such as KCl and NaCl may be used. Once fractions are collected, they are loaded onto a reversed-phase trap followed by washing of salts via an HPLC pump. A RP gradient is then used to move peptides from the RP trap to a RP analytical column that is the electrospray ionization source for a tandem mass spectrometer.

SCX-RP/MS/MS

245

Multiple samples

Nanoflow HPLC (200–300nL/min) kV

Sample from HPLC-autosampler

ESI into MS RP

RP trap

SCX

RP

200–300 nL/min flow rate

Waste Waste

FIGURE 11.2 Online strong cation exchange RP/MS/MS configuration. Strong cation exchange and reversed-phase chromatography may be coupled using the configuration shown in this figure. Multiple digested protein samples are loaded onto a SCX column and eluted onto a RP trap with volatile, ammonium acetate or formate, or nonvolatile salts, NaCl and KCl. A salt solution is then passed over the SCX column through the RP trap and to waste to move peptides from the SCX column to the RP trap. After washing the salts off the RP trap and into waste, the valves are reconfigured so that an RP gradient from a nanoflow pump is able to move peptides from the RP trap to the RP analytical column for tandem mass spectrometry analysis.

the ‘-’ represents the uncoupling of SCX and RP. There are variations to each of these approaches that will be described in this review.

11.1 SCX-RP/MS/MS In an off-line configuration, a complex peptide mixture from a proteomic sample is loaded onto a SCX column and fractions collected (Fig. 11.1). After the collection of fractions, they are then loaded into an autosampler and analyzed via the traditional RP/ MS/MS approach. Using this system, a variety of buffers and elution conditions may be used (Table 11.1). For example, one may use a volatile salt such as ammonium formate (Adkins et al., 2002; Blonder et al., 2004; Fujii et al., 2004; Yu et al., 2004; Qian et al., 2005a and b) or ammonium acetate (Cutillas et al., 2003; Coldham and Woodward, 2004), collect SCX fractions, lyophilize, resuspend in low acetonitrile and acid, and then directly analyze via RP/MS/MS. In most of the cases, when ammonium acetate or ammonium formate are used, a 20-minute wash period is used to remove the ammonium acetate or ammonium formate prior to the reversed-phase gradient (Table 11.1). However, because fractions are collected and can be buffer exchanged,

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COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

Biphasic column SCX

RP

Triphasic column RP

Sample loading in pressurization vessel

SCX

RP

Split-triphasic column RP

SCX

RP M-520 Inline filter assembly

Low ACN

High ACN

Low ACN and salt

Column connection

HPLC 100 µL/min flow rate

ESI into MS Waste

RP

SCX

RP

200–300 nL/min flow rate kV

FIGURE 11.3 Biphasic and triphasic MudPIT configuration. In the MudPIT system, a biphasic or triphasic microcapillary column with both SCX and RP packing materials is prepared and loaded off-line in a pressurization vessel. The split-triphasic column uses larger diameter-fused silica for the RP/SCX section in front of an Upchurch Scientific (Oak Harbor, WA) M-520 Inline filter assembly. Columns are prepared, samples loaded, and loaded columns are washed off-line in pressurization vessels. Using a triphasic column, the first step of a MudPIT analysis is a reversed-phase gradient to move peptides from the RP to the SCX. Then the first salt bump is run moving peptides from the SCX to the RP followed by RP gradients that elute peptides into the mass spectrometer. In this approach, a volatile salt, such as ammonium acetate or ammonium formate, must be used.

NaCl or KCl (Peng et al., 2003; Ballif et al., 2004; Beausoleil et al., 2004; Wilmarth et al., 2004; DeSouza et al., 2005; Vitali et al., 2005) may be used for the SCX fractionation, in spite of the incompatibility of these salts with mass spectrometers. When using KCl, for example, the sample must be desalted off-line (Ballif et al., 2004; Beausoleil et al., 2004), on the RP column before MS/MS acquisition (DeSouza et al., 2005; Vitali et al., 2005), with a vented column (Peng et al., 2003), or with a RP-trap (Vollmer et al., 2004; Wilmarth et al., 2004). The configuration with a RP-trap is shown in Fig. 11.1, and in this case, a flow splitter is used to reduce the flow rate from hundreds of microliters per minute to hundreds of nanoliters per minute. However, HPLC pumps of lower flow rate are now available and could eliminate the need for a flow splitter. Examples using KCl that require desalting include the identification of the human saliva proteome (Wilmarth et al., 2004), the B. infantis proteome (Vitali et al., 2005), phosphoproteins from HeLa cells (Beausoleil et al., 2004), and phosphoproteins from mouse brain (Ballif et al., 2004). One of the important points to consider is that

247

Ammonium formate

Ammonium formate

Ammonium formate

Ammonium acetate

Ammonium acetate Ammonium formate Phosphate/KCl Phosphate/KCl Phosphate/KCl Phosphate/KCl Phosphate/KCl

Phosphate/KCl

NaCL, no buffer

Mouse cortical neuron culture

Mouse natural killer cells

Human plasma, mammary epithelial, and hepatocyte cells Salmonella typhimurium

Human urinary peptides Human plasma Yeast lysate HeLa phosphoproteins Mouse brain phosphoproteins Human saliva Bifidobascterium infantis

Cancer markers

Escherichia coli

Drying RP trap Vented column Off-line Off-line RP trap On analytical column On analytical column RP trap

On analytical column On analytical column On analytical column On analytical column No

MSD Trap XCT

QSTAR

Q-TOF LCQ DECA XP LCQ DECA XP LCQ DECA XP LCQ DECA XP LCQ Classic Q-TOF

LCQ

LCQ DECA XP

LCQ DECA XP

LCQ DECA XP

LCQ DECA XP

ProQUANT/ ProICAT Spectrum Mill

MASCOT MASCOT SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST

SEQUEST

SEQUEST

SEQUEST

SEQUEST

SEQUEST

Database search engine*

(Vollmer et al., 2004)

(DeSouza et al., 2005)

(Qian et al., 2005a; Qian et al., 2005b) (Coldham and Woodward, 2004) (Cutillas et al., 2003) (Fujii et al., 2004) (Peng et al., 2003) (Beausoleil et al., 2004) (Ballif et al., 2004) (Wilmarth et al., 2004) (Vitali et al., 2005)

(Blonder et al., 2004)

(Yu et al., 2004)

(Adkins et al., 2002)

Reference

*The LCQ and LCQ DECA are products of Thermo Electron Corporation (San Jose, CA); the Q-TOF is a product of Waters (Beverly, MA); the QSTAR, ProQUANT, and ProICAT are products of Applied Biosystems (Foster City, CA); and Spectrum Mill and the MSD TRAP XCT are products of Agilent Technologies (Palo Alto, CA).

Ammonium formate

Human blood serum

Mass spectrometer*

Buffer/Salt

Applications

Desalting

Examples of Off-line Strong Cation Exchange On-line RP/MS/MS

TABLE 11.1

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COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

phosphopeptides tend to be enriched in early fractions (Beausoleil et al., 2004). As an example of the off-line approach with KCl, in the analysis of the S. cerevisiae proteome by Peng et al. (2003), 1 mg of yeast protein lysate was digested and separated into 80 one-minute fractions during an 80 min SCX gradient. Each fraction was then analyzed via an autosampler by RP/MS/MS with a vented column using 60 min, 90 min, 120 min, or 150 min RP gradients, depending on the complexity of each fraction, for a total of 135 h of RP/MS/MS time (Peng et al., 2003). In the end, 7537 unique peptides and 1504 proteins were identified (Peng et al., 2003). Recent applications using ammonium formate and no desalting include an analysis of a human blood serum proteome (Adkins et al., 2002), human blood plasma proteomes (Fujii et al., 2004; Qian et al., 2005), and a mouse cortical neuron proteome (Yu et al., 2004). The flexibility of the off-line approach allows for the incorporation of additional separation techniques to undertake, in effect, three-dimensional analyses by using size exclusion chromatography of proteins, collecting fractions, digesting proteins, separating peptides by off-line SCX followed by fraction collection, and analyzing each fraction by RP/MS/MS (Jacobs et al., 2004). The main limitation of the off-line SCX—online RP/MS/MS approach is the need to transfer each SCX fraction to an autosampler for RP/MS/MS, which can be a time consuming process. An advantage of this system is the ability to carry out salt gradients, rather than pulses or bumps, and to use phosphate buffers with KCl or NaCl, which are well-characterized SCX elution buffers. In addition, with the off-line SCX, large SCX columns with large sample capacities may be used, and organic solvent in the SCX buffer may be used to improve separations. Lastly, many laboratories have been set up to use RP/MS/MS with peptides from digested gel slices using an autosampler. It has been easier to simply do the off-line SCX on an HPLC and collect fractions that can then be placed into an autosampler and RP/MS/MS system already up and running in a lab.

11.2 SCX/RP/MS/MS In the first of the two general online configurations, the SCX and the RP/MS/MS are not directly coupled with any fractions collected from the SCX. The first description of complex peptide mixture analysis via SCX/RP/MS/MS used this approach and KCl to analyze S. cerevisiae ribosomes (Link et al., 1999). In an arrangement of this approach, multiple digested samples could be placed into an autosampler for SCX fractionation onto a RP trap followed by elution of the RP trap onto a RP microcolumn coupled directly to MS/MS (Fig. 11.2). The use of the RP trap allows for the use of phosphate and KCl/NaCl for the SCX analysis, as shown in a C. trachomatis proteome analysis (Skipp et al., 2005), or ammonium chloride, as shown in a S. cerevisiae analysis (Li et al., 2005) (Table 11.2). Ammonium formate (Nagele et al., 2003; Vollmer et al., 2003; Xiang et al., 2004) and ammonium acetate (Gu et al., 2004; Tyan et al., 2005a and b) may also be used with this configuration (Table 11.2). The configuration in Fig. 11.2 is one that is a generally available from many high-performance liquid chromatography (HPLC) and mass spectrometry manufacturers. Advantages of this system are that it is flexible with respect to the SCX elution conditions that can be used and no handling of fractions is needed, meaning it is a more automated approach than the one described in

249

Ammonium formate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium chloride Phosphate/KCl Phosphate/sodium acetate No buffer/NaCl

Human cancer cell lines Human erythrocytes Human pleural effusion PUMA-induced apoptosis S. cerevisiae Chlamydia trachomatis Human lung fibroblasts and gliomas S. cerevisiae

LCQ DECA LCQ DECA LCQ DECA QSTAR XL LCQ DECA QTOF Global Ultima LCQ MSD Ion Trap XCT

2 RP traps

LCQ MSD Trap SL

Mass spectrometer*

On-column RP trap RP trap RP trap RP trap RP trap RP trap

On-column RP trap

Desalting

Spectrum Mill

SEQUEST SEQUEST SEQUEST MASCOT SEQUEST MassLynx Proprietary

SEQUEST MASCOT

Database search engine*

(Nagele et al., 2004)

(Link et al., 1999) (Nagele et al., 2003; Vollmer et al., 2003) (Xiang et al., 2004) (Tyan et al., 2005a) (Tyan et al., 2005b) (Gu et al., 2004) (Li et al., 2005) (Skipp et al., 2005) (Davis et al., 2001)

Reference

The LCQ and LCQ DECA are products of Thermo Electron Corporation (San Jose, CA); the Q-TOF and MassLyx are products of Waters Corporation (Beverly, MA); the QSTAR XL is a product of Applied Biosystems (Foster City, CA); and Spectrum Mill and the MSD TRAP XCT is a product of Agilent Technologies (Palo Alto, CA).

*

KCl Ammonium formate

Buffer/Salt

Examples of On-line Strong Cation Exchange RP/MS/MS

S. cerevisiae ribosome E. coli

Applications

TABLE 11.2

250

COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

MudPIT

251

Fig. 11.1. In addition, one area that has yet to be characterized is the use of sequential RP columns with large dead volumes. It seems unlikely that peptides eluting from the RP trap would reconcentrate on the RP column directly in front of the mass spectrometer diminishing the value of the second RP column. This needs further analytical investigation. 11.3 MudPIT The MudPIT approach has arguably driven the field of proteomics to adopt SCX/RP/ MS/MS-based approaches. MudPIT is a shotgun proteomics approach that incorporates SCX/RP/MS/MS in a fully online fashion (Fig. 11.3) (Link et al., 1999; Washburn et al., 2001; Wolters et al., 2001). In this approach, first a bi/triphasic microcapillary column packed with RP and SCX HPLC grade materials is loaded with a complex peptide mixture generated from a biological sample (Fig. 11.3). Typically, this column is made of 100 mm inner diameter and 365 mm outer diameter fused silica. Next, the packed and loaded column is interfaced with a quaternary HPLC pump that acts as the ion source for a tandem mass spectrometer. In each chromatographic step, peptides are directly eluted from the biphasic microcapillary column, ionized, and then analyzed in the tandem mass spectrometer. The biphasic SCX/RP column that directly elutes into a tandem mass spectrometer was first described for the analysis of the S. cerevisiae proteome using a phosphate buffer and KCl (Link et al., 1999). A series of methodological improvements and the use of ammonium acetate (Washburn et al., 2001; Wolters et al., 2001) led to a large-scale analysis of the S. cerevisiae proteome that detected and identified 5540 peptides and 1484 proteins from three different fractions of the yeast proteome run on three different MudPIT columns for a total of 83 h of SCX/RP/MS/MS

3 FIGURE 11.4 MudPIT analsysis of yeast. The chromatograms of a 15-cycle MudPIT analysis of a heavily washed insoluble fraction from the S. cerevisiae, prepared as described in the materials and methods. The four buffer solutions used for the chromatography were 5% ACN/0.02% HFBA (buffer A), 80% ACN/0.02% HFBA (buffer B), 250 mM ammonium acetate/5% ACN/0.02% HFBA (buffer C), and 500 mM ammonium acetate/5% ACN/0.02% HFBA (buffer D). Cycle 1 (Fig. 11.4a) consisted of a 70 min gradient from 0% to 80% buffer B and a 10 min hold at 80% buffer B. Each of the next 12 cycles were 110 min with the following profile: 5 min of 100% buffer A, 2 min of X% buffer C, 3 min of 100% buffer A, a 10 min gradient from 0% to 10% buffer B, and a 90 min gradient from 10% to 45% buffer B. The 2 min buffer C in cycles 2–13 were as follows: Cycle 2—10% (Fig. 11.4b), Cycle 3—20% (Fig. 11.4c), Cycle 4—30% (Fig. 11.4d), Cycle 5—40% (Fig. 11.4e), Cycle 6—50% (Fig. 11.4f), Cycle 7— 60% (Fig. 11.4g), Cycle 8—70% (Fig. 11.4h), Cycle 9—80% (Fig. 11.4i), Cycle 10—90% (Fig. 11.4j), Cycle 11—90% (Fig. 11.4k), Cycle 12—100% (Fig. 11.4l), and Cycle 13—100% (Fig. 11.4m). Cycle 14 (Fig. 11.4n) consisted of a 5 min 100% buffer Awash followed by a 20 min 100% buffer C wash, a 5 min 100% buffer Awash, a 10 min gradient from 0—10% buffer B, and a 90 min gradient from 10—45% buffer B. Cycle 15 (Fig. 11.4o) was identical to Cycle 14 (Fig. 11.4n), except that the 20 min salt wash was with 100% buffer D. The chromatograms shown are representative of those obtained from samples of comparable complexity to the one described (reprinted with permission from Wolters et al., 2001). Copyright 2001 American Chemical Society.

252

COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

600 A

Hits in cycle

500 400 300 200 100 0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

9

10 11 12 13 14 15

Cycle 400

New unique hits in cycle

350

B

300 250 200 150 100 50 0 1

2

3

4

5

6

7

8 Cycle

FIGURE 11.5 The number of peptide identifications from each of the 15 cycles from the data displayed in Figure 11.4 is shown. In Figure 11.5a and 11.5b, Cycle 1 corresponds to Figure 11.4a, Cycle 2 corresponds to Figure 11.4b,. . ., and Cycle 15 corresponds to Figure 11.4o. Figure 11.5a displays the total number of peptide identifications in each cycle. These peptide identifications are not necessarily unique because a large number of peptides are identified multiple times during a typical MudPIT analysis. The total number of peptides identified in this sample was 5738. Figure 11.5b displays the total number of new unique peptide identifications from each cycle in the sample. The total number of unique peptides identified in this sample was 2114 (reprinted with permission from Wolters et al., 2001). Copyright 2001 American Chemical Society.

time (Washburn et al., 2001). An example of these fractions is a heavily washed insoluble fraction, as shown in Fig. 11.4. Each of these fractions contained at least 200 identifiable peptides (Fig. 11.5a) that provided new unique peptides to the running tally of 2114 peptides detected and identified from the analysis of the heavily washed insoluble fraction (Fig. 11.5b). Ammonium acetate is the salt predominantly used in MudPIT applications (Table 11.3), but ammonium formate could also be used. Examples (Table 11.3) of the analysis of proteomes via MudPITand a biphasic column

253

*

Biphasic/triphasic Triphasic Triphasic Triphasic Triphasic

Ammonium acetate

Ammonium acetate

Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic Biphasic

Phosphate/KCl Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium acetate Ammonium bicarbonate Ammonium acetate Ammonium acetate Ammonium acetate

LCQ DECA/LTQ

LCQ DECA

LCQ LCQ DECA LCQ DECA

LCQ LCQ LCQ DECA LCQ DECA LCQ DECA LCQ DECA QSTAR LCQ DECA/LTQ LCQ DECA

Mass spectrometer*

SEQUEST

SEQUEST

SEQUEST SEQUEST SEQUEST

SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST SEQUEST SONAR SEQUEST SEQUEST

Database search engine

(Ram et al., 2005)

(Cai et al., 2005)

(McDonald et al., 2002) (Graumann et al., 2004) (Mayor et al., 2005)

(Link et al., 1999) (Washburn et al., 2001) (Florens et al., 2002) (Kislinger et al., 2003) (Pan et al., 2004) (Wenner et al., 2004) (Gaucher et al., 2004) (Sandhu et al., 2005) (Breci et al., 2005)

Reference

The LCQ, LCQ DECA, and LTQ are products of Thermo Electron corporation (San Jose, CA), and the QSTAR is a product of Applied Biosystems (Foster City, CA).

Bovine microtubule S. cerevisiae protein complexes S. cerevisiae poylubiquitin conjugates HeLa histone acetyltransferase complex Microbial Biofilm

S. cerevisiae proteome S. cerevisiae proteome P. falciparum proteome Mouse organs Mouse heart Human cerebrospinal fluid Human heart mitochondria Breast cancer cells S. cerevisiae proteome

Buffer

Application

Bi/Triphasic

Examples of Biphasic and Triphasic MudPIT Analyses

TABLE 11.3

254

COUPLED MULTIDIMENSIONAL CHROMATOGRAPHY

include the P. falciparum (Florens et al., 2002), mouse hearts (Pan et al., 2004), human cerebrospinal fluid (Wenner et al., 2004), and breast cancer cells (Sandhu et al., 2005). One of the problems with the biphasic MudPIT column is that digested peptide mixtures typically contain salts and urea, which requires off-line desalting prior to loading onto a biphasic column. This is an additional sample handling step that likely leads to sample loss and increases the time of analysis. As a result, the triphasic column using RP/SCX/RP was developed to carry out online desalting in the first dimension (McDonald et al., 2002). The triphasic column or the split-three phase column (Fig. 11.3) is gaining in popularity because it reduces sample handling. The triphasic column appears to be particularly useful for analyzing protein complexes (Graumann et al., 2004; Cai et al., 2005; Mayor et al., 2005). The split-three phase column allows for the use of larger inner diameter capillaries (250 mm inner diameter and 365 mm outer diameter fused silica) that allow for more RP/SCX material, and thus permits large sample quantities to be loaded. This proved useful in a recent analysis of a microbial biofilm (Ram et al., 2005). In my laboratory, we exclusively use the triphasic columns and split-three phase columns for proteomic analysis. Although the MudPIT approach requires the use of salt pulses or bumps and is not compatible with SCX gradients using phosphate buffers and KCl/NaCl, it has proved an effective approach for biological discovery.

11.4 ALTERNATIVE FIRST DIMENSION APPROACHES The use of two-dimensional chromatography coupled to MS/MS with SCX and RP as the chromatographic approaches has proven powerful and is gaining widespread acceptance. This has led researchers to investigate alternatives to SCX/RP/MS/MS for shotgun proteomics, which include coupling liquid chromatography and capillary electrophoresis (reviewed in Evans and Jorgenson, 2004, and described in Chapter 16 by Issaq). In one approach, anion exchange (AE) chromatography is used instead of SCX. Mawuenyega et al. (2003) performed a large-scale protein identification of C. elegans proteome using AE with a tris buffer and NaCl coupled to a RP trap followed by RP/MS/MS on a Q-TOF2, and used MASCOT for protein identification. This technological platform has also been used to analyze the Escherichia coli proteome (Taoka et al., 2004). Taking advantage of the ability of titanium oxide to selectively retain water soluble organic phosphates, Pinkse et al. (2004) used a titanium oxide column connected to a RP precolumn that eluted onto a RP analytical column in a specified application for phosphopeptide analysis. Another alternative with additional advantages uses isoelectric focusing of peptides followed by RP/MS/MS (Cargile et al., 2004a and b; Cargile and Stephenson, 2004; Chen et al., 2002; Chen et al., 2003a and b; Essader et al., 2005). When using isoelectric focusing one can couple capillary isoelectric focusing to RP/MS/MS for proteomic analysis (Chen et al., 2002; Chen et al., 2003a and b), or one can use immobilized pH gradient strips to separate peptides followed by RP/MS/MS analysis (Cargile et al., 2004a and b; Cargile and Stephenson, 2004; Essader et al., 2005). The

REFERENCES

255

potential additional advantage with this approach is the use of the pI of peptides in strengthening protein identification (Cargile et al., 2004; Cargile and Stephenson, 2004). 11.5 CONCLUSION With the increasing popularity of multidimensional chromatography coupled to tandem mass spectrometry, more and more researchers will explore innovations in chromatography and mass spectrometry to improve proteome analysis via shotgun proteomics. On the chromatographic side, effort is underway to dramatically increase the peak capacity of liquid chromatography by using smaller particles and ultrahigh pressure, greater than 50,000 psi, liquid chromatography (Mellors and Jorgenson, 2004; Patel et al., 2004, also see Chapter 7). When ultrahigh pressure chromatography systems are used in place of current HPLC systems, it is believed that far more complex peptide mixtures will be able to be analyzed in a given time period with a shotgun proteomics system. In addition, the use of higher temperatures should facilitate faster two-dimensional chromatography (Stoll and Carr, 2005). However, if the faster chromatography exceeds the ability of mass spectrometers to acquire tandem mass spectra, valuable information will be lost. This requires innovations in mass spectrometry, which most instrumentation manufacturers constantly pursue. The reason for the need of all these innovations is that comprehensive proteome analysis remains elusive because of the huge dynamic ranges in protein abundance and the even larger analytical challenge of posttranslational modification analysis from complex protein mixtures. However, as newer innovative mass spectrometers become available and are coupled to SCX and RP for proteome analyses, comprehensive proteomic analysis may become more and more possible.

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chip and identification using two-dimensional electrospray ionization tandem mass spectrometry. J. Proteome Res. 4, 748–757. Tyan, Y.C., Wu, H.Y., Lai, W.W., Su, W.C., Liao, P.C. (2005b). Proteomic profiling of human pleural effusion using two-dimensional nano liquid chromatography tandem mass spectrometry. J. Proteome Res. 4, 1274–1286. Vitali, B., Wasinger, V., Brigidi, P., Guilhaus, M. (2005). A proteomic view of Bifidobacterium infantis generated by multi-dimensional chromatography coupled with tandem mass spectrometry. Proteomics 5, 1859–1867. Vollmer, M., Horth, P., Nagele, E. (2004). Optimization of two-dimensional off-line LC/MS separations to improve resolution of complex proteomic samples. Anal. Chem. 76, 5180–5185. Vollmer, M., Nagele, E., Horth, P. (2003). Differential proteome analysis: two-dimensional nano-LC/MS of E. coli proteome grown on different carbon sources. J. Biomol. Tech. 14, 128–135. Washburn, M.P., Wolters, D., Yates, J.R., 3rd (2001). Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247. Wenner, B.R., Lovell, M.A., Lynn, B.C. (2004). Proteomic analysis of human ventricular cerebrospinal fluid from neurologically normal, elderly subjects using two-dimensional LC-MS/MS. J. Proteome Res. 3, 97–103. Wilmarth, P.A., Riviere, M.A., Rustvold, D.L., Lauten, J.D., Madden, T.E., David, L.L. (2004). Two-dimensional liquid chromatography study of the human whole saliva proteome. J. Proteome Res. 3, 1017–1023. Wolters, D.A., Washburn, M.P., Yates, J.R., 3rd (2001). An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73, 5683–5690. Xiang, R., Shi, Y., Dillon, D.A., Negin, B., Horvath, C., Wilkins, J.A. (2004). 2D LC/MS analysis of membrane proteins from breast cancer cell lines MCF7 and BT474. J. Proteome Res. 3, 1278–1283. Yu, L.R., Conrads, T.P., Uo, T., Kinoshita, Y., Morrison, R.S., Lucas, D.A., Chan, K.C., Blonder, J., Issaq, H.J., Veenstra, T.D. (2004). Global analysis of the cortical neuron proteome. Mol. Cell. Proteomics 3, 896–907.

12 DEVELOPMENT OF ORTHOGONAL 2DLC METHODS FOR SEPARATION OF PEPTIDES Martin Gilar, Petra Olivova, Amy E. Daly, and John C. Gebler Waters Corporation, Milford, MA 01757, USA

12.1 INTRODUCTION Identification and quantitation of proteins in proteome samples present a great challenge even for state-of-the-art liquid chromatography–tandem mass spectrometry (LC–MS/MS) (Aebersold and Mann, 2003; Peng et al., 2003; Von Haller et al., 2003). It is estimated that in the human proteome approximately 10–20 thousand proteins are expressed at any given time (Anderson and Anderson, 2002; Wehr, 2002). In the case of shotgun proteomics (Wolters et al., 2001), when protein samples are digested with specific proteolytic enzymes prior to analysis, the complexity may reach 100–200 thousands peptides or more. No separation technique is currently capable of resolving such a complex sample in a single analysis. Consequently, the sample eluting from the LC column and entering the MS instrument at any given point of analysis is a rich mixture of peptides, making a complete and comprehensive MS/MS analysis difficult. An additional challenge of proteome analysis is the dynamic range. For example, the range of protein concentration in serum spans eleven orders of magnitude (Anderson and Anderson, 2002), severely exceeding the current dynamic range of mass spectrometers. Various separation techniques have been combined to enhance the resolution of peptides (or proteins) and decrease the complexity of proteomic samples to more Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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manageable levels. Among other things, 2D LC methods have been utilized (Kachman et al., 2002; Peng et al., 2003; Vollmer et al., 2004; Wagner et al., 2002). The consensus is that an efficient 2DLC separation improves the prospects for LC–MS/MS analysis of highly complex peptide samples over a simpler (less efficient) 1DLC setup (Essader et al., 2005; Liu et al., 2004; Man et al., 2005; Von Haller et al., 2003; Wehr, 2002). However, due to the nature of analysis and the lack of systematic studies utilizing comparable samples, it is difficult to judge the level of improvement, measured as the number of identified peptides/proteins, and the achievable limit of detection. Separation performance of gradient chromatography can be described by peak capacity (P), which is the maximum number of peaks that can be theoretically resolved on a column in a given gradient time (Dong and Tran, 1990; Ghrist et al., 1987; Neue et al., 2001; Snyder and Stadalius, 1986; Stadalius et al., 1987). Recent reports evaluated the peptide separation efficiency using both conventional and custom-made columns. It appears that a maximum peak capacity of 1DLC is somewhere between 1000–1600 peptides in a single run (Gilar et al., 2004; Shen et al., 2005). Extending the gradient time (using shallower gradients) and utilizing longer columns has diminished peak capacity gains, and therefore it is not economical. 2DLC seems to offer a larger separation power. Its peak capacity is defined as a multiplication of peak capacities of two chosen separation dimensions (Giddings, 1987). For example, when combining SCX-HPLC (typical peak capacity is 50 for a 50 min analysis) with RP-HPLC (peak capacity may be 100 for a 50 min analysis), the total 2D peak capacity is 5000. In reality, this value also depends on the orthogonality of separation (Gilar et al., 2005b; Liu et al., 1995; Slonecker et al., 1996). To date little research effort has been focused on the investigation of orthogonality of LC modes for peptides, even for the most common 2DLC based on strong cation exchange (SCX) and RP modes (Gilar et al., 2005; Peng et al., 2003). Therefore, the peak capacity of 2DLC for the separation of peptides can only be approximately estimated. Combining 2DLC with MS for proteomic research translates into a 3D separation technique with peak capacity exceeding the chromatographic separation space. Although MS can be viewed as another separation technique, the peak capacity estimates usually take into account only chromatographic resolution. This is understandable since it is not trivial to discern the ultimate LC–MS/MS separation performance due to the impact of dynamic range and degree of component overlap. Parameters, such as the speed of MS/MS acquisition, must be also considered. The effect of MS/MS duty cycle limitations on the overall number of identified peptides has only recently been systematically studied (Liu et al., 2004). The overlap of eluting peptides significantly affects the data dependent MS/MS acquisition (DDA) and creates a bias for more abundant components in the mixture. Because of the nature of DDA, MS/MS becomes more reproducible for less complex (sufficiently resolved) samples. The aim of this chapter is to evaluate the orthogonality of selected 2DLC systems for the separation of peptides. The orthogonality of different chromatographic modes was quantitatively characterized using a novel geometric approach. Practical peak capacity was calculated from the theoretical peak capacity and the knowledge of

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263

orthogonality, better defining the achievable separation performance of 2DLC systems. Finally, the peak capacity concept was extended into 2DLC–MS, where the duty cycle of MS/MS analysis was taken into account and used to define the overall system peak capacity.

12.2 PREVIOUS WORK In our earlier report (Gilar et al., 2004), we studied the peak capacity of several 1DLC systems using three approaches: (i) decreasing the gradient slope (tg) extending the gradient time, while keeping the column length fixed, (ii) increasing the column length (L) with proportional increase in gradient time, and (iii) employing columns packed with smaller sorbent particles. Because the gains in peak capacity with the increase of tg and L are not linear, the first two strategies have diminishing returns. The third strategy is limited by the operational pressure. A predicted maximum achievable peak capacity in single-dimensional (1D) RP-LC is within the range of 1400–1600 (Gilar et al., 2004). Other authors developed ultrahigh performance LC methods using the abovementioned strategies and dedicated pumps capable of achieving high operational pressures (100–500 MPa) (MacNair et al., 1999; Shen et al., 2002; Tolley et al., 2001). A peak capacity of 1500 was reported when using a 200 cm column and 33 h gradient (Shen et al., 2005). These papers clearly demonstrate both the strength and the limitations of 1DLC. Although impressive separations have been produced, further extensions in column and gradient length do not yield significantly improved separations. The observed trends are in a good agreement with a peak capacity prediction model based on gradient theory (Gilar et al., 2004; Neue et al., 2001). It is difficult to conceive 1DLC techniques capable of the separation of tens or hundreds of thousands of components desirable for proteome research. In general, 2DLC is expected to provide a greater peak capacity (P2D) than single-dimensional LC. The P2D can be calculated theoretically by multiplying the peak capacity values of the first (P1) and second (P2) LC dimensions (Eq. 1) (Giddings, 1987). P2D ¼ P1 P2

ð12:1Þ

This concept assumes that each fraction (peak) collected in the first dimension further separates in the second dimension with regular spacing and that the entire 2D separation space is evenly covered by eluting peaks. More realistically, the peaks would be distributed randomly: over the 2D separation space some peaks are likely to coelute, while some area will remain vacant of peaks. Therefore, Equation12.1 represents an idealized peak capacity estimate although the real number of resolved peaks is lower. Most importantly, the peak capacity proposed by Equation 12.1 is achievable when the chromatographic modes used for separation are completely orthogonal. The orthogonality of common LC modes for peptide separation is not well known, but in most cases it is not ideal. Several reports suggest that even dissimilar modes such as SCX and RP do not separate peptides in an orthogonal fashion, and some peak

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clustering is observed in the 2DLC separation space (Gilar et al., 2005; Peng et al., 2003; Wagner et al., 2002). Similar situations have been observed for comprehensive 2DLC systems described for the separation of small molecules. A limited orthogonality is often achieved, despite the considerable effort to identify the most promising separation modes for the first and second LC dimensions (Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003). The consequences of limited orthogonality in 2DLC on peak capacity have not been extensively investigated; however, it is clear that the achievable peak capacity will be lower than that proposed by Equation 12.1. In contrast to comprehensive 2DLC (Murphy et al., 1998; Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003), a typical experiment in proteome research involves the analysis of only a limited number of fractions, further sacrificing the achievable peak capacity of the chosen 2DLC system. For example, when collecting 10 fractions in the first LC dimension, its peak capacity is reduced to 10, even if the theoretical column peak capacity is 100. While fraction oversampling is important to maintain the component resolution in the 2D chromatogram (Murphy et al., 1998), it also splits the peptides into several consecutive fractions, reduces their signal, and decreases the number of MS (MS/MS) identifiable peptides. A frequent fractionation, in conjunction with the time-consuming second-dimension LC–MS/MS analyses, also increases the overall length of 2DLC analysis. Since the detection limit and sample throughput are two primary concerns in proteomic analysis, the fraction collection frequency is often limited to 1–5 peak widths (Gilar et al., 2005b). Fractionation frequency has to be taken into account when estimating the 2DLC peak capacity. Our recent study investigated the impact of orthogonality on practical 2DLC peak capacity (Gilar et al., 2005a). Selected LC modes were chosen for separation of tryptic peptides and their selectivity was correlated in 2D separation space. Several mathematical methods have been proposed in literature (Liu et al., 1995; Slonecker et al., 1996), employing complementary descriptors, such as informational similarity, percentage of synentropy, peak spreading angle, and practical peak capacity. However, these approaches are rather complex and not suitable for the description of 2DLC systems with apparent peak clustering. Therefore, we have developed a simpler geometric approach utilizing a single descriptor for description of 2DLC orthogonality (percent of orthogonality). This geometric approach has been employed to evaluate the potential of selected 2DLC systems (Gilar et al., 2005a).

12.3 DEVELOPING ORTHOGONAL 2DLC METHODS 12.3.1

LC Selectivity for Peptides: Experimental Design

In an effort to identify promising LC modes for 2DLC separation of tryptic peptides several traditional, as well as novel, LC modes have been evaluated, as earlier reported

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(Gilar et al., 2005a). Chromatographic modes included reversed-phase (RP) chromatography, size exclusion chromatography (SEC), hydrophilic interaction chromatography (HILIC), and strong cation-exchange (SCX) chromatography. The experiment was designed as a single-dimensional LC experiment with direct MS detection. Narrow bore 150 2.1 mm columns (with the exception of SEC) at a flow rate 0.2 mL/min were used. Five different mixtures of peptides were prepared by digesting following proteins with trypsin: yeast enolase (ENOL), bovine hemoglobin (BH), bovine serum albumin (BSA), rabbit phosphorylase b (PHOSP), and yeast alcohol dehydrogenase 1 (ADH). These five digestion standards were sequentially injected on LC–MS. Tryptic peptides of interest were identified (according to their unique mass), their chromatographic retention was recorded, and the retention data for different LC modes were vizualized in 2D plots. The sample is described in more detail in an earlier published report (Gilar et al., 2005a). The 2D retention maps were constructed for promising LC modes simulating 2DLC selectivity (Gilar et al., 2005a, b). Although the data were acquired in a singledimensional LC setup, the 2D retention maps (Fig. 12.2) are identical and interchangeable with those acquired in a 2DLC setup using a frequent fraction collection in the first LC dimension and reinjection of the fraction in a second LC–MS dimension. The simpler and less time-consuming data generation in 1DLC is useful as long as the peptide retention can be confirmed with direct MS detection. The LC modes and separation conditions evaluated are described in Table 12.1. Analysis using reversed-phase chromatography at low pH was carried out under conditions typically used for 2DLC–MS/MS analysis of peptides. The mobile phases for other chromatographic conditions were also chosen to be compatible with MS detection, including SCX LC, where the peptides were eluted with volatile ammonium formate buffer. An example of chromatograms illustrating the separation of phosphorylase b tryptic digest (90 tryptic peptides) for selected LC modes is shown in Fig. 12.3. The sequential analysis of all five tryptic digests (only peptides larger than four amino acids were included in the study) yielded a dataset of 196 peptides common to all 2DLC experiments (Gilar et al., 2005a). Retention data were then normalized according to Equation 12.2, where RTmin and RTmax represent the retention times of the first and last eluting peptides in the set. RTiðnormÞ ¼

RTi RTmin RTmax RTmin

ð12:2Þ

The retention times RTi are converted to normalized RTi(norm); the values of RTi(norm) range from 0 to 1. The normalization serves two purposes. First, it allows for a comparison of different chromatographic data in a uniform 2D retention space, regardless of absolute retention time values. Second, it removes the empty space in the 2D separation plot, where no peaks elute. The voids can be caused by the LC system gradient delay, column void volume, or gradient spanning outside the useful range (for example, a gradient of 0–100% acetonitrile in RPLC, while practically all tryptic peptides elute within 0–50% acetonitrile).

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TABLE 12.1 Chromatographic Modes and Conditions Used for LC–MS Study of Tryptic Peptides Separation Selectivity LC mode

Column type

Column L i.d; dp. (mm mm; mm)

RP C18

Atlantis dC18

150 2.1; 3

HILIC

Atlantis HILIC

150 2.1; 3

SEC

YMC diol, 60 A

SCX

PolySULFO A, 300 A

Other RP stationary embedded polar group (EPG) phenyl (PH), pentafluorophenyl (PFP)

RP C18, high pH

a

phases: Symmetry Shield RP18 XTerra Phenyl Prototype sorbent

XTerra MS C18, or XBridge MS C18

750 4.6; 5 (3 columns in series) 150 2.1; 5

Elution conditionsa 0.2% Formic acid, 0–42% acetonitrile in 50 min 10 mM ammonium formate, pH 4.5, 90–48% acetonitrile in 50 min 40 mM ammonium formate, pH 4.5, 20% acetonitrile 25% acetonitrile, gradient 40–300 mM of ammonium formate in 40 min

150 2.1; 3.5

Same as RP C18

150 2.1; 3.5 150 2.1; 5

Same as RP C18 10 mM ammonium formate, pH 3.25, 0–42% acetonitrile in 50 min 20 mM ammonium formate, pH 10, 0–42% acetonitrile in 50 min

150 2.1; 3.5

Flow rate was 0.2 mL/min, and separation temperature was 40 C for all experiments.

12.3.2

Investigation of 2DLC Orthogonality for Separation of Peptides

Separation selectivity in LC depends on various factors. The most important is the choice of the stationary and mobile phases (Chen et al., 2004; Guo et al., 1987). In addition, the separation temperature (Hancock et al., 1994) and gradient slope (Chloupek et al., 1994) have also been shown to have a moderate impact on LC selectivity. We have evaluated a set of different LC stationary phases utilizing different separation modes, as listed in Table 12.1. While the choice of a dissimilar separation mode has a primary impact on LC selectivity, it is not a guarantee of separation orthogonality (Tanaka et al., 2004; van der Horst and Schoenmakers, 2003; Venkatramani and Zelechonok, 2003). The guidelines for the selection of highly orthogonal LC modes have not been satisfactorily specified in the scientific literature (at least not for the separation of peptides). The problem is highlighted in the following example. Although SEC resolves peptides by their size and

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267

RP retains molecules according to their hydrophobicity, some correlation clearly exists between these two LC modes. Apparently, the peptide size loosely correlates with hydrophobicity; in general, the bigger peptides elute earlier in SEC and later in RP. Similarly, HILIC and RP are also likely to exhibit some selectivity correlation, as their retention mechanism is related to hydrophilicity (HILIC) or lack of hydrophilicity (RP). A second avenue toward the orthogonal 2DLC separation relies on the choice of the mobile phase as another principal factor affecting the separation selectivity (Chen et al., 2004; Guo et al., 1987; Young and Wheat, 1990). We have evaluated the impact of different ion-pairing agents and the mobile phase pH. The pH appears to be the most promising tool to generate alternative selectivity in RP (Gilar et al., 2005). Additional separation parameters, such as temperature and gradient slope, were previously investigated by other authors (Chloupek et al., 1994; Hancock et al., 1994). While noticeable changes in the separation of critical pairs of peptides have been reported (Chen et al., 2004; Chloupek et al., 1994; Guo et al., 1987; Hancock et al., 1994; Young and Wheat, 1990), the overall impact on separation selectivity is not dramatic (Gilar et al., 2005a; Gilar et al., 2005b). The gradient slope and temperature are not likely to generate a degree of orthogonality useful for 2DLC applications. A summary of the 2DLC orthogonality study is outlined in Fig. 12.2. Normalized 2D plots constructed for an identical set of 196 tryptic peptides are used to compare the investigated 2DLC systems. The x-axis retention data are common for all retention maps; the data were acquired using a RP C18 column at conditions typically used for second-dimension LC–MS analysis. The y-axis data vary according to the LC mode representing the first separation dimension. In the case of ideal (orthogonal) separation, one should observe that the entire 2D separation space randomly covered with eluting peptides (Fig. 12.1). This is clearly not the case in any of the investigated scenarios shown in Fig. 12.2. These results lead to several conclusions. As discussed in more detail in a previous study (Gilar et al., 2005b), altering the ionpairing agents (e.g., using trifluoroacetic acid instead of formic acid) and the RP stationary phase (C8, C18, embedded polar group RP18 or phenyl type of sorbent) has some impact on separation selectivity, but the overall impact on 2DLC orthogonality is minor. An example can be seen in Figure 12.2a for the C18 versus phenyl column. Most of the peptides are distributed along the diagonal of the 2D plot, as expected for a 2DLC system with low orthogonality. Out of the different RP modes, only the pentafluorophenyl stationary phase showed larger differences of selectivity compared to a C18 sorbent (Fig. 12.2b). Nevertheless, the orthogonality of separation is still rather weak and not promising for 2DLC applications. A careful inspection of Fig. 12.2 reveals that none of the investigated 2DLC systems are in fact completely orthogonal. The data points do not randomly cover the entire separation surface and large parts of the separation space remain unpopulated by peptides. Some degree of clustering is evident even for completely dissimilar separation modes. Therefore, the assumption of ideal 2DLC orthogonality, made by many researchers, is not valid and the peak capacity of commonly used 2DLC combinations is overestimated.

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(b)

Normalized RT

Second LC dimension

(a)

First LC dimension Normalized RT

FIGURE 12.1 Geometric approach to orthogonality. Normalized separation space is divided into the number of bins equal to the number of separated compounds (10 10 bins, peak capacity ¼ 100). Part (a) represents a nonorthogonal 2DLC system; surface coverage is 0.1, and orthogonality is 0%. Part (b) represents ideally orthogonal separation (random data); surface coverage is in average 0.63, and orthogonality is 100%.

The lack of orthogonality in 2DLC is not surprising. The tryptic peptides have some common physicochemical properties such as average length, number of charges, hydrophobicity, among others. Fig. 12.2 shows an interesting insight into the separation selectivity of chosen LC modes and deserves further discussion. SEC-RP 2DLC method has been proposed (Opiteck et al., 1997) as an alternative to SCX-RP for achieving an orthogonal 2DLC separation of peptides. Our study of SECRP selectivity. (Fig. 12.2d) revealed some correlation between size (SEC elution order) and hydrophobicity of peptides (C18 retention). Larger peptides eluting earlier in SEC are more retained in RP mode, and vice versa. Additional, nonspecific interaction with the sorbent seems to play some role in separation. We observed anomalous retention for some hydrophobic peptides, eluting outside of the expected time window or even outside of the inclusion/exclusion window. Although the secondary interaction improved the overall 2DLC orthogonality (causing the larger spread in data points in Fig. 12.2d), some peptides were incompletely recovered from the column. The addition of acetonitrile and ammonium formate buffer into the SEC mobile phase was necessary to control the peptides recovery and carryover. Fig. 12.2e illustrates a promising orthogonality of the HILIC-RP combination in 2DLC. At first this could be surprising, but a more judicious look reveals a pattern in the HILIC retention behavior. It appears that the separation is based, at least in part, on the peptide charge. As the HILIC experiment was carried out on a bare silica column, this could be explained by the role of negatively charged silanols, which can interact with positively charged peptides. Because the peptides with a higher charge (3þ, 4þ, 5þ . . . )

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269

FIGURE 12.2 Normalized retention time plots for investigated 2DLC systems. The area used for separation is highlighted in gray. Peak capacity of 2D space is 14 14 (196 bins); normalized retention of 196 tryptic peptides is recorded. The charge or pI of peptides is shown in figure legends, if appropriate. (See color plate.)

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

are better retained onthesorbent than the less charged (1þ, 2þ) ones, the retention relies, in part, on the ion-exchange chromatography mechanism. The increase in buffer concentration in the mobile phase counteracted this additional charge-tocharge retention mechanism to a certain extent and lowered the apparent orthogonality (data not shown). Therefore, it appears that the combination of two separation mechanisms is responsible for the high orthogonality observed for the chosen HILIC-RP 2DLC system. Currently, the most common 2DLC approach is based on a combination of SCX-RP modes. The orthogonality of SCX-RP was investigated and is illustrated in Fig. 12.2f. The principal retention mechanism is the charge-based interaction of peptides with the SCX sorbent. In general, the peptides carrying a low charge (1þ) elute first, followed by charged species 2þ, 3þ, 4þ . . . (Fig. 12.2f). Because trypsin cleaves peptides at the C terminus of basic amino acids (arginine or lysine) and their N terminus contributes another charge (NH2 group), the majority of peptides have a 2þ charge. Singly charged peptides are rare. They are either C terminal peptides (without arginine or lysine at the C terminus), originating from a nontryptic cleavage, or their net charge is reduced by a posttranslational modification (e.g., phosphorylation) (Beausoleil et al., 2004). Peptides carrying a larger charge (3þ and higher) contain histidine(s) in their sequence, or they are so-called missed-cleaved peptides (having a non-cleavable sequence motif consisting of multiple arginines/ lysines). Only limited populations of tryptic peptides have a 4þ or higher charge. This narrow range of peptide charges presents the main limitation for SCX separation; the peptides tend to elute in clusters according to their charge. Figure 12.2f shows the orthogonality of a SCX-RP 2DLC method. As expected, the most abundant groups of 2 þ (66%) and 3 þ (28%) charged peptides form relatively tight clusters (Fig. 12.2f) and the 2D separation space is not uniformly covered. In an extreme case, all the peptides with the same charge would elute under a single peak. Fortunately this is not the case, the trends emerging from the plot suggest that peptide retention also depends on their length. While large peptides (more hydrophobic and better retained in RP) are relatively less retained in SCX chromatography, the short peptides of the same charge more strongly interact with the SCX sorbent. This behavior implies that charge density (which is greater for the shorter peptides) plays an important role in the retention mechanism (Gilar et al., 2005b). Figure 12.2d,e, and f illustrate the 2DLC methods that employ different modes in both LC dimensions. An alternative approach is to use similar (or identical) LC stationary phases while altering the mobile phase. We explored the impact of mobile phase pH using the same sorbent (C18) in both LC dimensions (Gilar et al., 2005; Toll et al., 2005). As evident from Fig. 12.2c, the pH is a potent tool for altering the selectivity of separation. This is in agreement with the accepted theory of RP separation for charged solutes, nevertheless, the degree of orthogonality is rather surprising. The orthogonality is comparable to other 2DLC scenarios using dissimilar LC separation modes. To illustrate the separation mechanism in high/low pH RP-RP 2DLC (Fig. 12.2c), the peptides are divided into the three groups according to their pI. The pI values were calculated using the method of Shimura et al. (2002). As expected, the acidic peptides

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271

are more retained at low pH when they are less charged (more hydrophobic), whereas basic peptides are more retained at high pH. The “neutral peptides” (pI 5.5–7.5) are affected by pH as well. The pI values are the sum of contributions of all ionizable amino acids; changing the pH from 10 to 3 alters the ionization of amino acid functional groups (pKa 3.6–12.5). The pH used in the first C18 separation dimension was rather high (pH 10), however, no peptide loses or carryover, due to on-column precipitation, were observed. Peak shape was comparable to peptide analysis at low pH. Modern stationary phases, based on hybrid silica and stable alkyl bonding chemistry, are well suited for chromatography at extreme pH without compromising column lifetime or analysis-to-analysis reproducibility (Wyndham et al., 2003). 12.3.3

Geometric Approach to Orthogonality in 2DLC

The retention maps summarized in Fig. 12.2 allow one to visually compare the degree of separation orthogonality. Several promising 2DLC setups for the separation of tryptic peptides have been identified. However, to quantitatively compare the data orthogonality and estimate an achievable 2DLC peak capacity, more rigorous mathematical tools are needed. The description of the degree of retention data correlation is more complicated than it appears. For example, the 2D retention maps cannot be characterized by a simple correlation coefficient (Slonecker et al., 1996) since it fails to describe the datasets with apparent clustering (Fig. 12.2f). Several mathematical approaches have been developed to define the data spread in 2D separation space (Gray et al., 2002; Liu et al., 1995; Slonecker et al., 1996), but they are nonintuitive, complex, and use multiple descriptors to define the degree of orthogonality. We have recently developed an alternative geometric approach suitable for the description of 2DLC orthogonality (Gilar et al., 2005a). The approach uses a single descriptor (percent of orthogonality) and is based on the concept of area covered by eluting peaks in the normalized 2D separation space. Briefly, the normalized separation space is divided into rectangular bins corresponding to the number of separated components (in this fashion the datasets of different sizes can be compared). Each bin essentially corresponds to a peak area. Normalized separation space is superimposed with the dataset and the bins containing peak(s) are summed. The degree of area coverage describes the orthogonality of an interrogated 2D system. The greater the use of separation space, the greater the orthogonality and the practical peak capacity (Gilar et al., 2005a). In their seminal work from 1983, Davis and Giddings used a statistical theory to define the number of peaks observable in 1DLC separation upon the injection of a sample of different complexity on a column of a given peak capacity (Davis and Giddings, 1983). The theory was later extended into 2D separation space (Davis, 2005; Shi and Davis, 1993), also discussed in Chapter 2 of this book. The theory implies that when the 1D or 2D separation space is randomly covered with the number of peaks equal to the separation space peak capacity (area), the normalized surface coverage is

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

an average of 0.63 (due to partial peak overlap) (Davis, 2005). Remaining space is unused for the separation and remains devoid of peaks. Similar mathematical solution can be derived from a Poisson distribution of random events in 2D space. The probability that 2D separation space will be covered by peaks in ideally orthogonal separation is analogical to an example where balls are randomly thrown in 2D space divided into uniform bins. The general relationship between the number of events K (number of balls, peaks, etc.) and the number of bins occupied F (bins containing one or more balls, peaks, etc.) is described by Equation 12.3, where N is the number of available bins (peak capacity in 2DLC). 1 K ð12:3Þ F ¼ NN 1 N In the special case considered here, where the number of events K is equal to number of bins (N ¼ K; peak capacity is equal to the number of separated components), this formula can be rearranged into Equation 12.3. F 1 ¼ 1 1 ð12:4Þ N N The ratio F/N is the degree of area coverage; for a N approaching infinity, the F/N value is 0.63. The separation space coverage of 0.63, therefore, represents an ideally orthogonal separation. The degree of orthogonality for such coverage is regarded to be 100%. Both above-mentioned theoretical solutions are useful for peak capacity estimation of ideally orthogonal 2DLC. However; they fail to describe the incompletely orthogonal systems. Therefore, we developed the geometric orthogonality concept (Gilar et al., 2005a), outlined in Fig. 12.1. The surface coverage with peaks (data points) is calculated as a sum of bins containing data points (grayed out). Fig. 12.1b shows an example of a randomized dataset; 100 normalized retention data points were inserted into the separation space with a peak capacity of 100 (number of bins). The randomized datasets were generated in silico using a random number generator function (MS Excel). The average area used for the separation obtained in repeated simulations matched the stochastic prediction (0.63) (Davis, 2005). The opposite case, completely nonorthogonal separation, is presented in Fig. 12.1a. In this scenario, both separation dimensions are identical (nonorthogonal); the data are aligned along the diagonal of the separation space. The area coverage of 2D space averaged 0.1; the assigned orthogonality value is 0%. Fig. 12.1a and b, with area coverage 0.1–0.63, represents the achievable ranges of orthogonality (0–100%). The orthogonality % for any 2D separation scenario could then be calculated from Equation 12.5, P pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ bins Pmax O% ¼ 100 ð12:5Þ 0:63 Pmax P where bins is the number of bins in a 2D plot containing data points, and Pmax is the total peak capacity obtained as a sum of all the bins. For the rectangular separation space (peak capacity is the same in both dimension, P1 ¼ P2), the Pmax can be

273

Retention time, min

Retention time, min

TIC 45

SEC P = 14

TIC 50

C18, pH 2.6 P = 115

10

(e)

0

(b)

Retention time, min

Retention time, min

TIC 60

HILIC P = 79

60

TIC

PFP P = 115

0

(f)

0

(c)

Retention time, min

Retention time, min

TIC 45

SCX P = 51

TIC 50

P = 115

C18, pH 10

FIGURE 12.3 Examples of LC–MS analysis of phosphorylase b tryptic digest (approximately 100 peptides). The LC modes used for analysis were (a) C18, pH 2.6, (b) PFP reversed phase, (c) C18, pH 10, (d) SEC, (e) HILIC, and (f) SCX. For details see Table12.1.

30

(d)

0

(a)

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

TABLE 12.2 Theoretical Peak Capacity, Orthogonality, and Practical Peak Capacity of Investigated 2DLC Setups. Second Dimension was in All Cases Carried Out Using C18 Column and Typical LCMS Compatible Elution Conditions First LC dimension

Second LC dimension C18; pH2:6; P ¼ 115

1DLC peak capacitya 2D number of binsb 2D normalized area fractionc 2DLC orthogonality %d 2DLC theoretical peak capacity P2De 2DLC practical peak capacity Npf 2DLC practical peak capacity Npg (10 fractions collected in 1st D)

Phenyl 115

PFP 115

C18 pH 10 115

SEC 14

HILIC 79

SCX 51

30

52

80

86

100

81

0.15

0.27

0.41

0.44

0.51

0.41

13 13225

31 13225

53 13225

58 1610

69 9085

54 5865

1984

3571

5422

708

4633

2405

172

311

472

506

587

472

a

Calculated according to (Gilar et al., 2004). See Fig. 12.2, used bins are highlighted in gray. c Number of bins used for separation divided by total number of bins (196). d See Equation 12.5. e Calculated from Equation 12.1. f See Equation 12.6. g Equation 12.6, P1 is equal to number of collected fractions (P1 ¼ 10). b

pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ calculated as P2; thus, the Pmax value is equal to the number of bins intersected by a diagonal line (e.g., Fig. 12.3a). Using Equation 12.5 one can describe the orthogonality of different 2DLC data shown in Fig. 12.2 with quantitative values. The data points of 196 tryptic peptides were projected in the normalized space divided into 14 14 bins (196 bins in total). Please note that regardless of the number of bins in the normalized separation space, the area is always one. The rectangular bins used for the 2DLC separation surface coverage calculation are highlighted in gray in Fig. 12.2. Number of bins, surface coverage, and % or orthogonality for different 2DLC scenarios are also shown in Table 12.2. The highest orthogonality was observed for HILIC-RP scenario (69%), followed by SEC-RP (58%). The most common 2DLC approach used in proteome research based on SCXRP combination (Fig. 12.2f) is 54% orthogonal, which is comparable to RP-RP separation shown in Fig. 12.2c (53%). When comparing the orthogonality values listed in Table 12.2, one may assume that the HILIC-RP combination is the most efficient 2DLC setup for the separation of

DEVELOPING ORTHOGONAL 2DLC METHODS

275

peptides. However, besides the orthogonality, the peak capacities in both separation dimensions have an impact on the overall achievable 2DLC peak capacity. This is reflected by Equation 12.6, which defines the 2D practical peak capacity Np by taking into account an impact of surface coverage (orthogonality) and peak capacities P1, P2. P bins ð12:6Þ Np ¼ P1 P2 Pmax Table 12.2 summarizes the practical peak capacities of all 2DLC systems investigated in Fig. 12.2. It is apparent that despite the relatively large orthogonality of the SEC-RP setup, the overall practical 2DLC peak capacity value is reduced by a low separation efficiency of SEC. The importance of high peak capacity in both separation dimensions is highlighted in the case of RP-RP 2DLC (employing different pH). It provides the highest achievable Np of all systems, despite the fact that its orthogonality falls behind HILIC-RP and SEC-RP 2DLC methods. SCX-RP 2DLC has comparable orthogonality to the RP-RP setup, but due to the lower peak capacity of the SCX column, its achievable practical peak capacity Np is lower. 12.3.4

Practical 2DLC Considerations in Proteome Research

Although comprehensive 2DLC is the preferable setup from the point of achievable peak capacity (Gilar et al., 2004; Murphy et al., 1998) and is often used for the analysis of small molecules (Gray et al., 2004; Schoenmakers et al., 2005; Tanaka et al., 2004), the 2DLC analysis in proteome research usually consists of only 10–20 fractions and employs time-consuming analyses in the second LC dimension. The differences in this approach are forced by several factors. First, the comprehensive analysis is difficult to design in the nano- and capillary-LC scale, especially when both separation dimensions employ a gradient elution. Second, the duty cycle of MS/MS peptide analysis in the second dimension has to be taken into consideration. Therefore, further discussion will be carried out from the perspective of 2DLC–MS, as is typically done for proteomic applications. 2DLC can be practiced either in an online or an off-line setup. Both approaches have their distinct advantages and disadvantages. Some researchers favor an online approach because of the ease of use and minimal sample manipulation (Wagner et al., 2002; Washburn et al., 2001; Wolters et al., 2001). The necessary requirements are (i) mobile phase composition compatibility between the first and the second LC dimension and (ii) volume compatibility between fractions submitted for analysis and the scale of the second LC dimension. Although these requirements seem trivial, only two online 2DLC–MS approaches for separation of peptides have been described in the literature: SEC-RP (Opiteck et al., 1997) and SCX-RP 2DLC systems (Bushey and Jorgenson, 1990; Wagner et al., 2002; Washburn et al., 2001; Wolters et al., 2001). Both SEC and SCX modes operate in principle with aqueous mobile phases that could be transferred directly to the second RP dimension. However, nonspecific interactions of peptide with the sorbent makes it advisable to include organic solvents in the mobile phase (typically 5–25% acetonitrile) (Alpert and Andrews, 1988; Burke et al., 1989; Gilar et al., 2005; Peng et al., 2003; Winther and Reubsaet, 2005). This effectively

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

eliminates the focusing effect of the second RP dimension and leads to a considerable peptide loss (Masuda et al., 2005). An off-line 2DLC setup (Gilar et al., 2005b; Peng et al., 2003) eases the restrictions of mobile phase compatibility. For example, the addition of 25% acetonitrile in SCX mobile phases is acceptable. The collected fractions can be either evaporated or diluted to minimize the negative effect of organic solvents on second RPLC dimension. The flexibility of the off-line 2DLC setup was especially useful in the presented study, since a majority of the investigated LC modes use solvents that cannot be injected directly on a second (RPLC) separation dimension. While the choice of a 2DLC method has a great impact on practical peak capacity, it is important to notice that the Np values shown in Table 12.2 are based on the assumption of frequent fraction collection. When the number of collected fractions transferred from the first to second dimension is limited to the level typical in 2DLC proteomic analysis (e.g., 10), the practical peak capacity Np (Equation 12.6) decreases. The importance of peak capacity in the first dimension is eliminated (P1 is equal to the number of fraction), and the Np largely depends on orthogonality. This scenario is illustrated in Table 12.2; the practical peak capacity for the most efficient 2DLC system is between 500–600. Interestingly, the SEC-RP approach becomes a viable choice for 2DLC. The only advantage then of using an efficient LC mode in the first dimension is the lower degree of fraction overlap. When the peaks are significantly narrower than the fraction collection window, they are less likely to be divided between consecutive fractions (Gilar et al., 2005b). As discussed above, in a typical proteomic experiment with a limited number of fractions collected, the practical peak capacity of 2DLC is well below 1000. This resolution is dramatically lower than the values considered in literature. 12.3.5

Evaluation of Selected 2DLC MS/MS Systems

We have successfully used partial evaporation of the fractions collected from the first LC dimension for an off-line 2DLC-MS/MS analysis of 17 digested proteins (Olivova et al., 2005). Three different modes were applied as a first dimension (150 1 mm or 150 2.1 mm columns): high pH RP, HILIC–LC, and SCX (with 25% acetonitrile in mobile phase). After the organic content was reduced and concentrated, the fractions were injected on the capillary RPLC–MS/MS system. The concentration of proteins varied between 30 fmole and 10 pmole; the lower concentration represented the MS instrument limit of detection for peptides. The amount of the 17-protein digest sample injected was identical for all three 2DLC experiments. An alternate low energy MS and high energy MS/MS scanning was used for MS/MS analysis (2.2 s per scan) (Silva et al., 2005). MS chromatograms of collected fractions are shown in Fig. 12.4. No peak distortion or peptide breakthrough was observed. All three 2DLC approaches are promising alternatives for proteomic research and show useful orthogonality. The highest number of peptides/proteins (221/15) was identified by RP-RP, followed by HILIC-RP (164/13) and SCX-RP (146/12) in 2DLC–MS/MS. The greater amount of peptide/protein identification in the RP-RP experiment is likely related to higher peptide recovery from the first LC dimension. In addition, a

277

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3

5

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8

40

UV 220 nm

7

1st D separation, C18 column, pH 10

0

Minutes

8 4

Minutes

7

3

45

6

2

45

TIC

5

1

0

2nd D LC–MS, C18 column, pH 2.6

FIGURE 12.4 Comparisons of three off-line 2DLC methods for the separation of 17 protein tryptic digest. (a) RP-RP 2DLC separation based on pH differences in both separation dimensions, (b) HILIC-RP 2DLC, (c) SCX-RP 2DLC. The large peak eluting close to the column void volume (in all three 1st D LC analyses) is a UV signal of alkylating and reducting agents. The first fractions in HILIC and SCX contained no peptides; their LC–MS chromatogram is not shown. First RP-LC fraction contains mostly small hydrophilic peptides.

0

1

(a)

278

FIGURE 12.4

0

1

(b)

3

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(Continued )

2

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1st D separation, HILIC

45 0 Minutes

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2nd D LC-MS, C18 column, pH 2.6

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FIGURE 12.4

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2nd D LC-MS, C18 column, pH 2.6

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

more efficient separation (less peak tailing) and lower fraction overlap in the RP-RP mode reduces the dilution of peptides, leading to the loss of identification of less abundant peptides (Gilar et al., 2005b) as their concentration falls below the MS limit of detection. In our experience, the RP-RP 2DLC method is a robust and efficient approach for the separation of complex peptide samples, which can supplement the most common current 2DLC technique based on SCX-RP chromatographic modes.

12.3.6

Peak Capacity in 2DLC-MS/MS

12.3.6.1 Chromatographic Versus MS/MS Peak Capacity In an earlier publication, we employed the RPLC gradient theory to predict peak capacity for peptide separations (Gilar et al., 2004). Different column length/efficiency and gradient slopes were considered. It appears that the theory reliably described the peptide behavior; it was utilized to model the separation productivity in 2DLC. The model predicts that the best productivity (defined as the number of separated peptides per unit of time) is achieved when using a frequent fraction collection in the first dimension (Murphy et al., 1998) in conjunction with a comprehensive and efficient analysis in the second LC dimension. The earlier study concluded that it is feasible to separate approximately 10,000–15,000 peaks within 8 h when using 6 min long analysis in the second dimension (Gilar et al., 2004). The proposed estimate has several limitations. When taking into account the limited orthogonality of investigated 2DLC modes, the practical peak capacity is reduced approximately to half. It needs to be also emphasized that a full separation power of the first LC dimension is realized only when the number of collected fractions exceeds its peak capacity (Murphy et al., 1998). If the number of fractions analyzed is low, the achievable chromatographic peak capacity suffers. A comparison of theoretical and practical peak capacity values, summarized in Table 12.2, leads to a conclusion that even the most promising 2DLC setups do not provide for the peak capacity needed to fully resolve a complex proteomic sample. As a result, the eluent entering the MS source typically contains multiple coeluting peptides. While chromatographic peak capacity is not adequate to resolve hundreds of thousands of components, many researchers argue that MS itself is an additional separation dimension with an orthogonal selectivity (separation is based on mass-tocharge ratio). Therefore, the combined resolution of LC and MS is greater than the chromatographically defined peak capacity. The question therefore stands: What is the achievable peak capacity of the 2DLC-MS/MS system? 12.3.6.2 MS/MS Duty Cycle Typical MS/MS analysis is a serial process, relying on the selection of precursors (peptides) in MS mode, followed by high-energy fragmentation in MS/MS. This process is termed data dependent acquisition (DDA). The duty cycle for the completion of MS and MS/MS cycles (the time necessary for MS/MS spectrum acquisition) is of primary importance. When the separation performance is viewed from the mass spectrometry perspective, the peak capacity can be characterized by the number of MS/MS scans, yielding successful

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identification of peptides. In this scenario, the peak capacity of the LC–MS/MS system is given by the DDA duty cycle, which is virtually independent of LC performance. Although this approach is simplistic, it is useful for evaluating the overall peak capacity of a 2DLC–MS/MS system. Over the years, MS/MS duty cycle of modern MS instruments has constantly been improving, but for simplicity we assume it is equal to 1 s. Considering this it is possible to identify up to 60 peptides per minute and up to 3600 peptides in a LC-MS/MS analysis of 1 h. It is important to mention that only a small percentage of MS/MS scans typically yield a spectrum of sufficient quality that can be matched against a protein database and can result in peptide identification. Extending the concept of peak capacity defined by MS/MS, one may arrive to the conclusion that it is the overall LC analysis time that decides the number of identified peptides regardless of whether the analysis is practiced in a 1D- or 2DLC setup. As the time of analysis increases, more MS/MS scans can be accomplished during the experiment. In other words, a 10-h long 1DLC should be equivalent to a 10 h long 2DLC experiment (10 fractions collected in the first dimension and analyzed in 1 h long LC–MS run). This assumption correlates well with the practical experience in many proteomic laboratories. However, shallow gradients in 1DLC runs will result in wider peaks (Shen et al., 2005), and the peptides will be more dilute than in a typical 2DLC run, where the second dimension analysis is short, producing sharp (intense) peaks (Gilar et al., 2004). Thus, the favorable detection limit of peptides is expected in the latter case. 12.3.6.3 Achievable MS/MS Peak Capacity Proteomic samples are often more complex than the peak capacity defined by a MS/MS duty cycle. As a consequence, data-dependent MS/MS analysis undersamples the mixture and provides results that do not represent a full complement of peptides in the mixture. As the automated DDA acquires MS/MS spectra based on the MS precursor intensity, the protein identification is generally biased toward the most abundant ones. Understandably, the low abundant peptides are often not selected for DDA although they may be present in detectable quantities. In addition, the precursors selected for MS/MS from a complex sample may be polluted with other ions of similar masses. Therefore, the MS/MS spectra may contain foreign fragments that may lead to false positive/negative identification via the database search. Typically, only a small fraction of MS/MS scans (10–25%) yield a useful identification of peptides (Liu et al., 2004; Peng et al., 2003). The maximum LC–MS peak capacity calculated for a DDA duty cycle of 1 s is shown in Table 12.3. The number of MS/MS scans exceeds 100,000 for 10 h long 1D/2DLC experiment, but the number of identified peptides is typically lower. When considering the 25% success rate of a database search and the limited 2DLC orthogonality, the number of identified peptides is not more than 4500 in a 10 h experiment. The proteins of high abundance are often identified by multiple peptides and high statistical confidence. On the contrary, many medium and low abundant proteins are typically identified by only a single peptide. This is a direct result of the semirandom nature of the DDA algorithm (Liu et al., 2004), as discussed above. In addition, these peptide hits often cannot be confirmed by subsequent DDA experiments conducted

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

TABLE 12.3 The Peak Capacity Estimate of 2DLC–MS/MS Based on the MS/MS Duty Cycle (1 s per MS/MS spectra acquisition)

1DLC (60 min analysis) 2DLC 10 fractions 60 min 2DLC 20 fractions 120 min

MS maximum peak capacity, Number of MS/MS spectra (one precursor per second)

MS practical peak capacity, Number of identified peptides (25% success rate, 63% orthogonality in 2DLCa)

3600

900

36,000

4500

144,000

18,000

a

The 63% orthogonality represents a situation in which only half of the 2DLC separation space was covered with eluting peaks (see Eq. 12.6). The practical MS peak capacity in 2DLC is reduced to 50% (in addition to 25% MS/MS success rate), since the chromatogram in the second dimension is incompletely covered with eluting peaks (e.g., Figures 12.4(a) and 12.5).

even in the same laboratory and on the same day (Cargile et al., 2004; Maynard et al., 2004; Omenn et al., 2005; Von Haller et al., 2003). A parallel MS and MS/MS data acquisition approach introduced recently is a promising alternative to DDA, alleviating some of the inherent experimental drawbacks, such as undersampling, and providing for qualitative and quantitative protein analysis at the same time (Silva et al., 2005, 2006). 12.3.7

Considerations of Concentration Dynamic Range

The enormous dynamic range of proteins in the sample represents an additional difficulty in proteome analysis. The best example is serum with a protein abundance ranging over eleven orders of magnitude (Anderson and Anderson, 2002). To detect the low abundant species, one has to load a sufficient amount of digest on a column to meet the limit of detection (LOD) of the MS instrument. Some reports published used up to 2.5 L of plasma with an extensive fractionation of intact proteins prior to LC–MS analysis on the peptide level (Rose et al., 2004). One benefit of off-line 2DLC is the flexibility to operate the first LC dimension at a desirable scale with larger columns, providing a sufficient mass load capacity (Peng et al., 2003). The second dimension is typically carried out on nano-LC scale. One can, in principal, process more material in the 2DLC setup than in 1DLC, thereby improving the peptide LOD and overall MS signal. However, this approach is still limited by column mass load capacity, since the peptides deriving from both high abundant and low abundant proteins are present in the same sample. Consequently, the capillary or nanocolumns in second dimension may still be severely overloaded before the desirable concentration of low abundant peptides is reached. Figure 12.5 illustrates the problem of using human serum digest analysis. The digest was fractionated using a 2.1 150 mm C18 column at pH 10 (50 mL serum

DEVELOPING ORTHOGONAL 2DLC METHODS

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Minutes

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Fr1 0–5mm

25–30mm

5–10mm

30–35mm

10–15mm

35–40mm

15–20mm

40–45mm

20–25mm

Fr10 45-50mm

70 0

Minutes

BPI 100

FIGURE 12.5 Human serum tryptic digest analysis. Fractionation in the first LC dimension was performed using a C18 column at pH 10. Fractions were analyzed using NanoEase 0.3 150 mm Atlantis d18 column. Approximately 66 mg (400 pmole of serum albumin peptides) was injected on column. Arrow points to a selected albumin peptide illustrating a local column mass overloading. Ten-5mm wide fractions were collected in Ist LC dimension.

volume equivalent was injected on first dimension LC, total peptide content 3.3 mg, data not shown), and 1 mL of serum volume equivalent (66 mg of peptides) was injected on a 0.3 150 mm C18 column. MS chromatograms of collected fractions (Fig. 12.5) reveal that lower abundant peptides elute as narrow peaks, whereas the albumin peptides (400 pmole per peptide injected on column) give rise to 5 min broad peaks. This has a negative impact on achievable chromatographic peak capacity, while also decreasing MS accuracy due to MS signal saturation. Loading even more sample on the second dimension column would result in a complete deterioration of chromatographic separation and sample breakthrough. Figure 12.5 illustrates a typical problem of analysis of minor components present in a matrix of highly abundant ones. Despite the availability of large LC–MS peak capacity (Table 12.3), the number of peptides detected in a serum/plasma digest does not exceed several hundreds (Kapp et al., 2005). These peptides typically match 30–50 high abundant proteins. We believe that the majority of remaining proteins/peptides in the sample are present at concentrations well below the LOD of MS instrument. Depleting albumin and other major proteins from the sample combined with 2DLC–MS/MS analysis offer a promising solution (Bjorhall et al., 2005; Zolotarjova

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DEVELOPMENT OF ORTHOGONAL 2DLC METHODS

et al., 2005) and can extend the limit of detection by one or two orders of magnitude; however, serum remains the most challenging sample for proteomic research.

12.4 CONCLUSIONS The consensus among proteome researchers is that separation is an essential part of complex protein analysis methods. A simplification of complex samples using 2DLC is beneficial for state-of-the-art MS/MS analysis. While 2DLC potentially provides a higher peak capacity than 1DLC, the orthogonality of separation has to be taken into consideration. We have evaluated several 2DLC methods for the separation of peptides and constructed 2D retention maps suggesting that it is difficult to find any two modes with complete orthogonality. The geometric approach for the quantification of orthogonality and practical peak capacity estimates indicate that the most suitable 2DLC approaches are based on a combination of SCX-RP and HILIC-RP chromatographic modes. In addition, we found that RP-RP 2DLC based on widely different pHs, in both separation dimensions, may also be a promising approach for proteomic research. It has been argued that in a typical 2DLC proteomic experiment, with only a limited number of fractions submitted for analysis in the second LC dimension, chromatographic peak capacity is less than 1000. This value is considerably lower than the expected sample complexity. Additional resolution is offered by MS, which represents another separation dimension. With the peak capacity defined as the number of MS/MS scans (peptide identifications) accomplished within the LC analysis time, the MS-derived peak capacity was estimated to be in an order of tens of thousands. While the MS peak capacity is virtually independent of LC separation performance, the complexity of the sample entering the MS instrument still defines the quality of MS/MS data acquisition. The primary goal of 2DLC separation is to reduce the complexity of the sample (and concentrate it, if possible) to a level acceptable for MS/MS analysis. What is the “acceptable” level of complexity to maintain the reliability and the repeatability of DDA experiments remains to be seen.

ACKNOWLEDGMENT The authors thank Aleksander Jaworski and Jessica Fridrich for helpful suggestions to the manuscript.

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13 MULTIDIMENSIONAL SEPARATION OF PROTEINS WITH ONLINE ELECTROSPRAY TIME-OF-FLIGHT MASS SPECTROMETRIC DETECTION Steven A. Cohen and Scott J. Berger Life Sciences R&D, Waters Corporation, Milford, MA 01757, USA

13.1 INTRODUCTION Multidimensional chromatography is being more widely used as scientists study biological systems of ever-increasing complexity. The emerging field of proteomics, where the goal is to qualitatively and/or quantitatively describe the entire protein complement of a system, presents some of the most challenging separation problems (e.g., Anderson and Anderson, 2001; Wang et al., 2005; Wang and Hanash, 2005). A typical sample from a cellular extract, tissue homogenate, or biological fluid may comprise thousands of proteins, with a tremendous variation in physical properties. Protein size can range from small soluble proteins with molecular weights of 103 Da to large protein complexes of 106 Da (Peng and Gygi, 2001). Solubility and charge characteristics vary widely, and can influence protein chromatographic properties and recovery during fractionation. Membrane proteins are often poorly soluble in common aqueous buffers, and as a consequence present exceptionally challenging issues with recovery and chromatographic efficiency. At the other extreme, the small soluble proteins may be highly charged and present recovery difficulties for ion-exchange (IEX) separations.

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Separation difficulties are further exacerbated by the wide variation in protein concentrations present in most samples (Gygi et al., 2000; Anderson and Anderson, 2001; Wang et al., 2005; Wang and Hanash, 2005). Typical protein concentrations in cells vary by 6 orders of magnitude, and the variation in plasma can be as high as 1011. Analysis of low concentration analytes is often masked by a single protein or group of proteins that dominate the sample. For example, in serum or plasma, albumin is the predominant protein, comprising approximately 60% of the total protein mass, and the 10 most abundant proteins comprise roughly 90% of the total mass present (Anderson and Anderson, 2002). Sample complexity is also greatly increased because of the presence of multiple forms of many proteins arising from alternative transcriptional editing, cotranslational protein processing, and posttranslational modifications (Anderson and Anderson, 2001; Brunet et al., 2003; Jensen, 2004; Wang and Hanash, 2005). These modified forms can also be present over a significant dynamic range, and minor variations in structure often lead to very similar retention characteristics such that resolving the various forms is not an easy task. The limitations of using one separation mode for purifying a single protein from a crude biological sample have been apparent to biochemists for decades, and virtually all protein purification schemes, except for some affinity purifications, employ multiple steps to produce a purified sample. The goal of proteomic studies is often to create a comprehensive picture of proteins in a sample, and multiple steps of fractionation can be critical to achieving this aim. This is particularly true in those studies that seek to combine information at the intact protein level (top-down proteomics) (Nemeth-Cawley et al., 2003; Kelleher, 2004; Whitelegge et al., 2002) with identification based on peptide tandem mass spectrometry (bottom-up proteomics) (Ducret et al., 1998; Hamler et al., 2004). No single chromatographic mode possesses the resolving power to separate the hundreds to thousands of protein species present in typical samples. This has rekindled an interest in multidimensional protein separations, an area pioneered by Jorgenson and coworkers (Bushey and Jorgenson, 1990; Opiteck et al., 1997, 1998), and now finding great utility for proteomics. In addition, there are several recent reviews covering protein MDLC (Apffel, 2004; Evans and Jorgenson, 2004; and Chapter 8 by Evans and Jorgenson) that describe practical aspects of linking various orthogonal chromatographic modes. Multidimensional separations are most useful when the different separation modes are orthogonal in nature, with true orthogonality (defined as lack of correlation between analyte retention in the two modes) (Slonecker et al., 1996; also see Chapters 3 and 12 by Davis and Gilar et al., respectively) yielding systems with peak capacities that are the product of the individual separation dimensions (Giddings, 1984, 1987; Bushey and Jorgenson, 1990; Liu et al., 1995). Thus, the choice of chromatographic modes, such as IEX or reversed-phase (RP), is one of the essential elements for optimizing the resolving power of a MDLC system. Fortunately, several potential separation modes rely on completely different retention mechanisms that, when coupled in a 2D system, provide excellent orthogonality. For example, researchers have successfully coupled first dimension separations by size exclusion

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chromatography (SEC) (Opiteck et al., 1997, 1998), IEX chromatography (Bushey and Jorgenson, 1990; Liu et al., 2002; Millea et al., 2005, 2006), affinity chromatography (Geng et al., 2001) and chromatofocusing (Chong et al., 2001) with a second dimension reversed-phase chromatographic (RPLC) step. Several other chapters in this book provide detailed descriptions of various 2D protein separations, including those coupling LC with CE (Chapter 16), 2D capillary electrophoresis (Chapter 15), and chromatofocusing with RPLC (Chapter 10). In addition, Evans and Jorgenson (Chapter 8) provide a broad overview of 2D-LC protein separations. This chapter will focus specifically on our research using an IEX–RP configuration in conjunction with electrospray-time-of-flight mass spectrometry (ESI–TOF MS), which provides an excellent combination of chromatographic resolution, orthogonal separation, and compatibility with the online protein mass determination.

13.2 CHROMATOGRAPHIC PARAMETERS In the first section of the chapter, we will discuss the advantages and disadvantages of various operating parameters of the first dimension IEX operation, such as anion- and cation-exchange separations, as well as a comparison of step versus linear gradient elution for the initial dimension. Configuration issues, such as coupling the two dimensions, are presented in the second section. Because the buffers used for IEX are typically weak eluents in RPLC, concentration of the eluting components on RP columns is readily accomplished, but different configurations for this transfer step have their own beneficial consequences. The RP step itself employs MS-compatible mobile phases, such as dilute formic acid-acetonitrile gradient systems, and meets the requirement for introducing a salt-free sample into the MS interface. However, residual salt from the IEX step will interfere with the analysis. Approaches to removing the salt are discussed. Downstream post-run processing of the 2D separation, including the generation of peptides for further analysis of the eluting proteins, is also an important aspect for proteomics analysis, and is taken into consideration.

13.3 ANALYTE DETECTION AND SUBSEQUENT ANALYSIS A 2D system coupled with a TOF-MS detector provides not only resolution for a large number of protein components, but also yields accurate intact molecular weight information (e.g., Opiteck et al., 1997; Liu et al., 2002; Millea et al., 2005). Moreover, by splitting the effluent just prior to the MS interface, a small portion can be diverted for MS analysis, whereas the bulk of the sample can be collected for subsequent analysis, following enzymatic digestion, to provide positive identification and characterization of the proteins present in the fraction. The ESI–MS of an intact protein yields a series of ions with m/z values corresponding to sequentially charged species (Fenn et al., 1989). Algorithms and software for the deconvolution of these peaks into a single neutral mass have been available for many

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years, but the enormous number of spectra produced by a comprehensive 2D protein, poses new challenges for data reduction. For simple systems, a total ion chromatogram (TIC) generated from individual RPLC steps can be sufficient to permit automated peak identification (Liu et al., 2002). This scheme often fails with highly complex mixtures, such as whole cell extracts that exhibit overlapping peaks and a much higher dynamic range. The approach taken for these complicated samples has been to apply a time-based segmentation of the data to automate spectral deconvolution of resulting LC–MS analysis (Millea et al., 2006). This preserves chromatographic resolution, and provides a means to evaluate the distribution of a protein among multiple fractions. The combination of this top-down proteomics approach, which generates information on the structure of the intact protein, with a bottom-up approach for protein identification (using MS/MS data of tryptic peptides from the collected fractions) has been particularly useful for identifying posttranslational modifications, cotranslational processing, and proteolytic modifications in a number of proteins. Examples from our work will be shown to illustrate this hybrid methodology for proteomics analysis.

13.4 BUILDING A MULTIDIMENSIONAL PROTEIN SEPARATION Multidimensional separations in proteomics have most often been targeted toward peptide-level (or bottom-up) analysis. This has been effective for simplifying samples for subsequent mass-spectrometric analysis, and addressing the limitations of mass spectrometry to acquire MS/MS data from online separations. Peptide-level separations do not significantly address issues of the fundamental discontinuities between proteomic sample dynamic range (106–1012) and the dynamic range of detection in modern mass spectrometers (typically 103–104). The peptides from abundant proteins are distributed throughout a fractionation, and thus the dynamic range of individual fractions closely resembles that of the original sample. In the end, the largest improvements in such peptide-level separations can be seen when fractions (of lower sample mass than the original sample) are overloaded with respect to these abundant components to facilitate the identification of lower abundance components. Separations at the intact protein level can effectively resolve abundant proteins from those of lesser abundance, but introduce the additional complications of keeping proteins soluble and maintaining or preventing protein interactions. It can also be challenging to maintain the consistency of protein–sorbent interactions over multiple chromatographic runs, which is potentially compromised by incomplete desorption of strongly retained or poorly solubilized sample components, such as lipids or membrane proteins. Whether the separation is used for fractionation or for direct analysis of a sample, separation methodologies must balance the benefits of more extensive fractionation with the capability of extracting quantitative information from the data. Each processing or separation step demonstrates some variation for overall analyte recovery, and distribution, and these effects are multiplied with each additional dimension of sample processing. In the following sections, we will detail various aspects of several IEX/reversed-phase multidimensional chromatographic separation

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methodologies we have developed for the resolution and analysis of complicated protein mixtures. 13.4.1 Selection of an Ion-Exchange–Reversed-Phase Separation System for Protein-Level Separations The IEX chromatography possesses several attractive features as a first-dimension separation mode in an MDLC scheme. These features include the following: (1) The capability to concentrate dilute biological samples prior to elution. (2) The ability to generate separation peak capacities on the order of 10–100 peaks. (3) High orthogonality with reversed-phase chromatography. (4) Compatibility with many of the additives used to prevent protein interactions, and maintain sample stability and solubility (detergents, denaturants, and reductants, and protease inhibitors). (5) Flexibility to address proteins over the wide range of size and pI. (6) Commercial availability of high binding capacity sorbents with moderate to high pressure tolerance. On the downside, eluent pH, ionic strength, and the presence of additives are often incompatible with downstream analysis methods, particularly those involving massspectrometric analysis. Thus, the pairing of IEX methods with a second-dimension reversed-phase separation can accomplish not only the goals of orthogonal separation and increased peak capacity, but also the practical objectives of sample cleanup and removal of interfering substances prior to analysis. The practical implementation of this chromatographic pairing will be discussed in detail in subsequent sections of this chapter. 13.4.2

Chromatographic Sorbent Considerations

Anion-exchange chromatography appears, in the literature, to be the preferred separation mechanism for complex protein separations. In most of the organisms studied, there is an asymmetrical distribution of protein pI, with 70% of typical cellular proteins having pI below 7 (Gianazza and Righetti, 1980). Thus, anionexchange chromatography, at or near neutral pH, would demonstrate superior utility for the separation of greater numbers of proteins. Notable exceptions to this rule would be classes of basic proteins dominated by proteins that interact with nucleic acids via electrostatic interactions (e.g., ribosomal proteins and histones). For these protein classes, cation-exchange chromatography has been demonstrated (e.g., Threadgill et al., 1987) to be a highly efficient mode of separation. Both large-pore and nonporous sorbents have been successfully applied for large biomolecule separations. The fundamental distinction between these two particle types is the balance between efficiency of mass transfer and loading capacity. Porous

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particles possess significantly greater surface area (approaching 50x) than comparable nonporous particles, which is typically manifested as higher analyte loading capacity. This extra binding capacity is achieved while sacrificing both separation efficiency and pressure resistance. Pressure limitations can be particularly notable when using porous polymeric-based ion-exchangers, and can significantly limit potential choices for the second-dimension RP column configurations. Capacity limitations are typically more acute with nonporous RP sorbents than IEX sorbents, and column overloading can manifest during a multidimensional separation when protein content varies significantly between steps or segments of an IEX first dimension. 13.4.3

Chromatographic Behavior of Proteins

Proteins are all “large molecules’’ in chromatographic terms, and typically produce multisite interactions with a chromatographic sorbent. Regnier described the cooperative nature of such interactions in the 1980s as part of the stoichiometric displacement model (Geng and Regnier, 1984; Drager and Regnier, 1986). The model argues that an ideal protein achieves significant chromatographic linear velocity only when a critical elution strength, necessary to break all interactions between protein and sorbent, has been reached. This translates into the expectation that proteins can be maintained on a column at subcritical eluent strength for extended periods without degrading separation quality once the critical elution strength is achieved. Thus, the use of both step and linear gradients in the first-dimension IEX separation should prove useful for protein separations. In reality, many proteins demonstrate mixed mode interactions (e.g., additional hydrophobic or silanol interactions) with a column, or multiple structural conformations that differentially interact with the sorbent. These nonideal interactions may distribute a component over multiple gradient steps, or over a wide elution range with a linear gradient. These behaviors may be mitigated by the addition of mobile phase modifiers (e.g., organic solvent, surfactants, and denaturants), and optimization (temperature, salt, pH, sample load) of separation conditions.

13.5 COMPREHENSIVE MULTIDIMENSIONAL CHROMATOGRAPHIC SYSTEMS In general, a comprehensive separation strategy implies the desire to resolve/analyze all components within a sample. In the specific context of a multidimensional chromatographic method, the term is more narrowly applied to indicate that all analytes introduced to the first-dimension separation are also subjected to a seconddimension separation. There are two basic configurations used by our laboratory to carry out comprehensive multidimensional (IEX/RP) protein separations—IEX— Dual Column RP system and IEX—Dual Trap RP system (Figs. 13.1 and 13.2), respectively. The IEX—Dual Column RP system configuration shown (Fig. 13.1) utilizes independent pumping systems for each separation dimension. One pumping system

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has responsibility for sample injection onto the first-dimension column, and for generation of the first-dimension gradient separation. The effluent from the firstdimension column is directed into a 10-port two-position valve, where flow is delivered to one of the two reversed-phase columns plumbed into the valve, and on to waste. Flow from the second-dimension pumping system is simultaneously directed through the valve and the second reversed-phase column to fraction collection and/or mass analysis. The actuation of this “ column selection’’valve (V1) alternates the roles of the two reversed-phase columns between capture of the first-dimension effluent and RP analysis of the previous first-dimension IEX sampling. The second-dimension effluent in Fig. 13.1 is typically directed through a second two-position valve (V2) to divert salts and other hydrophilic substances that do not retain on a second-dimension column. This is necessary when separations are coupled with online ESI—MS analysis. The valve is initially switched to the divert position when a second-dimension column is switched in-line, and maintained in this position until residual salts and hydrophilic components are washed from the column. During this period of desalting/cleanup, the second-dimension pumping system is held at initial gradient conditions. Following the desalting step, the valve is actuated, the RP gradient initiated, and the second-dimension flow is returned to mass detection and/or fraction collection. The IEX—Dual Trap RP system configuration (Fig. 13.2) directs first-dimension IEX effluent to two alternating reversed-phase trap columns (typically 2.1 10 mm

FIGURE 13.1 Schematic of a comprehensive 2DLC (IEX/RP) configuration with alternating second-dimension analytical RP column sampling of first-dimension eluent. A salt diversion valve is present to divert salts from the IEX dimension to waste, and prevent contamination of downstream collected fractions or mass analyzer.

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FIGURE 13.2 Schematic of a comprehensive 2DLC (IEX/RP) configuration with alternating sampling of first-dimension IEX eluent by two RP trap columns, and a single downstream analytical RP column. A salt diversion valve is present to divert salts from the IEX dimension to waste, and prevent contamination of downstream collected fractions or mass analyzer.

cartridges) rather than the higher resolution analytical columns employed for the previous configuration. A single analytical reversed-phase column is placed downstream of the salt-divert valve. To prevent rapid pressure changes in the system, the salt-divert waste line now contains a pressure restrictor roughly equal in backpressure to the analytical column. This configuration is typically employed when the firstdimension IEX column has limited pressure tolerance, as is the case with most porous polymeric IEX phases, and cannot withstand the high backpressure of a modern small particle RP typically used in the second-dimension. This configuration also permits rapid desalting/washing of the reversed-phase trap column, which can be useful for protecting silica-based reversed-phase chemistries from attack by alkaline eluent employed for anion-exchange chromatography. We have found that the dual-trap configuration is not significantly more difficult to execute, nor appreciably less efficient than the dual-column format. The most common failure mode appears to be fouling of one of the trap columns. We have observed that the trap column that receives components in the flow through IEX fraction is usually the first to show performance degradation with complicated proteomic samples. This is the initial fraction containing those components (including likely nonproteinaceous components such as lipids) that are not retained on the IEX column under loading conditions. Performance degradation of a trap column is often

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diagnosed by observing increased system backpressure when the affected column is in line with the first-dimension IEX column. To maintain parallel equivalent function, the trap columns are typically replaced as pairs. In contrast to the two previous multidimensional reversed-phase LC systems, a third online configuration (IEX–Multiple RP) has been described in the literature (Masuda et al., 2005), where each first-dimension elution step or first-dimension gradient segment is captured by a unique second-dimension column. This “Gattling Gun’’ configuration effectively decouples the two separation dimensions, and could more aptly be described as an approach for “online fraction collection.’’ Two functional distinctions arise from this third methodology: A single chromatographic system could potentially support both separation dimensions, and that the maximum peak capacity for the first-dimension is directly limited by the number of seconddimension columns in the system. Although the fluidics of such a system is rather straightforward (sets of columns are plumbed between two synchronized multiposition valves, e.g., a seven-port six-position valve would support six RP columns), we have not explored this configuration because of the lack of scalability inherent in this approach.

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13.6 COUPLING 2DLC WITH ONLINE ESI–MS DETECTION Mass spectrometry provides an information-rich and concentration-sensitive detection scheme that can yield component-level characterization of complex protein mixtures. Interfacing multidimensional separations to mass spectrometry provides a significant increase in analytical capability over UV detection, permitting the tracking of individual proteins throughout a separation scheme, and resolving data from proteins with overlapping elution profiles. This ability to simultaneously characterize multiple analytes provides an extra analytical dimension to the separation, increasing peak capacity by fivefold or greater in most systems. Subsequent figures will provide examples where multiple coeluting or partially coeluting proteins are resolved during a 2DLC/MS analysis. The use of the salt-divert valve (Figs. 13.1 and 13.2; V2) is critical to applications involving online MS analysis, as non-volatile salts can be strongly bound to proteins during ionization, producing salt adduct peaks in resulting mass spectra. These adducts complicate qualitative characterization of a protein mixture, and can significantly degrade signal response of the nonadducted protein. Figure. 13.3 contains two spectra obtained during 2DLC(SCX/RP)/MS analysis of yeast ribosomal proteins, where insufficient removal of nonvolatile salts (7 column volumes under initial RP gradient conditions) resulted in a series of detected salt adducts (þ22 Da mass differences, consistent with a series of sodium adducts) dominating the deconvoluted spectrum for RPL17 protein (Fig. 13.3a). More extensive salt removal (14 column volumes) significantly eliminated the adduct signals, and the resulting deconvoluted mass spectrum is dominated by the signal of the intact protein (Fig. 13.3b). Organic solvents and mobile phase modifiers present in the chromatographic eluents can also be a source of adducts formed during LC/MS analysis.

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Employing mass spectrometry as a detector may ultimately require a tradeoff between optimizing separation peak capacity and MS sensitivity. In particular, trifluoroacetic acid (TFA), a strong reversed-phase ion pairing agent, will typically produce very narrow (concentrated) protein peaks during reversed-phase chromatography, but will also strongly suppress electrospray ionization of biomolecules (Apffel et al., 1995). Formic acid, a weaker ion-pairing agent, typically produces inferior chromatography, but up to 30-fold greater MS response than TFA (Huber and Premstaller, 1999). When these effects were compared by our group for the LC/MS analysis of yeast ribosomal proteins, shown in Figure 13.4, we observed that the TIC signal was suppressed threefold in 0.1% TFA versus 2% formic acid even when chromatographic peak widths were substantially reduced (Liu et al., 2002). The effect of acidic modifier choice and concentration on chromatographic performance is sorbent dependent, and may be one additional parameter to consider during sorbent selection.

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FIGURE 13.4 Total ion chromatograms from the 1D LC/MS analysis of a yeast ribosomal protein fraction separated using 0.1% TFA (Panel a) and 0.1% formic acid (Panel b) as mobile phase modifiers. TFA produced narrower, more concentrated, peaks for mass analysis that did not overcome the significant electrospray ionization suppression associated with using this modifier for LC/MS studies, resulting in an overall reduction in component intensities.

Data files obtained from the LC/MS and 2DLC/MS analysis of intact protein mixtures can often reach gigabyte size, even when modern data compression routines are employed. Large datasets can be challenging to deal with from both a processing time and storage requirement. The end goal of the data processing workflow is to reduce voluminous retention time, m/z, intensity data down to a set of protein components with characteristic retention time and intensities. The central engine of this data workflow is the process of spectral deconvolution. During spectral deconvolution, sets of multiply charged ions associated with particular proteins are reduced to a simplified spectrum representing the neutral mass forms of those proteins. Our laboratory makes use of a maximum entropybased approach to spectral deconvolution (Ferrige et al., 1992a and b) that attempts to identify the most likely distribution of neutral masses that accounts for all data within the m/z mass spectrum. With this approach, quantitative peak intensity information is retained from the source spectrum, and meaningful intensity differences can be obtained by comparison of LC/MS runs acquired and processed under similar conditions. To process the LC/MS data more efficiently, we have automated this deconvolution functionality using a Visual Basic macro (termed AutoME or Automated Maximum

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FIGURE 13.5 The total ion chromatogram and deconvoluted protein mass map for a 1D LC/MS analysis of yeast ribosomal proteins. The bubble size is proportional to component intensity.

Entropy) operating within the MassLynx 4.X data processing software (Waters). Chromatographic runs are time-segmented, and the summed mass spectra for each segment are deconvoluted, centroided, and thresholded to produce a set of deconvoluted neutral mass—intensity pairs for that processing segment. When the width of processing segments is a fraction of a chromatographic peak width, protein components can be recognized as a set of mass/intensity pairs present over adjacent processed segments. The resulting datasets are most commonly visualized as a virtual 2D protein mass map with retention time as the independent variable, deconvoluted mass as the dependent variable, and bubble size equal to component intensity. This plot was generated using the Microsoft Excel bubble plot display. This process was used to produce a 2D protein mass map for a single-dimension LC/MS analysis of yeast ribosomal proteins as shown in Figure 13.5, along with the associated TIC plot displayed above. The correspondence between TIC signal and component intensities is clearly indicated. However, the map reveals that samples of moderate complexity can contain regions where components are not sufficiently resolved to permit TIC or UV chromatographic data to reveal component-level detail, tracking, and quantitation between samples. Deconvoluted mass spectral data can provide the additional selectivity needed to distinguish coeluting analytes, and

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FIGURE 13.6 The total ion chromatogram and deconvoluted protein mass map for a 2DLC (SCX/RP)/MS analysis of yeast ribosomal proteins. The bubble size is proportional to component intensity.

monitor complex samples at the component level. The data were processed by summing mass spectral time segments that spanned a fraction of chromatographic peak width, and revealed the underlying chromatographic profiles for each of the components detected during the analysis (e.g., Fig. 13.5, inset). Processing LC/MS data by this approach naturally extends to the characterization of multidimensional LC/MS data. The protein mass map of a 2D(SCX/RP) LC/MS analysis of the same ribosomal protein fraction is displayed in Figure 13.6. The resulting orderly series of reversed-phase cycles, corresponding to individual SCX elution steps, can be visualized on the associated TIC plot. It can be seen that the shorter, steeper RP gradients produce not only narrower, more concentrated peaks than with the 1D separation of equivalent run length, but also considerable periods of time where no protein data are collected. These periods where no data are collected occur as the column is being regenerated in a low percentage organic mobile-phase before it can again capture components from the first-dimension separation. The resulting “overhead’’ from column regeneration reduces the practical peak capacity below what the narrow peaks could theoretically yield. A system could be configured with an additional isocratic LC pump and valving to produce rapid off-line regeneration of a RP column at the cost of significantly increased system complexity. It should

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be noted that the percentage of the total cycle time utilized for system overhead is directly related to the ratio of system regeneration time to second-dimension analysis time, and can limit the practical lower limits for the rate of second-dimension sampling of the first-dimension. The capability to analyze 2DLC data by the automated process, and the ability to resolve component-level behaviors within complex protein separations enabled us to undertake a subsequent series of comparative experiments where protein behaviors within a cellular lysate were analyzed using both step and linear gradients in a 2DLC/MS experiment. This will be described in the following sections of this chapter. 13.6.1 Interactions between the Two Dimensions of Chromatography (Step Vs. Linear) The choice between a step and linear first-dimension gradient mode fundamentally changes the interaction between the first-dimension IEX and second-dimension RP separations. Assuming the ideal chromatographic behavior of proteins according to the stoichiometric displacement model (see above), it is expected that proteins will either elute or retain on the IEX column during an isocratic salt elution step, independent of the length for which that step is maintained. During step gradient chromatography, the two separation dimensions would be effectively decoupled, and extended high-resolution separations in the second-dimension could be applied. In this mode, the maximum peak capacity of the first-dimension IEX separation is limited to the number of gradient steps applied, and the bulk of the system separation capacity would be obtained from the second-dimension RP separation. In contrast, with a system using a linear gradient for the IEX separation, the first and second dimensions are temporally coupled, in that the ratio of gradient lengths for the first and second dimension determines the effective peak capacity of a first-dimension separation. A more rapid second-dimension RP analysis cycle can sample the IEX separation with greater frequency, and make more efficient use of first-dimension peak capacity. Thus, the use of a linear gradient for the first-dimension IEX separation will provide peak capacities limited by three factors, the IEX column peak capacity, the gradient slope, and the rate at which the first-dimension effluent is sampled. As with all 2D separations, frequent sampling of the first-dimension is highly desirable (Murphy et al., 1998), but rapid RP analysis can cause a significant reduction in seconddimension peak capacity because of the steep gradient employed to increase the firstdimension sampling rate. Oversampling of the first dimension distributes components over several second-dimension cycles, thus diluting the concentration of an individual component in a specific second-dimension cycle, effectively increasing detection limits and potentially limiting the dynamic range of analysis. Secondarily, this requires combining data from multiple cycles, with a possible consequence of reducing the quantitative capacity of the analysis. In addition, oversampling in combination with online MS detection may generate an incredibly large dataset for postrun analysis that significantly increases the required processing time. Maintaining a suitable balance between the desire to maximize resolution and the practicality of

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analyzing the resulting dataset in a timely fashion can have a significant effect on the choice of first-dimension fraction size and total peak capacity achieved. Analysis of a purified ribosomal protein extract by strong cation exchange (denaturing conditions)-reversed-phase 2D LC/MS analysis (nine step IEX gradient, dual column configuration) under denaturing conditions showed that 67 of 91 (74%) identified ribosomal proteins were observed in a single reversed-phase cycle. In this study (Liu et al., 2002), the occurrence of a protein being distributed between adjacent IEX fractions was roughly twice as likely as second-dimension RP carryover distributing a component into multiple reversed-phase cycles. Analysis of all detected components revealed that only 6 of 120 (5%) proteins were observed in more than two RP cycles. Recent studies by our group have found that up to half of the proteins in a more complex and heterogeneous sample, Escherichia coli cytosol, exhibited nonideal behaviors in one or both chromatographic dimensions of a nondenaturing SAX-RP (dual trap/analytical) MDLC system (Millea et al., 2005). These include the splitting of an analyte between two consecutive second-dimension cycles, where a protein elution band is split by the activation of valve 1, and analyte carryover in the RP dimension, which results in a protein being observed in two nonadjacent (cycle n and n þ 2) cycles, or a combination of these two phenomena. In this study, the separation behavior of the top 100 abundant proteins (by LC/MS response) were followed over the course of triplicate analyses, using either an eight-step gradient or a corresponding linear gradient for the SAX first dimension. Replicate experiments showed that protein mixtures exhibit reproducible chromatographic elution patterns using both modes of multidimensional separation. Overlaid TIC data from triplicate analyses of a 2D (Step) LC/MS experiment are presented in Figure 13.7, and demonstrates the overall consistency of elution profiles, with only minor variability between replicates of the same salt step. There was, however, variation noted with respect to the relative intensity distribution of components that distributed across multiple RP cycles. Figure. 13.8 contains 2D protein map data from a single RP cycle of the linear (Fig. 13.8a) and step (Fig. 13.8b) 2DLC/MS analysis of the E. coli cytosol. In this example, the step gradient corresponds to the beginning and end salt concentrations for the linear segment. In Fig. 13.8, the deconvoluted protein data from replicate runs are represented by red- and green-colored spots, where data in intersection are visible in orange. Within each experimental type, the same components are typically observed in the replicate analysis, with some minor variation in component intensity. Although many of these components are common to the same step and linear cycle, many more differences are evident. Under the conditions used for this study, 70 of the top 100 proteins resolved by a linear 2D SAX/RP separation appeared in a single RP cycle, whereas only 51 of the top 100 proteins in the step-gradient separations are well-behaved. Surprisingly, the negative effects of the step-gradient separation were observed as both individual firstdimension SAX peak-splitting, and second-dimension RP carryover events, rather than peak-splitting effects alone. The 28 proteins that showed nonideal separation behaviors under both gradient modes exhibited roughly equal contributions of peaksplitting in the SAX dimension and carryover in the RP dimension. Such results likely

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FIGURE 13.7 Total ion chromatogram reproducibility for three 2DLC (SAX-Step Gradient/ RP)/MS analyses of an E. coli cytosolic fraction.

indicate the need for more denaturing separation conditions, or the need for additional solubilizing agents to promote more uniform column—protein interactions. 13.6.2

Recognizing Increased Selectivity in 2DLC Separations

Total theoretical peak capacity for the 1D and 2D LC/MS analyses of the yeast ribosomal protein sample was calculated as 240 and 700, respectively. Individual separation peak capacities were calculated by dividing the total separation time by the average peak width at baseline, and the 2D peak capacity determined as the product of the peak capacity of the two dimensions. These theoretical calculations rely on optimal use of the two-dimensional separation space, which in turn is dependent upon the lack of correlation between the component retention times in the two separation modes. Thus, the maximum use of the theoretical peak capacity is not only dependent on the selection of chromatographic modes based on different physicochemical

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FIGURE 13.8 Duplicate 2DLC(SAX/RP)/MS analyses of an E. coli cytosolic fraction were conducted using either a linear (Panel a) or step gradient (Panel b) IEX gradient first-dimension. Resulting protein mass maps show reproducibility of components over duplicate runs for one mode (merged image within each panel), and between gradient modes (Panel a vs. Panel b) from a single RP cycle corresponding to a common salt range in the first-dimension). In each panel the first run is displayed in red, the second in green, and overlapping data in orange. (See color plate.)

characteristics of the analytes, but also relies on the mixture of analytes itself possessing a sufficiently broad range of those physicochemical properties. Despite this threefold increase in peak capacity, the observed number of resolved protein peaks only rose from 53 to 80, even though a total of approximately 125 distinct proteins were detected in both experiments. This can be attributed at least in part to the similar chemical properties of the ribosomal proteins themselves, which are nearly all strongly basic molecules with a smaller range of both ionic character and hydrophobicity compared

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to a more diverse set of proteins such as would be expected in a more complex total cellular extract. The narrow pI range of ribosomal proteins is in contrast to cellular extracts that consist of many more acidic and neutral pI proteins, and these extracts are more effectively fractionated by anion-exchange than by cation-exchange columns. A subsequent section in this chapter details such an analysis of an E. coli extract.

13.7 EXPANDING MULTIDIMENSIONAL SEPARATIONS INTO A “MIDDLE-OUT” APPROACH TO PROTEOMIC ANALYSIS

Q5

The ability to resolve and characterize complicated protein mixtures by the combination of 2DLC and online mass spectrometry permits the combination of sample fractionation/simplification, top-down protein mass information, and bottom-up peptide level studies. In our lab, the simplified fractions generated by 2D(IEX–RP)LC are digested and analyzed using common peptide-level analysis approaches, including peptide mass fingerprinting (Henzel et al., 1993; Mann et al., 1993), matrix-assisted laser desorption/ionization (MALDI) QTOF MS/MS (Millea et al., 2006), and various capillary LC/MS/MS methodologies (e.g., Ducret et al., 1998). The intact mass of a protein represents the contribution of all modifications to the primary protein structure, but the combinatorial nature of the potential modifications limits the utility of intact mass to conclusively produce an initial protein identification. Although some labs have started to develop top-down intact protein MS/MS approaches to overcome this obstacle (Nemeth-Cawley et al., 2003; Kelleher, 2004), we have chosen to use a supplemental peptide level analysis as the primary tool for identifying proteins within a complex sample. Intact masses corresponding to a collected 2DLC fraction are compared to the theoretical mass of identified proteins to assign likely modification(s). These, of course, can be validated by targeted analysis of the digested 2DLC fraction that produced the initial identification. This hybrid approach was first applied to an enriched fraction of yeast ribosomal proteins using SCX/RP 2DLC proteins fractionation where roughly 10% of the effluent was directed to an ESI-TOF mass spectrometer for intact mass analysis, and the remainder collected for further analysis (Liu et al., 2002). The collected fractions were digested and subjected to MALDI peptide mass fingerprint (PMF) analysis. The simplicity of the sample coupled with the low dynamic range of ribosomal proteins in the purified sample meant that fractions on the order of 5–6 peak widths could be collected without exceeding the capabilities of PMF to analyze the resulting fractions as they only contained at most six proteins. Figure 13.9 is a demonstration of the synergy obtained by combining intact protein analysis with the peptide-level analysis of corresponding digested fractions. The data were obtained by diverting 90% of the column effluent of a 2DLC/ MS yeast ribosomal protein separation to fraction collection for digestion and protein mass fingerprint analysis, and directing the remaining 10% of eluent for ESI-TOF MS intact protein mass analysis. Fraction 35 was collected over 2 min of the RP gradient corresponding to the third SCX step. During this elution window,

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FIGURE 13.9 Eluent was collected (Fraction 35) over a 2 min segment of a reversed-phased LC separation corresponding to the third salt step of a 2DLC(SCX/RP)/MS analysis of yeast ribosomal proteins. The total ion chromatogram for this step is shown in Panel a. Deconvolution of the MS data acquired over this period reveals six significant components (Panel b) that can be assigned to known yeast ribosomal protein using the intact protein mass data, and supplementary protein MALDI mass fingerprint data (Panel c) obtained from the tryptic digest the collected fraction.

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FIGURE 13.9

Continued

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only four TIC peaks were observed (Fig.13.9a) that revealed five ribosomal proteins (rpL38, rpL31A, rpL31B, rpL33A, and rpL33B) following mass spectral deconvolution (Fig. 13.9b). A sixth component (20307.0 Da) was also observed that did not correspond to any known modified or unmodified ribosomal proteins. MALDI PMF analysis (Fig. 13.9c) confirmed the presence of both rpL31 and rpL33 isoforms, but not of the smaller and more basic (9 kD, pI 11) rpL38 protein. Arrows point to the peptides that contained protein isoform differences. In each case, the intact mass was able to demonstrate the proteolytic processing of the N-terminal methionine (-Met) and the lack of acetylation on the penultimate alanine of both subunits. The PMF data also demonstrated high sequence coverage (59%) for rpL20A/B, for which no intact mass data had been assigned. This prompted us to further examine the sequence of rpL20 to conclude that the unidentified mass corresponds to a truncated form of rpL20 lacking three (MYL, rpL20B) or nine (MKILVILSV, rpL20A) amino acids from the N-terminus. Surprisingly, these regions contain all differences between these two isoforms, and the “processed’’ fragment is identical for both isoforms. In a more recent paper (Millea et al., 2006) we have applied this combined analytical approach to a more complicated cellular protein mixture. In this study the soluble protein fraction of E. coli was resolved using a step-gradient SAX/RP 2DLC fractionation with 80% of flow diverted for online ESI–TOF MS analysis. Collected fractions were digested and analyzed using MALDI QTOF MS and data-dependent MS/MS analysis. The increased complexity of the sample coupled with increased dynamic range of E. coli cytosolic proteins required collecting fractions at approximately the same volume as the chromatographic peaks to analyze the resulting fractions. Using this approach, it was possible to identify N-terminal protein processing, proteolytic maturation of proteins, and posttranslational protein modifications, and to distinguish between closely related protein isoforms. In this study, the bias towards detection of lower than average molecular weight proteins (<50 kD) was apparent, with stronger bias displayed at the level of intact protein mass analysis. This is likely a result of combined contributions of sample preparation issues, chromatographic recovery, and higher sensitivity of ESI–MS with lower molecular weight proteins. Ongoing research into all of these areas is aimed at developing a more generic approach for fractionating and analyzing even more complex biological fractions.

13.8 FUTURE DIRECTIONS IN PROTEIN MDLC This chapter has presented several comprehensive 2DLC approaches combining a first-dimension IEX separation and a second-dimension RP separation for the analysis of complex protein mixtures typical in proteomics studies. Online ESI– TOF/MS detection provided sensitive detection and valuable qualitative information (MW) for proteins eluting from the MDLC system. Coordinated fraction collection and subsequent MS analysis of peptides produced by proteolysis of the fractions provided in-depth information on protein identification and a mechanism

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to identify the location of posttranslational modifications suggested by intact protein mass analysis. To date, these methods have only been applied to somewhat simple proteomics samples such as a yeast ribosomal protein fraction or E. coli soluble proteins. The extension of the methodology to mammalian systems, where protein variation and the extent of posttranslational modification greatly exceed that found in unicellular systems, remains a serious challenge. The entire analytical workflow, from protein separation to MS detection and postrun data analysis, could be targeted for improvement. 13.8.1

Q6

Protein Chromatography

Although multidimensional protein separations continue to show great promise for fractionating proteomics samples and providing the means for increasing the information available from these complex samples, experiments in our lab and publications in the literature have suggested a number of areas for improving protein MDLC in conjunction with proteomics studies. First and foremost is that although we and others (e.g., Opiteck et al., 1997, 1998; Millea et al., 2005, 2006) have shown that protein 2DLC especially combined with MS detection is capable of generating significant improvements in overall system peak capacity, both the theoretical and achieved peak capacities, even with highly efficient RPLC, do not approach that achieved with complex peptide mixtures (see e.g., Gilar et al., Chapter 12). Separation efficiency for proteins is almost always significantly lower than that achieved with small molecules. This is especially evident in IEX where peak widths can exceed several minutes, thus negating the advantages of frequent fraction collection from a first-dimension IEX separation. Jorgenson (private communication) has shown that the beneficial application of smaller particle columns used in reversedphase chromatography has not been observed with IEX, and has speculated that slow desorption kinetics may be responsible. Multipoint interactions of large biomolecules in IEX (Drager and Regnier, 1986), with varying affinities for the individual interaction sites, could be the source of the unfavorable desorption kinetics. Consequently, IEX peak capacities rarely approach those achieved in RPLC. In the present work we have limited the number of fractions from the IEX dimension to a maximum of 20, at least in part because the low IEX peak capacity and increase in the frequency of firstdimension sampling will only have a positive effect on the 2D peak capacity if separation efficiency in IEX can be significantly improved. Other multidimensional strategies, such as chromatofocusing (Chong et al., 2001) and liquid-phase isoelectrofocusing (Wall et al., 2000; Zuo and Speicher, 2004) that employ an RPLC second dimension, are similarly limited by the separation power of the first dimension, wherein typically fewer than 20 fractions are sampled. Clearly boosting the firstdimension separation capability speed and resolution would be a significant benefit to protein MDLC. In contrast to IEX, RPLC protein separations show good efficiency using either nonporous (Wall et al., 2000) or porous (Liu et al., 2002; Millea et al., 2005) silica columns, with peak capacities of approximately 100. Although not equivalent to small molecule separations, including peptides, this performance is not the main

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limitation for the RP step. The major performance issues are analyte recovery and carryover (ghosting), which are still problematic with a number of proteins (e.g., Millea et al., 2005). This is the case even with nonporous packings designed to overcome such issues. Despite over 25 years of experience and improvements in separating proteins by RPLC, significant hurdles remain in optimizing these performance criteria. One complication in efficiently eluting proteins from RP columns is that the proteins are not highly soluble in the organic solvent (typically acetonitrile) used to effect elution. This can reduce recovery and lower separation efficiency, especially for proteins that are strongly retained. This is most evident with large and/or hydrophobic proteins (e.g., membrane proteins), which are problematic in many protein fractionation schemes. Millea et al. (2005) described the typical observations with poorly behaving protein analytes in a 2DLC system. The MDLC system described in this chapter shows a definite bias towards the analysis and identification of proteins below 60 kD, which may be a consequence, at least in part, to their less than ideal chromatographic properties in RPLC. Poorly soluble hydrophobic proteins remain the biggest challenge to establishing effective MDLC schemes for proteomics. Classically, biochemists have resorted to detergent additives in the elution buffers, and some limited studies (Wall et al., 2000; Chong et al., 2001) have shown utility for the detergent octyl glucopyranoside in MDLC. Membrane and other hydrophobic protein recoveries, however, were not determined, so the improvement in recovering and chromatographing these proteins with high efficiency is still in question. Thus, advances in separating and recovering large hydrophobic proteins by RPLC would provide significant benefits for proteomics studies using MDLC. 13.8.2

MS Analysis of Proteins

Large biomolecules inherently pose problems for analysis by MS. As protein size increases, it becomes more difficult to balance conditions needed for the removal of solvent and adduct molecules with the conditions capable of efficiently generating intact protein ions. Such front-end issues are further complicated by the decreased sensitivity toward mass detection of larger macromolecular ions. These issues continue to be addressed, and even larger molecules and complexes are being studied using MALDI- and ESI-based mass detectors (Wenzel et al., 2005; Benesch and Robinson, 2006). We can anticipate that future technological advances in MS instrumentation will overcome some of the remaining limitations on protein analysis. The ongoing development of top-down protein MS/MS capabilities (MS/MS) should prove quite valuable to researchers looking to identify and characterize proteins fractionated by 2DLC separations. Such methods are currently limited by restrictions on the maximum size of proteins analyzed, as well as analysis time-requirements that limit coupling of these methods with online LC analysis. Investigators from labs, such as Kelleher, McLafferty, Hunt (Coon et al., 2005; Meng et al., 2005; Han et al., 2006), and others are rapidly addressing these issues, and their methods will likely be adopted by many other researchers over the next few years.

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Data Interpretation

One of the major obstacles to efficient analysis of protein LC/MS separations has been the reduction of voluminous amounts of raw MS data to provide information on protein mass. Spectral deconvolution is a computationally intensive process, and the computing power has only recently been available to permit analysis of such complex datasets. Although low resolution overviews of datasets can be generated at roughly half the rate at which the data was acquired, high resolution of an hour of LC/MS data can require more than a day of data processing. Continuing improvements in computing power should begin to address this workflow limitation, but we must always recognize that increasing processing capabilities will usually lead to even more complex datasets being submitted for analysis. For now, the most economical and practical approach is to process datasets or subsets of a single data set with multiple computer systems. Our development of an automated data processing tool for protein LC/MS data (AutoME) has been essential for efficient analysis of the quality of our 2DLC separations. Other examples of similar approaches can be found in the scientific literature (Wall et al., 2001; Lee et al., 2002; Williams et al., 2002; Hamler et al., 2004). The further development of this approach requires an equal effort to generate the bioinformatics tools required to identify and characterize the processed mass spectral data, and to combine data from protein-level and peptide-level workflows. Tools such as the ProSight (LeDuc et al., 2004) have begun to address basic processing of protein MS and MS/MS data, but additional progress is needed before 2DLC/MS datasets can provide a significant return for the average researcher.

13.9 CONCLUSION MDLC has become a valuable tool for fractionating and analyzing complex protein mixtures within a liquid-phase gel-free proteomic work flow. Multiple commercial chromatographic systems, including those with integrated online high resolution TOF MS detection and fraction collection, have proven capable of performing complete sample analyses, often within several hours. By contrast, data analysis of such complex samples can last days to weeks. By combining the data from protein 2DLC/MS systems with peptide information from collected fractions, structural features such as posttranslational modifications and protein processing events can be readily identified. Our results reflect the “early’’ days of the role of 2DLC/MS in a proteomic context, and we believe we have finally developed the tools to identify many of the deficiencies of 2DLC that can now be addressed by our lab and others. In addition to improving the chromatographic performance of 2DLC/MS systems, the production of new bioinformatic tools supporting protein-level LC/MS studies and combined protein–peptide workflows should further the utility of these approaches, and extend the techniques to a wider base of analytical and biological scientists.

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Giddings, J.C. (1984). Two-dimensional separations: concept and promise. Anal. Chem. 56, 1258A–1264A. Giddings, J.C. (1987). Concepts and comparisons in multidimensional separation. J. High Resolut. Chromatogr. Chromatogr. Commun. 10, 319–323. Gygi, S.P., Corthals, G.L., Zhang, Y., Rochon, Y., Aebersold, R. (2000). Evaluation of twodimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. USA 97, 9390–9395. Hamler, R.L., Zhu, K., Buchanan, N.S., Kreunin, P., Kachman, M.T., Miller, F.R., Lubman, D.M. (2004). A two-dimensional liquid-phase separation method coupled with mass spectrometry for proteomic studies of breast cancer and biomarker identification. Proteomics 4, 562–577. Han, X., Jin, M., Breuker, K., McLafferty, F.W. (2006). Extending top-down mass spectrometry to proteins with masses greater than 200 kilodaltons. Science 314, 109–112. Henzel, W.J., Billeci, T.M., Stults, J.T., Wong, S.C., Grimley, C., Watanabe, C. (1993). Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc. Natl. Acad. Sci. USA 90, 5011–5015. Huber, C.G., Premstaller, A. (1999). Evaluation of volatile eluents and electrolytes for highperformance liquid chromatography-electrospray ionization mass spectrometry and capillary electrophoresis-electrospray ionization mass spectrometry of proteins. I. Liquid chromatography. J. Chromatogr. A 849, 161–173. Jensen, O.N. (2004). Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol. 8, 33–41. Kelleher, N.L. (2004). Top-down proteomics. Anal. Chem. 76, 197A–203A. LeDuc, R.D., Taylor, G.K., Kim, Y.B., Januszyk, T.E., Bynum, L.H., Sola, J.V., Garavelli, J.S., Kelleher, N.L. (2004). ProSight PTM: an integrated environment for protein identification and characterization by top-down mass spectrometry. Nucleic Acids Res. 32, W340–W345. Lee, S.W., Berger, S.J., Martinovic, S., Pasa-Tolic, L., Anderson, G.A., Shen, Y., Zhao, R., Smith, R.D. (2002). Direct mass spectrometric analysis of intact proteins of the yeast large ribosomal subunit using capillary LC/FTICR. Proc. Natl. Acad. Sci. USA 99, 5942–5947. Liu, H., Berger, S.J., Chakraborty, A.B., Plumb, R.S., Cohen, S.A. (2002). Multidimensional chromatography coupled to electrospray ionization time-of-flight mass spectrometry as an alternative to two-dimensional gels for the identification and analysis of complex mixtures of intact proteins. J. Chromatogr. B 782, 267–289. Liu, Z., Patterson Jr., D.G., Lee, M.L. (1995). Geometric approach to factor analysis for the estimation of orthogonality and practical peak capacity in comprehensive two-dimensional separations. Anal. Chem. 67, 3840–3845. Mann, M., Hojrup, P., Roepstorff, P. (1993). Use of mass spectrometric molecular weight information to identify proteins in sequence databases. Biol. Mass Spectrom. 22, 338–345. Masuda, J., Maynard, D.M., Nishimura, M., Ueda, T., Kowalak, J.A., Markey, S.P. (2005). Fully automated micro-and nanoscale one- or two-dimensional high-performance liquid chromatography system for liquid chromatography-mass spectrometry compatible with non-volatile salts for ion exchange chromatography. J. Chromatogr. A 1063, 57–69. Meng, F., Forbes, A.J., Miller, L.M., Kelleher, N.L. (2005). Detection and localization of protein modificationsbyhighresolutiontandemmassspectrometry.MassSpectrom.Rev.24,126–134. Millea, K.M., Kass, I.J., Cohen, S.A., Krull, I.S., Gebler, J.C., Berger, S.J. (2005). Evaluation of multidimensional (ion-exchange/reversed-phase) protein separations using linear and step gradients in the first dimension. J. Chromatogr. A 1079, 287–298.

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14 ANALYSIS OF ENANTIOMERIC COMPOUNDS USING MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Renee J. Soukup and Daniel W. Armstrong Department of Chemistry, Iowa State University, Ames, IA, USA

The development of chiral stationary phases for the routine separation of enantiomeric compounds sparked interest in determining the enantiomeric composition of chiral molecules in complex matrices, especially in biological samples, such as plasma, urine, other plant or animal tissues, and environmental samples. Indeed, the new-found ability to do routine pharmacokinetic and pharmacodynamic studies on chiral drugs provided much of the impetus for the Food and Drug Administration (FDA) to issue the first guidelines for the development of stereoisomeric drugs (United States Food and Drug Administration, 1992). Normally, chiral stationary phases alone do not provide sufficient peak capacity and selectivity to separate the enantiomers of interest from diastereomers, metabolites, and the multitude of interfering matrix components found in biological samples. Often, extensive sample preparation is needed to prevent the irreversible adsorption of contaminants on the chiral stationary phase (CSP). Coupling achiral columns to chiral columns was an approach adopted by researchers to solve these problems. This chapter reviews the development and use of achiral-chiral column liquid chromatography and gives examples of its applications.

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

319

320

ANALYSIS OF ENANTIOMERIC COMPOUNDS

14.1 ONLINE ACHIRAL-CHIRAL LC-LC Like other multidimensional chromatographic techniques, achiral-chiral LC-LC couples two columns of different selectivities, with one of the columns containing a chiral stationary phase. Wainer 1989 discussed achiral-chiral LC-LC shortly after its inception. General multidimensional chromatography has been previously reviewed (Cortes, 1990; Mondello et al., 2002; Evans and Jorgenson, 2004), and will not be discussed in great detail here. Although it is not the focus of this chapter, it is worth mentioning that achiral-chiral multidimensional gas chromatography can be found in applications involving environmental and forensic/toxicological samples (Mondello et al., 2002). It is very popular for the enantiomeric determination of chiral polychlorinated organic pollutants (POPs) from various matrixes (Glausch et al., 1994; Glausch et al., 1995; Glausch et al., 1996; Gomera and Gonzalez, 2003; Bordajandi et al., 2005; Seemamahannop et al., 2005). A brief description of the methods used in multidimensional liquid chromatography as it pertains to achiral-chiral liquid chromatography separations is provided. Evans and Jorgenson have outlined three general methods for accomplishing multidimensional LC separations. These methods are (1) directly coupled-columns, (2) heart-cutting, and (3) comprehensive column-switching multidimensional LC. As the name suggests, in directly coupled-column achiral-chiral LC-LC configurations two columns are coupled together in series with one of the columns used for chiral separations. Although this is the simplest approach in regard to instrumentation, one mobile phase must provide the desired separations on both columns. In practice, this can be problematic as the mobile phase that provides optimum resolution on the achiral column may not produce optimal (or any) enantioresolution with the chiral stationary phase containing column. Also, the selectivity and peak capacity are not as high for columns coupled in series (as for other approaches) it is only used for “cleaner” samples with less complicated matrixes. Column-switching multidimensional LC-LC uses valves to connect the column or columns together. Columns can be connected in any order with different mobile phases, but greater instrumentation complexity usually is necessary. Using Evan’s and Jorgenson’s definitions, directly coupled-columns and column-switching are comprehensive multidimensional techniques that subject the entire injected sample to separation in both dimensions, while heart cutting does not. Usually only one or a few selected component(s) in a sample are chiral. Therefore, it is not usually necessary for the entire sample to be exposed to the column containing the CSP. Thus, the heartcutting approach is the most common method used in achiral-chiral LC-LC. In this approach, a desired segment of the eluent from the first column (containing the chiral analyte) is usually directed via a switching valve onto the second column (containing the chiral stationary phase) for subsequent separation of the enantiomers. This approach allows the use of different individually optimized mobile phases for each of the columns. However, it should be noted that even though different mobile phases can be used for the separation on each column, the mobile phases must be compatible with one another. This will be discussed subsequently. Also, the heart-cutting technique saves the chiral column from unnecessary wear and/or contamination,

ONLINE ACHIRAL-CHIRAL LC-LC

321

thereby improving its longevity. Due to the evolution of technical terms, some cited references use the term column-switching to describe an assay that employs the heartcutting approach. An example of a basic system configuration for the heart-cutting technique is shown in Figure 14.1a in which column A is the achiral column and column B is the column containing the CSP. The achiral column is equilibrated with the mobile phase from pump A while the chiral column is equilibrated with the mobile phase from pump B. The switching valve located between the two columns is initially set so that the flow from column A is directed through a detector to waste. At the desired time, the valve switches the flow from the achiral column to chiral column and then at a designated time switches back again. The chiral separation is then achieved on the column containing the CSP with the mobile phase from pump B. Detectors are usually located at the end of each column to monitor both the achiral and chiral separations. Additional columns, pumps, and valves in various arrangements can be used to modify the basic configuration to meet the needs of a particular analysis objective. As shown in Figure 14.1b, a third column (C8) was added to a system

(a)

Detector A

Pump A

MP A

Waste Switching valve

Column A

MP B

(b)

Detector B

Column B

Waste

Pump B

Recorder 1

Sample

Recorder 2 S P E

InJ

Pump A 200 MeCN 800 water 1 HOAC

UV 266 nm

C8, 250 x 4.6 mm

Pump B & 200 MeCN 800 water 1 HOAC

Analysis: 40% B Washing: 100% B 1 mL/min

Q1

SV1

UV 266 nm

C18, 100 x 4.6 mm

Pump C 380 MeOH 320 water 4 HOAC 0.5 mL/min

SV2

Fluorescence ex. 265 nm, em. 315 nm

Cyclobond 1, 250 x 4.6 mm

Pump D 800 MeCN 200 MeOH 2 HOAC 1 TEA 1 mL/min

FIGURE 14.1 (a) Diagram of an achiral-chiral system in a heart-cut configuration. MP: mobile phase. (b) Diagram of a triple column system. Figure 14.1b reprinted from Armstrong et al. (1993) with permission by John Wiley & Sons.

322

ANALYSIS OF ENANTIOMERIC COMPOUNDS

that was already using a C18 and chiral column. This additional column was necessary to remove a matrix interferent that coeluted with pipecolic acid. This is just one example how the basic achiral-chiral system can be modified to improve separation quality. Another common configuration includes short trap columns located between the achiral and chiral stationary phase containing columns to reconcentrate the analyte before enantiomeric separation. Achiral-chiral LC-LC is most often used to separate the desired analyte from interfering components, such as matrix components, metabolites, excess derivatization reagent, or other impurities. Separating such interferents from the analyte allows for better analyte quantification or enantiomeric ratio determination. Also, achiral columns are seen as a way to protect the more expensive chiral columns from matrix components that might become irreversibly retained and deteriorate column performance. Short achiral columns (trap columns) are sometimes used to reconcentrate the chiral analyte after a previous separation (either chiral or achiral) as a type of online enrichment. Configurations that combine an achiral column for increased selectivity and trap column(s) for online enrichment are relatively common, though this type of configuration requires more columns and increases complexity. Sample pretreatment also is an important consideration in achiral-chiral LC-LC as in traditional LC. Because proteins can become adsorbed on both the achiral and chiral stationary phases, direct injection of matrixes containing proteins is rarely practiced. Exceptions can be found in the cases where the achiral column involved is of the restricted access media (internal surface reverse phase) type of column, which are designed not to retain proteins. Otherwise, proteins are generally removed by precipitation and centrifugation. Solid phase extraction (SPE) is commonly practiced as well. In addition to serving as a preconcentration step, SPE is used to retain the analyte while washing away much of the matrix components. This works particularly well if the analyte has previously been derivatized for fluorescence or UV detection. Derivatization with a chromophore or fluorophore not only helps with detection, but also makes a compound more hydrophobic. This makes SPE and sample cleanup much easier and effective. SPE is also useful in removing and concentrating analytes from a water-containing matrices for subsequent separation using normal phase chromatography. Many times an analyte must be derivatized to improve detection. When this derivatization takes place is incredibly important, especially in regards to chiral separations. Papers cited in this chapter employ both precolumn and postcolumn derivatization. Since postcolumn derivatization takes place after the enantiomeric separation it does not change the way the analyte separates on the chiral stationary phase. This prevents the need for development of a new chiral separation method for the derivatized analyte. A chiral analyte that has been derivatized before the enantiomeric separation may not interact with the chiral stationary phase in the same manner as the underivatized analyte. This change in interactions can cause a decrease or increase in the enantioselectivity. A decrease in enantioselectivity can result when precolumn derivatization modifies the same functional groups that contribute to enantioselectivity. For example, chiral crown ethers can no longer separate amino acids that have a derivatized amine group because the protonated primary amine is

APPLICATIONS

323

necessary for enantioselectivity. Therefore, it is necessary that a chiral separation method is developed for the analyte in its derivatized form. In achiral-chiral LC-LC, the mobile phases used with the achiral and chiral columns must be miscible with one another. Since the enantiomeric separation is usually the most difficult to optimize, it is usually the separation that dictates the mode of operation of the total analysis. Thus, it makes sense that a chiral column that operates in the normal phase mode would require an achiral column that also works in the normal phase mode. Polar organic mode chiral separations are universal in that they can be paired with an achiral column that operates in either the reverse phase or normal phase mode. The choice of the achiral column is always determined after selecting the chiral column and the mode of operation. As with traditional liquid chromatography, different achiral columns will give different selectivity. Additionally, the injected matrix must also be miscible with the solvents used in the separations. For normal phase mode separations, all water must be removed from the injected matrix. Since many of the complex matrixes, such as plasma, urine, and other biological fluids contain a large amount of water, this requires more time consuming sample preparation. However, water can be injected into a polar organic or reverse phase mode separation. Even within the same mode, mobile phases that are very different can cause large disturbances in the baseline. Oda et al., (1991) solved this problem by inserting a dilution tube followed by a trap column in order to dilute the mobile phase used on the achiral column. Following the dilution tube, a trap column was used to reconcentrate the analyte of interest before the enantiomeric separation.

14.2 APPLICATIONS 14.2.1

Analysis of Enantiomers in Plasma and Urine

The bulk of the literature involving achiral-chiral column liquid chromatography involves the analysis of enantiomers in plasma and urine samples. Many of the enantiomers in these studies are pharmaceutical compounds and/or their chiral metabolites. These methods are commonly used in pharmacodynamic and/or pharmacokinetic studies. These types of studies require methods in which the analytes can easily be quantified with high degrees of accuracy and precision. Sensitivity also is a major issue in these studies as the concentrations of drug and particularly their metabolites in plasma and urine are often quite low. Although the exact instrumental configuration of the achiral-chiral system may vary with the analyte to be separated and the employed chiral stationary phase, the goal of separating and quantifying the enantiomers remains the same. One typical example of this type of work is the separation of the enantiomers of ketorolac from its chiral metabolites contained in plasma and urine (Diaz-Perez et al., 1994). Figure 14.2 is an example of how a C18 column and a column containing human serum albumin were directly coupled in order to separate the chiral analysis in urine. Before coupling the achiral and chiral column, the S-ketorolac enantiomer was not separated from the metabolite enantiomers. Table 14.1 is a summary of the drug enantiomers, chiral metabolites, or related

324

ANALYSIS OF ENANTIOMERIC COMPOUNDS

FIGURE 14.2 Achiral-chiral separation of S-Ketorolac (S-1), R-Ketorolac (R-1), p-hydroxyketorolac enantiomers (2,3) and internal standard Naproxen (4). (a) Urine blank (b) blank urine spiked with 1.5 mg/mL each of racemic ketorolac and p-hydroxyketorolac (c) Nondeconjugated urine sample taken from a patient who received 10 mg of oral racemic ketorolac. Reprinted from Diaz-Perez et al. (1994) with permission from John Wiley & Sons.

compounds in the literature that were analyzed using achiral-chiral column liquid chromatography. The columns used as well as the mode of operation are given. Mass spectrometry (MS) is also being used to add another dimension of analysis to achiral-chiral analysis. Recently, an achiral-chiral column-switching LC/LC-MS/MS method was reported for the pindolol enantiomers in human serum (Motoyama et al., 2002) and phenprocoumon metabolites (Kammerer et al., 1998). For analytes that have very poor chromophores or cannot naturally fluoresce, MS detection can be more sensitive for the underivatized form of the analyte. Also, MS detection can be particularly useful when very similar analytes that differ in mass (such as some amino acids and metabolites) cannot be satisfactorily separated chromatographically,

325

Ketoprofen Ketorolac

1,4-Dihydropyridine calcium antagonist Dichloroprop Diperodon Fluoxetine 5-p-Hydroxyphenyl-5-phenylhydantoin p-Hydroxyketorolac Ibutilide Isofamide

Chlorpheniramine Chlorthalidone

RP RP PO RP RP RP RP NP RP RP RP

RP PO RP RP RP NP RP NP RP

Modea Chiral column

Perphenylcarbamate Teicoplanin (Chirobiotic T) a1-Acid glycoprotein a1-Acid glycoprotein a1-Acid glycoprotein Chiracel OD a1-Acid glycoprotein Chiralpak AD Phenyl (w/b-cyclodextrin in mp) Ion exchange (Nucleosil SA) Ovamucoid C18 Teicoplanin (Chirobiotic T) C18 Teicoplanin (Chirobiotic T) C18 Chiralpak AD-RH C18 C18 (w/b-cyclodextrin in mp) C18 Human serum albumin Cyanopropyl, C18 3,5-Dinitrobenzoyl-D-phenylglycine Racemic naphthylalanine Chiracel OD C1 a1-Acid glycoprotein C18 Ovamucoid C18 Human serum albumin

Size exclusion ADS restricted access Cyanopropyl, C18 Cyanopropyl C1 Racemic naphthylalanine C1 Cyanopropyl Cyanopropyl

Achiral column(s)

Analysis of Chiral Drugs, Chiral Metabolites, and Related Compounds

Bupropion metabolite Cyclophosphamide

Bupivicaine

Atenolol

Compound

TABLE 14.1

(continued )

Fujitomo et al., 1993 Schneiderheinze et al., 1999b Hrobonova et al., 2002 Guo et al., 2002 Eto et al., 1994 Diaz-Perez et al., 1994 Hsu and Walters, 1995 Masurel and Wainer, 1989 Corlett and Chrystyn, 1994 Oda et al., 1992 Diaz-Perez et al., 1994

He et al., 1993 Lamprecht et al., 2000 Walhagen and Edholm, 1991 Clark et al., 1991 Suckow et al., 1997 Masurel and Wainer, 1989 Corlett and Chrystyn, 1996 Hiep et al., 1998 Walhagen and Edholm, 1991

References

Q2

Q1

326

NP RP RP RP

NP NP RP RP RP

RP RP

Metyrapone Metyrapol Montelukast Norverapamil Omeprazole

Oxazepam Pantoprazole

5-Methyltetra-hydrofolate Methylphenobarbital

Metoprolol

RP RP RP RP NP NP

Modea

RP RP RP RP

(Continued)

5-Methyl-tetrahydrofolate

Mecoprop Mefloquine

Leucovorin

Compound

TABLE 14.1

Cyanopropyl, C18 C18

Silica Silica Biomatrix extraction HISEP Restrict access (BSA, C8)

Phenyl C18 C18 Internal surface reverse phase Silica Cyanopropyl, C18 C18 Restricted access (BSA, C8)

Phenyl C18 C18 C18 Cyanopropyl Cyanopropyl

Achiral column(s)

Chiracel OD a1-Acid glycoprotein a1-Acid glycoprotein Amylose tris (3,5-dimethyoxyphenylcarbamate) Chiracel OJ Chiracel OJ a1-Acid glycoprotein a1-Acid glycoprotein Amylose tris (3,5-dimethyoxyphenylcarbamate) a1-Acid glycoprotein Chiracel OJ-R

Bovine serum albumin a1-Acid glycoprotein Bovine serum albumin Chiracel OJ-R

Bovine serum albumin a1-Acid glycoprotein Bovine serum albumin Teicoplanin (Chirobtiotic T) Napthylurea Napthylurea

Chiral column

(continued)

Walhagen and Edholm, 1989 Tanaka and Yamazaki, 1996

Chiarotto and Wainer, 1995 Chiarotto and Wainer, 1995 Lui et al., 1997 Chu and Wainer, 1989 Cass et al., 2003

Kim et al., 2000b Walhagen and Edholm, 1989 Walhagen et al., 1989b Cassiano et al., 2002

Wainer and Stiffen, 1988 Silan et al., 1990 Schleyer et al., 1995 Schneiderheinze et al., 1999b Gimenez et al., 1990 Gimenez et al., 1990 and Bourahla et al., 1996 Wainer and Stiffen, 1988 Silan et al., 1990 Schleyer et al., 1995 Ceccato et al., 1998

References

Q3

327

RP

Pindolol

NP NP NP RP RP RP RP RP RP

b

a

Rojkovicova et al., 2003a Rojkovicova et al., 2003b

Vancomycin (Chirobiotic V) Teicoplanin aglycone (Chirobiotic TAG) Chira Grom 2

Bovine serum albumin a1-Acid glycoprotein

Chiracel OD-H Sumichiral OA-4700 Sumichiral OA-4900 b-Cyclodextrin b-Cyclodextrin a1-Acid glycoprotein C18 (w/b-cyclodextrin in mp) a1-Acid glycoprotein Ovamucoid

Motoyama et al., 2002

Phenyl-carbamate b-cyclodextrin

Chu and Wainer, 1988 McAleer et al., 1992

Walters and Buist, 1998 Kim et al., 2005 Kim et al., 2000a, 2001 Edholm et al., 1988 Walhagen et al., 1989a Walhagen and Edholm, 1989 Walhagen and Edholm, 1991 Chu and Wainer, 1989 Oda et al., 1991

Kammerer et al., 1998

Mangani et al., 1997

References

a1-Acid glycoprotein

Chiral column

Mode of operation for the chiral column. NP: normal phase; PO: polar organic; RP: reverse phase. Discussed in the Section 14.4.1.

Warfarin

RP RP

Silica, Cyanopropyl Silica Silica Phenyl Phenyl Cyanopropyl, C18 Phenyl HISEP C18, dilution tube ovamucoid as trap ISRP C8

PR

Phenprocoumon metabolites Reboxetine Salmeterol Tertbutaline

Verapamil

C18

PO PO

Pinkerton RA, nonporous C18 Silica-based strong cation exchanger C18 C18

Achiral column(s)

Phenylcarbamate acid derivatives

RP

Modea

Compound

328

ANALYSIS OF ENANTIOMERIC COMPOUNDS

but can be resolved using the mass spectrometer because of the differences in the masses of the analytes. Because plasma and urine are both aqueous matrixes, reverse-phase or polar organic mode enantiomeric separations are usually preferred as these approaches usually requires less elaborate sample preparation. Protein-, cyclodextrin-, and macrocyclic glycopeptide-based chiral stationary phases are the most commonly employed CSPs in the reverse phase mode. Also reverse phase and polar organic mode are more compatible mobile phases for mass spectrometers using electrospray ionization. Normal phase enantiomeric separations require more sample preparation (usually with at least one evaporation-to-dryness step). Therefore, normal phase CSPs are only used when a satisfactory enantiomeric separation cannot be obtained in reverse phase or polar organic mode. Measuring levels of endogenous enantiomers is also possible using achiral-chiral techniques. Ichihara et al. (1999) determined the levels of D- and L-lactate in rat serum using a heart-cutting achiral-chiral system. Lactate in rat serum was derivatized for fluorescence detection with 4-(N,N-dimethylaminosylfonyl)-7-piperazino-2,1,3benzoxadiazole followed by liquid-liquid extraction to remove excess derivatization reagent. The hydroxyl group of lactate was then acetylated to enhance resolution of the two enantiomers on a Chiracel OD-RH column. A reverse-phase (C8) column was used as the achiral column. The D-lactate levels of rats before fasting was found to be higher than after fasting for 24 and 48 h.

14.3 AMINO ACIDS 14.3.1

Physiological Fluids or Tissues

Achiral-chiral LC-LC has also been used to examine the levels of D-amino acid or similar compounds in various biological fluids and tissues. In the early 1990s, the level of D-amino acids was determined in human urine using a chiral crown ether column with postcolumn derivatization (Armstrong et al., 1991). This study prompted a larger probe into the D-amino acid levels in human plasma, urine, cerebrospinal fluid, and amniotic fluid using a naphthylethyl carbamoyl-b-cyclodextrin, b-cyclodextrin (i.e., the Cyclobond series), or a chiral crown ether column (Armstrong et al., 1993a). Proline and pipecolic acid were derivatized with 9-fluorenylmethyl chloroformate (FMOC) chloride before being injected into the achiral/chiral system. After phenylalanine, tyrosine, and tryptophan were enantiomerically resolved on the chiral crown ether column, these amino acids were derivatized postcolumn for fluorescence detection. One of the amino acids examined in this study, pipecolic acid, was extremely difficult to separate from impurities. The heart-cut portion of eluent of a C8 column was transferred to a C18 column before the pipecolic acid was once again transferred to b-cyclodextrin column for the enantiomeric separation. This configuration was used in a follow-up study where the levels of D-pipecolic acid were studied in persons with peroxisomal deficiencies (Armstrong et al., 1993b). Achiral-chiral LC-LC was used in a study involving laboratory rodents that examined the factors that affected the

AMINO ACIDS

Q1

329

observed levels of selected D-amino acids (Armstrong et al., 1993c). The achiral column was a C18 column and the chiral column was a chiral crown ether (for phenylalanine, tyrosine, tryptophan, and leucine) or napthylethyl carbamoyl b-cyclodextrin (for proline) column. Derivatization for fluorescence detection was carried out as in previous studies (Armstrong et al., 1991, 1993a). Dietary (diet, antibiotics) and conditional factors (age, pregnancy, and advanced cancer) all affected D-amino acid concentrations. It was also found that for some strains of rodents, the sex of the rodent affected the amount of D-amino acids observed. More recently, achiral-chiral LC-LC was used to determine the amount of D-proline and D-leucine in brain tissue of mice lacking the D-amino acid oxidase enzyme (Hamase et al., 2001) and D-alanine in the rat central nervous system and periphery (Morikawa et al., 2003). The desired tissues were homogenized and then centrifuged with the resulting supernatant used for the analysis of the amino acids. The amino acids were derivatized with 4-fluoro-7-nitro-2,1, 3-benzoxadiazole for fluorescence detection. In these studies, the derivatized amino acid enantiomers were separated from the matrix using a microbore C18, transferred to a loop, and then separated on p-comlex type column (Sumichiral OA-2500R or Sumichiral OA-2500S) operated in the reverse-phase mode. Figure 14.3 shows the achiral and enantiomeric separation of alanine from the rat central nervous system tissues. It should be noted that if many of these amino acid studies (or analogous studies) were done today, the CSPs of choice would probably utilize the teicoplanin (Chirobiotic T) or teicoplanin aglycone (Chirobiotic TAG) chiral selectors. These newer CSPs now dominate amino acid enantiomeric separations (Table 14.2).

FIGURE 14.3 (a) Achiral separation of NBD-alanine in the rat cerebrum. (b) The chiral separation of the alanine heart cut from the achiral separation. Reprinted from Morikawa et al. (2003) with permission from Elsevier.

Q1

330

C18

C18

Microbore C18 C18

PO

PO

PO

PO PO

PO

C18

RP

Leucine

Methionine

C18

PO

Glutamic acid

C18

C18

C18

PO

Aspartic acid

C18

C18

PO

PO

Microbore C18 C18

Achiral column(s)

PO PO

Modea

Asparagine

Alanine

Amino Acid

TABLE 14.2 Analysis of Amino Acids

Sumichiral OA-2500S b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) g-Cyclodextrin (Cyclobond II) Chiral crown ether (Crownpak CRþ) b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) Sumichiral OA-2500S Naphthylethyl carbamate b-Cyclodextrin (Cyclobond RN) b-Cyclodextrin (Cyclobond I)

Chiral column

Rundlett and Armstrong, 1994 Armstrong et al. (1991, 193c)

Yes No

Hamase et al., 2001 Armstrong et al., 2001c

Yes

(continued)

Kullman et al., 1999

Kullman et al., 1999

Yes

Yes

Ekgorg-Ott and Armstrong, 1996

Yes

Pawlowska and Armstrong, 1994

Kullman et al., 1999

Yes

Yes

Kullman et al., 1999

Armstrong et al., 2001c

Yes Yes

Morikawa et al., 2003 Kullman et al., 1999

References

Yes Yes

Precolumn derivitization

Q2

331

C18

C18

C18

PO

PO

PO

Phenylthiocarbamoylated amino acidsb Pipecolic acid Proline

C18

PO

Phenylalanine

C8

C8, C18 C18

C18

C18

RP

PO PO

PO

PO

C18

RP

Phenylalanine

Achiral column(s)

Modea

Amino Acid

TABLE 14.2 (Continued)

b-Cyclodextrin Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN)

Chiral crown ether (Crownpak CRþ) b-Cyclodextrin (Cyclobond I) b-Cyclodextrin (Cyclobond I) Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) Phenyl-carbamoylated b-cyclodextrin

Chiral column

Ekgorg-Ott and Armstrong, 1996 Kullman et al., 1999 Armstrong et al., 2001c

Yes Yes

Yes

Ekgorg-Ott and Armstrong, 1996

Yes

(continued )

Pawlowska and Armstrong, 1994

Armstrong et al. (1993a, 1993b) Armstrong et al. (1993a, 1993c)

Yes

Yes Yes

Iida et al., 1997

Pawlowska and Armstrong, 1994

Yes

Yes

Armstrong et al. (1991, 1993a, 1993c)

References

No

Precolumn derivitization

332

Microbore C18 C18

C18

Diol C18

PO

PO PO

PO

RP

RP RP

PO

Proline

Theanine

Tryptophan

Tyrosine

Valine

C18

Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) Sumichiral OA-2500S Naphthylethyl carbamate b-cyclodextrin (Cyclobond RN) g-Cyclodextrin (Cyclobond II) Chiral crown ether (Crownpak CRþ) Human serum albumin Chiral crown ether (Crownpak CRþ) b-Cyclodextrin (Cyclobond I)

Chiral column

Yes

Kullman et al., 1999

Soltes and Sebille, 1997d Armstrong et al. (1991, 1993c)

Armstrong et al. (1991, 1993c)

No No No

Ekgorg-Ott et al., 1997

Hamase et al., 2001 Armstrong et al., 2001c

Kullman et al., 1999

References

Yes

Yes Yes

Yes

Precolumn derivitization

b

Mode of operation for the chiral column. NP: normal phase; PO: polar organic; RP: reverse phase. Included amino acids were alanine, arginine, aspartic acid, asparagine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threanine, tryptophan, tyrosine, and valine. c Reference discussed in the Section 14.4.1. d Reference discussed in the Section 14.5.

a

C18

Modea

Amino Acid

C18

Achiral column(s)

(Continued)

TABLE 14.2

AMINO ACIDS

14.3.2

333

In Food, Beverages, and Other Products

The enantiomeric composition of selected amino acids has also been explored in a variety of consumer goods. Once again, achiral-chiral LC-LC has been used as one method to separate the amino acids from the complex matrix. In addition, the achiralchiral system was useful in separating the derivatized amino acids from excess derivatization reagent. The total amount and enantiomeric ratios of leucine, phenylalanine, and proline were determined in a variety of honey samples (Pawlowska and Armstrong, 1994). The FMOC derivative of proline was purified by solid phase extraction on a C18 cartridge. Before derivatizing leucine and phenylalanine with FMOC-Gly-Cl, a strong cation SPE cartitridge removed sugars from an acidified diluted honey sample. Following derivatization, the amino acids were further purified using a C18 SPE cartridge. A C18 and either a Cyclobond b-cyclodextrin or naphthylethyl carbamoyl b-cyclodextrin CSP was used in the analysis. The CSPs were operated in the polar organic mode. The total level of the amino acids and the amount D-amino acids present varied not only with the botanical and geographical origins of the honey, but also with processing, storage, and other conditions. Rundlett and Armstrong (1994) examined processed foods for free D-glutamate using achiral-chiral LC-LC. The glutamic acid was derivatized with 9-fluroenylmethylcarbonylglycine chloride for more sensitive detection. Derivatized glutamate was separated first on a reverse phase C18 and then switched to a g-cyclodextrin and eluted with a polar organic mobile phase. The level of D-glutamate in foods with monosodium glutamate (MSG) added by the manufacturer had higher levels of total glutamate but lower D-glutamate concentrations. The highest levels of D-glutamate were found in fermented products, such as sauerkraut juice and soy sauce. However, these products tended to have the least amount of total glutamate. Malt beverages (beers) are fermented and so the enantiomeric composition of three amino acids in these beverages was studied by Ekgorg-Ott and Armstrong (1996). Derivatization was carried out in a similar fashion as in Pawlowska and Armstrong (1994). A reverse-phase separation was carried out on C18 and a polar organic separation on the naphthylethyl b-cyclodextrin column. While the concentration of Dphenylalanine and D-leucine could be as high as ten percent of that of the L-enantiomer, the enantiomeric ratios didn’t vary as much as the absolute concentrations of the individual amino acids. The enantiomeric composition of several amino acids was also determined in three strains tobacco leaf, as well as some smokeless tobacco products (chewing tobacco and snuff) and filtered and nonfiltered cigarettes (Kullman et al., 1999). Tobacco leaves and chewing tobacco were cut up and ground in a mortar before being used in the extraction procedure. Cigarette tobacco was also ground while the snuff was used as received. The extract was filtered and then derivatized with 9aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC). The AQC derivatized amino acids were separated on a C18 column and the individual amino acid enantiomers separated on various b-cyclodextrin columns. Some of the highest reported levels of D-amino acids were reported in this study. In fact, the amino acid alanine was racemic in one tobacco sample. Theanine (n-ethylglutamic acid) is the main amino acid found in tea and its enantiomeric composition determine by

334

ANALYSIS OF ENANTIOMERIC COMPOUNDS

Ekgorg-Ott et al. (1997). An interesting trend was discovered when considering the relative amount of D-theanine present in the samples. The teas of the highest grades consistently contained the lowest amounts of D-theanine. The theanine achiral-chiral system configuration included a C18 column operated in the reverse-phase mode and a g-cyclodextrin CSP in the polar organic mode.

14.4 OTHER APPLICATIONS

Q2

Q3

Phenylthiocarbamoyl derivatives of 18 chiral amino acids were separated on a C8 column connected in series to a phenylcarbamoylated b-cyclodextrin column (Iida et al., 1997). The C8 column separated the derivatized amino acids from one another entering the chiral column. Under this configuration several enantiomers of adjacent amino acids coeluted resulting in poor resolution. However, this configuration was successful in determining the amino acid sequence and chirality of the amino acids in a D-amino acid containing peptide. With the increased popularity of LC-MS, the problem of overlapping enantiomer peaks from other amino acids has largely been resolved. The mass spectrometer can act as an additional dimension of separation (based on mass to charge ratio). Thus, only amino acids having the same mass-to-charge ratio must be separated achirally (see Desai and Armstrong, 2004). This additional dimension of separation also has implications for the applications in the matrices discussed previously. With the ability of the mass spectrometer to discriminate on the basis of mass, this lessens the need for complete achiral separation. For example, an LC-MS method was recently developed to study the pharmacokinetics of theanine enantiomers in rat plasma and urine without an achiral separation before the enantiomeric separation (Desai et al., 2005). In such matrices, proteins must still be removed by appropriate sample preparation. Many of the methods for determining amino acids in complex matrices reported above use precolumn derivatization to increase sensitivity followed by achiral-chiral LC-LC. The CSPs used are mainly from the cyclodextrin class of CSPs. Since these projects were completed, a new class of CSPs, the macrocyclic glycopeptide class, has been developed (Armstrong, 1997; Armstrong and Zhang, 2001). These CSPs excel at separating native amino acids (with either a primary or secondary amino group). The need for derivatization for increased sensitivity is reduced by using mass spectrometry. 14.4.1

Analysis of Enantiomers from Plant and Environmental Sources

Like plasma and urine, matrixes from plant or environmental sources contain a vast diversity of components. Thus, achiral-chiral LC-LC is also useful for analysis involving samples from these sources. Stalcup et al. (1991) studied the enantiomeric purity of scopolamine extracted from Datura sanguinea in both homogenized plant leaves and commercial extracts. A reverse-phase separation on a C18 column separated the scopolamine from other alkaloid and matrix components while the enantiomeric separation (also in the reverse-phase mode) was carried out on two coupled b-cyclodextrin columns or a single acetylated b-cyclodextrin column. The single

OTHER APPLICATIONS

335

acetylated b-cyclodextrin (Cyclobond AC) column was found to as effective as the two b-cyclodextrin columns as the higher enantioselectivity observed on the acetylated column. Scopolamine from air-dried and lyophylized Datura sanguinea leaves was found to be about 0.7% D-scopolamine and commercial extracts were even higher. Within the limits of the employed method, the fresh homogenized plant leaf extract was found to be enantiomerically pure while the commercial extracts were found to contain some of both enantiomers. Subsequently, the commercial extract procedures were examined to find the source of racemization of the scopolamine. The enantioselective biodegradation of some phenoxyalkanoic herbicides (mesoprop and dicloroprop) was explored in broadleaf weeks, turf grass, and soil (Schneiderheinze et al., 1999). A C18 reverse-phase separation isolated the herbicide that was then switched to a teicoplanin CSP operated in the reverse-phase mode for enantiomeric separation. These separations are shown in Figure 14.4. The turf grass did not enantioselectively degrade the herbicides while the S-enantiomers were preferentially degraded in the broadleaf weeds and soil. The enantiomers of the fungicide epoxiconazole in tap and surface water or extracted from soil samples were determined using a C18 column and heart cut transferred to a microcrystalline cellulose acetate for the chiral separation (Hutta et al., 2002). The achiral-chiral method allowed for the direct injection of the water samples or methanol extracts (from the soil). Even the chirality of amino acids in aerosol/dust found in laboratory and residential enclosures has been examined (Armstrong et al., 2001). A C18 column was used to separate the various amino acids. The amino acids were then heart cut to either a b-cyclodextrin or naphthylethyl carbamoyl b-cyclodextrin column. The aerosol/dust was almost

FIGURE 14.4 Achiral separation (a) of rye grass extract containing 2-(2,4-diclorophenoxy) propionoic acid (2,4-DP) on a C18 column and subsequent chiral separation (b) of the heart-cut portion on a Chirobiotic T CSP. Reprinted from Schneiderheinze et al. (1999) with permission John Wiley & Sons.

336

ANALYSIS OF ENANTIOMERIC COMPOUNDS

entirely (99% or greater) the L-enantiomer of the amino acids. Thus, aerosol/dust can pose problems for the trace determination of the enantiomeric purity of amino acids.

14.5 MISCELLANEOUS APPLICATIONS

Q4

The following applications highlight the vastly different ways achiral-chiral LC-LC can be used. Many of these applications involve analytes that are structurally related. These structurally related compounds may be impurities from the synthesis of the analyte of interest using either traditional laboratory or biological methods. Achiralchiral separations can be useful in separating these related analytes. Ferretti et al. (1988) used an amino column coupled to a derivatized amylose column (Chiralpak AS) operated in the reverse-phase mode to separate the enantiomers of the antifungal agent voriconazole from several chiral impurities and one achiral impurity. Three of the chiral impurities are the other enantiomer and corresponding diastereomers of voriconazole. More chiral impurities result from a chlorinated voriconazole. Additionally, this multidimensional method could baseline separate all but two of the chiral impurities into their respective enantiomers. These separations are shown in Figure 14.5. Various chiral lumazines produced from the parent pterins by an enzymatic reaction were separated using achiral-chiral multidimensional LC-LC by Klein et al. (1994). A C18 column and a flavoprotein column were used in the reverse-phase mode to achieve the separation of the threo forms of the lumazines. The flavoprotein column was unable to resolve the erythro forms. Mancini et al. (2004) reported the use of achiral-chiral LC-LC to separate the various forms of allethrin. A silica gel column was used to separate the allethrin into cis, trans isomers. Then, the cis or trans peak was switched onto a Chiralcel OJ column to separate the stereoisomers. The other set of isomers was analyzed in a subsequent run on the achiral-chiral system. Recently, propionyl L-carnitine and its related impurities were separated using a strong cation-exchange column coupled in series to a teicoplanin aglycone column using a reverse phase mobile phase (D’acquarica et al., 2004). Also reported in this work was the successful application of an achiral-chiral mixed-bed column made with the achiral and chiral stationary phases in a 1 : 1 ratio. This column was found to give similar results to the individual achiral and chiral columns connected in series. Since the mixed-bed column was made by mixing the two types of stationary phases, the ratio of the achiral to chiral phase can be adjusted to change selectivity. This mixed-bed approach to achiral-chiral chromatography may be very helpful for applications where a 1 : 1 ratio of achiral:chiral stationary phase material is not optimal. Achiral-chiral chromatography has been used to study the differences in enantiomeric binding of proteins. Shibukawa et al. (1995) studied the enantioselective binding of chiral drugs to human, bovine, and rat serum albumins using high-performance frontal analysis. This application uses a diol-silica column that retains the drug in the micropores of the stationary phase while confining the protein to the interstial spaces between the packing particles. Under appropriate conditions on this column, a zonal

MISCELLANEOUS APPLICATIONS

337

FIGURE 14.5 Separations involving voriconazole (1), its mirror image (2), related diastereomers (3), chlorinated impurities (4), and an achiral impurity 5. (a) Achiral separation of compounds 1–5 on an amino column with hexane/ethanol mobile phase (b) Chiral separation of compounds 1–5 on Chiralpak As column with hexane/ethanol mobile phase (c) Achiral-chiral multidimensional separation with the amino and chiral column coupled in series. Reprinted from Ferretti et al. (1998) with permission from Vieweg Verlag.

peak with a plateau region will elute. The plateau region contains the drug in the same concentration as the unbound drug in the sample solution. A known volume of this plateau region was transferred to a C4 column to concentrate the drug enantiomers and then eluted and switched on to two b-cyclodextrin columns connected in series for

338

Q5

ANALYSIS OF ENANTIOMERIC COMPOUNDS

enantiomeric separation. All of the serum albumins were found to exhibit enantioselective binding, though there were differences in binding among the different species. Soltes and Sebille (1997) attempted to find the binding sites of D- and Ltryptophan on human serum albumin (HSA) using different fragments of the HSA protein. Using the Hummel and Dreyer method, racemic tryptophan was dissolved in the mobile phase used on the achiral size exclusion column. The desired protein or fragment was injected that produces a negative peak corresponding to the decreased amount of tryptophan in the mobile phase. At this minimum an aliquot was switched onto a chiral column made of HSA bonded to silica. Thus the enantiomeric ratios could be determined. Achiral-chiral chromatography has also been accomplished using subcritical fluid chromatography (Phinney et al., 1998). In this work, the structurally related b-blockers, 1,4-benzodiazepines, and two cold medicines were separated using methanol or ethanol modified carbon dioxide mobile phases. The b-blockers were separated using cyanopropyl and Chiracel OD columns connected in series. Likewise, an amino bonded phase and Chiracel OD column were used for the separation of the 1,4-benzodiazepines. Guaifenesin and phenylpropanolamine from cough syrup were separated on cyanopropyl and Chiralpak AD columns in series. Two chiral columns have even been coupled together (Dungelova et al., 2004). In this case, two chiral columns with opposite chiral selectivity were connected via a T connector. The observed enantioselectivity could be tuned by changing the flow rates between the two columns. Two chiral columns were also connected to improve the enantioresolution of a series of potential intermediates of b-lactam antibiotics (Hwang et al., 2003). When used as single columns, neither the Chiracel OD, Chiracel OJ, or (R,R)-WhelkO CSPs gave satisfactory resolution. The Chiracel OD and (R,R)WhelkO CSPs could be coupled together because the same enantiomer eluted first on both columns (as determined by a polarimetric detector). Together the columns separated the analytes to near baseline. Up to four chiral columns (operated in SFC mode) have been coupled for the separation of multiple chiral drugs at once (Sandra et. al., 1994). A Chiralcel AD, Chiralcel OD, and Chirex 3002 were serially coupled and with trifluoroacetic acid and triethylamine the mobile phase both acid and basic analytes were able to be separated. Even some barbiturates, which separate best in the reverse-phase mode with cyclodextrin columns were separated in SFC mode with the serially coupled columns. Adding another CSP (a brush type with p-acceptor characteristics) separated some amphetamines.

14.6 CONCLUSION Achiral-chiral multidimensional chromatography remains one of the best ways to separate chiral analytes from interfering matrix components or other compounds. The flexibility offered by different operation modes, stationary and mobile phases, and configurations allows analysis methods to be tailored to the analytical problem. By offering possible configurations for both online sample cleanup and concentration, achiral/chiral LC/LC reduces manual sample preparation. The ability to be coupled to

REFERENCES

339

MS detection allows for sensitive detection without derivatizing samples, also reducing sample preparation. Achiral/chiral LC/LC, especially when coupled to MS detection, can be expected to continue to play an important role in the analysis of enantiomers that are present in biological and/or environmental samples.

REFERENCES Q6

Armstrong, D.W. (1997). The evolution of chiral stationary phases for liquid chromatography. LC-GC. 15, S20–28. Armstrong, D.W., Zhang, B. (2001). Chiral stationary phases for HPLC. Anal. Chem. 73, 557A–561A. Armstrong, D.W., Duncan, J.D., Lee, S.H. (1991). Evaluation of D-Amino acid levels in human urine and in commercial L-amino acid samples. Amino Acids 1, 97–106. Armstrong, D.W., Gasper, M.P., Lee, S.H., Zukowski, J., Ercal, N. (1993a). D-Amino acid levels in human physiological fluids. Chirality 5, 375–378. Armstrong, D.W., Zukowski, J., Ercal, N., Gasper, M.P. (1993b). Stereochemistry of pipecolic acid found in the urine and plasma of subjects with peroxisomal defieciencies. J. Pharm. Biomed. Anal. 11, 881–886. Armstrong, D.W., Gasper, M.P., Lee, S.H., Ercal, N., Zukowski, J. (1993c). Factors controlling the level and determination of D-amino acids in the urine and plasma of laboratory rodents. Amino Acids 5, 299–315. Armstrong, D.W., Kullman, J.P., Chen, X., Rowe, M. (2001). Composition and chirality of amino acids in aerosol/dust from laboratory and residential enclosures. Chirality 13, 153– 158. Bordajandi, L.R., Korytar, P., De Boer, J., Gonzalez, M.J. (2005). Enantiomeric separation of chiral polychlorinated biphenyls on b-cyclodextrin capillary columns by means of heart-cut multidimensional gas chromatography and comprehensive two-dimensional gas chromatography: applications to food samples. J. Sep. Sci. 28, 163–171. Bourahla, A., Martin, C., Gimenez, F., Singhasivanon, V., Attanath, P., Sabchearon, A., Chongsuphajaisiddhi, T., Farinotti, R. (1996). Stereoselective pharmacokinetics of mefloquine in young children. Eur. J. Clin. Pharmacol. 50, 241–244. Cass, Q.B., Lima, V.V., Oliveira, R.V., Cassiano, N.M., Degani, A.L.G., Pedrazzoli, P., Jr. (2003). Enantiomeric determination of the plasma levels of omeprazole by direct plasma injection using high-performance liquid chromatography with achiral-chiral columnswitching. J. Chromatogr. B 798, 275–281. Cassiano, N.M., Cass, Q.B., Degani, A.L.G., Wainer, I.W. (2002). Determination of the plasma levels of metyrapone and its enantiomeric metyrapol metabolites by direct plasma injection and multidimensional achiral-chiral chromatography. Chirality 14, 731–735. Ceccato, A., Boulanger, B., Chiap, P., Hubert, P., Crommen, J. (1998). Simultaneous determination of methylphenobarbital enantiomers and Phenobarbital in human plasma by online coupling of an achiral precolumn to a chiral liquid chromatographic column. J. Chromatogr. A 819, 143–153. Chiarotto, J.A., Wainer, I.W. (1995). Determination of metyrapone and the enantiomers of its chiral metabolite metyrapol in human plasma and urine using coupled achiral-chiral liquid chromatography. J. Chromatogr. B 665, 147–154.

340

Q7

Q8

ANALYSIS OF ENANTIOMERIC COMPOUNDS

Chu, Y.Q., Wainer, I.W. (1988). The measurement of warfarin enantiomers in serum using coupled achiral/chiral high performance liquid chromatography. Pharm. Res. 5, 680–683. Chu, Y.Q., Wainer, I.W. (1989). Determination of the enantiomers of verapamil and norverapamil in serum using coupled achiral-chiral high-performance liquid chromatography. J. Chormatogr. 497, 191–200. Clark, B.J., Hamdi, A., Berrisford, R.G., Sabanathan, S., Mearns, A.J. (1991). Reversed-phase and chiral high-performance liquid chromatographic assay of bupivacaine and its enantiomers in clinical samples after continuous extraplural infusion. J. Chromatogr. 553, 383–390. Corlett, S.A., Chrystyn, H. (1994). Enantiomeric separation of R- and S-isosfamide and their determination in serum from clinical subjects. J. Chromatogr. B 654, 152–158. Corlett, S.A., Chrystyn, H. (1996). High-Performance liquid chromatographic determination of the enantiomers of cyclophosamide in serum. J. Chromatogr. B 682, 337–342. Cortes, H.J. (1990). Multidimensional Chromatography: Techniques and Applications. Marcel Derker Inc., New York. D’acquarica, I., Gasparrini, F., Giannoli, B., Badaloni, E., Galletti, B., Giorgi, F., Tinti, M.O., Vigevani, A. (2004). Enantio- and chemo-selective HPLC separations by chiral-achiral tandem-columns approach: the combination of CHIROBIOTIC TAG and SCX for the analysis of propionyl carnitine and related impurities. J. Chromatogr. A 1061, 167–173. Desai, M.J., Armstrong, D.W. (2004). Analysis of native amino acid and peptide enantiomers by high-performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry. J. Mass. Spec. 39, 177–187. Desai, M.J., Gill, M.S., Hsu, W.H., Armstrong, D.W. (2005). Pharmacokinetics of theanine enantiomers in rats. Chirality 17, 154–162. Diaz-Perez, M.J., Chen, J.C., Aubry, A.F., Wainer, I.W. (1994). The direct determination of the enantiomers of Ketorlac and parahydroxyketorlac in plasma and urine using enantioselective liquid chromatography on a human serum albumin-based chiral stationary phase. Chirality 6, 283–289. Dungelova, J., Lehotay, J., Krupcik, J., Cizmarik, J., Welsch, T., Armstrong, D.W. (2004). Selectivity tuning of serially coupled (S,S) Whelk-O1 and (R,R) Whelk-O1 columns in HPLC. J. Chromatogr. Sci. 42, 135–139. Edholm, L.E., Lindberg, C., Paulson, J. (1988). Determination of drug enantiomers in biological samples by coupled column liquid chromatography and liquid chromatography-mass spectrometry. J. Chromatogr. 424, 61–72. Ekgorg-Ott, K.H., Armstrong, D.W. (1996). Chirality evaluation of the concentration and enantiomeric purity of selected free amino acids in fermented malt beverages (beers). Chirality 8, 49–57. Ekgorg-Ott, K.H., Taylor, A., Armstrong, D.W. (1997). Varietal differences in the total and enantiomeric composition of theanine in tea. J. Agric. Food. Chem. 45, 353–363. Eto, S., Noda, H., Noda, A. (1994). Simultaneous determination of antiepileptic drugs and their metabolites, including chiral compounds, via b-cyclodextrin inclusion complexes by a switching chromatographic technique. J. Chromatogr. B 658, 385–390. Evans, C.R., Jorgenson, J.W. (2004). Multidimensional LC-LC and LC-CE for high-resolution separations of biological molecules. Anal. Bioanal. Chem. 378, 1952–1961. Ferretti, R., Gallinella, B., La, T.F., Zanitti, L. (1988). Direct resolution of a new antifungal agent, voriconazole(UK-109,496) and its potential impurities, by use of coupled achiralchiral high-performance liquid chromatography. Chromatographia 47, 649–654.

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Ichihara, H., Fukushima, T., Imai, K. (1999). .Enantiomeric determination of D- and L-lactate in rat serum using high-performance liquid chromatography with a cellulose-type chiral stationary phase and fluorescence detection. Anal. Biochem. 269, 379–385. Iida, T., Matsunaga, H., Fukushina, T., Santa, T., Homma, H., Imai, K. (1997). Complete enantiomeric separation of phenylthiocarbamoylated amino acids on a tandem column of reversed and chiral stationary phases. Anal. Chem. 69, 4463–4468. Kammerer, B., Kahlich, R., Ufer, M., Laufer, S., Gleiter, C. (2005). Achiral-chiral LC/LC-MS/ MS coupling for determination of chiral discrimination effects in phenprocoumon metabolism. Anal. Biochem. 339, 297–309. Kim, K.H., Kim, H.J., Hong, S.-P., Shin, S.D. (2000a). Determination of tertbutaline enantiomers in human plasma by coupled achiral-chiral high performance liquid chromatography. Arch. Pharm. Res. 23, 441–445. Kim, K.H., Kim, H.J., Kang, J.-S., Mar, W. (2000b). Determination of metoprolol enantiomers in human urine by coupled achiral-chiral chromatography. J. Pharm. Biomed. Anal. 22, 377–384. Kim, K.H., Kim, H.J., Kim, J.-H., Shin, S.D. (2001). Determination of terbutaline enantiomers in human urine by coupled achiral-chiral performance liquid chromatography with fluorescence detection. Chromatogr. B 751, 69–77. Kim, K.H., Yun, H.W., Kim, H.J., Park, H.J., Choi, P.W. (1998). Coupled column chromatography in chiral separation of salmeterol. Arch. Pharm. Res. 21, 212–216. Klein, R., Tatischeff, I., Tham, G., Mano, N. (1994). Chiral lumazines: preparation, properties, enantiomeric separation. Chirality 6, 564–571. Kullman, J.P., Chen, X., Armstrong, D.W. (1999). Evaluation of the enantiomeric composition of amino acids in tobacco. Chirality 11, 669–673. Lamprecht, G., Kraushofer, T., Stoschitzky, K., Lindner, W. (2000). Enantioselective analysis of (R)- and (S)-atenolol I urine samples by a high-performance liquid chromatography column-switching setup. J. Chromatogr. B 740, 219–226. Lui, L., Cheng, H., Zhao, J.J., Rogers, J.D. (1997). Determination of montelukast (MK-0476) and its S-enantiomer in human plasma by stereoselective high-performance liquid chromatography with column switching. J. Pharm. Biomed. Anal. 15, 631–638. Mancini, F., Fiori, J., Bertucci, C., Cavrini, V., Bragieri, M., Zanotti, M.C., Liverani, A., Borzatta, V., Andrisano, V. (2004). Stereoselective determination of allethrin by twodimensional achiral/chiral liquid chromatography with ultraviolet/circular dichroism detection. J. Chromatogr. A 1046, 67–73. Mangani, F., Luck, G., Fraudeau, C., Verette, E. (1997). Online column-switching highperformance liquid chromatography analysis of cardiovascular drugs in serum with automated sample clean-up and zone-cutting technique to perform chiral separation. J. Chromatogr. A 762, 235–241. Masurel, D., Wainer, I.W. (1989). Analytical and preparative high-performance liquid chromatographic separation of the enantiomers of isofamide, cyclophoshamide, and trofosfamide and their determination in plasma. J. Chromatogr 490, 133–143. McAleer, S.D., Chrystyn, H., Foondun, A.S. (1992). Measurement of the (R)- and (S)-isomers of warfarin in patients undergoing anticoagulant therapy. Chirality 4, 488–493. Mondello, L., Lewis, A.C., Bartle, K.D. (2002). Multidimensional Chromatography, John Wiley & Sons, New York. Morikawa, A., Hamase, K., Zaitsu, K. (2003). Determination of D-alanine in the rat central nervous system and periphery using column-switching high-performance liquid chromatography. Anal. Biochem. 312, 66–72.

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Stalcup, A.M., Faulkner, J.R., Jr, Tang, Y., Armstrong, D.W., Levy, L.W., Regalao, E. (1991). Determination of the enantiomericpurity of scopolamine isolated from plant extract using achiral/chiral coupled column chromatography. Biomed. Chromatogr. 5, 3–7. Suckow, R.F., Zhang, M.F., Cooper, T.B. (1997). Enantiomeric determination of the phenylmorpholinol metabolite of bupropion in human plasma using coupled achiral-chiral liquid chromatography. Biomed. Chromatogr. 11, 174–179. Tanaka, M., Yamazaki, H. (1996). Direct determination of pantoprazole enantiomers in human serum by reverse-phase high-performance liquid chromatography using a cellulose-based chiral stationary phase and column-switching system as a sample cleanup procedure. Anal. Chem. 68, 1513–16. United States Food and Drug Administration.(1992). FDA’s policy statement for the development of new stereoisomeric drugs. Chirality 4, 338–340. Wainer, I.W. (1989). The application of achiral/chiral coupled-column high-performance liquid chromatography to biomedical analysis. J. Pharm. Biomed. Anal. 7, 1033–1038. Wainer, I.W., Stiffin, R.M. (1988). Direct resolution of the stereoisomers of leucovorin and 5-methyltetrahydrofolate using a bovine serum albumin high-performance liquid chromatographic chiral stationary phase coupled to an achiral phenyl column. J. Chromatogr. 424, 158–162. Walhagen, A., Edholm, L.E. (1989). Coupled-column chromatography on immobilized protein phases for direct separation and determination of drug enantiomers in plasma. J. Chromatogr. 473, 371–379. Walhagen, A., Edholm, L.E. (1991). Chiral separation of achiral stationary phases with different functionalities using b-cyclodextrin in the mobile phase and applications to bioanalysis and coupled columns. Chromatographia 32, 215–223. Walhagen, A., Edholm, L.E., Kennedy, B.M., Xiao, L.C. (1989a). Determination of terbutaline enantiomers in biological samples using liquid chromatography with coupled columns. Chirality 1, 20–26. Walhagen, A., Edholm, L.E., Heeremans, C.E.M., van der Hoeven, R.A.M., Niessen, W.M.A., Tjaden, U.R., van der Greef, J. (1989b). Coupled column chromatography-mass spectrometry thermospray liquid chromatographic-mass spectrometric and liquid chromatographictandem mass spectrometric analysis of metoprolol enantiomers in plasma using phasesystem switching. J. Chromatogr. 474, 257–263. Walters, R.R., Buist, S.C. (1998). Improved enantioselective method for the determination of the enantiomers of reboxetine in plasma by solid-phase extraction, chiral derivitization, and column-switching high-performance liquid chromatography with fluorescence detection. J. Chromatogr. A 828, 167–176.

PART IV MULTIDIMENSIONAL SEPARATION USING CAPILLARY ELECTROPHORESIS

15 TWO-DIMENSIONAL CAPILLARY ELECTROPHORESIS FOR THE COMPREHENSIVE ANALYSIS OF COMPLEX PROTEIN MIXTURES James R. Kraly, Melissa M. Harwood, Megan Jones, and Norman J. Dovichi Department of Chemistry, University of Washington, Seattle, WA 98195, USA

15.1 INTRODUCTION Q1

For thirty years, two-dimensional gel electrophoresis has been the workhorse tool for protein analysis. In that technology, isoelectric focusing (IEF) in tube or strip format is used to separate proteins based on their isoelectric point. The isoelectric focusing gel is then physically placed at the top of a sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE) plate, and proteins are separated at right angles to the IEF gel; this separation is based on molecular weight. The second dimension separation is parallel, wherein all components in the isoelectric focusing dimension are simultaneously separated in the second dimension. Proteins are detected by staining the gel after the separation. As pointed out by Giddings, the spot capacity of a two-dimensional separation is given by the product of the peak capacity in the two separation dimensions, assuming that the two separation mechanisms are uncorrelated (Davis and Giddings, 1983). Isoelectric point and molecular weight are uncorrelated for complex protein mixtures, and IEF/SDS-PAGE provides very high spot capacity. Each dimension often achieves a peak capacity of 100, and the resulting spot capacity can approach 10,000. Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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In practice, a good two-dimensional gel can resolve a few thousand components (Li et al., 1999). Although classic two-dimensional gel electrophoresis provides exquisite peak capacity, it suffers from several limitations. First, the technology is labor-intensive and difficult to automate, which hampers applications to large-scale proteomics analyses (Hanash, 2000). Second, the technology has mediocre reproducibility. Software is available to morph images so that spots can be lined up; such software is expensive, difficult to use, and not always accurate in its alignment. To overcome this problem and to simplify quantitative comparisons between samples, Unlu et al. (1997) developed differential gel electrophoresis (DIGE), where two samples are each labeled with different fluorescent tags, pooled, separated on the same gel, and scanned at characteristic wavelengths to resolve the components. This technology has been commercialized by Amersham. Third, stains produce limited dynamic range and sensitivity (Hanash, 2000). A good stain and scanner might produce two orders of magnitude dynamic range, with detection limits in the picomole range. The dynamic range of the proteome extends for many orders of magnitude, and the limited dynamic range of staining technology limits study of low abundance proteins.

15.2 PREVIOUS WORK 15.2.1

Miniaturized IEF/SDS-PAGE

There has been interest in miniaturizing and automating electrophoresis of proteins. Ruchel (1997) reported a miniaturized system where proteins are first separated by isoelectric focusing in millimeter diameter tubes. The tube’s contents are transferred to a slab gel that is a few centimeters on a side. This technology was used to separate the proteins from a single giant neuron from Aplasia californicus. More recently, native fluorescence has been used to resolve 200 proteins from a similar miniaturized electrophoresis system (Sluszny and Yeung, 2004). Poehling reported a microscale two-dimensional gel electrophoresis system 25 years ago (Poehling and Neuhogg, 1980; Neuhoff, 2000). In that system, separation in the IEF dimension was performed in thin, gel-filled tubes. After IEF, the gel was transferred to a 3 cm 3.5 cm polyacrylamide gel, where proteins were separated by SDS-PAGE. Several hundred components were resolved from a few micrograms of protein homogenate. Neukirchen et al. (1982) from the Max-Planck Institute employed a similar miniaturized IEF/SDS-PAGE system that was roughly 2 cm 2 cm. Silver staining was used to detect spots containing as little as 10 pg of protein, and electrophoresis was used to separate the proteins contained within a single Drosophila egg. Whitesides reported a microfabricated device where isoelectric focusing was performed in a horizontal channel and SDS-PAGE was performed in a series of vertical channels machined at right angles to the IEF channel (Chen et al. 2002).

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Sweedler reported a two-dimensional separation, where fluorescein thiocarbamyl derivatives of peptides were separated by capillary zone electrophoresis in the first dimension (Liu and Sweedler, 1996). The outlet of the capillary was wiped across the top of an SDS-PAGE gel, where peptides were then separated based on their size.

15.2.2

One-Dimensional Capillary Electrophoresis for Protein Analysis

Hjerten introduced free zone electrophoresis nearly 50 years ago. In this early technology, compounds, including proteins, were separated in a large tube, 4 mm inner diameter, and in the absence of a sieving matrix (Hjerten, 1958, 1967). To reduce band broadening due to convection, the tube was rotated about its central axis. Detection was by means of UV absorbance, either by scanning the transmission detector along the length of the tube or by allowing the analyte to flow past a fixed detector at the tube’s outlet. Jorgenson reported the use of glass capillaries for free solution electrophoresis 25 years ago (Jorgenson and Lukacs, 1981, 1983). A plug of analyte was introduced into a buffer-filled capillary and separated at high electric fields. Capillaries of 75 mm inner diameter were employed, and detection of labeled amino acids and peptides was based on fluorescence. 15.2.2.1 Free-Solution Electrophoresis of Proteins in the Presence of Surfactants The addition of an anionic surfactant, such as SDS, to the buffer helps to solubilize proteins, minimizing their adsorption to the capillary walls. The interaction of SDS with proteins was extensively studied in the 1970s, yielding conflicting results. Reynolds reported that standard proteins tended to bind SDS at a constant ratio of 1.4 g SDS per gram of protein (one SDS molecule per two amino acid residues), and the binding is by the monomer rather than by micelles (Reynolds and Tanford, 1970). Since each monomer carries a single negative charge, the charge induced by SDS swamps the native charge of the uncomplexed protein, and all proteins should have a constant charge-to-molecular mass ratio. Electrophoretic mobility depends on the ratio of charge to size, and the mobility of SDS-complexed proteins is expected to be constant and independent of molecular weight. Takagi determined the free solution mobility of 14 SDS–peptide and SDS–protein complexes. For proteins of molar mass greater than about 10 kDa, the mobility was indeed observed to be constant ( 2.85 10 4 cm2 V-1 s 1) (Karim et al., 1994), whereas lower molecular weight peptides tended to have more negative mobilities. Both Reynolds and Karim worked at neutral pH, with denatured proteins, and with reduced disulfide bonds. Under these conditions, proteins are in a random coil conformation (Mattice et al., 1976), so that their hydrodynamic radius is monotonically related to their molar mass. Takagi et al. (1975) reported that the binding isotherm of SDS to proteins strongly depends upon the method of denaturing disulfide bonds. Presumably, protein–SDS complexes are not fully unfolded when disulfide bonds are left intact, which breaks the relationship between molar mass and hydrodynamic

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radius of unreduced proteins complexed with SDS. Even for reduced proteins, anomalous SDS binding characteristics are observed for proteins with few hydrophobic residues, high glycine and cationic residue content, histones, and highly glycosylated proteins (Mattice et al., 1976; Hayashi and Nagai, 1980; Rizzo, 1985; Karim et al., 1994; Tejero-Diez et al., 1999). Gelamo and Tabak (2000) reported a dramatic decrease in the binding of SDS to reduced bovine serum albumin at pH 9.0 compared to pH 7.0. However, this phenomenon is expected only if disulfide bonds are reduced; there is little difference in the charge of the protein at these two pHs if the disulfide bonds are intact. We have investigated the electrophoretic separation of SDS-protein complexes both under submicellar and micellar conditions, and we observed quite rich electropherograms from cellular protein homogenates (Lee et al., 1998; Zhang et al., 2000). These results differ dramatically from those reported by Reynolds and Karim. However, our experiments differed from the earlier experiments in three important ways. First, we did not reduce disulfide bonds, which lead to the retention of some secondary structure within the protein, so that the viscous drag experienced by the molecule is not monotonically related to the protein’s molecular weight. Second, we performed our separation at pH 9.0, where the binding of SDS decreases dramatically compared to the binding at neutral pH (Gelamo and Tabak, 2000). Third, we used relatively low concentrations of SDS, which may not completely denature the protein; Whitesides has recently reported dramatic differences in the mobility of a set of proteins separated at modest concentrations of SDS (Gudiksen et al., (2006a,b). 15.2.2.2 Capillary SDS-Sieving Electrophoresis In the presence of a sieving matrix, mobility decreases monotonically with molecular weight for SDS-complexed proteins. This relationship is the basis of SDS-PAGE separation of proteins. Hjerten (1983) reported the use of crosslinked polyacrylamide gels for the capillary electrophoretic separation of proteins. However, crosslinked polymers are quite rigid, and the capillary has a short lifetime. Widhalm et al. (1991) reported the use of noncrosslinked polyacrylamide for protein separation in fused silica capillaries. This matrix has low viscosity and can be replaced between separations, greatly facilitating automation of the separation. Awide range of noncrosslinked polymers has been used for size-based protein separations. Noncrosslinked polymers do not form a gel, and it is inappropriate to refer to this separation as gel electrophoresis. A number of names have been used for the method. In an effort to standardize nomenclature, IUPAC has used the term capillary sieving electrophoresis. 15.2.2.3 CSE and Micellar Electrokinetic Capillary Chromatogarphy (MECC) of Complex Protein Homogenates Detection UV absorbance detection is typically used for capillary electrophoresis. However, the short optical pathlength of the capillary results in poor detection limits

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and limited dynamic range. Although native fluorescence can be used to detect proteins and peptides that contain aromatic amino acids, we prefer to employ fluorescence labeling chemistry, which generates highly fluorescent products that can be excited with low-cost and highly reliable lasers that operate in the visible portion of the spectrum. Most labeling chemistry focuses on reaction with the e-amine of lysine residues. We find that conventional fluorescent labels are not satisfactory for this application; they must be used at high concentration, which results in large amounts of unreacted reagent. That material, and impurities present at the parts per million level, generate large background signals that interfere in protein analysis. Furthermore, complete labeling of all lysine residues requires heroic efforts (Liu et al., 2001). As a result, most labeling reactions produce a complex mixture of reaction products that generates complex electropherograms (Zhao et al., 1992; Craig and Dovichi, 1998; Richards et al., 1999). Instead, we employ fluorogenic reagents for labeling. In particular, we find that the fluorogenic reagent 3-(2-furoyl)quinoline-2-carboxaldehyde (FQ) to be particularly useful. Fluorogenic reagents are nonfluorescent until they react with a primary amine, resulting in very low background signals. FQ has the fortuitous property of converting cationic lysine residues into neutral products. SDS appears to ion pair with unreacted lysine residues, also creating neutral products, which results in high-efficiency separations of labeled proteins. Similar behavior has been reported by Whitesides and coworkers (Gudiksen et al., 2006) for other alkylating agents that produce neutral products. Separations Figure 15.1 presents the CSE and MECC separation of the protein homogenate prepared from the hTERT cell line. The separations were both performed at an electric field of 1000 V/cm in 30 mm ID, 20 cm long capillaries. CSE requires the use of a coating to reduce electroosmosis, which otherwise would pump the sieving matrix from the capillary. The use of covalent coatings is difficult with these narrow inner-diameter tubes (Lee and Wright, 1980). We instead use a dynamic coating with the reagent UltraTrol to suppress electroosmosis. The separations are complete in about 2.5 min, and peak efficiencies exceed 250,000 plates for both MECC and CSE separations. The separation window is quite short, particularly for the CSE dimension, with the majority of components migrating in a 25 s window. 15.2.2.4 Capillary Isoelectric Focusing Early work on miniaturized electrophoresis employed Drummond glass pipets to perform isoelectric focusing. Modern capillary isoelectric focusing was inaugurated by Hjerten and Zhu (1985), who reported isoelectric focusing in 100 mm inner-diameter fused silica capillaries. Proteins were detected by UV absorbance, either by scanning the capillary after focusing, or by mobilization of proteins past a fixed detector by manipulation of the cathodic or anodic buffer.

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Signal (arb)

(a)

1

1.5

2

2.5

(b)

1

1.5

2

2.5

Minutes

FIGURE 15.1 One-dimensional capillary electrophoresis separation of a protein homogenate prepared from the hTERT cell line. Both separations were preformed in 30 mm ID, 145 mm OD, 20 cm long capillaries at 20,000 V. (a) Micellar electrokinetic chromatography performed with a 100 mM CHES, 100 mM Tris, and 15 mM SDS buffer at pH 8.7. Sample is electrokinetically injected with 0.25 kV for 1 s (b) Capillary sieving electrophoresis performed in 5% Dextran (513 kDa), 100 mM CHES, 100 mM Tris, 3.5 mM SDS, pH 8.7.

15.3 TWO-DIMENSIONAL CAPILLARY SEPARATIONS FOR ANALYSIS OF PEPTIDES AND PROTEINS 15.3.1 Capillary Liquid Chromatography Coupled with Capillary Electrophoresis for Analysis of Unlabeled Peptides and Proteins

Q3

These systems rely on various combinations of size-exclusion chromatography, reversed-phase chromatography, and zone electrophoresis to characterize amines, peptides, and proteins (Yamamoto et al., 1989; Bushey and Jorgenson 1990; Larmann et al., 1993, Moore and Jorgenson, 1995; Optick and Jorgenson, 1997). Haleem Issaq reviews these separations in Chapter 16 of this book. 15.3.2

Two-Dimensional Capillary Electrophoresis for Analysis of Proteins

15.3.2.1 Instrument We reported the first two-dimensional capillary electrophoresis system for protein analysis (Michels et al., 2002, Hu et al., 2004, Michels

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Interface Capillary 1

Capillary 2 Laser

First calillary buffer

Power supply 1

Second calillary buffer

Power supply 2

Lens filter Photo detector

Electrical ground

Sheath-flow cuvette

To waste Computer

FIGURE 15.2 Instrumentation for two-dimensional capillary electrophoresis. Two capillaries are connected in a buffer-filled interface so that their ends are separated by 30 mm. The injection end of the first capillary is placed in a buffer filled vial that is in contact with the highvoltage power supply 1. The interface is connected with wide-bore tubing to a second vial, which is connected to the second power supply. The distal end of the second capillary is placed in a postcolumn sheath-flow cuvette, which is held at ground potential. A 473 nm solid-state laser is used to excite fluorescence, which is detected with a single-photon counting avalanche photodiode. A computer controls the power supplies and records the signal from the photodetector.

et al., 2004). Our first report employed free-solution electrophoresis with a submicellar concentration of SDS in both dimensions; neutral pH was used in the first capillary and a very basic pH was used in the second dimension. Currently, we employ capillary sieving electrophoresis in the first dimension and free-solution electrophoresis with SDS near its critical micelle concentration in the second. In this system, shown in Fig. 15.2, two capillaries are used for the separation. During separation, the injection end of the first capillary is placed in a microcentrifuge tube containing running buffer and the distal end is placed in an interface. The second dimension capillary is placed within the interface, which holds the two capillaries so that they are coaxial and separated by a few micrometers. The interface is also connected through a 1 mm inner-diameter tube to another centrifuge tube, which contains running buffer for the second dimension separation. The distal end of the second capillary is placed within a postcolumn sheath-flow cuvette detector. The heights of the buffer reservoirs and the sheath-flow waste container are held at the same height to minimize formation of a siphon through the capillaries, which would degrade the separation efficiency. The interface is constructed by macromachining grooves in a Plexiglas block. These grooves are about 1 mm wide and deep, and are milled using an appropriately sized bit. Two nested sets of tubes are placed within the capillary; these tubes align the

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25 kV 20 kV 15 kV

Power supply 2 (interface)

Transfer 1 (1 s)

Transfer 2 (1 s)

Transfer 3 (1 s)

First dimension Second prerun dimension (80 s) separation

10 kV 5 kV Power supply 1 (injection box) 0 kV

Cycle 1 (11 s)

Cycle 2 (11 s)

Cycle 3 (11 s)

Sheath-flow cuvette Time

FIGURE 15.3 Timing diagram. The potential is shown at the injection reservoir (dashed line), the interface (solid line), and the detection cuvette (dotted line). The interface is held at high potential continually and the cuvette is held at ground potential. As a result, analyte always migrates through this capillary. The movement of analyte in the first dimension capillary is controlled with the injection reservoir power supply. Analytes are injected under the action of high voltage or pressure. Once the analytes are injected, they are subjected to a prerun, until the fastest moving analytes approach the end of the first capillary. A series of voltage pulses is used to periodically transfer a fraction from the first to the second capillary.

separation capillaries. A microscope slide is glued to the top of the Plexiglas block, forming a leak-tight interface. Two high-voltage power supplies are used to drive the separation. The first power supply applies high voltage through a platinum electrode to the injection buffer reservoir for the first dimension separation. The second power supply applies potential to the interface through its buffer reservoir. The sheath flow cuvette is held at ground potential. A typical voltage timing-diagram is shown in Fig. 15.3. Once the sample is injected, voltage is applied to the first capillary for several minutes until the fastest migrating components approach the distal end of that capillary. Contents of the first capillary are held stationary by applying equal voltage to the first and second electrodes, resulting in a zero net potential across that capillary. After this prerun, the voltage is programmed to periodically pulse a plug of analyte into the interface. This fraction is then drawn into the second capillary for further separation. In our current configuration, the separation window in the CSE dimension is roughly 200 s in duration, and roughly 200 pulses of 1 s duration are required for the contents of the CSE capillary to be transferred to the second dimension. A constant potential is applied across the second dimension capillary, typically 10,000–20,000 V. Under this constant voltage, any analyte present within the interface is driven into the second dimension capillary for separation. Detection is by laser-induced fluorescence in a postcolumn sheath-flow cuvette. 15.3.2.2 Data Presentation The raw electrophoresis data consist of a long file of the intensity trace recorded by the fluorescence detector (Fig. 15.4). The trace consists of a pseudoperiodic set of peaks, where successive peaks are generated by a component, which is transferred in several fractions from the first to the second

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Corrected signal, MHz

30

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FIGURE 15.4 Computer record of a two-dimensional capillary electrophoresis analysis of a protein homogenate prepared from a biopsy obtained from the fundus of a Barrett’s esophagus patient. The data were generated by performing 1 s transfers between capillaries and a 9 s second-dimension separation. The first-dimension separation employed the same buffer as the CSE separation in Fig. 15.1 and the second-dimension separation employed the same buffer as the MECC separation in Fig. 15.1.

capillary. Figure 15.5 presents a close-up of the data, which shows a region where two components are comigrating in the first dimension but are separated in the second. The data can be considered to be an intensity plot recorded on a cylinder, where the angular position corresponds to the MECC time and the vertical distance corresponds

Corrected signal, MHz

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FIGURE 15.5 Close-up of a 3.5 min. long stretch of the data of Fig.15.4. Two components are migrating from the first capillary and are outlined with dashed curve. The components are transferred over 6 cycles. They have different migration times in the second dimension separation, which allows their resolution.

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to the CSE time. To visualize the data, this cylinder is slit vertically and flattened. The angular position for the cut is arbitrary and is usually chosen to minimize the number of intense components that are bisected. Note that drift in the second dimension separation time can result in a component appearing immediately to the left of the cut on one electropherogram and to the right on another. The data can be visualized in several formats. In a gel image, the optical density at each point is related to the fluorescence intensity; false color images can be used to improve the dynamic range of visualization. We usually employ a logarithmic compression to help visualize the wide dynamic range of the data; the image can be processed to saturate the most intense components, allowing observation of less intense components. In a landscape image, the data are presented as a surface, where the height at each point is proportional to the fluorescence intensity; compression algorithms are not used with this visualization method, although the image can also be processed to saturate the most intense components. 15.3.3

High-Speed Two-Dimensional Capillary Electrophoresis

Our initial experiments required about 8 h to complete the two-dimensional separation. This long period results in drift due to temperature changes and buffer evaporation. We have modified the instrument to generate faster separations (Kraly et al., 2006). Shorter and narrower inner-diameter capillaries are now employed, which allow operation at 1000 V/cm, and dramatically improves the separation time. We employ a 10 s seconddimension separation period, and the separation is now complete in 40 min. Figure 15.6 presents the data of Fig. 15.4 in the form of a gel image. The optical density in the image is proportional to the logarithm of the fluorescent intensity; this

FIGURE 15.6 Gel image of the data in Fig. 15.4, where the density of the plot is proportional to the logarithm of the fluorescence intensity observed in each cycle. The data are plotted as a gray scale for low intensity components and a blue-green-yellow-red scale for the most intense components.

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FIGURE 15.7 Landscape image of the data of Fig. 15.4. The height of each point is proportional to the intensity observed at that time.

processing helps compress the very wide dynamic range of the data. To further accentuate low-amplitude components, the gray-scale image has been overexposed; the overexposed regions are superimposed with a color scale, which helps visualize the more intense components. The molecular weight scale is obtained by the analysis of standard proteins. The data consist of a set of spots, which are well-resolved components, and long, thin smears. Figure 15.7 presents the same data in the form of a landscape image; this image helps visualize the more intense components. The data are dominated by a few highamplitude components. Figure 15.8 presents the same data, expanded by a factor of 30 to highlight the low-amplitude components. Figure 15.9 presents the same data, expanded by another factor of 30 for a total amplification of 1000 to highlight the lowest-amplitude components. The dynamic range for this data approaches 250,000,

FIGURE 15.8 Expanded view. The data of Fig. 15.7 have been expanded by a factor of 30 to highlight components of intermediate intensity.

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FIGURE 15.9 Expanded view. The data of Fig. 15.7 have been expanded by a factor of 1000 to highlight the lowest amplitude components. The dynamic range of the measurement exceeds 250,000.

which allows study of low abundance proteins in the presence of highly abundant proteins. This sample is a biopsy taken from the fundus of a patient with Barrett’s esophagus under informed consent. The biopsy was fixed in 70% ethanol within 15 s of sampling and was homogenized within 5 min. It is possible to obtain the sample, process it, and complete the electrophoresis within 1 h. The biopsy is roughly 3 mm long, 1 mm wide and 500 mm deep. Its total protein content is 150 mg. This homogenate is prepared in 400 mL of solution and can be used for dozens of analyses. We have analyzed the run-to-run reproducibility of the two-dimensional capillary electrophoresis system. The relative standard deviation in both MECC and CSE dimensions is better than 2% for the 50 most intense components in a set of five runs, and a simple gel alignment algorithm provides a twofold improvement in precision. We also fit a Gaussian surface to the spots. The average spot width, expressed as the standard deviation of the Gaussian, was 1.6 transfers in the CSE dimension and 0.19 s in the MECC dimension. Each peak migrating from the sieving capillary was sampled roughly three times as it was transferred to the second-dimension capillary. 15.3.4

The Analysis of a Single Fixed Cell

This instrumentation has exquisite sensitivity, which allows the analysis of single cancer cells (Hu et al., 2004). Our earlier work employed slow separation conditions and a rather primitive photodetection system. Our current system takes roughly 1 h to complete the two-dimensional capillary electrophoresis separation and employs stateof-the-art photodetectors. Figure 15.10 presents the two-dimensional capillary electrophoresis fingerprint of a single MCF-7 breast cancer cell. This cell had been fixed in 70% ethanol before analysis.

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FIGURE 15.10 Landscape image of a two-dimensional capillary electrophoresis analysis of the protein content of a single fixed MCF-7 breast-cancer cell. The data consist of a few highamplitude components.

FIGURE 15.11 of 30.

Expanded view. The data of Fig. 15.10 have been expanded by a factor

FIGURE 15.12 of 1000.

Expanded view. The data of Fig. 15.10 have been expanded by a factor

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The data are dominated by a few low molecular weight components. Figure 15.11 presents an image that has been amplified by a factor of 30; many more components are visible. Figure 15.12 presents the same data amplified by another factor of 30 for a total amplification of 1000, and a sea of peaks is visible. An unsupervised routine was used to isolate all local maxima in the data; 190 components were resolved with amplitude greater than 10 times the standard deviation of the background signal.

15.4 CONCLUSIONS Two-dimensional capillary electrophoresis of complex protein samples requires careful attention to detail. Tissues and cells should be fixed to prevent degradation. Most conventional fixatives are inappropriate because they produce covalent crosslinks, which are difficult to reverse. We find that ethanol produces decent results when the sample is homogenized with high concentrations of SDS. Fluorescent labeling requires care. Unless the protein is completely labeled, which requires heroic effort, incomplete labeling converts each protein into a complex mixture of reaction products. We find that the use of FQ as the labeling reagent and the use of an SDScontaining buffer produces extremely high-efficiency separations of labeled proteins. High-efficiency separations of FQ-labeled proteins are only achieved in the presence of an anionic surfactant, such as SDS. As a result, capillary isoelectric focusing is not useful for the analysis of these proteins. Instead, we employ capillary sieving electrophoresis and micellar electrokinetic capillary chromatography for our two-dimensional electrophoresis. Extremely high dynamic range and exquisite sensitivity is produced by laserinduced fluorescence of FQ-labeled proteins. The dynamic range exceeds 250,000, and the detection limit is in the high yoctomole range for FQ-labeled proteins.

15.5 ABBREVIATIONS CSE DIGE FQ IEF ID PAGE SDS

capillary sieving electrophoresis differential gel electrophoresis 3-(2-furoyl)quinoline-2-carboxaldehyde isoelectric focusing inner diameter polyacrylamide gel electrophoresis sodium dodecyl sulfate

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16 TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION Haleem J. Issaq and Timothy D. Veenstra Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA

16.1 INTRODUCTION The resolution of a complex mixture, for example, a serum proteome that may contain up to 20,000 proteins with a concentration dynamic range of 1010 (Anderson and Anderson, 2002), which when digested may result in 400,000–600,000 peptides, cannot be achieved using a single chromatographic or electrophoretic procedure because they do not possess the required peak capacity. To increase the peak capacity, a combination of two or more orthogonal separation techniques are used. According to Giddings (1987), the peak capacity of an orthogonal multidimensional separation is the product of the peak capacities of its component one-dimensional methods. Mass spectrometry (MS) is the technique of choice for the sensitive detection and identification of peptides. Because a mass spectrometer can perform mass measurements on several, but not many, coeluting peptides, multidimensional separation becomes a critical aspect of mass spectral identification of peptides in a proteome. Therefore, limiting the number of coeluting peptides results in an increase in the number of peptides that can be identified. High performance liquid chromatography (HPLC) and capillary electrophoresis (CE) are two instrumental separation techniques that are applicable to the separation of proteins and peptides. The advantage of HPLC and CE techniques is that they afford the analyst the freedom to resolve a complex mixture by different routes employing different Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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separation mechanisms based on physical and chemical properties of the solute mixture (Issaq, 1990). Different modes of HPLC (reversed-phase, ion exchange (IEX), size exclusion, affinity, and hydrophobic interaction) and CE (capillary zone, isoelectric focusing, isotachophoresis, affinity, and micellar) have been used in multidimensional formats for the fractionation and/or separation of peptides and proteins. Multidimensional HPLC and CE procedures are automatable, sensitive, reproducible, fast, and quantitative. HPLC and CE separations can also be coupled with different detection systems, including UV/Vis, mass spectrometry (MS), or laser-induced fluorescence (LIF). In proteomic studies, the multidimensional system selected should allow the analysis of the whole sample, and not only selected parts of the first-dimension fraction using heart-cutting procedures where only peaks of interest are transferred from the first column to a second column for further separation. The discussion in this chapter will be limited to illustrative and published two-dimensional HPLC–CE procedures in which HPLC is the first dimension and CE is the second dimension. 16.2 OFF-LINE VERSUS ONLINE An initial important aspect of multidimensional separation is the choice of the experimental setup. The 2D HPLC–CE techniques discussed here are performed by injecting the sample onto the HPLC column, and then sequentially introducing the resulting fractions individually onto the CE column. The whole process can be carried out off-line or online. Off-line methods involve the collection of fractions from the first dimension and then analyzing them by the second dimension. In an online 2D separation, fractions eluting from the HPLC column are directly transferred to the CE column for final analysis. Both methods have their advantages and limitations. The advantages of an online approach are speed and high throughput; however, such approaches have stringent requirements. These include that the second dimension must be faster than the first dimension to accommodate online sampling, the columns used are restricted in dimensions that limit the amount of sample that can be efficiently loaded, the procedure does not allow reanalysis of fractions, and it requires special instrumental modifications and fast data acquisition. In addition, run times and the choices for mobile-phase compositions tend to be more limited than they are with off-line 2D approaches. An offline approach, although slower, and may lead to some sample loss, has several advantages. The procedure uses commercially available equipment that does not require instrumental modifications and allows the reanalysis of collected fractions. It is simple, easy to perform with no restrictions on HPLC column dimensions or packing materials, and there is no limitation on the number or volume of the collected fractions. Our group preferred the off-line approach because of its simplicity and the complexity of the proteomic samples being analyzed, such as the human serum proteome and cell lysates. 16.3 HPLC FRACTIONATION HPLC fractionation is the first step (dimension) in the two-dimensional analysis of complex peptide mixtures. This step is generally carried out by gradient elution

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chromatography, not capillary electrophoresis, because the sample volume that can be analyzed by HPLC (mL) is larger than that by CE (nL). Analytical HPLC would allow more concentrated fractions to be collected, which is especially important in the analysis of peptides/proteins of a cell or serum proteome where the concentration dynamic range is 108–1010. HPLC fractionation is achieved by using mobile-phase gradients whereby proteins or peptides are differentially eluted by changing the organic modifier concentration (RP), the ionic strength of the buffer (hydrophobic interaction chromatography and IEX), or the pH of the mobile phase (IEX). Issaq et al. (2002) published a comprehensive review of protein/peptide fractionation procedures.

16.4 2D HPLC–CE The 2D methods that employ RP–HPLC in the first dimension and CE in the second dimension are a powerful combination because they combine two high resolving techniques with different separation mechanisms, hydrophobicity for RP–HPLC and charge-to-size ratio for capillary zone electrophoreosis (CZE). Among the various chromatographic modes (RP, IEX, size exclusion, etc.), RP–HPLC has several advantages: it possesses the highest resolving power, the mobile phase is free of salts, and it is amenable to direct mass spectral analysis. This is one of the main reasons why it is normally the second (last) dimension in any 2D or multidimensional chromatographic separation schemes. In comparison to HPLC, CE has many advantages, including higher column efficiency, speed, and sensitivity when LIF or MS is the method of detection. As the CE column is constructed from open tubular fused silica capillary, contamination because of carryover effects from separation to separation is minimized. Moreover, the time between consecutive CE experiments is much shorter than LC-based separations with gradient elution, because the CE capillary column does not require reequilibration between analyses. Different HPLC modes have been coupled with different CE modes as will be discussed in Section 16.6. Online interfacing of an HPLC with a CE is not a trivial problem. Most analytical HPLC instruments use columns that are not compatible (mm i.d.) with CE columns (mm i.d.). However, the introduction of micro- and nano-HPLC systems, where narrow, under 100 mm i.d., fused silica capillaries are used, simplified the transfer of effluents from the first-dimension (HPLC) column into the second separation (CE) column and prevented peak broadening. Many attempts have been made for coupling the two columns together using valves (Bushey and Jorgenson, (1990a), flow gating (Hooker and Jorgenson, 1997), and other instrumental approaches. For a detailed discussion, the reader should consult a review by Ehala et al., (2004). Online coupling of RP-HPLC to CE was pioneered and developed by Jorgenson and coworkers, employing different chromatographic modes as the first dimension and CZE as the second dimension (Bushey and Jorgenson, (1990a); Larmann et al., 1993; Moore and Jorgenson, (1995a); Hooker and Jorgenson, 1997; Lewis et al., 1997; Evans and Jorgenson, 2004). In most of the reported applications of two-dimensional

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RPLC-CZE, fractions are collected off-line from the first dimension and subsequently subjected to CZE analysis (Issaq et al., 1999 and 2001; Shen et al., 2001; He et al., 2002; Sanz-Nebot et al., 2002; Rodriguez et al., 2003; Yang et al., 2003). Details of selected applications will be discussed subsequently. Robson et al. (1999) developed a simple interface for gradient CEC-HPLC.

16.5 CE–MS DETECTION Detection of the effluent in a 2D system is carried out at the end of the second dimension’s column. UVand LIF are the most widely used and the simplest methods of detection for CE separations because they are performed on-column. MS detection, unlike UV and LIF, is carried out on the effluent as it exits the CE column. The direct coupling of CE with mass spectrometry has shown great potential in proteomic research (Janini et al., 2004). The method of choice for detection of peptides is MS-electrospray ionization (ESI). However, ESI requires a special interface between the CE column and the mass spectrometer that has proven not to be a simple matter (Issaq et al., 2004). Although UV and LIF allow the detection of peptides and proteins, they are not used for protein sequencing or peptide identification, and hence tandem mass spectrometry (MS/MS) is the technique of choice. The marriage of CE and MS is an extremely powerful tool for the separation, identification, and characterization of peptides and proteins. However, the online interfacing of CE with MS is a challenging problem and not as easy as that of CE–UV or CE–LIF where oncolumn detection is carried out. The requirements of an online CE–ESI-MS system are a CE instrument, a mass spectrometer, an interface that will allow for the continuous transfer of analytes from the CE column outlet to the MS source, a closed CE electrical circuit for electrophoretic separation of the analyte mixture, and a closed electrical circuit for the generation of a continuous, stable, and uniform fine spray stream of the column effluent that will permit sensitive MS detection of the solutes. An ideal interface is constructed of a single capillary that incorporates both the CE separation column and the spray tip to form one continuous unit to eliminate any dead volume that may lead to peak broadening and affect the quality of separation. In addition, the design should preserve the electric circuits of both the CE system and the spray tip. Also, it would be advantageous if no external solvent is added to the system (sheath liquid) that would dilute the analyte concentration and affect the detection sensitivity. Electrospray ionization (ESI) is ideally suited as a detection technique for the online interfacing of liquid-phase separations (HPLC and CE) to MS, because it facilitates the transfer of analytes from the liquid phase of the HPLC or CE column to the gas phase of the MS. Also, it allows the detection of high molecular weight species, such as peptides. Three interface designs have been developed in the past 18 years for coupling CE with MS. The first CE–MS interface, coaxial sheath flow, was introduced by Smith and his group in 1987 (Olivares et al., 1987) and was improved upon in later work (Smith et al., 1988). Coaxial sheath flow is formed using two concentric metal capillaries, whereby the CE terminus and the makeup flow line are inserted into the

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inner capillary. The delivery of the sheath gas is through space between the inner and outer capillaries. In 1989, Henion and coworkers (Lee et al., 1988) introduced the liquid junction interface, where a tee forms a junction between the CE terminus and a makeup flow line that transfers the resolved analytes to the ion source of the MS. Smith and coworkers (Wahl et al., 1994) realized the limitations of sheath liquid flow and developed a sheathless interface. Although then there have been many improvements on the design of these three interfaces (Cai and Henion, 1995; Lewis et al., 1997; Kelly et al., 1997; Ding and Vouros, 1999; Bergstrom et al., 2003), many of these CE–MS interfaces have certain limitations, namely they are hard to fabricate, not durable, poorly reproducible, difficult to operate, or cause peak broadening. A simple sheathless CE–ESI-MS interface that offers significant improvements in terms of ease of fabrication, durability, and maintenance of the integrity of the CE-separated analyte zones over existing interfaces was developed in our laboratory. The design, shown in Fig.16.1, incorporates the separation column, an electrical porous junction, and the spray tip on a single piece of a fused silica capillary (Janini et al.,2003). The sample is preconcentrated directly within the CE capillary followed by its electrophoretic separation and detection using a true zero dead-volume sheathless CE–MS interface (Fig.16.1). ESI is accomplished by applying an electrical potential through an easily prepared porous junction across a 3–4 mm length of fused silica. A stable electrospray is produced at nanoliter flow rates generated in the capillary by electrophoretic and electroosmotic forces. The interface is particularly well suited for the detection of low femtomole levels of proteins and peptides. In this design, spray tips were made by applying heat from a microtorch while gently pulling on a capillary. The resulting long tapered tip is later trimmed to the desired inside diameter using a glass tube cutter. The polyimide

Extraction disk

Fused silica sleeve Epoxy glue

ESI power supply

Plexlglass reservoir

CE capillary MS Plexlglass slide Spray tip

CE power supply CE buffer Etched segment

FIGURE 16.1 Schematic diagram of the CE capillary with on-column mSPE cartridge– sheathless ESI-MS interface. (reprinted with permission from Analytical Chemistry).

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coating was removed from a 3–4 mm section of the capillary at a distance of 5 cm from the spray tip end, and the capillary was trimmed to a total length of 60 cm. The interface was constructed by etching the exposed section of the capillary with hydrofluoric acid (HF) to reduce the outside diameter without affecting the inside diameter. Over the course of the reaction (6 h), the capillary wall thins to about 15–20 mm. Although this porous junction created by HF etching is fragile, it is durable because it is firmly held inside the reservoir, which also serves as the buffer reservoir for closing the CE circuit and providing the spray voltage. Capillaries with different inside and outside diameters were evaluated to optimize the performance of the CE–MS system, resulting in a mass limit of detection of 500 amol for tandem MS analysis of a standard peptide mixture using a 20 mm i.d. capillary. In this design, on-column sample enrichment is incorporated into the sheathless interface (Janini et al., 2003). A miniaturized solid-phase extraction (mSPE) cartridge, made of reversed-phase material, was attached to the CE capillary near the injection end as shown in Fig. 16.1. This design was used in a 2D HPLC–CE sheathless ESI-MS/MS for the analysis of a protein digest of low molecular weight human serum (Janini et al., 2004). High molecular weight serum proteins were first precipitated by the addition of methanol to the serum sample. The depleted serum sample was then digested overnight with trypsin and separated by RP-HPLC. Thirty fractions from this separation were collected and each analyzed by CZE–ESI-MS/MS, resulting in 130 unique proteins being identified. Figure 16.2 shows representative base peak electropherograms of two fractions, numbers 15 and 20. The mSPE–CE–MS system was tested for the separation and identification of peptides generated from tryptic digestion of a protein mixture. A solution of 0.1 mg/ mL each of horse heart cytochrome c and equine apomyoglobin (10 mM each) was digested with trypsin. After diluting the sample 1000-fold, 5.5 mL (corresponding to 55 fmol of each protein digest) was loaded onto the mSPE cartridge and analyzed by CE–tandem MS. A number of peptides were identified from each of the protein samples. The total protein coverage obtained for apomyglobin and cytochrome c was 61% and 67%, respectively. In contrast, no peaks were observed and no peptides were identified when the same diluted solution was analyzed with an identical CE capillary that was not equipped with an mSPE cartridge. The same mSPE-equipped capillary was also used for the analysis of peptides generated from an in-gel tryptic digestion of two distinct proteins.

16.6 APPLICATIONS Jorgenson and coworkers (Bushey and Jorgenson,1990a; Moore and Jorgenson, 1995a,b; Lewis et al., 1997) developed many elegant online multidimensional HPLC–CE approaches. Bushey and Jorgenson (1990a) were the first to report the use of an online HPLC/CE system for the separation of tryptic digests of ovalbumin with fluorescence detection, shown in Fig. 16.3. The entire system is automated and operated under computer control. The two modes of separation were RPLC, using a

APPLICATIONS

371

FIGURE 16.2 Representative base peak electropherograms from CZE runs of RPLC fractions. (a) Fraction #15 (5 peptide identifications) and (b) fraction #20 (19 peptide identifications). Column, bare fused silica capillary, 60 cm 180 mm OD 30 mm i.d.; separation voltage, 15 kV; observed CZE current, 1.91 mA; running electrolyte, 200 mm acetic acid þ 10% isopropanol; temperature, 22 C; injection time, 10 s at 2 psi (4 nL total injection volume); supplementary pressure, 2 psi; flow rate, 25 nL/min; spray voltage, 1.5 kV (reprinted with permission from Electrophoresis).

1 mm i.d. column, as the first dimension and CZE as the second dimension. Effluent from the RP column, operated under gradient conditions, fills a loop on a computercontrolled six-port valve. The second pump flushes this loop material over the grounded (anode) end of a 50 mm i.d. CZE capillary at specific intervals. S

L1 L2 V1

W

M

C1

P1 A

B

V2

A

P2 CZE

W D

A

B µA

GB

HV

FIGURE 16.3 Schematic of 2D LC/CZE instrumentation: A is 0.012 M potassium phosphate buffer, pH 6.9, and B is acetonitrile; P1, microgradient syringe pump; M, 52-mL mixer; V1, Valco six-port manual injection valve; S, injection syringe; L1, 50-mL loop; C1, reversed-phase column; P2, Waters Associates model 6000A piston pump; V2, grounded six-port electrical actuated Valco valve; L2, 10-mL loop; CZE, CZE capillary; D, fluorescence detector; IB, interlock box; mA, microammeter; GB, grounding box; HV, high-voltage power supply (reprinted with permission from Analytical Chemistry).

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TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION

Electromigration injections are performed on the CZE portion of the system from a flowing stream. Fluorescence detection is used on the CZE capillary. The 2D system has a much higher resolving power and peak capacity, estimated at 420, which is an order of magnitude better than either of the two dimensions alone. The system was further improved by using a narrower inner diameter fused silica capillary as the CZE column (Bushey and Jorgenson,1990b). The separation of a tryptic digest of albumin is shown in Fig. 16.4. Moore and Jorgenson (1995a) used online RPLC/CZE separations to resolve complex mixtures of peptides. In these comprehensive procedures, the second

FIGURE 16.4 (a) Three-dimensional representation of 2D RPLC–CZE separation of fluorescamine-labeled tryptic digest of ovalbumin. (b) Contour plot of the same dataset. Tic marks on the injection number axis represent five injections, 5 min each (reprinted with permission from Analytical Chemistry).

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dimension separation was designed to be much faster than the first dimension. As described in Chapters 1 and 5, this order enables the second dimension (CZE) to analyze many fractions eluting from the HPLC column without creating a bottleneck at the final separation stage. For example, the effluent from a 15-min RP-HPLC run is sampled 60 times by the CZE system employing a high speed optical injection system that may be thought of as an “inverse’’ injection method (Moore and Jorgenson,1995a ). Briefly, the system is based on an argon ion laser. The samples are tagged with fluorescein isothiocyanate (FITC). The power of the beam obtained from the laser is split, with 95% directed into a gating beam, focused nearer the injection end of the capillary and a probe beam, and 5% of the laser power is focused nearer the exit end of the capillary. To make an injection, the gating beam is momentarily blocked with a computer-controlled shutter. This allows a small aliquot of material to pass through, where its components are resolved and detected. This gating system was developed because mechanical injection methods are too slow for the fast CZE separation. Later, Hooker and Jorgenson (1997) designed their instrument (a schematic is shown in Fig. 16.5) with a new interface for the coupling of microHPLC with CZE based on the original flow- gated design developed in their laboratory where the new interface is made from clear plastic. This allows for the direct observation and routine manipulation of the micro-HPLC and CZE capillaries. As with the original design, a transverse flow of CZE buffer controls analyte injection onto the CZE capillary. A split injection/flow system is used to deliver a nL/s flow rate to the capillary RPLC column from the gradient LC pump. The capillary HPLC column has 50 mm i.d. and 76 cm length, and the electrophoresis capillary has 17 mm i.d., L ¼ 25 cm, and l ¼ 15 cm, where l is the length of the capillary from the inlet to the detection window. The valve is air-actuated and controls the flow of flush buffer. A

µA Electrophoresis capillary

RPLC capillary

High voltage

LIF

RPLC pump Flow gated interface

Waste

Buffer Computer

Waste Waste Flush pump

Air actuated valve Waste

Computer – valve interface

FIGURE 16.5 Schematic of instrumental setup for 2D micro-RPLC–CZE. A split injection/ flow system is used to deliver a nanoliter per second flow rate to the micro-RP-HPLC column from the gradient LC pump. The HPLC microcolumn has 50 mm i.d. and 76 cm length, and the electrophoresis capillary has 17 mm i.d., L ¼ 25 cm, and I ¼ 15 cm. The valve is air-actuated and controls the flow of flush buffer (reprinted with permission from Analytical Chemistry).

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TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION

Stainless steel tubing

µA 1/16" channel

RPLC capillary

CZE capillary

1/16" o.d. Teflon sleeve

Lexan

PEEK tubing

FIGURE 16.6 Schematic of the clear flow gating interface. The interface was constructed inhouse from a 1 in. diameter, 0.5 in. thick Lexan disk. The disk is clear, which allows direct observation of the capillaries in the stream of flush buffer. The capillaries are sleeved in 0.0625 in. o.d. Teflon tubing and this tubing is held in place by Lite Touch fittings (not shown). The cross-flow of buffer prevents LC effluent from electromigrating onto the CZE capillary until an injection is desired (reprinted with permission from Analytical Chemistry).

detailed schematic of the new flow gating interface is shown in Fig. 16.6. The interface was constructed from clear polycarbonate (Lexan) and consisted of a 1 in. diameter, 0.5 in. thick Lexan disk through which 0.0625 in. channels have been bored. The capillaries were sleeved inside 0.007 in. i.d., 0.0625 in. o.d. Teflon tubing. This tubing fits snugly in the 0.0625 in. flow channels and was held in place with the removable nut and ferrule system. The capillaries extended out from the Teflon sleeves into the flow channel, where the transverse flow of run buffer was present. An HPLC pump was used to deliver run buffer to the interface. The buffer exited the interface through stainless steel tubing. A microammeter was placed between this stainless steel tubing and electrical ground to monitor electrophoresis current. This online instrumental setup was applied for the 2D separation of fluorescein isothiocyanate-derivatized human urine; the results are shown in Fig. 16.7. This separation used overlapped CZE runs, which means that two samples are injected on the column, one after the other, separated by a period of time that will allow the separation of the first-injection components from the second-injection components that are present in the capillary at any point prior to detection. This was accomplished by making an injection in a time interval of exactly half of the actual CZE run time. To overlap the CZE runs, an injection was made every 29 s. For the overlapping to work,

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FIGURE 16.7 Gray-scale image of a two-dimensional micro-RPLC–CZE separation of FITC-tagged human urine. A gradient of 15–30% acetonitrile in 240 min was run on the 76 cm long, 50 mm i.d. LC microcolumn. CZE injections were performed at intervals of 29 s and were at 3 kV for 3 s. The CZE run potential was 30 kV. The electrophoresis buffer used was 10 mM phosphate, 0.22% TEA, 15% CH3CN, and pH 10.50 (reprinted with permission from Analytical Chemistry).

nothing can migrate prior to 29 s and all species must migrate between 29 and 58 s. Overlapping of the CZE runs allows one to sample the HPLC dimension twice as often, which means a saving in time and effort. Although Jorgenson and his coworkers selected the online approach to 2D HPLC–CE, we preferred the off-line approach owing to its simplicity and ease of operation. In the first publication from our laboratory (Issaq et al., 1999), we used RP-HPLC for the fractionation of a tryptic digest of a mixture of cytochrome c and myoglobin and CZE for the second dimension with LIF detection. The instrumental setup was assembled from commercially available equipment. Fractions of the effluent from the HPLC column were collected into microtiter plates using an automatic microfraction collector. The fractions were then dried under vacuum at room temperature, reconstituted, and analyzed by a single-capillary CE instrument (Issaq et al., 1999). Figures 16.8 and 16.9 shown the HPLC chromatogram and the

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TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION

1350

1150

950

Signal

750

550

350

150

–50

0

10

20 Time, min

30

40

FIGURE 16.8 HPLC chromatogram of cytochrome c and myoglobin digest, using a 250 cm 4.6 mm ODS C18 Vydac column and a linear mobile-phase gradient, 5–50% B, in 50 min. Buffer A was 0.1% TFA in water and buffer B was 0.1 TFA in acetonitrile. UV detection was carried out at 214 nm, at room temperature (reprinted with permission from Electrophoresis).

0.0190

Absorbance

0.0150 0.0110 0.0070 0.0030 –0.0010 2

4

6

8 10 Time, min

12

17

1E

FIGURE 16.9 Electropherogram of the cytochrome c and myoglobin digest. CZE was performed with a 31 cm 50 mm fused silica capillary. The separation buffer was 50 mM phosphoric acid titrated to pH 2.1 with 50 mM sodium hydroxide. Injection was carried out by vacuum at 5 s 0.1 psi and detection at 214 nm (reprinted with permission from Electrophoresis).

APPLICATIONS

Background

0.0060

0.0040

0.0020

0.0020

0.0000

0.0000 2

5

8

11

Fraction 21

0.0060

0.0040

14

0.0090

2

5

8

11

14

0.0200 Fraction 23

Fraction 7

Absorbance

377

0.0150 0.0060 0.0100 0.0050

0.0030

0.0000 0.0000 2

5

8

11

14

2

5

8

11

14

0.0045 Fraction 15

0.0200

Fraction 27 0.0030

0.0100 0.0015 0.0000

0.0000 2

5

8

11

14

2

5

8

11

14

Time, min FIGURE 16.10 CZE electropherograms of HPLC fractions collected at 7, 15, 21, 23, and 27 min. Injection was by vacuum for 20 s at 0.5 psi. Other conditions as in Fig. 16.7 (reprinted with permission from Electrophoresis).

CZE electropherogram of the peptide mixture. Both figures show the presence of a complex mixture that is not fully resolved into its individual components (peptides) in either method. As peptide mapping plays a vital role in the characterization of protein products, it is, therefore, important to employ methodologies such as 2D HPLC–CE that will give comprehensive separation of a complex peptide mixture, for example, a serum proteome digest. Figure 16.10 shows the electropherograms of five selected HPLC collected fractions. The advantages of multidimensional orthogonal separations are clearly obvious from this figure, whereby multiple peaks are observed in each 1-min collected fraction. Although this off-line approach

TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION

cap 90 cap 80 cap 70 cap 60 cap 50 cap 40 cap 30 cap 20 cap 10

HPLC elution time

378

165.8 181.8 197.8 213.8 229.8 245.8 261.8 277.8 293.8 CZE time, s

FIGURE 16.11 Multidimensional HPLC–CE separation of cytochrome c/myoglobin enzymatic digest presented in 2D format. For experimental details see text (reprinted with permission from Electrophoresis).

gave comprehensive and satisfactory results, it was time consuming because all collected fractions had to be analyzed separately by CZE. This limitation could be overcome by using a 96-array capillary instrument that allows the simultaneous analysis of all fractions in less than 15 min. In a later study, Issaq et al. (2001) employed a 96-array CE instrument equipped with an LIF detector to simultaneously analyze the 96 collected fractions of a tryptic digest of a mixture of cytochrome c and myoglobin with an analysis time of 10 min. Figure 16.8 shows the separation of each of the 96 capillaries. The CE separation mode can be either CZE for the separation of peptides (Issaq et al., 1999, 2001) or capillary gel electrophoresis for the separation of proteins (Issaq et al., 2001). A two-dimensional plot of the sample concentration is then constructed, the dimensions being the LC and CZE elution times of the respective peptides, as shown in Fig. 16.11. Intensity of the spots, that is, quantification, was represented by different colors (not shown in this black-and-white figure). The CZE array allowed the total HPLC/CE analysis to be completed in approximately 2 h. He et al. (2002) used an off-line HPLC/CE method to map cancer cell extracts. Frozen ovarian cancer cells (containing 107 cells) were reconstituted in 300 mL of deionized water and placed in an ultrasonic bath to lyse the cells. Then the suspension was centrifuged and the solubilized proteins were collected for HPLC fractionation. The HPLC separation was carried out on an instrument equipped with a RP C-4 column, 250 mm 4.6 mm, packed with 5-mm spherical silica particles. Extracted proteins were dissolved in 300 mL of DI water, and 100 mL was injected onto the column at a flow rate of 1 mL/min. Buffer A was 0.1% TFA in water and buffer B was 0.1% TFA in acetonitrile. A two-step gradient, 15–30% B in 15 min followed by 30–70% B in 105 min, was used. The column effluent was sampled every minute into a 96-well microtiter plate with the aid of an automatic fraction collector. After collection, the fractions were dried at room temperature under vacuum. The sample in each well was reconstituted before the CE analysis with 10 mL deionized water. The

379

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300

Absorbance, arb. unit

A 4 fraction

250 D 6 fraction

200

150

D 8 fraction

100 E 1 fraction

0

5

10 Migration time, min

15

20

FIGURE 16.12 CZE analysis of four HPLC fractions of cancer cell extracts (reprinted with permission from Journal of Chromatography A).

fractions were then analyzed by multiplexed (96-capillary) CZE and detected by UV absorption. Prior to the analysis by CZE, the reconstituted fractions were concentrated on-column using large volume sample stacking with polarity switching. By using a capillary array, HPLC fractions were simultaneously concentrated and separated by multiplexed CZE in 30 min. Figure 16.12 shows the separation of four different collected fractions. Chromatographic and electrophoretic modes other than RP, SEC, IEX, and CZE have been used in a 2D format for the separation of complex mixtures. For example, Amini et al. (2002) investigated the selectivity behavior of tryptic peptides on a Cu2þloaded immobilized metal ion affinity chromatography (IMAC) support. Histidinecontaining peptides from an albumin digest were separated by affinity chromatography in the first dimension and coupled off-line with capillary electrophoresis and matrix-assisted laser desorption ionisation–time-of-flight mass spectrometry (MALDI–TOF). Two of the five histidine-containing peptides, in addition to some nonhistidine-containing peptides, were captured by the IMAC. The IMAC-captured peptides were analyzed by micellar electrokinetic chromatography (MEKC) using low concentrations of SDS (10 mM), and characterized by MALDI–TOF. In a recent study (Mao and Zhang, 2003), a 2D separation system coupling capillary RP-HPLC to capillary isoelectric focusing (cIEF) is described for protein and peptide mapping. Complex protein/peptide samples, such as yeast cytosol and a BSA tryptic digest, were used to demonstrate the utility of this approach. The authors reported a peak capacity of more than 10,000. Derivatization of the peptides with FITC allowed sensitive detection (7 fm) by laser-induced fluorescence. Figure 16.13 is a threedimensional representation of the separation of BSA digest using 2D capillary HPLC– cIEF. Two-dimensional HPLC/CE separations are not limited to column HPLC/ column CE, as reported by Yang et al. (2003), who were able to couple capillary

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TWO-DIMENSIONAL HPLC–CE METHODS FOR PROTEIN/PEPTIDE SEPARATION

FIGURE 16.13 Three-dimensional display of RPLC–cIEF of BSA peptides. RP column: 30 cm long 250 mm i.d. packed with C8-bonded 5 mm silica particles. Flow rate 2 ml/min. cIEF conditions: 35 cm 75 mm i.d. fused silica capillary and on-column LIF detection at 30 cm. Buffer 10 mM H3PO4 as anolyte and 20 mM NaOH as catholyte. Applied voltage, 12 kV, 10 min focusing, gravity mobilization from anode to cathode (6 cm height difference). Time is given from the start of mobilization (reprinted with permission from Electrophoresis).

HPLC with microchip electrophoresis. Capillary RPLC was used as the first dimension, and chip CE as the second dimension to perform fast sample transfers and separations. A valve-free gating interface was devised simply by inserting the outlet end of LC column into the cross-channel on a specially designed chip. Laser-induced fluorescence was used for detecting the FITC-labeled peptides of a BSA digest. The capillary HPLC effluents were continuously delivered every 20 s to the chip for CE separation.

16.7 CONCLUDING REMARKS It is clear from the above discussion that separation science, chromatography, and electrophoresis, give the analyst a wide array of options that can be used to resolve a complex mixture of peptides employing online or off-line multidimensional separation strategies. It is also abundantly clear that no single chromatographic or electrophoretic procedure is capable of resolving the complex mixture of peptides that results from a global digest of a proteome. Combining two or more orthogonal separation procedures dramatically improves the overall resolution and results in a

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larger number of peptide identifications. In conclusion, multidimensional separation of peptides combined with mass spectrometry is an important aspect of proteomic research.

ACKNOWLEDGMENT This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. NO1-CO-12400.

REFERENCES Amini, A., Chakraborty, A., Regnier, F.E. (2002). Simplification of complex tryptic digests for capillary electrophoresis by affinity selection of histidine-containing peptides with immobilized metal ion affinity chromatography. J. Chromatogr. B 772, 35–44. Anderson, N.L. and Anderson, N.G. (2002). The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867. Bergstrom, S.K., Samskog, J., Markides, K.E. (2003). Development of a poly(dimethylsiloxane) interface for on-line capillary column liquid chromatography–capillary electrophoresis coupled to sheathless electrospray ionization time-of-flight mass spectrometry. Anal. Chem. 75, 5461–5467. Bushey, M.M., Jorgenson, J.W. (1990a). A comparison of tryptic digests of bovine and equine cytochrome c by comprehensive reversed-phase HPLC–CE. J. Microcolumn Separations 2, 293–299. Bushey, M.M., Jorgenson, J.W. (1990b). Automated instrumentation for comprehensive twodimensional high-performance liquid chromatography/capillary zone electrophoresis. Anal. Chem. 62, 978–984. Cai, J., Henion, A. (1995). Capillary electrophoresis–mass spectrometry. J. Chromatogr. 703, 667–692. Ding, J., Vouros, P. (1999). Advances in CE/MS. Anal. Chem. 71, 378A–385A. Ehala, S., Kaljurand, M., Kudrjashova, M., Vaher, M. (2004). Stroboscopic sampling in comprehensive high-performance liquid chromatography–capillary electrophoresis via a pneumatic sampler. Electrophoresis 25, 980–989. Evans, C.R., Jorgenson, J.W. (2004). Multidimensional LC–LC and LC-CE for high-resolution separations of biological molecules. Anal. Bioanal. Chem. 378, 1952–1961. Giddings, J.C. (1987). Concepts and comparisons in multidimensional separation. J. High Resolut. Chromatogr. Chromatogr. Commun. 10, 319–323. He, Y., Yeung, E.S., Chan, K.C., Issaq, H.J. (2002). Two-dimensional mapping of cancer cell extracts by liquid chromatography–capillary electrophoresis with ultraviolet absorbance detection. J. Chromatogr. A 979, 81–89. Hooker, T.F., Jorgenson, J.W. (1997). A transparent flow gating interface for the coupling of microcolumn LC with CZE in a comprehensive two-dimensional system. Anal. Chem. 69, 4134–4142.

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Issaq, H.J. (1990). Recent advances in multimodal thin layer chromatography. Trends Anal. Chem. 9, 36–40. Issaq, H.J., Chan, K.C., Cheng, S.L., Qingbo, L. (2001). Multidimensional high performance liquid chromatography–capillary electrophoresis separation of a protein digest: an update. Electrophoresis 22, 1133–1135. Issaq, H.J., Chan, K.C., Janini, G.M., Muschik, G.M. (1999). A simple two-dimensional high performance liquid chromatography/high performance capillary electrophoresis set-up for the separation of complex mixtures. Electrophoresis 20, 1533–1537. Issaq, H.J., Janini, G.M., Chan, K.C., Veenstra, T.D. (2004). Sheathless electrospray ionization interfaces for capillary electrophoresis—mass spectrometric detection: advantages and limitations. J. Chromatogr. A 1053, 37–42. Issaq, H.J., Janini, G.M., Conrads, T.P., Janini, G.M., Veenstra, T.D. (2002). Methods for fractionation, separation and profiling of proteins and peptides. Electrophoresis 23, 3048– 3061. Janini, G.M., Chan, K.C., Conrads, T.P., Issaq, H.J., Veenstra, T.D. (2004). Two-dimensional liquid chromatography–capillary zone electrophoresis—sheathless electrospray ionization-mass spectrometry: evaluation for peptide analysis and protein identification. Electrophoresis 25, 1973–1980. Janini, G.M., Zhou, M., Yu, L.-R., Blonder, J., Gignac, M., Conrads, T.P., Issaq, H.J., Veenstra, T.D. (2003). On-column sample enrichment for capillary electrophoresis sheathless electrospray ionization mass spectrometry: evaluation for peptide analysis and protein identification. Anal. Chem. 75, 5984–5993. Janini, G.M., Conrads, T.P., Wilkens, K.L., Issaq, H.J., Veenstra, T.D. (2003). A sheathless nanoflow electrospray interface for on-line capillary electrophoresis mass spectrometry. Anal. Chem. 75, 1615–1619. Kelly, J.F., Ramaley, L., Thibault, P. (1997). Capillary zone electrophoresis–electrospray mass spectrometry at submicroliter flow rates: practical considerations and analytical performance. Anal. Chem. 69, 51–60. Larmann, J.P., Lemmo, A.V., Moore, A.W., Jorgenson, J.W. (1993). Two-dimensional separations of peptides and proteins by comprehensive liquid chromatography–capillary electrophoresis. Electrophoresis 14, 439–447. Lee, E.D., Muck, W., Henion, J.D., Covey, T.R (1988). On-line capillary zone electrophoresis– ion spray tandem mass spectrometry for the determination of dynorphin. J. Chromatogr. 458, 313–321. Lewis, K.C., Opiteck, G.J., Jorgenson, J.W., Sheeley, D.M. (1997). Comprehensive on-line RPHPLC–CZE–MS of peptides. J. Am. Soc. Mass. Spectrom. 8, 495–500. Mao, Y., Zhang, X.M. (2003). Comprehensive two-dimensional separation system by coupling capillary reverse-phase liquid chromatography to capillary isoelectric focusing for peptide and protein mapping with laser-induced fluorescence detection. Electrophoresis 24, 3289– 3295. Moore, A.W., Jorgenson, J.W. (1995a). Rapid comprehensive two-dimensional separations of peptides via RP-HPLC-optically gated capillary zone electrophoresis. Anal. Chem. 67, 3448–3455. Moore, A.V., Jorgenson, J.W. (1995b). Comprehensive three-dimensional separation of peptides using size exclusion chromatography/reversed phase liquid chromatography/optically gated capillary zone electrophoresis. Anal. Chem. 67, 3456–3463.

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Olivares, J.A., Nguyen, N.T., Yonker, C.R., Smith, R.D. (1987). On-line mass spectrometric detection for capillary zone electrophoresis. Anal. Chem. 59, 1230–1232. Robson, M.M., Bartle, K.D., Myers, P. (1999). Simple interface for gradient elution CEC and coupled HPLC-CEC. Chromatographia 50, 711–715. Rodriguez, R., Manes, J., Pico, Y. (2003). Off-line solid-phase microextraction and capillary electrophoresis mass spectrometry to determine acidic pesticides in fruits. Anal. Chem. 75, 452–459. Sanz-Nebot, V., Benavente, F., Barbosa, J. (2002). Liquid chromatography–mass spectrometry and capillary electrophoresis combined approach for separation and characterization of multicomponent peptide mixtures: application to crude products of leuprolide synthesis. J. Chromatogr. A 950, 99–111. Shen, Y., Berger, S.J., Smith, R.D. (2001). High-efficiency capillary isoelectric focusing of protein complexes from Escherichia coli cytosolic extracts. J. Chromatogr. A 914, 257–264. Smith, R.D., Barinaga, J.C., Udseth, H.R. (1988). Improved electrospray ionization interface for capillary zone electrophoresis–mass spectrometry. Anal. Chem. 60, 1948–1952. Wahl, J.H., Gale, D.C., Smith, R.D. (1994). Sheathless capillary electrophoresis-electrospray ionization mass spectrometry using 10 mm I.D. capillaries: analyses of tryptic digests of cytochrome c. J. Chromatogr. 659, 217–222. Yang, X.H., Zhang, X.M., Li, A.Z., Zhu, S.Y., Huang, Y.P. (2003). Comprehensive twodimensional separations based on capillary high-performance liquid chromatography and microchip electrophoresis. Electrophoresis 24, 1451–1457.

PART V INDUSTRIAL APPLICATIONS

17 MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS Frank Rittig BASF Aktiengesellschaft, Polymer Research, 67056 Ludwigshafen, Germany

Harald Pasch Deutsches Kunststoff-Institut (German Institute for Polymers), 64289 Darmstadt, Germany

17.1 INTRODUCTION State-of-the-art polymeric materials possess property distributions in more than one parameter of molecular heterogeneity. Copolymers, for example, are distributed in molar mass and chemical composition, while telechelics and macromonomers are distributed frequently in molar mass and functionality. It is obvious that n independent properties require n-dimensional analytical methods for accurate (independent) characterization of the different structural parameters. In chromatography, the separation efficiency of any single separation method is limited by the efficiency and selectivity of the separation mode, that is, the plate count of the column and the phase of the selected system. Adding more columns will not overcome the need to identify more components in a complex sample, due to the limitation of peak capacities. The peak capacity in an isocratic separation can be described, following Grushka (1970), as given in Equation (17.1): pﬃﬃﬃﬃ N Vp ð17:1Þ ln n ¼ 1þ V0 4 Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright 2008 John Wiley & Sons, Inc.

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

where n is the peak capacity, N is the plate number, Vp is the pore volume, and V0 is the interparticle volume. The corresponding peak capacity of an n-dimensional separation is enormously higher due to the fact that each dimension contributes to the total peak capacity as a factor, and not as an additive term for single-dimension methods, as described in Equation 17.2: ntotal ¼ P ni sinði1Þ qi

ð17:2Þ

where ntotal represents the total peak capacity, ni the peak capacity in dimension i, and qi is the angle between the two dimensions. The angle between dimensions is determined by the independence of the methods; a 90 angle is obtained by two methods, that are completely independent of each other and will, for example, separate two properties solely on a single parameter without influencing themselves. In the case of a two-dimensional system, the peak capacity is given by Equation 17.3: pﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃ N1 Vp;1 N2 Vp;2 ln ln n2D ¼ n1 n2 sinq ¼ 1 þ 1þ sinq ð17:3Þ 4 4 V0;1 V0;2 This effect is illustrated in Fig.17.1. Multidimensional chromatography separations can be done in planar systems or coupled-column systems. Examples of planar systems include two-dimensional thin-layer chromatography (TLC) (Consden et al., 1944; Grinberg et al., 1990), where successive one-dimensional TLC experiments are performed at 90 angles with different solvents, and 2D electrophoresis, where gel electrophoresis is run in the first dimension followed by isoelectric focusing in the second dimension (O’Farrell, 1975; Anderson et al., 1981; Celis and

FIGURE 17.1 Schematic contour map representation of increased resolution and peak capacity in 2D separations (peaks in each dimension are indicated by black bars at the axes).

INTRODUCTION

389

Bravo, 1984). Hybrids of these systems, where chromatography and electrophoresis are used in each spatial dimension, were reported nearly 40 years ago (Efron, 1959). Belenkii and coworkers reported on the analysis of block copolymers by TLC (Gankina et al., 1991; Litvinova et al., 1991). Two-block copolymers of styrene and t-butyl methacrylate were separated first with regard to chemical composition by TLC at critical conditions, followed by a SEC-type separation to determine the molar masses of the components. The main problem using planar methods is the difficulty in detection and collection of fractions among other less critical problems, such as homogeneous preparation of chromatographic media. However, the detection problem exists also for the coupledcolumn methods, mainly because of fraction dilution by each stage in a multidimensional separation system. Another aspect is the adjustment of chromatographic time bases between the different dimensions so that first-dimension peaks may be sampled an adequate number of times by the next dimension separation system. This aspect has been recently studied in detail (Murphy et al., 1998), and is covered in detail in Chapters 2 and 6. In 2D-column chromatography systems, an aliquot from a column or channel is transferred into the next separation method in a sequential and repetitive manner. Storage of the accumulating eluent is typically provided by sampling loops connected to an automated valve. Many variations of this theme exist, which use various chromatographic and electrophoretic methods for one of the dimensions. In addition, the simpler “heart-cutting’’ mode of operation takes the eluent from a first-dimension peak, or a few peaks, and manually injects them into another column during the firstdimension elution process. A partial compilation of these techniques is given here (Balke, 1984; Bushey and Jorgenson, 1990; Cortes, 1990; Gankina et al., 1991; Larmann et al., 1993; Liu et al., 1993; Kilz et al., 1995; Venema et al., 1997). This is further discussed in the Chapter 5. The use of different modes of liquid chromatography facilitates the separation of complex samples selectively with respect to different properties, such as hydrodynamic volume, molar mass, chemical composition, architecture, or functionality. Using these techniques in combination, multidimensional information on different aspects of molecular heterogeneity can be obtained. If, for example, two different chromatographic techniques are combined in a “cross-fractionation’’ mode, information on CCD and MMD can be obtained. Literally, the term “chromatographic cross-fractionation’’ refers to any combination of chromatographic methods capable of evaluating the distribution in size and composition of copolymers. An excellent overview on different techniques and applications involving the combination of SEC and gradient HPLC was published by Gl€ockner (1991). In the SEC mode, the separation occurs according to the molecular size of a macromolecule in solution, which is dependent on its chain length, chemical composition, solvent, and temperature. Thus, molecules of the same chain length but different composition may have different molecular sizes (hydrodynamic volumes). Since SEC separates according to hydrodynamic volume, SEC in different eluents can separate a copolymer in two diverging directions. This principle of

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

“orthogonal chromatography’’ was suggested by Balke and Patel(1980,1983) (Balke, et.al 1982). The authors coupled two SEC instruments together so that the eluent from the first one flowed through the injection valve of the second one. In the first instrument, a separation with regard to molecular size was carried out, while in the second instrument, by changing the mobile-phase composition, separation was mainly driven by the chemical composition of the polymer. Since “orthogonality’’ requires that each separation technique is totally selective toward an investigated property, it seems to be more advantageous to use a sequence of methods in which the first dimension separates according to the chemical composition. In this way quantitative information on CCD can be obtained, and the resulting fractions eluting from the first dimension are chemically homogeneous. These homogeneous fractions can then be analyzed independently in the SEC mode in the second dimension to get the required MMD information. In such cases, SEC separation is strictly separating according to molar mass, and quantitative MMD information can be obtained. This chapter discusses the present state of multidimensional chromatography from an industrial perspective. The fundamentals of the different LC modes are briefly described, and information on the experimental setups for multidimensional chromatography is given. In two sections, representative examples are discussed where multidimensional chromatography is used for the detailed analysis of complex polymers with industrial relevance. As will be shown, the experimental approach in an industrial laboratory might differ significantly from the analysis of model compounds due to the fact that the available times and resources for the investigation might be quite limited.

17.2 PRINCIPLES OF MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY AS APPLIED TO POLYMER ANALYSIS Every chromatographic process is controlled by the equilibrium distribution of the solute between the mobile and stationary phases. The retention volume Vr, describing the volume of mobile phase that is required to elute the analyte from the column, is given by Equation 17.4: Vr ¼ V0 þ VKd

ð17:4Þ

where V0 is the interstitial volume of the column, V the volume of the stationary phase, and Kd is the distribution coefficient describing the distribution of the analyte between the mobile and stationary phases. Kd is related to Gibbs free energy DG (Gl€ockner, 1991) through Equation 17.5: Kd ¼ expðDG=RTÞ ¼ exp ðDS=R DH=RTÞ

ð17:5Þ

DG is always negative, and the decrease in free energy can be due to adsorption effects (change in DH) or entropic interactions (change in DS). DS is always operating when the polymer chain cannot occupy all possible conformations in a pore (confined space) due to the limited size of the pore relative to the size of the macromolecule. In a real

PRINCIPLES OF MULTIDIMENSIONAL LIQUID

391

chromatographic system both effects (DH and DS) can be present. In an ideal system the distribution coefficient can be described as the product of the enthalpic distribution coefficient (KLAC) and the entropic distribution coefficient KSEC: Kd ¼ KLAC KSEC

ð17:6Þ

In an ideal SEC experiment, enthalpic effects are not operating (DH ¼ 0) and, thus, KLAC equals 1. Accordingly, the distribution coefficient of ideal SEC is only a function of DS, and Kd is given by Equation 17.7: KSEC ¼ exp ðDS=RÞ

ð17:7Þ

In a real LC system, the distribution coefficient is a function of the entropic and enthalpic terms. Therefore, the retention volume is described by Equation 17.8 (Pasch and Trathnigg, 1998): VR ¼ V0 þ Vp KLAC KSEC þ Vstat KLAC

ð17:8Þ

where Vp is the pore volume and Vstat the volume of the “interactive part’’ of the total stationary phase. Considering adsorptive interactions of the analyte with the active groups of the stationary phase (liquid adsorption chromatography), Gl€ockner (1991) pointed out that there is a fundamental difference between the behavior of low molar mass compounds and macromolecules. While for the former typically one molecule interacts with one active site of the stationary phase, for polymers a multiple attachment mechanism is described. This mechanism is operating due to the fact that macromolecules typically contain a large number of interactive groups (functional groups, repeat units). The polymer chain is retained as long as one interactive group is still adsorbed at the stationary phase. Kd can be described by the probability p for each interactive group (repeat unit) to be adsorbed: Kd ¼ p=ð1 pÞ Kd ¼ ðKd;monomer þ 1Þn 1 Vr

ð17:9Þ ð17:10Þ

where Kd,monomer is the distribution coefficient of the interactive unit and n is the number of interactive units. Consequently, a linear increase in molar mass (number of interacting units) is leading to an exponential increase in retention volume. As has been pointed out, both entropic and enthalpic interactions affect the chromatographic behavior of macromolecules. They are adjusted to the required type of separation by selecting appropriate stationary and mobile phases. In a third mode of liquid chromatography of polymers, liquid chromatography at the critical condition (LCCC) (Entelis et al., 1985, 1986; Pasch, 1997), the adsorptive interactions are fully compensated by entropic interactions. This mode is also referred to as liquid chromatography at the critical point of adsorption. Hence, TDS is equal to DH and therefore, DG becomes zero. Kd is 1 irrespective of molar mass and, consequently, homopolymer molecules of different molar masses coelute in one chromatographic

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

peak. The energetic conditions to achieve this mode of chromatography rely on a very sensitive equilibrium, which is determined by the type of stationary phase, the mobile phase composition, and the temperature. Complex polymers are distributed in more than one molecular property, for example, comonomer composition, functionality, molecular topology, or molar mass. Liquid chromatographic techniques can be used to determine these properties. However, one single technique cannot provide information on the correlation of different properties. A useful approach for determining correlated properties is to combine a selective separation technique with an information-rich detector or a second selective separation technique. The combination of different chromatographic techniques was first introduced as a setup, where SEC in the first dimension was combined with HPLC in the second dimension. An overview on this approach was given by Pasch and Trathnigg (1998) and shall not be reviewed here. Another more recent approach is combining an HPLC separation with regard to chemical composition or functionality in the first dimension with SEC for molar-mass analysis in the second dimension (Pasch and Kilz, 2003). The schematic flow diagram of this type of two-dimensional chromatography is given in Fig. 17.2. The complex polymer sample (different colors

FIGURE 17.2 Schematic flow sheet of a 2D separation using a HPLC system in the first dimension and a SEC system in the second dimension (Kilz and Pasch, 2000). (See color plate.)

EXPERIMENTAL

393

represent different chemical structures, different sphere sizes represent different molar masses) is separated with regard to its composition by LCCC in the first dimension followed by separation with regard to molecular size by SEC in the second dimension.

17.3 EXPERIMENTAL A 2D experimental setup is composed of two independent HPLC systems that are connected to each other by an electrically (or pneumatically)-driven fraction transfer device. Typically, in the first dimension a detector is not used. In the case of an off-line system after the first dimension a fraction collector is used. The most efficient way to connect the first and second dimensions is to use an automatic fraction transfer valve (Kilz, 1992; Kilz et al., 1993, 1995). A schematic presentation of a 2DLC experimental setup is shown in Fig. 17.3. The valve that transfers fractions from the first to the second chromatographic dimension can be controlled electrically or pneumatically. It allows a complete transfer of all eluting polymer fractions from the first to the second dimension by choosing the proper flow rates in both dimensions (Kilz and Pasch, 2000). The function of such a transfer valve is shown in Fig. 17.4. As it has been mentioned previously, there are different sequences that can be used in a 2D experiment. During the 1980s, in most cases SEC has been performed first (Balke,1982 ; Balke and Patel, 1980; Ogawa and Sakai,1983 ), followed by HPLC in the second dimension. In this experimental setup, the heart-cut approach was very frequently used; meaning, that only selected fractions were transferred into the second dimension.

FIGURE 17.3 Schematic experimental setup of a 2D chromatographic experiment.

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.4

Configuration of an automatic fraction transfer valve. (See color plate.)

In recent years, the sequence of HPLC in the first dimension and SEC in the second dimension is favored. Due to the fact that SEC experiments with new columns can be conducted in a very short period of time (Stout et al., 1983; Diehl et al., 2003; Pasch and Kilz, 2003; Gabriel et al., 2004), a complete transfer of all fractions from the first dimension into the SEC column became possible. The advantages of using HPLC in the first dimension include higher sample loads into HPLC as compared to SEC and the fact that the HPLC separation can be tuned without interference of the SEC mobile phase. In the case of using SEC in the first dimension, each fraction is dissolved in a thermodynamically good solvent when injected into HPLC and breakthrough peaks can occur (Jiang et al., 2002). If SEC is in the second dimension, the injected solvent from the HPLC will simply be separated from the polymer fraction. Still, the solvent composition of the first dimension can interfere with the SEC separation. Therefore, it is advisable to have at least one common solvent component, for example, tetrahydrofuran, in both the HPLC and SEC separations. Typically, a 2D experiment is quite time consuming. Using high performance column technology and a fully automated system, one 2D experiment can take up to 10 h. But there is still room for improvement by using high throughput column designs and fast data-sampling detectors, such as an evaporative light scattering detector (ELSD) (Stout et al., 1983; Diehl et al., 2003; Pasch and Kilz, 2003; Gabriel et al., 2004). Detection will always remain an important issue in 2DLC. The injected sample is diluted twice and the total dilution factor is quite high. As has been shown in the majority of applications, ELS and UV detectors have sufficient high sensitivities to identify components in the range of <1%. Unfortunately, not all polymer species can be detected with ELS and UV detectors. When using a UV detector, the macromolecules must contain UV chromophores and the mobile phase must be UV transparent. The use of an ELSD is not limited to chromophores, but the signal intensity is not linearly dependent on sample concentration (Schultz and Engelhardt, 1990) and low molar mass species may evaporate together with the eluent.

ANALYSIS OF ALKYLENE OXIDE-BASED POLYMERS

395

17.4 ANALYSIS OF ALKYLENE OXIDE-BASED POLYMERS 17.4.1

Amphiphilic Polyalkylene Oxides

Fatty alcohol and fatty acid ethoxylates are amphiphilic compounds that are commonly used as nonionic surfactants and emulsifiers in many applications, such as cosmetic and care products and in textile fabrication. They serve as antistatic lubricants and viscosity regulators. Fatty alcohol ethoxylates (FAE) have a hydrophilic PEO polymer chain and a hydrophobic fatty alcohol or aryl end group. Typically, they are prepared by anionic polymerization of ethylene oxide in the presence of mixtures of fatty alcohols having different chain lengths. Therefore, macromolecules with different end groups are formed. Moreover due to imperfections of the reaction system, in addition to the desired a-alkyl(aryl)oxy-w-hydroxy terminated oligomers, further functionalities such as a,w-dihydroxy, a,w-alkyl(aryl)oxy and cyclic oligomers are obtained (see Fig. 17.5). As the properties of fatty alcohol ethoxylates strongly depend on the chemical composition, functionality, and molar mass, it is important to determine these parameters to analyze the corresponding structure–property relationships. The separation of FAE according to the alkyl(aryl) end groups is possible in liquid chromatography by operating at the critical point of adsorption. The separation is typically done on a RP-18 stationary phase and a mobile phase of methanol–water or acetonitrile–water. Under these conditions, the separation is independent of molar mass and occurs exclusively with regard to the end groups. An excellent separation of the different functionality fractions can be achieved on Nucleosil-type RP-18 columns, as has been shown by Adrian (1998). Using a column size of 25 0.46 cm i.d. and a flow rate of 1 mL/min, the analysis time is around 30–40 min per sample (Pasch and Zammert, 1994; Pasch and Trathnigg, 1998; Keil et al., 2001). Further details of AE surfactants are given in Chapter 18.

A lk(A r)

OH +

Alk( Ar)

O

secondary reactions

H Alk( Ar)

(O CH2 CH2 )n

OH

additional functionality fractions

( O CH2 CH2 ) n O H ( O CH2 CH2 ) n OAlk( Ar)

( O CH2 CH2 ) n

FIGURE 17.5

Reaction scheme for the synthesis of alkyl(aryl) ethoxylates.

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.6 Chromatogram of a technical octylphenoxy PEO sample separated by LCCC, stationary phase: RP-18, mobile phase: methanol–water 86:14% by volume (reprinted from Adrian et al., 1998, with permission of Advanstar Communications, UK).

As a typical application, the separation of an octylphenoxy-terminated PEO with respect to the terminal groups by LCCC is presented in Fig. 17.6. Similar to previous investigations on RP-18 stationary phases (Gorshkov et al., 1990; Pasch and Zammert, 1994), the critical eluent composition was achieved with methanol–water 86:14% by volume. Five well-separated peaks appeared in the chromatogram, which could be identified by MALDI–TOF mass spectrometry as being different functionality fractions. Accordingly, separation took place strictly with respect to the chemical structure of the end groups. In agreement with the expected composition of the sample, the most intense elution peak can be assigned to the a-octylphenoxy-w-hydroxy functionality fraction 2. The other elution peaks can be assigned to the following structures: Peak 1

C4 H9

O

CH 2 CH 2 O

H n

Peak 2

C8 H1 7

O

CH 2 CH 2 O

H n

Peak 3

C4 H9

O

CH 2 CH 2 O

C4 H9 n

Peak 4

C4 H9

O

CH2 CH2 O

C8 H1 7 n

ANALYSIS OF ALKYLENE OXIDE-BASED POLYMERS

Peak 5

C8 H1 7

O

CH 2 CH 2 O

397

C8 H1 7 n

Having this type of selective separation at hand, it could be combined with SEC as the molar mass selective technique using the experimental setup described in Fig. 17.3. In this 2DLC experiment, LCCC was used in the first dimension followed by SEC in the second dimension. The resulting contour diagram LCCC versus SEC is presented in Fig. 17.7, see also Adrian et al. (1998). At the abscissa, the retention volume of the SEC runs is given, whereas the ordinate gives the retention volume of the LCCC. The peak height is assigned to a color code, meaning that equal colors are equivalent to equal peak intensities. The contour plot clearly shows five spots corresponding to the five functionality fractions, compared to Fig. 17.6, with fraction 2 being the main fraction containing the a-octylphenoxy-w-hydroxy oligomers. In addition, a,w-di(octylphenoxy) oligomer fractions and fractions having butylphenoxy end groups are obtained. It is obvious that the fractions have very similar molar masses. The 2D experiment yielded separation with respect to functionality and molar mass, and FTD and MMD could be determined quantitatively. For calculating FTD, the relative concentration of each functionality fraction must be determined. These concentrations are equivalent to the volume of each peak in the contour plot. With the

FIGURE 17.7 Contour plot of the two-dimensional separation of an octylphenoxy terminated PEO sample (reprinted from Adrian et al., 1998, with permission of Advanstar Communications, UK). (See color plate.)

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

appropriate software this can be done easily. Determination of the MMD for each fraction was possible after calibrating the second dimension with PEO calibration standards. Calculation of the MMD could then be achieved in the usual way, taking one chromatogram for each functionality fraction, preferably from the region of the highest peak intensity. In similar approaches, other polyalkylene oxides have been analyzed by 2D chromatography. Murphy et al. (1995) separated polyethylene glycols and Brij-type surfactants according to chemical composition and molar mass by reversed-phase HPLC versus SEC. The analysis of methacryloyl-terminated polyethylene oxides by LCCC versus SEC was described by Kr€ uger et al. (1996). The functionality-type separation was conducted on a reversed-phase system at a critical eluent composition of acetonitrile–water 43:57 (v/v). The functionality fractions, including polyethylene glycol, a-methoxy-w-hydroxy, a-methoxy-w-methacryloyloxy, and a,w-di(methacryloyloxy) PEO, were identified by MALDI–TOF mass spectrometry. Technical C13, C15-alkoxy-terminated PEO was analyzed by Pasch and Trathnigg (1998) using LCCC versus SEC. Further increase in resolution of the chromatographic modes and the detector sensitivity could be achieved by another approach, as has been shown by Trathnigg et al. (Trathnigg et al., 2002; Trathnigg and Rappel, 2002). They analyzed FAE by a combination of LCCC and liquid exclusion–adsorption chromatography (LEAC). Transfer of fractions from the first to the second dimension was achieved using the full adsorption–desorption technique. The peaks of interest in the first dimension were trapped on a short precolumn before injecting them into the second dimension. This trapping allowed for focusing and reconcentration of the fractions. In this way, a full resolution of the oligomers was achieved in the second dimension. As both dimensions are run isocratically, density and RI detection could be applied. One of the important issues in an industrial laboratory is the time that is required for a specific analysis. Even the best analytical procedure is only of interest if the measurement can be conducted in a reasonable time. As was pointed out earlier, the time required to separate a FAE with regard to FTD with conventional columns is typically 30–40 min. In difficult cases, the separation may take longer than 1 h. Considering that materials research is frequently using high-throughput experimentation techniques where large numbers of samples are produced in a relatively short period of time, analytical techniques that are used must work adequately fast. In a detailed study, Pasch et al. investigated the effect of the stationary phase and the chromatographic conditions on the time requirements for the end group analysis of FAE (Pasch et al., 2005). For the assessment of the chromatographic performance of the stationary phases, a set of FAE with different end groups and different degrees of polymerization was used, including C10-, C12-, C13-, C15-, C16-, and C18-FAE. These polyethylene oxides were mixed to give a sample of complex composition. For a reference experiment the mixture was separated by LCCC using conventional column technology resulting in a baseline separation of all components, see Fig. 17.8. The time requirement for this separation, however, was about 140 min.

EXCIPIENTS

399

C12

1,0

Detector intensity

0,8

0,6

Nonylphenol

PEG

C13

C10 C11

4

0,4

8

12

16

20

24

28

0,2 C15

0,0

0

20

40

C16

60

C18

80

100

120

140

Time, min

FIGURE 17.8 LCCC separation of FAE with different end groups, stationary phase: M&N Nucleosil C18, 25 0.46 cm i.d., mobile phase: MeOH:H2O 88:12% by volume, flow rate: 1 mL/min (reprinted from Pasch et al., 2005, with permission of European Polymer Federation).

In order to reduce the time requirements for this separation, gradient instead of isocratic elution was used. As revealed in Fig. 17.9, using this approach the total separation time was decreased to about 50 min. To find out what the best suited column in terms of good column performance and minimum analysis time is, column screening tests were conducted with a range of columns from different producers. The best performance in terms of required analysis time was obtained with Chromolith (Merck, Darmstadt, Germany), a monolithic silica rod with macro- and mesopores that allows high flow rates and low backpressures. Accordingly, this stationary phase could be used also at very high flow rates up to 8 mL/min. Figures 17.10 and 17.11 show the gradient slopes for flow rates of 1 mL/min and 8 mL/min and the corresponding separations on the Chromolith C18 with a column length of 10 cm, respectively. As can be seen, sufficient separation of all components of the complex FAE mixture could be achieved even at a flow rate of 8 mL/ min. The time requirement for such a separation is less than three minutes and fits perfectly the requirements of high-throughput screening.

17.5 EXCIPIENTS The preferred and easiest intake of medicines is by oral ingestion in the form of tablets. Tablets today contain a combination of an active pharmaceutical ingredient and a (polymer) excipient – the “inactive’’ ingredient that delivers the pharmaceutical active

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

100 % MeOH

1,0

Detector signal

0,8

C 18

C12

0,6

0,4

C16 C13

Nonylphenol

C15 C14

0,2 PEG

C10 C11 0,0 0

10

20

30

40

50

Time, min FIGURE 17.9 Gradient LC separation of the FAE mixture, stationary phase: M&N Nucleosil C18, 25 0.46 cm i.d., mobile phase: gradient of MeOH–H2O, flow rate: 1 mL/min (reprinted from Pasch et al., 2005, with permission of European Polymer Federation).

compound (McGinity, 1989; Banker and Rhodes, 1996). A tablet coating has many functions and requirements, such as to protect the contents of the tablet during transport and storage, to ease the identification by the use of a colored coating, to mask the taste, and to enhance the swallowability. Another important function of a tablet 100 1 mL/min 8 mL/min

95

% MeOH

90

85

80

75

0

2

4

6

8 Time, min

10

12

14

FIGURE 17.10 Gradients for the separation of the FAE mixture on a monolithic column, stationary phase: Chromolith C18, 10 0.46 cm i.d., mobile phase: MeOH:H2O.

401

EXCIPIENTS 1 mL/min

0,8 0,6 0,4 0,2 0,0

8 mL/min

1,0 Detector signal

Detector signal

1,0

0,8 0,6 0,4 0,2

0

2

4

6

8 10 12 14 16 18 20

0,0 0,0

0,5

Time, min

1,0

1,5

2,0

Time, min

FIGURE 17.11 Gradient separation of FAE with different end groups, stationary phase: Chromolith C18, 10 0.46 cm i.d., mobile phase: MeOH:H2O (reprinted from Pasch et al., 2005, with permission of European Polymer Federation).

coating is to obtain a controlled release of the active pharmaceutical ingredient in the stomach or intestine. An instant release system is generally used in the stomach, whereas both “instant’’ and sustained release systems are used in the intestines. Instant release systems are used to obtain a fast effect of an active pharmaceutical compound. This can be achieved by using sugar, hydroxypropyl methyl cellulose, poly(vinyl pyrrolidone-co-vinyl acetate), or poly(vinyl alcohol) as a material for the tablet coating. The polymers dissolve readily and the tablet content is released. Recently, a new class of excipients based on ethylene oxide–vinyl alcohol copolymers (see Figure 17.12) has been developed (PEO-g-PVA). The combination of PVA and PEO should result in an excellent instant-release tablet coating. One way to O O)H

n

HO

(

O ) co

n– x

MeOH / Initiator

O) H

(

x

O

)

HO

(

O

(

O O

m

HO

(

O ) co

n-x

O

O) H

(

x

Catalyst (–MeOAc)

)

OH OH

(

m

FIGURE 17.12 Synthetic route for the preparation of PEO-g-PVA copolymers.

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MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

obtain a copolymer of PVA and PEO is the grafting of vinyl acetate (VAc) onto PEO and subsequent hydrolysis of the acetate groups. The grafting reaction of VAc onto PEO is hardly known and very little details are given in the literature (Kahrs and Zimmermann, 1962; Xie and Xie, 1999). The most similar reaction described is the grafting of acrylic acid onto EO–PO copolymers (Bromberg,1998a, 1999). Assuming an analogous reaction sequence (Bromberg, 1998a, 1998b), the following mechanism is proposed for the formation of the PEO-g-PVA copolymers. For analyzing structure–property relationships, a variety of PEO-g-PVA copolymers were prepared, differing in the VAc-to-PEO ratio and the molar mass of PEO. The analysis of the copolymers by IR and 1 H- and 13 C-NMR showed the presence of both O absorption was still present and was explained by a PEO and PVA. A small C nonquantitative saponification. SEC showed polydispersities (Mw/Mn) of around 5, with a small tailing to the low molar mass side. The latter was probably caused by the relatively low molar mass PVA homopolymer formed by the chain transfer reaction of VAc, both to the PEO and its acetate functionality. One of the main requirements of the PEO-g-PVA copolymers is that no free (nongrafted) PEO is present. Three methods have been used to determine this, that is, extraction, liquid chromatography, and mass spectrometry. Due to PVA, PEO, and the slightly grafted PEO-g-PVA copolymers having similar solubility properties, extraction experiments were relatively difficult and results not very reliable. Gradient HPLC measurements were performed, using a THF-water eluent; however, a complete separation of PEO and PEO-g-PVA could not be obtained (Adler, 2004). Liquid chromatography under critical conditions resulted in the desired separation (Adler, 2004). The critical point for PEO was obtained at an eluent of MeOH–water of 82.5:17.5% by volume. The LCCC chromatograms in Fig. 17.13 show that PEO is well separated from the copolymer fractions that elute at elution volumes between 1.5 and 2.8 mL. 900 800

600

PSS WinGPC scientific V 6.20, Instanz #1

Spannung [mV]

700

500 400 300 200 100 0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Elutionsvolumen [ml] FIGURE 17.13 LCCC analysis of PEO and PEO-g-PVA, stationary phase: Nucleosil C18, mobile phase: MeOH:H2O 82.5:17.5% by volume, samples: PEO (black), copolymer 1 (red) and 2 (blue). (See color plate.)

POLYETHER POLYOLS

403

FIGURE 17.14 2D separation of PEO-g-PVA copolymer 2, first dimension: LCCC, second dimension: SEC (reprinted from Gutzler et al., 2005, with permission of Wiley-VCH Publishers, Germany). (See color plate.)

As can be seen, there are significant differences between copolymers 1 and 2. Copolymer 1 having a high PEO-to-PVA ratio exhibits a quite broad distribution with regard to chemical composition and a significant amount of nongrafted PEO. In contrast, copolymer 2 having a low PEO-to-PVA ratio does not contain free PEO. To obtain an even better separation, SEC was performed in a second dimension following the LCCC analysis. As has been described previously, the resolution of a 2D experiment may be significantly higher as compared to the single chromatographic separations. A typical experimental result is shown in Fig. 17.14 (Gutzler et al., 2005). The comparison of the 2D plot of a graft copolymer with the 2D plot of the precursor PEO shows clearly that the graft copolymer sample does not contain any free PEO. This result was also confirmed by MALDI–TOF mass spectrometry. Next to the requirement of being PEO free, the PEO-g-PVA copolymers showed a good combination of film-forming properties, a fast dissolution, and a low solution viscosity in water. The phase separated morphology, as demonstrated by TEM, DSC, DMTA, and WAXS experiments, provided the PEO-g-PVA copolymers with relatively constant mechanical properties.

17.6 POLYETHER POLYOLS Statistical and block copolymers based on ethylene oxide (EO) and propylene oxide (PO) are important precursors of polyurethanes. Their detailed chemical structure, that is, the chemical composition, block length, and molar mass of the individual blocks may be decisive for the properties of the final product. For triblock copolymers HO (EO)n(PO)m(EO)nOH, the detailed analysis relates to the determination of the total molar mass and the degrees of polymerization of the inner PPO block (m) and the outer PEO blocks (n).

404

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

The application of LCCC to block copolymers is based on the consideration that the Gibbs free energy DGAB of a block copolymer AnBm is the sum of the contributions of block A and block B, DGA and DGB, respectively. X DGAB ¼ ðnA DGA þ nB DGB þ cAB Þ ð17:11Þ where cAB describes the interactions between blocks A and B. Assuming no specific interactions between A and B (cAB ¼ 0), the change in Gibbs free energy is only a function of the contributions of A and B. X ðnA DGA þ nB DGB Þ ð17:12Þ DGAB ¼ By the use of chromatographic conditions, corresponding to the critical point of homopolymer A, block A in the block copolymer will be chromatographically invisible, and the block copolymer will be eluted solely with respect to block B. DGA ¼ 0

DGAB ¼

ð17:13Þ

X

nB DGB

KdAB ¼ KdB

ð17:14Þ

ð17:15Þ

At the critical point of homopolymer B, block B will be chromatographically invisible, and the block copolymer will be eluted solely with respect to block A. DGB ¼ 0

DGAB ¼

X

KdAB ¼ KdA

ð17:16Þ

nA DGA

ð17:17Þ

ð17:18Þ

Triblock copolymers of the ABA0 type may be analyzed similar to the analysis of diblock copolymers. The two possible cases for this type of investigation are (a) the analysis with respect to the inner block B using the critical conditions of the outer blocks A and A0 , and (b) the analysis of the outer blocks A and A0 using the critical conditions of the inner block B. It is particularly useful to carry out experiments at the critical point of A and A0 . The separation then occurs with respect to the chain length of B, yielding fractions that are monodisperse with respect to B and polydisperse with respect to A and A0 . These fractions can be analyzed selectively with respect to the outer blocks A and A0 in separate experiments (Entelis et al., 1986; Adrian et al., 1998; Pasch and Augenstein, 1993, 1994, 2002; Pasch, 1997, 2000, 2004; Kilz and Pasch, 2000).

POLYETHER POLYOLS

405

FIGURE 17.15 LCCC separation of a PEO–PPO–PEO triblock copolymer with regard to the PPO block, stationary phase: Nucleosil RP-18, eluent: methanol-water 86:14% by volume.

On the basis of this approach, a triblock copolymer of ethylene oxide (EO) and propylene oxide (PO), HO(EO)n(PO)m(EO)nOH was analyzed with respect to the PPO inner block and the PEO outer blocks by LCCC and SEC (Adrian et al., 1998). For the selective separation of the block copolymer with respect to the PPO block, experiments were conducted using chromatographic conditions, corresponding to the critical point of PEO. These could be established on a RP-18 stationary phase when an eluent of methanol–water 86:14 (v/v) is used. The separation of the triblock copolymer at the critical point of PEO is shown in Fig. 17.15. Under these conditions, the polyethylene oxide blocks behave chromatographically “invisible’’ and retention of the block copolymer is solely directed by the polypropylene oxide block, yielding fractions of different degrees of polymerization (m) with respect to PPO. The assignment of the peaks was based on comparison with the chromatogram of a polypropylene glycol. Every peak was uniform with respect to m, but each one had a distribution in block length with respect to the PEO blocks (n). To identify fractions, they were collected and subjected to mass spectrometry. The first fraction contained polyethylene glycol and block oligomers with a degree of polymerization m(PO) ¼ 1–3. The second fraction was homogeneous with respect to PO and contained m(PO) ¼ 4, while fraction 3 resulted from m(PO) ¼ 5. The chromatogram in Fig. 17.15 reflects the oligomer distribution of the PO inner block, but does not give any information on the chain lengths of the PEO outer blocks or the total molar mass. This lack of information can be compensated by 2D liquid chromatography. Fig. 17.16 represents the contour plot of the 2D separation of the block copolymer in the sequence LCCC versus SEC. The separation with respect to the PO block length is now obtained along the ordinate, while the abscissa reflects

406

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.16 2DLC separation of a PEO–PPO–PEO triblock copolymer, first dimension: LCCC, second dimension: SEC. (See color plate.)

the differences in total molar mass. As can be seen, the total molar mass of the fractions increased with the length of the PO block. As the molar mass of the PO block was known for each fraction, the molar mass of the EO blocks could be calculated from the total molar mass (Adrian et al. 1998). 17.7 ANALYSIS OF CONDENSATION POLYMERS Condensation reactions are performed using at least one bifunctional monomer. One may distinguish between reactions where one monomer having two different functional end groups, that is, an AB monomer, or two monomers with two identical end groups (AA and BB) are involved. A typical product made by using an AB monomer is polyamide 6 (perlon). Using AA and BB monomers, a larger variety of polymers is accessible; most prominent examples are polyesters and polyamide 6.6 (nylon). All polycondensation products have in common that their molar mass distribution corresponds to a Schultz-Flory distribution (Schaefgen and Flory, 1948). The molar mass distribution is rather broad and corresponds to a polydispersity index (Mw/Mn) of two and higher. Experimentally, polydispersity indices of >2 have been found due to the fact that by-products such as cyclic oligomers are formed that are not considered by the theory (Schaefgen and Flory, 1948). The generally high polydispersity of polycondensation polymers makes it difficult to obtain narrowly distributed reference materials, while the polymers, discussed in previous sections, can be synthesized with quite narrow molar mass distributions (Mw/Mn < 1.05). In order to obtain narrowly distributed

407

POLYAMIDES

condensation polymers, preparative SEC fractionations must be conducted (Coulier et al., 2005). This approach is rather time consuming. Additionally, the solubility of some of the polymers is rather limited and, therefore, restricts the solvents, that can be used for chromatography. This fact also limits the application of HPLC techniques and, consequently, the number of publications dealing with liquid chromatography and multidimensional techniques of polycondensation polymers is rather limited. In fact, only polyesters have been studied extensively by gradient and isocratic chromatographic techniques (Cools et al. 1996; Philipsen 1998; Kr€uger et al. 1996; Philipsen et al., 1996, 1997a, 1997b; Mengerink et al., 2001, 2002). Two-dimensional chromatography and other multidimensional techniques have not been used frequently. The following sections will deal with novel chromatographic procedures for polyamides, aromatic, and aliphatic polyesters. 17.8 POLYAMIDES Polyamides are macromolecules with acidamide units CONH, where the chemical structure of the other parts of the monomers can be aliphatic and/or aromatic. Similar structures are found in nature, for example, polypeptides. Although in principle a large number of potential polyamide structures can be produced, only a few polyamides are produced in industrial scale. The most prominent aliphatic polyamides are polyamide 6 and polyamide 6.6. Polyamides are used in a broad range of applications as performance polymers in medicine, textile, and car manufacturing industries. In 2003, the European production of polyamides was approximately 3 million tons for technical applications. Of the total polyamide consumption, 94% was polyamide 6 and polyamide 6.6. Polyamide 6 is produced by ring opening polycondensation of e- caprolactame. If no other reactants are used, the polymer chains contain one carboxylic acid and one amine end group. O n

N

n H2O

O

n H2N

HO

OH

O

N H

n

H + nH O 2

Formation of polyamide 6

Polyamide 6.6 is produced by cocondensation of adipic acid and hexamethylene diamine. If a 1:1 ratio of acid to amine is used, the polymer chains are terminated by one acid and one amine end group. O

n H2N

OH

NH2 + n HO O

H

O

H N

N H

n

O

+ n H2O Formation of polyamide 6.6

OH

408

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

Statistically, other end groups can also be formed: H

O

H N

N H

NH 2

nN

H

O and

O

HO O

N H

O

H N

n OH

O

This is particularly the case when nonstoichiometric ratios of acid-to-amine are used. Typically, cyclic oligomers are also present in the reaction products. Due to the fact that different end groups can be formed during the polycondensation, the reaction products may exhibit a functionality-type distribution in addition to the molar mass distribution. Although SEC is suitable to analyze the molar mass distribution, it does not yield information on different end groups. For the determination of the functionality-type distribution, other types of liquid chromatography must be used. A major challenge in using interactive chromatography for polyamides is to find a suitable mobile phase (Mengerink et al., 2001, 2002; Weidner et al., 2004). Polyamides form semicrystalline morphologies that limit the solubility in organic solvents. Besides hot phenol, formic acid, and trifluoroethanol (TFE) (Mori and Barth, 1999), 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) represents a suitable solvent for polyamides (Chen et al., 2002). These solvents are mainly used to analyze the molar mass distribution of polyamides by SEC. A first example of applying interactive chromatography to the analysis of polyamide 6 was published recently by Mengerink et al. 2001. They established the critical conditions of polyamide 6 and tried to separate the macromolecules with regard to their end-group functionality. The critical conditions were obtained on a stationary phase of Nucleosil 50–5 (Macherey–Nagel) by using a mobile-phase composition of formic acid/i-propanol 81.6:18.4 v/v. For establishing the critical conditions, they used linear polyamides of different molar masses and a series of cyclic oligomers. As is shown in Fig. 17.17, they were able to separate the linear oligomers that eluted first from the cyclic oligomers that eluted as a second small peak. The identification of the different functionality fractions was conducted by MALDI–TOF mass spectrometry (Mengerink et al., 2001). They found the pentamer to be the highest cyclic oligomer in the samples. Using conventional reversed-phase HPLC, it is possible to separate the low molar mass oligomers of polyamide 6. As can be seen in Fig. 17.18, cyclic oligomers up to the nonamer were resolved. A C18-modified silica gel was used as the stationary phase and the mobile phase was a water–acetonitrile mixture run under gradient conditions (Horbach, 1998). Unfortunately, by this procedure only the low molar mass oligomers can be analysed. Information on the total sample cannot be obtained due to insolubility of the higher oligomers in the mobile phase. Comparing the two different HPLC procedures, the results of Mengerink et al. (2001) are superior since data on the total polymer samples can be obtained.

POLYAMIDES

409

FIGURE 17.17 Separation of a typical PA 6 sample at critical conditions (reprinted from Mengerink et al., 2001, with permission from Elsevier).

FIGURE 17.18 RP-HPLC separation of cyclic PA 6 oligomers (Horbach, 1998, with permission of Carl Hanser Publisher).

410

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.19 2D contour plot of a modified polyamide 6, first dimension: LCCC, second dimension: SEC. Area 1: unmodified chains, area 2: modified chains, area 3: cyclic oligomers. (See color plate.)

The trade-off might be that the selectivity of the gradient HPLC method for the individual cyclic oligomers is better. One way to increase resolution of the LCCC procedure is to couple this type of separation with a SEC separation in a 2D experimental setup. The higher selectivity of the 2DLC separation could help to resolve individual oligomers of different functionalities. LCCC can also be used to separate polyamide 6 samples, where the end groups were modified with benzoic acid. In addition to the cyclic oligomers and linear oligomers with one acid and one amine end group, further oligomers were formed that contain benzoic amide end groups. The LCCC separations were performed on two Supelco Discovery C8 columns using a mobile phase of HFIP–methanol 70:30 v/v. The LCCC separation was coupled to a SEC separation in a 2DLC setup, see Fig. 17.19. The SEC separations were performed using HFIP þ 0.05% ammonium acetate as the mobile phase. The contour plot clearly shows two major and one minor spots corresponding to the three functionality fractions. By using MALDI–TOF MS, peak 1 was identified to be regular polyamide and peak 2 to be polyamide 6 having an amide and a benzoic amide end group. The low molar mass, “shoulder’’ in peak 3 is due to cyclic oligomers. The elution of the regular polyamide occurs nearly independent of molar mass, meaning the chromatographic conditions are at least close to LCCC conditions. The LCCC separation is mainly based on polarity differences of the different end groups. Peak 2 shows a strong molar mass dependence. A further increase in molar mass may even jeopardize the separation since the high molar mass species of this fraction may coelute with the regular polyamide. Similar separations can also be achieved for polyamide 6.6 as has been shown for rather low molar mass samples by Mengerink et al. (2002). As is shown in Fig. 17.20 for

POLYAMIDES mAU 250 200 150 100 50 0

411

amine–acid

Amine rich polyamide 6.6 amine–amine

acid–acid

cycs 0

mAU 800

2.5

5

7.5

10

Acid rich polyamide 6.6

12.5

15

17.5

22.5

min

20

22.5

min

20

22.5

min

20

amine–acid

amine–amine

600 400 200

acid–acid

cycs

0 0

2.5

5

7.5

10

12.5

15

mAU

cyclic

Cyclic polymers of polyamide 6.6

250 200 150 100 50 0 0

2.5

5

7.5

17.5

10

12.5

15

17.5

FIGURE 17.20 LCCC separation of low molar mass polyamide 6.6 samples. Top trace: amine rich polyamide 6.6 (Mw ¼ 10 kg/mol), middle trace: acid rich polyamide 6.6 (Mw ¼ 6 kg/mol), lower trace: cyclic polymer of polyamide 6.6 (Mw ¼ 700) (Mengerink et al., 2002; with permission from Elsevier).

a sample with a molar mass of 10,000 g/mol, LCCC is capable of resolving the different functionality fractions. The mobile phase in this case was HFIP/10 mM phosphoric acid 89.5:10.5 v/v. A Nucleosil C18 300–5 column was used as the stationary phase. Mengerink et al. (2002) showed that the LCCC conditions are very sensitive to slight changes of the energetic balance such as changes in temperature. However, the excellent separation into the different functionality fractions would make the system a good candidate for 2DLC separations provided that it also can be applied for higher molar masses. Using the same chromatographic system as given in Fig. 17.19 we were able to investigate modified polyamide 6.6 by 2DLC. The samples were modified by amidation with propionic acid, introducing additional functionality fractions to the system. In total, seven different species were expected to be present in the samples (see Table 17.1). Although seven different functionality fractions are expected to be present in the sample, the contour plot in Fig. 17.21 reveals only two different elution areas. The fraction that elutes at about 3 mL in the first dimension (ordinate direction) can be assigned tentatively to a PA 6.6 having an acid and an amine end group by comparing the results with the plot in Fig. 17.19. Accordingly, the later eluting fractions are due to the other different linear functionality fractions, as shown in Table 17.1. To resolve the linear oligomer fractions, Weidner et al. (2004) proposed an alternative approach. He used the combination of LCCC and MALDI–TOF to identify the different functionalities. Weidner performed a separation according to chemical composition by LCCC, and used a spray interface (LC-Transform from LabConnections) to deposit the chromatographic fractions on a MALDI–TOF target. In the second step, each individual

412

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

fraction of the chromatogram was analyzed by MALDI–TOF MS and the different functionality fractions were identified. In Fig. 17.22 a series of MALDI–TOF mass spectra corresponding to different elution volumes are depicted. There are two different oligomer series present in all spectra. The oligomer series can be identified by calculating the masses of the end groups and assigning them to specific chemical structures (Pasch and Schrepp, 2003; Weidner et al., 2004). In the present example, the two species are the propionic amide-acid (R-am-ac) and the propionic amide-propionic amide polyamides (R-am-am-R). The use of MALDI– TOF MS as a structure-sensitive detector allows the resolution to be indirectly enhanced since several species coelute, as shown in Fig. 17.21. The polarity of the

FIGURE 17.21 2DLC contour plot of a modified polyamide 6.6, first dimension: LCCC, second dimension: SEC, experimental conditions; see discussion of Fig. 17.19. (See color plate.)

POLYAMIDES

413

FIGURE 17.22 Series of MALDI–TOF MS spectra obtained at elution volumes between 5 mL and 8 mL (as indicated).

end group determines the elution profile, because with decreasing molar mass the impact of the end groups on the elution becomes stronger. On the contrary, the elution of the different functionality fractions does not strictly follow the polarities of the end groups. The acid end group is very polar, while the two propionic amide end groups are assumed to be less polar. To study the elution behavior of the different functionality fractions more in detail, a 2D plot using the MALDI–TOF MS data for the molar mass dimension has been constructed. After normalization of the mass spectra, the different mass peaks with their corresponding intensities were plotted as a function of the LCCC elution volumes and a 2D contour diagram where LCCC is plotted against MALDI– TOF is obtained. The contour diagram given in Fig. 17.23 gives a much more detailed insight into the structural peculiarities of the sample, revealing different functionality fractions and their corresponding molar masses. Six of the expected seven functionality fractions (see Table 17.1) were identified. Only the polyamide having two amine end groups was not found. The elution of the polyamide components appears in two main groups. The first peak (approx. 3 mL in ordinate direction) in Figs. 17.21 and 17.23, respectively, is narrow and composed of polyamide species having at least one amine end group. It cannot be distinguished between a polyamide having the propionic amide–amine and acid amine end group combination. At elution volumes larger than 3 mL in the ordinate direction, all species without amine end groups elute. The elution order is R-am-am-R, ac-am-R, ac-ac, meaning the components elute according to the polarity of the end groups. The less polar components elute first and the polyamides having more polar end-group

414

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.23 2D contour plot of a propionic acid modified polyamide 6.6 (assignments according to Table 17.1), first dimension: LCCC, second dimension: MALDI–TOF. (See color plate.)

combinations elute later. Thus, the LCCC separation is dominated by the presence of an amine end group. The specific effect of the amine end groups has been found previously by Mengerink et al. (2002).

17.9 AROMATIC POLYESTERS Aromatic polyesters are prepared typically by polycondensation of a diol (frequently ethanediol or butanediol) and a diacid or diester (frequently terephthalic acid or its dimethyl ester). The resulting polymers may exhibit functionality-type distributions due to the formation of oligomers with different combinations of hydroxy, acid, or ester groups. The HPLC analysis of complex polyesters based on adipic acid, isophthalic acid and di-propoxylated bisphenol A has been presented by Philipsen et al. (1996,1997a, 1997b,1998,1999). They studied the mechanisms of normal-phase and reversed-phase gradient HPLC and their application to aromatic copolyesters. Using a reversed-phase mechanism, Philipsen showed for low molar mass oligomers (Mw ¼ 5 kg mol1) that separation takes place with regard to the degree of polymerization and the end groups. Philipsen et al. (1998) studied the separation mechanism of polyesters using normalphase gradient liquid chromatography by screening various stationary phases and applying quarternary gradients (heptane to dichloromethane to THF to methanol). Philipsen (1999) studied copolyesters made by transesterification. A reversed-phase gradient LC system (Novopak C18, gradient water þ AcOH–THF) was used to evaluate the molar mass and oligomer distributions. For low molar mass oligomers, detailed information on chemical composition was obtained. For high molar mass fractions, however, it appeared difficult to assign differences in chemical composition. Phillipsen and Wubbe also studied copolyesters having two hydroxy end groups by the combination of SEC and normal-phase gradient LC. For the same samples, he used a normal-phase system to separate the cyclic oligomers from the diols and the acid-terminated oligomers.

AROMATIC POLYESTERS

415

In a second step, the fractions from HPLC were reinjected into a SEC system and the molar masses of the fractions were analyzed. As was expected, the molar mass of the cyclic oligomers was rather low. For the polyester fractions having diol end groups, it was found that their molar masses were lower than the polyester fractions containing one or two acid end groups. These results are in agreement with the results obtained by Kilz et al. (1995) and Kr€ uger and coworker. (Kr€ uger and Schulz, 1994), but cannot be explained by theory (Schaefgen and Flory, 1948; Bovey and Winslow, 1979). Polycarbonates have also been studied recently with regard to chemical heterogeneity. Polycarbonates are polycondensation products of phosgene and aliphatic or aromatic dihydroxy compounds. CH3

O

O

O

n

CH3 Polycarbonate based on bisphenol A

Polycarbonates are important thermoplastic materials with a high optical transparency. Typically, polycarbonates are produced either by the reaction of bisphenol A with phosgene, or in a melt transesterification process using diphenyl carbonate and bisphenol A with a subsequent distillation of the by-product phenol. In order to obtain stable products with well-defined molar masses, polycarbonates are endcapped with tert-butyl alcohol or t-butyl phenol. The melt process is superior due to the absence of solvents and phosgene, but needs higher temperatures and more sophisticated equipment (King, 2000). A number of side reactions take place, particularly at long reaction times and high temperatures. These include discoloration and branching (Oba et al., 2000). The most accepted mechanism of thermal rearrangements of polycarbonates is the Kolbe–Schmidt reaction that results in the following by-products: O

CH3 PC [ O

] PC

O CH3

OH CH3

O

PC [ O

] PC

O CH3

O ]

PC

O

CH3 PC [ O

O CH3

] PC CH3 O ] PC

O CH3

By-products of polycarbonate made by the melt process according to Oba et al. (2000) and Marks et al. (2000)

416

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

FIGURE 17.24 2DLC plots of a virgin polycarbonate (a) and a hydrolytically degraded polycarbonate (b) first dimension: LCCC, second dimension: SEC (from Coulier et al., 2005; with permission from Elsevier). (See color plate.)

The molecular heterogeneity of poly(bisphenol A)carbonate and its hydrolytic degradation has been investigated by means of multidimensional liquid chromatography by Coulier et al. (2005). In the first dimension, gradient and isocratic elution profiles were tested. It was shown that better selectivity was achieved with the gradient elution technique that occurred mainly with regard to different oligomers. LCCC conditions could also be established by fractionating a virgin polycarbonate according to molar mass and using the fractions as reference materials for finding the critical conditions. Two-dimensional experiments were performed where LCCC was used in the first dimension. In the second dimension, SEC was used to obtain molar mass information. The 2D contour plots for a virgin polycarbonate and a sample after hydrolysis are shown in Fig. 17.24. It is to be noted that the abscissa and ordinate are reversed compared to the previously shown 2D plots. The peak at low retention volume (LCCC dimension) contains mainly cyclic oligomers and linear oligomers with two tert-butyl end groups. None of the byproducts in the reaction scheme of the by-products shown above were found. The hydrolytically degraded sample shows an additional peak that was assigned to species having one t-butyl end group and one OH end group. According to the 2D plots, the molar mass broadened, but there was no strong decrease in molar mass. Interestingly, no evidence of polycarbonate having two OH end groups was found. Such end groups would be expected if degradation starts at the chain end. It can be speculated, therefore, that degradation starts somewhere along the polymer chain. To conclude, the LCCC method was superior to the gradient LC method because the separation took place mainly according to the end groups. The selectivity of LCCC is lower than the gradient technique; however, the 2D plots give a clear picture of the molecular heterogeneity of the samples. The method appearsto be suitable for in-depth studies of the hydrolytic degradation of polycarbonates.

417

ALIPHATIC POLYESTERS

17.10 ALIPHATIC POLYESTERS Aliphatic polyesters are typically prepared from aliphatic bifunctional alcohols and acids. O

n HO

a

OH + n COOH

b

COOH

HO

a

COOH

O

b n

+ n H2O Formation of aliphatic polyesters

If the polyester synthesis is performed with equimolar amounts of diol and diacid, then, in addition to hydroxy, carboxy-terminated oligomers, dihydroxy- and dicarboxy-terminated oligomers are formed, as shown below. In a thermodynamic equilibrium, the molar ratios of the three functionality fractions should be 2:1:1, respectively.

HOOC

b

O

a

O

O

O

O

O

COOH b n

HO and

a

O

b n

O

a

OH

Other structures like cyclic oligomers can also be formed. For the analysis of the functionality-type distribution of the reaction products, different types of interaction chromatography can be used. Kr€ uger et al. (Kr€ uger and Schulz, 1994; Kr€ uger et al., 1996) investigated polyesters that were prepared from adipic acid and hexane diol by gradient HPLC and LCCC. The LCCC conditions were obtained by investigating well-defined laboratory samples in the range of 1–6 kg/mol having two hydroxy end groups. The authors showed that in addition to the main (dihydroxy) oligomer series other functionality fractions were formed, including cyclic oligomers and oligomers with alkyl, alkylene, and ether end groups. To analyze the SEC behavior of the different functionality fractions, these fractions were collected from LCCC and reinjected into SEC, see Fig. 17.25. The species of the fractions are given in Table 17.2. Using SEC columns with sufficiently high resolution, it was possible to separate the functionality fractions into single oligomers. From the oligomer separations, calibration curves for the different functionalities were obtained that were used for molar mass calculation. A comparison of the calibration curves showed that the cyclic oligomers and the linear oligomers with alkyl end groups had lower hydrodynamic volumes compared to the other functionality fractions. This clearly indicates that differences in end-group functionality have a strong effect on the SEC behavior, and they must be considered when investigating this type of sample by SEC. Other interesting polyesters of practical relevance are polylactides that are considered to be biologically degradable. Polylactides are prepared by a ring opening

418

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

37.867

19.623 31.992

16.970

13.987

27.819

(a)

7

8

30

20

10

0

fraction 1 2 3 4 5 6

(b)

Fr. 9 Fr. 8 Fr. 7 Fr. 6 Fr. 5 Fr. 4 Fr. 3 Fr. 2 Fr. 1

SEC

LACCC

FIGURE 17.25 LCCC separation of a poly(1,6-hexanediol adipate) (a) and SEC analysis of the functionality fractions (b) from Kr€uger et al., 1994 (Copyright 1994 from Journal of Liquid Chromatography, 1994, p. 17 by Kr€uger et al. Reproduced by permission of Taylor & Francis Group, LLC., http://www.taylorandfrancis.com).

ALIPHATIC POLYESTERS

TABLE 17.2 in Fig. 17.25

419

Functionality Fractions in Poly-Hexanediol Adipinate Found

Fraction

End-group Combination

1 2 3 4 5 6 7 8 9

alkyl–alkyl cyclic oligomers alkyl–alcohol acid–acid alcohol–acid alcohol–alcohol Ether Ether Hexane diol

polymerization of, typically, L-lactide. The basic structure of a poly(L-lactide) is shown below: O RO

O n

OH

O Poly(L-lactide), R=alkoxide group as initiator fragment

Polylactides are used for biomedical applications (Barrows, 1990; Kaharas et al., 1994; Hartmann, 1998) because of their biodegradability. Kr€uger et al. (19996, 2003) studied the thermal degradation of polylactides using LCCC and MALDI–TOF MS. As could be expected, the authors found cyclic oligomers in addition to the regular linear chains. The linear oligomers had carboxylic end groups before and after the thermal treatment. During the thermal treatment, new chemical structures were not formed. Only the quantitative composition of the samples changed. Kr€uger et al. (2005) also studied polylactides where one end group has been modified by long alkyl and aromatic groups. Using interaction chromatography the degree of modification could be evaluated. Biela et al. (2002, 2003) prepared and analyzed linear and star-shaped polylactides. Using LCCC, star-shaped samples were separated with regard to the number of arms. Essentially, this separation was driven by the number of hydroxy groups that constituted the end group of each arm. Two-dimensional LC was used to show that the LCCC separation was exclusively driven by chemical composition irrespective of molar mass. Other industrial applications of 2DLC are known and have been used for applications from quality assurance to synthesis research. These applications will drive 2DLC into new areas as many of the applications used in industry cannot easily be obtained by other means. The analysis of polymers and other industrially useful molecules will be aided by further developments in 2DLC column and instrumental methodologies.

420

MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY IN INDUSTRIAL APPLICATIONS

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18 THE ANALYSIS OF SURFACTANTS BY MULTIDIMENSIONAL LIQUID CHROMATOGRAPHY Robert E. Murphy Kroungold Analytical, Inc., Encinitas, CA 92024, USA

Mark R. Schure Theoretical Separation Science Laboratory, Rohm and Haas Company, Springhouse, PA 19477-0904, USA

18.1 INTRODUCTION The use of HPLC for the analysis of simple anionic, cationic, and nonionic surfactants is well known (Schick, 1967; Jungermann, 1970; Linfield, 1973; Schmitt, 2001). Ionic surfactants typically contain a long alkyl chain connected to a charged ionic group. The ionic group renders the alkyl group soluble in aqueous media and the alkyl group allows oily molecules to be solubilized. The transport of the oily molecules is therefore facilitated into aqueous solutions. A common example is washing of one’s hands in soap and water with the subsequent transfer of oily dirt and grease, which are hydrophobic, into the water phase. Nonionic surfactants contain a similar hydrophobic alkyl chain, but the hydrophilic group is generally a polyethylene oxide (PEO) polymer of various lengths. The hydrophilic-to-lyophilic balance (HLB) is commonly used to characterize surfactants, and it reflects the partitioning of a molecule of interest between a polar (water) and nonpolar (oil) medium (Griffin, 1949, 1954). This may be facilitated in many cases by the use of subtle changes in molecular architecture; for

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright Ó 2008 John Wiley & Sons, Inc.

425

426

THE ANALYSIS OF SURFACTANTS

example, the hydrophobic portion may be a polymer of polypropylene oxide (PPO) and the water-soluble region may be PEO. The two monomers PEO and PPO differ by one methylene (CH2) group. Many surfactants have constant structure; that is, the surfactant has a uniform structure with both the (charged) head and alkyl tail groups having constant length. Other surfactants, especially those with polymeric constituents, have distributions of chain lengths but have charged groups of constant size, for example, sulfate or sulfonate. In many cases, the analysis of this length distribution is of tantamount importance and may be determined by some form of liquid chromatography, from reversed-phase liquid chromatography (RPLC) to size exclusion chromatography (SEC). The structure of a number of popular surfactants is shown in Table 18.1, which illustrates the concepts of two distinct molecular functionalities per molecule. Looking at the molecules in this table, one finds that some surfactants have more hydrophobic parts than other surfactants. As stated above, this variability differentiates many of the surfactants, and it is the tuning of the HLB for specific applications that promotes the diversity of surfactant structures. The presence of alcohol and ether groups with good water solubility in hydrocarbon chain surfactants is noted. In addition, it is found that sulfur-containing end groups for anionic surfactants and nitrogen-containing end groups for cationic surfactants are very common. As will be discussed further, the molecular structure region separated for characterization of surfactants includes hydrocarbon chain lengths with various groups embedded within the chains. When surfactant molecules contain more than one distribution, for example, a distribution of chain lengths in the hydrophobic and hydrophilic portions, twodimensional liquid chromatography (2DLC) is a very powerful method for complete analysis. One can get the full quantitation of the distribution of molecular size by using the 2DLC technique. For example, take a surfactant molecule like alcohol ethoxylates (AE’s) having a general structure of H--ðCH2 Þx --O--ðCH2 CH2 OÞy --H

TABLE 18.1

Common Surfactants with Structures

Type Anionic Cationic Amphoteric (Zwitterionic)

Nonionic

Chemical name Sodium dodecyl sulfate (SDS) Benzyldodecyldimethyl ammonium chloride 3-(N,NDimethyltetradecylammonio)propanesulfonate Triton

Structure

INTRODUCTION

427

Let us say that the alkyl chain length varies from 10 x 14 and the PEO chain from 5 y 7. Hence, there should by 5 different x values and 3 different y values. The product gives 15 different molecule structures. Using 2DLC it is possible to easily separate all combinations of the x and y valued isomers of the AE’s. Hence, a complete quantitation of the 15 different structures will be possible if the separation can be set up to separate independently the alkyl distribution from the PEO distribution. This is the general key to the utility of using 2DLC for separating complex surfactants with two distinct chain regions. An example of this from previous work is given in Fig. 18.1 (Murphy et al., 1998a) in which the 1D and 2D chromatograms of a polyethylene glycol (PEG) and Brij surfactant mixture are shown. The 1D chromatogram shows the first dimension chromatographic concentration profile on a reversed-phase column. The peaks a1 through a3 are shown to be fused into the first peak. The use of SEC as a molecular size separation system resolves peaks a1 through a3 in the second dimension. The use of only SEC would lead to a fusion of peaks a2, b, c, and d3.

FIGURE 18.1 The one- and two-dimensional chromatograms of a PEG and BrijÒ mixture. PEG 200 (a1, x ¼ 0, y ¼ 4.5), PEG 1000 (a2, x ¼ 0, y ¼ 23), and PEG 8000 (a3, x ¼ 0, y ¼ 182), b: BrijÒ 35 (x ¼ 12, y ¼ 23), c: BrijÒ 58 (x ¼ 16, y ¼ 20), d: BrijÒ 72 (d1, x ¼ 18, y ¼ 2), BrijÒ 76 (d2, x ¼ 18, y ¼ 10), and BrijÒ 78 (d3, x ¼ 18, y ¼ 20). The HPLC flow rate is 0.1 mL/min and the solvent composition is 98/2 (methanol/water). The SEC flow rate is 1 mL/min of tetrahydrofuran. The sampling time is 1.5 min and only 0.67 min of the SEC axis is shown. Reprinted from Murphy et al. (1998a) with permission of the American Chemical Society.

428

THE ANALYSIS OF SURFACTANTS

The mechanisms of the first- and second-dimension separations are independent here; that is, the alkyl chain length separation is independent of the PEO chain length. The retention in this case is often called orthogonal between the two dimensions. A discussion of orthogonal separations is given in Chapters 2, 3, 6, and 12. This independent nature of the two separation dimensions allows the full analysis of the alkyl and PEO components per molecule. Additional examples of the independence in separation axes will further demonstrate the importance of 2DLC for surfactant analysis.

18.2 ANALYTICAL CHARACTERIZATION METHODS Many different analytical separation techniques have been used to analyze surfactants for either the quantitation in a variety of matrices (Schroder, 2003; Petrovic et al., 2003; Jahnke et al., 2004) or the characterization of molecular compositions and mass distributions (Escott and Chandler, 1989; Jandera and Urbanek, 1995; Jandera et al., 1998). 1D separations are discussed in the following sections, and their potential as a dimension in 2DLC systems is evaluated, prior to the 2DLC separation section. The liquid-phase techniques discussed in this section are mainly used for characterization, but they equally apply to quantitative analysis with proper controls and calibration. Ionic surfactants that have a charged end group and an ethylene oxide distribution can be fully characterized using either ion-exchange chromatography (IEC), ion-pair liquid chromatography (IPLC), capillary gel electrophoresis (CGE) or capillary electrophoresis (CE). Ion-exchange chromatography is used to separate ionic and nonionic surfactants (Okada, 1990; Desmazieres et al., 1993), but more recently it has mainly been used in the preparative mode (Torres et al., 2001). Ion-pair liquid chromatography (Steinbrech et al., 1986; Escott and Chandler, 1989) operates similarly to IEC except that the charged analyte is paired with an oppositely charged mobile phase additive and then separated by RPLC as a neutral compound. It usually has improved resolution over IEC and is less damaging to pumps because of lower salt concentrations. Over the past 10 years, IEC and IPLC have been less used for surfactant analysis, and CE usage has increased due to its advantage of resolution and speed. CE has been used to separate the oligomers of sulfated PEG’s after derivatization to impart a charge and provide a UV chromophore (Wallingford, 1996). The analysis of an octylphenol ethoxylate sulfate by CGE and CE is described in the next section. Nonionic surfactants generally have a higher degree of complexity due to the lower grade of alcohol used in the production, thus producing an alkyl and ethylene oxide distribution. One-dimensional techniques can sometimes resolve most oligomers, but more complex samples need two-dimensional chromatography to fully separate and characterize the mixture (Murphy et al., 1998b). SEC can be used to analyze surfactants, but as they are generally of low mass and size, SEC cannot resolve individual oligomers below a mass of 600 (Rissler, 1996). In the SEC section, SEC is used to separate PEG’s of various mass, and its advantages in a two-dimensional HPLC system are examined.

ANALYTICAL CHARACTERIZATION METHODS

429

Liquid chromatography is most commonly used to characterize surfactants by the number of ethylene oxide units or alkyl chain length (Rissler, 1996). The ethylene oxide distribution analyses of nonionic surfactants are typically accomplished by normal phase liquid chromatography (NPLC) using aminopropyl silica or silica columns and aliphatic hydrocarbon–alcohol–water mobile phases (Rissler, 1996). The NPLC analysis of several AE’s is shown in the section on NPLC and the limitations of one-dimensional separations are discussed. In addition, 2DLC separations are performed on the same samples for comparison in the section on 2DLC. RPLC is generally used to separate surfactants by the alkyl chain length with C18bonded or C8-bonded silica and methanol–water mobile phases (Rissler, 1996), which is discussed in greater detail in the RPLC section. Gradient elution generally provides a greater range of oligomers that can be resolved, but detection becomes limited for nonionic surfactants without an ultraviolet chromophore; this is discussed in more detail in the section on detection methods. 18.2.1

CE and CGE

Capillary gel electrophoresis is becoming very widely used in the biotechnology field for the high resolution separation of DNA and peptides according to molecular weight, but it has limited application for the analysis of surfactants (Wallingford, 1996). CGE does result in an increase in the resolution per unit time over SEC for charged polymers (Poli and Schure,1992). Polymers with an ionizable repeat unit (like DNA) are separated at high pH conditions in CGE, where the oligomeric charge-to-solution frictional drag ratio is fairly constant. Hence, a free solution CE experiment would not yield a separation of these molecular distributions, and so a gel matrix through which the polymers can sieve is needed to enact the separation. This results in the elution of the smallest oligomers first since they can migrate the fastest through the gel, while larger oligomers take longer to permeate through the gel. Figure 18.2 shows an electropherogram of a CGE separation of an octylphenol ethoxylate sulfate surfactant. The oligomers are well resolved. The analytical use of CE has increased since its discovery because of the very high resolution obtainable and the fast separation speed. This technique has also been used to separate polymers according to molecular weight (Heinig et al., 1998 and 1999; Petrovic and Barcelo, 2003). In CE, oligomers are separated at a pH where the electrophoretic mobility, that is, charge-to-solution frictional drag ratio, varies due to the end group being the only charged functional group. If the response factor of each oligomer is known, then the calculation of the molar mass distribution is possible. Figure 18.3 shows the same alkylphenol ethoxylate sulfate as in Fig. 18.2 but run under CE conditions. The CE analysis time is less than 6 min, as compared to 40 minutes under CGE conditions, but with a lower resolution. CE would be a good candidate for the second dimension of a fast 2DLC system. For this particular example, the pH is 7.8 and the analyte elutes in order of decreasing oligomer number. Thus, the elution order is reversed from that in Fig. 18.2 due to the combined effect of high electroosmotic flow toward the detector and analyte mobility away from the detector. The smaller oligomers elute last as they migrate away from the detector faster than the

430

THE ANALYSIS OF SURFACTANTS

FIGURE 18.2 Capillary gel electrophoresis separation of an octylphenol ethoxylate sulfate (with an ethylene oxide chain length from 1 to 8). Run conditions: pH 8.3 (100 mM tris–borate, 7 M urea); 50 mm 75 cm J&W polyacrylamide gel capillary (PAGE-5, 5%T, and 5%C) run at 20 kV with a 5 kV injection for 5 s; UV detection at 260 nm.

larger oligomers. The oligomers are swept past the detector since the electroosmotic flow is larger in magnitude than the oligomer mobility. 18.2.2

SEC

Currently, the most widely used chromatographic technique for the molecular mass distribution analysis of a polymer is SEC. The widespread use of SEC is because of its

FIGURE 18.3 Capillary zone electrophoresis separation of octylphenol ethoxylate sulfate. Run conditions: pH 7.8 (25 mM phosphate); 75 mm 50 cm capillary run at 10 kV with a 5 kV injection for 20 s; UV detection at 225 nm.

ANALYTICAL CHARACTERIZATION METHODS

431

simplicity, ease of use, and ability to be automated (Yau et al., 1979). The calibration of SEC should be done using molecules of the same composition for accurate average molecular weight calculation. Since well-calibrated standards are not usually available for all the different polymer types, most analyses are performed to give a relative average molecular weight. Surfactant analysis operates in the low molecular weight range or at a smaller pore size compared to most polymers. SEC has been used for the past decade for characterization (Ysambertt et al. 1995, 2005) or combined with electrospray mass spectrometry for the analysis of ethoxylated surfactants (Prokai and Simonsick, 2000). In the last few years, the availability of smaller particle sizes has decreased run times. In combination with higher linear velocities, SEC analysis can be performed within 1 min (Murphy et al., 1998a). These fast analysis times make the technique well suited for the second dimension of a 2DLC system since faster runs correspond to higher sampling of the first dimension, thus higher 2D resolution, as discussed in Chapter 6. 18.2.3

NPLC

Surfactants are separated according to adsorption or partitioning differences with a polar stationary phase in NPLC. This retention of the polar surfactant moiety allows for the separation of the ethylene oxide distribution. Of all the NPLC packings that have been utilized to separate nonionic surfactants, the aminopropyl-bonded stationary phases have been shown to give the best resolution (Jandera et al., 1990). The separation of the octylphenol ethoxylate oligomers on an amino silica column is shown in Fig. 18.4. Similar to the capabilities of CE for ionic surfactants, the ethylene oxide distribution can be quantitatively determined by NPLC if identity and response factors for each oligomer are known.

FIGURE 18.4 NPLC chromatogram of Triton X-100. Conditions: Supelco LC-NH2 column, 15 cm 4.6 mm and 3 mm particles; linear gradient of 80/20/0 (heptane/tetrahydrofuran/ methanol) to 0/90/10 in 40 min at a ﬂow rate of 1 mL/min; evaporative light scattering detection.

432

THE ANALYSIS OF SURFACTANTS

FIGURE 18.5 Amino NPLC chromatograms of Novel II 1412-70 (a) and Neodol 25-12 (b). The inset shows an expanded view of the chromatograms from 28 to 38 min. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

The solute used in Fig.18.4 had a fixed alkyl chain length, and as the alkyl distribution increases, one-dimensional NPLC separations become more complex. In Fig. 18.5 the amino silica chromatograms of Novel II 1412-70 and Neodol 25-12 are shown, and their corresponding chemical compositions are given in Table 18.2 along with other AE’s. TABLE 18.2

Alkyl and Ethylene Oxide Compositions of Alcohol Ethoxylates

AE PEG 600 Triton X-100 BrijÒ 35 Novel II 1412-70 Neodol 25-7 Neodol 25-12 a

Narrow distribution.

Alkyl components None C8 benzene C12 C12 and C14 C12, C13, C14, and C15 C12, C13, C14, and C15

Average number of EO 14 10 23 10a 7 12

ANALYTICAL CHARACTERIZATION METHODS

433

The use of the amino NPLC column results in baseline separation of Novel II 141270 oligomers (Fig. 18.5a). The inset in Fig. 18.5 shows the retention order and alkyl chain length assignments, with the C14 end group eluting prior to the C12 end group. The NPLC separation of Neodol 25-12, shown in Fig. 18.5b, demonstrates an alkyl distribution for each ethylene oxide oligomer. There appears to be an optimum in the resolution around the tenth set of alkyl distributions eluting at approximately 50 min. The alkyl distributions eluting after 55 min start to coelute and are indistinguishable. Thus, one-dimensional NPLC cannot resolve both alkyl and ethylene oxide distributions of Neodol 25-12 throughout the entire range of interest. As far as 2DLC method development is concerned though, NPLC has the ability to resolve the ethylene oxide distributions of nonionic surfactants and would generally be useful for the first dimension of a 2DLC system because of its longer run times. 18.2.4

RPLC

For 30 years, RPLC has been used for the separation of surfactants based on the alkyl chain length (Parris et al., 1977, Parris, 1978). In the RPLC mode, surfactants elute in order of increasing hydrophobicity. Figure 18.6 shows the isocratic separation of a mixture of nonionic PEG and BrijÒ surfactants containing a variety of alkyl chain and ethylene oxide lengths. This separation is based on the alkyl chain length, with the C18 polymers eluting last, but without the resolution of the underlying PEG distributions.

900 800

CxH2x+1 (OC2H4)y OH

X=0 ELSD intensity

700 600 500 X = 12 400 X = 18

300

X = 16

200 100 0

5

10

15

20

25

30

35

40

45

50

55

Time, min

FIGURE 18.6 Reversed-phase liquid chromatogram of a mixture of PEG and BrijÒ surfactants: x ¼ 0 is a mixture of PEG 200 (y ¼ 4.5), PEG 1000 (y ¼ 23), and PEG 8000 (y ¼ 182); x ¼ 12 is BrijÒ 35 (y ¼ 23); x ¼ 16 is BrijÒ 58 (y ¼ 20); x ¼ 18 is a mixture of BrijÒ 72 (y ¼ 2), BrijÒ 76 (y ¼ 10), and BrijÒ 78 ( y ¼ 20). Run conditions: Zorbax SB-18 column, 7.5 cm 3.0 mm and 5 mm particles; mobile phase of 98/2 methanol/water at a ﬂow rate of 50 mL/min; evaporative light scattering detection.

434

THE ANALYSIS OF SURFACTANTS

Since NPLC can separate the ethylene oxide distribution and RPLC separates according to the alkyl distribution, the combination of these two techniques into a 2LDC system was shown to resolve both distributions in one analysis (Murphy et al., 1998b). This is discussed in the NPLC coupled to RPLC section.

18.3 DETECTION METHODS A variety of detectors are used in the one-dimensional HPLC analysis of surfactants from chemiluminescence to ultraviolet absorption depending on the chemical nature of the compound. All detectors used in one-dimensional HPLC should be applicable in 2DLC systems as long as the mobile phase conditions are compatible. Ultraviolet detection is most often used for alkyl phenol ethoxylates or alkyl benzene sulfonates due to the good absorption from the benzene ring. AE’s do not contain a UV chromophore, thus evaporative light scattering detection has been used for many years (Mengerink et al.,1991). Conductivity detection has been utilized for alkyl sulfates (Jiang and Liu, 1997). Zwitterionic and cationic surfactants containing nitrogen have been analyzed using chemiluminescent nitrogen detection (Harrison and Lucy, 2002). More recently, electrospray mass spectrometry has been the detector of choice for the analysis of surfactants (Jandera et al., 1998; Zhu et al., 2003 and 2004; Cheguillaume et al., 2004; Kuehn and Neubert, 2004). Identification of each oligomer allows for the full characterization of the samples. In addition, RPLC with tandem MS has been utilized for the characterization and detection of surfactants at low levels (Schroder, 2003 and Jahnke et al., 2004). This is the method of choice for the low level detection of surfactants and degradation products in wastewater (Petrovic et al., 2004).

18.4 2DLC Two-dimensional liquid chromatographic systems have been used for several years for characterization of surfactants (Murphy et al., 1998a; Trathnigg et al., 2002a and 2002b). Most applications involve the characterization of AE’s due to the dual distributions (alkyl and ethylene oxide) and the lack of full resolution in onedimensional separation systems. Analyzing several fractions from the first dimension is useful for group-type analysis (Haefliger, 2003). Off-line systems have been used to analyze AE’s by liquid chromatography under critical conditions (LCCC) in combination with SEC (Trathnigg et al., 2001). Additional information about LCCC is given in the previous chapter on industrial applications of 2DLC. Online or automated HPLC systems allow unattended operation for higher throughput and reproducibility (Murphy et al., 1998b). We will show several examples of the use of 2DLC for nonionic surfactants. The resulting resolution can be dramatically different depending on the two separation modes in the 2DLC system. As discussed previously, the separation of the hydrophobic groups can be accomplished with a reversed-phase column, and the separation of the

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hydrophilic ethylene oxide units can be done with either a size exclusion or a normal phase column. The combination of RPLC with SEC for the 2DLC separation of a mixture of BrijÒ surfactants and PEG (C0-15 AE’s) is described in the next section on RPLC coupled to SEC. The combination of RPLC with NPLC for the higher resolution 2DLC separation of BrijÒ, Novel, and Neodol surfactants (C12-15 AE’s) is described in the section on NPLC coupled to RPLC. 18.4.1

RPLC Coupled to SEC

The two-dimensional HPLC analysis of nonionic surfactants can be used to determine both the alkyl and EO distributions simultaneously. The axes in 2DLC correspond to retention along each chromatographic column and the concentration data are displayed as a contour for easier viewing. The ELSD intensity is represented by different grayscale shades. Using SEC as the second dimension allows for each peak separated in the first dimension, by their hydrophobicity, to be further separated by their size. This results in the size distribution along the SEC axis and the alkyl length distribution along the RPLC axis. The analysis of a mixture of PEG and BrijÒ alkyl ethoxylates by 2DLC was shown previously in Fig. 18.1. The one-dimensional separation according to alkyl chain lengths is the same as the one shown in Fig. 18.6. In this 2DLC system, the mixture is first separated according to alkyl chain length on the RPLC column and then further separated according to size or ethylene oxide chain length on the SEC column. The results of the SEC analysis were used to evaluate the optimum sampling rate and were discussed and presented in Chapter 6. The separation is not totally orthogonal, as shown in Fig. 18.1, and is typical of most 2DLC separations (Kilz et al., 1995). Low molecular weight polymers that can diffuse into the packing pores exhibit both hydrophobic and size exclusion mechanisms in RPLC, and this mixed mechanism is shown by the BrijÒ 70 series of peaks, d1 through d3. The lower molecular weight material (d1) is more retained on the RPLC column since it can further diffuse into the pores. 18.4.2

NPLC Coupled to RPLC

The use of 2DLC with NPLC and RPLC separation dimensions should increase the selectivity and the resolution over 1DLC and 2DLC with RPLC and SEC, and allow the separation of all the components of a multiple distribution AE. The use of NPLC instead of SEC in the 2DLC system will result in the separation of individual ethylene oxide oligomers rather than the single peak observed in the lower resolution size separation. If each dimension of a 2DLC system can separate an individual chemical distribution of the AE, then 2DLC has the selectivity to analyze independently each distribution along an axis. This increased selectivity should also make the interpretation of AE distributions easier since each oligomer class is aligned along an axis. In an effort to better resolve the ethylene oxide and alkyl distributions individually, silica NPLC and RPLC were investigated. Figure 18.7a and b shows the results of these

THE ANALYSIS OF SURFACTANTS

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FIGURE 18.7 One-dimensional HPLC chromatograms of Neodol 25-12: silica NPLC column (a) and C18 RPLC column (b). Reprinted from Murphy et al. (1998b) with permission of the American Chemical Society.

one-dimensional HPLC analyses of Neodol 25-12. The ethylene oxide distribution is resolved on a silica column using a reversed-phase gradient (Fig. 18.7a). This separation was attempted using other grades of silica (Zorbax Rx-SIL, Supelcosil LC-Si, and Micra NPS) resulting in worse resolution, suggesting that the Zorbax SIL has a different treatment or structure that improves the selectivity over other silicas. Similar results were shown for the analysis of alkylphenol ethoxylates, which could only be separated on Spherisorb silica using acetonitrile and water (Rissler 1994). Since water-acetonitrile gradients give better resolution over water-methanol gradients for the analysis of PEG’s, it was proposed that methanol shields residual

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silanol groups (through hydrogen bonding) better than acetonitrile, and thus the ethylene oxide can interact more strongly with the surface when acetonitrile gradients are used. The separation of the alkyl distribution of Neodol 25-12 is accomplished using an octadecyl-bonded silica column and reversed-phase gradient, as shown in Fig. 18.7b. Thus, using two one-dimensional techniques the bulk alkyl and bulk ethylene oxide distributions can be determined independently when the appropriate columns and solvent systems are used. This multitechnique method is commonly used to characterize materials by one particular distribution at a time and takes the analyst twice the amount of time because of preparing samples, solvents, and equipment twice. Moreover, the results are sometimes inconclusive due to poor resolution from overlapping distributions. As will be shown in the next set of figures, much more information can be obtained if we perform this type of analysis using a comprehensive 2DLC system rather than determining these distributions separately. Figure 18.8 shows the 2DLC chromatogram of Novel II 1412-70. Two distributions with different alkyl end groups are evident along the RPLC axis. The C14 end group, eluting at 0.75 min in the RPLC axis, is the major component since it is represented as a darker region in the contour plot. These data show that the EO distribution with each alkyl end group is similar and ranges between 125–160 min, with an average at approximately 140 min. Comparable data are seen in Fig. 18.5a on the amino NPLC column, where both distributions are resolved.

FIGURE 18.8 Two-dimensional HPLC (NPLC/RPLC) chromatogram of Novel II 1412-70 with the corresponding chemical structure and average EO distribution as supplied by the manufacturer. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

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THE ANALYSIS OF SURFACTANTS

FIGURE 18.9 Two-dimensional HPLC (NPLC/RPLC) chromatogram of Neodol 25-12 with the corresponding chemical structure and average EO distribution as supplied by the manufacturer. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

The 2DLC analysis of Neodol 25-12 is shown in Fig. 18.9, which was not fully resolved on the amino NPLC column in Fig. 18.5b. Four distributions with different alkyl end groups are clearly seen. The EO distributions have a similar average, but the C15 end group (eluting at 0.90 min in the RPLC dimension) appears to have a narrower EO distribution. This difference is due to using isocratic RPLC in the second dimension that results in broader peaks of lower intensity for later eluting components. Thus, the C15 end group has a lower ELSD intensity relative to the other end groups. The EO distributions are very similar and will be extracted and compared in a later figure. The EO distribution ranges between 120–180 min in the NPLC dimension, with an average at approximately 150 min. This EO distribution elutes later and is much broader than that of the Novel II 1412-70 distribution. This result is expected since Neodol 25-12 has a larger and broader EO distribution than Novel II 1412-70. The narrower distribution of Novel II 1412-70 is expected since the manufacturer sells this product specifically for the narrower distribution. The slant in the 2D chromatograms was attributed to the first dimension concave gradient. Figure 18.10 shows a 2DLC chromatogram of Neodol 25-7. The early eluting EO oligomers of Neodol 25-7 are not as well resolved as the later oligomers, since the first dimension gradient was optimized for Neodol 25-12 oligomers. The alkyl distribution is similar to that of Neodol 25-12. The EO distribution ranges between 112–160 min in the NPLC dimension, with an average at approximately 135 min. Figure 18.11 is a 2DLC chromatogram of BrijÒ 35. The 2DLC of BrijÒ 35 shows mainly the C12 end group distribution, with a C14 end group in lesser concentration and the C16 end group at an even lower concentration. The EO distribution elutes between

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439

FIGURE 18.10 Two-dimensional HPLC (NPLC/RPLC) chromatogram of Neodol 25-7 with the corresponding chemical structure and average EO distribution as supplied by the manufacturer. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

FIGURE 18.11 Two-dimensional HPLC (NPLC/RPLC) chromatogram of BrijÒ 35 with the corresponding chemical structure and average EO distribution as supplied by the manufacturer.

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THE ANALYSIS OF SURFACTANTS

FIGURE 18.12 Two-dimensional HPLC (NPLC/RPLC) chromatogram of PEG-600 with the corresponding chemical structure and average EO distribution as supplied by the manufacturer. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

160 and 260 min, which is much later then that of the Neodols and is due to the larger ethylene oxide average molecular weight of this product. Figure 18.12 shows a 2DLC chromatogram of PEG-600. This sample is presented to show where an AE without an alkyl end group elutes. PEG’s are commonly analyzed as an impurity in AE’s, thus this type of molecule needs to be resolved from the main component. It is interesting to note how the ethylene oxide oligomers of PEG-600 are well separated in this sample, whereas Neodol 25-7 (Fig. 18.10) eluting at the same time in the NPLC dimension is not as well resolved. Also, the NPLC retention of PEG-600 ranges between 80–170 min., averaging at 125 min., which is much earlier than any of the other AE’s. The PEG-600 has an average EO number of 14, which we would expect to elute between Neodol 25-12 and BrijÒ 35 in the NPLC dimension, but it elutes much earlier. This suggests that the first dimension retention mechanism is not confined to just EO separations but rather there is a mixed retention mechanism. Similar results were previously seen with a similar column and a gradient (Ibrahim and Wheals, 1996). Although PEG was not analyzed in this work, nonylphenol ethoxylates eluted after octylphenol ethoxylates, suggesting an alkyl contribution to retention. Since AE’s have both a hydrophobic and a hydrophilic section, the alkyl portion may be interacting with the hydrophobic siloxane functionality and the EO portion may be interacting with the hydrophilic silanol functionality of the silica. After collecting 2DLC chromatograms, the data may be presented as EO distributions of individual alkyl components. Figure 18.13 is a plot of the EO distributions across the C12 alkyl component of Neodol 25-12 from Fig. 18.9. The distributions across the C12 component are similar, but with different intensities. Individual NPLC

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FIGURE 18.13 One-dimensional NPLC chromatograms extracted across the C12 alkyl component from the 2DLC analysis of Neodol 25-12 shown in Fig. 18.9. Times listed are the individual NPLC chromatograms extracted from the 2DLC chromatogram.

runs may be extracted from the 2DLC data, but they are not as representative as the averaged distribution. Figure 18.14 shows the four alkyl components of Neodol 25-12. The EO distributions were constructed by averaging the NPLC runs across the listed RPLC times on the chromatograms. This average is constructed by summing the intensity values across the columns of the data matrix and then dividing by the number of columns summed. The EO distributions of the four components are similar, but resolution decreases with increasing alkyl content of the end group at the low end of the distribution. The resolution decrease is probably due to alkyl dominance in the retention mechanism at low EO length, as the silica NPLC column is unable to distinguish between a small number of EO units with a large alkyl end group. These distributions were not corrected

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THE ANALYSIS OF SURFACTANTS

160000 C , 0.38–0.45 12

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FIGURE 18.14 One-dimensional NPLC chromatograms extracted for each alkyl component from 2DLC analysis of Neodol 25-12 as shown in Fig. 18.9. Times listed are the range of chromatograms extracted and then averaged from the 2DLC chromatogram to produce each NPLC chromatogram. Reprinted from Murphy et al. (1998b), with permission of the American Chemical Society.

for the variation in intensity as a function of EO chain length. However, this effect has been studied previously (Trathnigg and Kollroser, 1997) and found not to have a significantly large effect for large EO chain lengths detected with ELS detection.

18.5 CONCLUSIONS The use of 2DLC (NPLC/RPLC) for the analysis of higher alkyl AE’s is clearly superior to that of one-dimensional NPLC. This 2DLC system is capable of

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simultaneously separating AE’s into alkyl and EO components. The average retention times in the NPLC dimension of the AE’s examined correspond fairly well with the number of EO units. The PEG-600 retention in the NPLC dimension does not correlate with the AE’s, which suggests a mixed retention mechanism on the silica column with a reversed-phase gradient. Software reconstruction of the ethylene oxide distributions allows the analysis of each alkyl component independently in a single run. The analysis of each EO distribution of a multiple alkyl AE should facilitate better characterization protocols. One-dimensional NPLC may provide sufficient resolution for less complicated AE (i.e., Novel II 1412-70 and BrijÒ 35), but 2DLC offers the selectivity to display the EO distribution of each end group independently, which is neither easy nor unambiguous to extract from one-dimensional data. 2DLC is a powerful technique not only to separate materials, but also to aid in identification, characterization, analytical trouble shooting, synthesis optimization, and quality control. In our particular application, we do not have an absolute method of calibration because the alkyl chain length affects the EO distribution retention. However, mass spectrometry would be an ideal third dimension. The automated combination of twodimensional chromatography and mass spectrometry is the next step toward the future of simultaneous separation and identification of very complicated samples.

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Ibrahim, N.M.A., Wheals, B.B., (1996). Oligomeric separation of alkylphenol ethoxylate surfactants on silica using aqueous acetonitrile eluents. J. Chromatogr. A 731, 171–177. Jahnke, A., Gandrass, J., Ruck, W. (2004). Simultaneous determination of alkylphenol ethoxylates and their biotransformation products by liquid chromatography/electrospray ionisation tandem mass spectrometry. J. Chromatogr. A 1035(1), 115–122. Jandera, P., Urbanek, J., Prokes, B., Churacek, J. (1990). Comparison of various stationary phases for normal phase high perfomance liquid chromatography of ethoxylated alkylphenols. J. Chromatogr. 504, 297–318. Jandera, P., Urbanek, J. (1995). Comparison of chromatographic behavior of oligoethylene glycol nonylphenyl ether non-ionic and anionic surfactants in reversed-phase highperformance liquid chromatography. J. Chromatogr. A 689(2), 255–267. Jandera, P., Holcapek, M., Theodoridis, G. (1998). Investigation of chromatographic behavior of alcohol ethoxylate surfactants in normal-phase and reversed-phase systems using high-performance liquid chromatography-mass spectrometry. J. Chromatogr. A 813(2), 299–311. Jiang, S.X., Liu, X. (1997). Reverse phase HPLC analysis of alkyl sulfonates with nonsuppression conductivity detection. J. Liq. Chromatogr. Related Technol. 20(13); 2053–2061. Jungermann, E. (1970). Cationic Surfactants. Marcel Dekker, New York. Kilz, P., Kruger, R., Much, H., Schulz, G. (1995). Two-Dimensional chromatography for the deformulation of complex copolymers. in Chromatographic Characterization of Polymers: Hyphenated and Multidimensional Techniques. T. Provder, H.G. Barth, M.W. Urban, Editors, American Chemical Society, Washington. DC. Kuehn, A.V., Neubert, R.H.H. (2004). Characterization of mixtures of alkyl polyglycosides (plantacare). by liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry. Pharm. Res. 21(12), 2347–2353. Linfield, W.M. (1973). Anionic Surfactants. Marcel Dekker, New York. Mengerink, Y., De Man, H.C.J., Van der Wal, S. (1991). Use of an evaporative light scattering detector in reversed-phase high-performance liquid chromatography of oligomeric surfactants. J. Chromatogr. A 552(1–2), 593–604. Murphy, R.E., Schure, M.R., Foley, J.P. (1998a). Effect of Sampling Rate on Resolution in Comprehensive Two-Dimensional Liquid Chromatography. Anal. Chem. 70(8), 1585– 1594. Murphy, R.E., Schure, M.R., Foley, J.P (1998b). One- and two-dimensional chromatographic analysis of alcohol ethoxylates. Anal. Chem. 70, 4353–4360. Okada, T. (1990). Chromatographic oligomer separation of poly(oxyethylenes) on potassium ion-form cation-exchange resin. Anal. Chem. 62(4), 327–331. Parris, N. (1978). Surfactant analysis by high performance liquid chromatography: I. A rapid analysis for mixtures of amphoteric surfactants and soap. J. Am. Oil Chem. Soc. 55(9), 675–677. Parris, N., Linfield, W.M., Barford, R.A. (1977). Determination of sulfobetaine amphoteric surfactants by reverse phase high performance liquid chromatography. Anal. Chem. 49(14), 2228–2231. Petrovic, M., Barcelo, D. (2003). Capillary electrophoresis in surfactant analysis. Comprehensive Anal. Chem. 40, 77–87.

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INDEX

Adsorption chromatography, 391 liquid exclusion adsorption chromatography (LEAC), 398 Afﬁnity chromatography, 98, 99 Albumin bovine serum albumin (BSA), 166, 265 Aliphatic polyamides, 407 polyesters, 417 Amino acids, 23, 265, 270, 271, 311, 328, 329, 332, 335, 336 arginine, 270 cysteine, 23 glycine, 23, 181, 333, 350 leucine, 329, 332, 333 lysine, 270, 332, 351 methionine, 230, 311, 332 n-terminal methionine, 311 phenylalanine, 328, 329, 331–333 proline, 23, 328, 329–333 tryptophan, 328, 329, 332, 338 tyrosine, 328, 329, 332

Amino NPLC column, 433, 437, 438 Aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC), 333 Ammonium acetate, 195, 196, 244–249, 251, 252, 253, 410 Amphiphilic compounds, 395 Analysis reproducibility, 223, 271 Analytical information markup language (AnIML), 114 Analytical instrument association (AIA) format, 113, 115 Anion-exchange see also Ion-exchange chromatography, 100, 109, 130, 180, 181, 192–202, 209, 225, 254, 295, 308 gradient(s), 179, 199, 200–202 Annexins, 237 Aplasia californicus, 348 Arginine, see Amino acids Aromatic amines, 103 amino acids, 351 polyesters, 414

Multidimensional Liquid Chromatography: Theory and Applications in Industrial Chemistry and the Life Sciences, Edited by Steven A. Cohen and Mark R. Schure. Copyright Ó 2008 John Wiley & Sons, Inc.

447

448

INDEX

Autocorrelation methods, 41 Autocovariance function (ACVF) methods, 28, 68, 74–85, 87, 88, 119 Band-broadening, 67, 163, 171 Bethe lattice, 29 Biomarkers, 209, 221, 222, 230, 231, 237 Biphasic SCX/RP column, 251 Block copolymers, 389, 403, 404 Bottom-up proteomics, 118, 203, 292 Bovine hemoglobin (BH), 265 Bovine serum albumin (BSA), 166, 265, 350, 389, 403 Brij® type surfactants, 398, 427, 432, 433, 435, 438–440, 443 Butylphenoxy end groups, 397 Capillary electrochromatography (CEC), 147 Capillary electrophoresis (CE), 5, 27, 96, 127, 349, 365, 367 electromigration injections, 372 interfaces, 103 isoelectric focusing, 224, 351 micellar electrokinetic chromatography (MEKC or MECC), 20, 106, 350 microchip electrophoresis, 380 SDS-sieving electrophoresis, 350 two-dimensional capillary electrophoresis, 356 with liquid chromatography, 352 Capillary gel electrophoresis (CGE), 428, 429 Cation-exchange see also Ion-exchange chromatography (CEX), 178 gradient, 181 peak capacity, 182 random access media-strong cationexchange (RAM-SCX), 212, 214, 215, 216, 217 strong cation-exchange (SCX) chromatography, 243, 262, 265 SCX-RP 2DLC method, 270 SCX-RP 2DLC systems, 275 SCX-RP chromatographic modes, 280 SCX gradients, 254 with reversed-phase, 181 with size exclusion, 178

Cayley tree, 29 Cellular protein homogenates, 350 Central nervous system tissues, 329 Cerebrospinal ﬂuid, 208, 214, 217, 253, 254, 328 Chemical heterogeneity, 415 Chemical variance, 28 Chemiluminescent nitrogen detection, 434 Chemometric analysis, 27, 28, 117 Chip-based MDLC systems, 29, 30 Chiral achiral-chiral system, 321, 322, 323, 324 chromatography, 338 crown ether column, 328 cyclodextrin column 325, 327–335, 337, 338 drugs, 319, 323, 325, 336–338 enantiomeric purity of amino acids, 336 enantioselectivity, 322, 323, 335, 338 metabolites, 319, 322–327 pindolol enantiomers, 324, 327 stationary phases, 319, 328, 336 S-ketorolac enantiomer, 323–325 Chromatofocusing, 3, 100, 109, 117, 224–234, 293, 312 Chromatogram subtraction, 110, 119, 234 Chromatographic data systems, 109–113 hardware, 97 methods, 127, 147, 177, 207, 221, 223, 261, 291, 319, 389 performance, 147, 190, 300, 314, 398 pressure limitations, 296 ChromolithTM columns, 140, 149, 155–157, 165–167, 216, 217, 399–401 Coaxial sheath ﬂow, 368 Comprehensive 2D chromatography, 4, 5, 13–15, 24, 29, 37, 93–109, 118, 127 Computer -generated map, 84, 87 hardware, 109–113 software, 81, 97, 100, 103, 106, 109–120, 194, 227–231, 293, 302, 348, 398, 443 Condensation polymers, 406, 407 reactions, 406 Continuous ﬂow electrophoresis, 224 Copolymers, 387, 389, 401–406

INDEX

Correlation, 5, 12, 15, 19–37, 42, 43, 54, 55, 77–79, 132, 156, 200, 231–235, 267–271, 292, 306, 392 Cross-fractionation, 96, 389 Cyano column, 161, 325–327, 338 see also Normal-Phase Liquid Chromatography Cyclic oligomers, 395, 406, 408, 410, 414–419 Cyclodextrin column 325, 327–335, 337, 338 Data large datasets, 114, 301 interpretation, 314 mass spectral data, 113, 114, 118, 302, 314 presentation, 96, 115, 116, 118, 354 storage, 114 Database searching algorithms, 27, 28, 243, 244 Datura sanguinea leaves, 334, 335 Dendrogram, 231, 232 Deseparation process, 24 Detectors, chemiluminescent, 434 diode array detector (DAD), 1, 17, 97, 114, 118 dynamic range, 29, 119, 141, 207, 223, 261, 262, 282, 292, 294, 304, 308, 311, 348, 351, 256, 357, 358, 360, 365, 367 evaporative light scattering (ELSD), 100, 109, 114, 138, 394, 431–434 fast data-sampling detectors, 394 ﬂuorescence, 100, 104–106, 181, 322, 348–356, 360, 366, 370, 372, 379, 380 Fourier transform-ion cyclotron resonance (FT-ICR), 16 ion mobility-mass spectrometry (IM-MS), 16 laser-induced ﬂuorescence (LIF), 100, 106, 354, 366, 379, 380 matrix-assisted laser desorption ionization (MALDI) with Time-of-ﬂight (TOF) detection, 152, 186, 210, 214–217, 229–236, 308–313, 379, 396, 398, 403, 408–414, 419

449

peptide mass ﬁngerprinting, 229, 308 tandem mass spectrometry (MS/MS), 243–255, 261, 292, 368 time-of-ﬂight mass spectrometry (TOF-MS), 16, 83, 192–194, 291–293, see also MALDI ultraviolet (UV) detection, 1, 3, 17, 97, 100, 109, 110, 114, 118, 179–188, 196–198, 212, 226–233, 277, 299, 302, 322, 349–351, 366–368, 376, 379, 394, 428–434 Detergent additives, 313 Dielectrophoresis, 6 Differential gel electrophoresis (DIGE), 348 Diffusion barrier, 211 Digraphs, 30 Dimensionality 2, 22–24, 36, 59, 68, 215 Diode array detector, 1,17, 97, 114, 118 Disulﬁde bonds, 225, 349, 350 Dithiothreitol (DTT), 225 DNA microarrays, 222 Drug enantiomers, 319, 323, 325, 336–338 Dynamic range, 29, 119, 141, 207, 223, 261, 262, 282, 292, 294, 304, 308, 311, 348, 351, 256, 357, 358, 360, 365, 367 Eight-port valve, 94, 96, 99, 100–102, 108, 177–181 Electrically-gated solute injection, 106 Electrically/pneumatically-driven fraction transfer device, 393 Electrophoresis, 60, 348 see also Capillary Electrophoresis continuous ﬂow electrophoresis, 224 differential gel electrophoresis (DIGE), 348 gel, 2, 11, 29, 60, 79, 80, 96, 141, 142, 177, 186–188, 191, 192, 202, 209, 222, 223, 347, 348, 388 isoelectric focusing (IEF), 2, 3, 11, 80, 96, 141, 224, 254, 347, 348, 351, 360, 366, 379, 388 polyacrylamide gel electrophoresis (PAGE), 60, 79, 80, 96, 141, 142, 177, 188, 191, 192, 202, 222, 233, 234, 347, 348, 350 Electrophoretic mobility, 64, 349, 429

450

INDEX

Electrospray ionization (ESI), 112, 152, 153, 158, 167, 168, 181, 186, 192, 194, 228, 230, 236, 244, 291, 293, 297, 300, 301, 308, 311, 313, 328, 368–370 TOF-MS, 186, 192, 228, 311 Elevated temperatures 109, 130, 139, 227 Emulsiﬁers, 395 Enantiomers see Chiral Enantiomeric purity of amino acids, 336 Enantioselectivity, 322, 323, 335, 338 Enthalpic interactions, 391 Entropic interactions, 391 Ethylene oxide (EO), 395, 398, 401, 403, 405, 425, 428–437, 440, 443 chain length, 442 distribution, 436, 438, 440, 441, 443 EO-PO copolymers, 402 EO-VA copolymers, 401 Escherichia coli, 181, 247, 254, 305 cytosol, 305 Euclidean norm, 17, 18, 143 Evaporative light scattering detector (ELSD), 100, 109, 114, 138, 394, 431–434 Extensible markup language (XML), 114 External porosity, 153, 155 Extra-column effects, 156, 157

Gas-liquid chromatography (GLC), 5, 14, 20, 21, 36, 60, 110, 114, 118, 320 Gel alignment algorithm, 358 Gel electrophoresis, 2, 11, 29, 60, 79, 80, 96, 141, 142, 177, 186–188, 191, 192, 202, 209, 222, 223, 347, 348, 388 Gel permeation chromatography (GPC), 13 see also Size-exclusion chromatography Generalized rank annihilation method (GRAM), 28 Gibbs free energy, 390, 404 Glutamic acid, see Amino acids Glycine, see Amino acids Gradient elution chromatography, 3, 14, 16, 27, 101, 102, 117, 130–142, 150, 152, 153, 158–160, 165–171, 178–181, 184, 191, 193–196, 198–203, 212–216, 224–226, 231, 233, 244–248, 251, 254, 262, 263, 265–267, 275, 280, 281, 293, 296–299, 303–311, 366–378, 389, 399–402, 407–410, 414, 416, 417, 429, 431, 436–440, 443 Graph theory, 30 Graphical user interfaces (GUIs), 110 Graphics packages, 117 Gray-scale contour plots, 116, 357, 375

Field-ﬂow fractionation (FFF), 6, 27, 106 Flow cytometry/cell sorting, 6 Flow-gating interface (FGI), 104, 367 Flow splitting, 104, 108, 170, 181, 293 Fluorescein isothiocyanate (FITC), 373, 374 Fluorescence detection, 100, 104–106, 181, 322, 348–356, 360, 366, 370, 372, 379, 380 Fluorescent labelling, 351 Fluorogenic reagents, 351 Four-port valves, 183 Four-position valve, 111 Fourier-based techniques, 74, 119 Fourier-space description, 5, 22, 74, 119 complex chromatograms, 22 Fourier transform-ion cyclotron resonance (FT-ICR), 16 Free-solution electrophoresis, 349, 353 Functionality-type distribution, 408, 414, 417

Heart-cutting technique, 5, 29, 94–99, 177, 320, 321, 328, 329, 335, 366, 389, 393 Heat shock proteins (HSPs), 237 Hemoglobin, 179 bovine 265 Hexaﬂuoroisopropanol (HFIP), 408 Hierarchical clustering, 231, 232 High-throughput analyses, 188, 208, 366 column designs, 394 screening, 398, 399 Histidine, see Amino acids History of 2D Chromatography, 13 Human serum, 216, 282, 323, 324, 338, 366, 370 Hydrodynamic volumes, 389, 417 Hydrophilic interaction chromatography (HILIC), 100, 141, 142, 265–270, 273, 274, 275–278, 284

INDEX

Hydrophilic-to-lyophilic balance (HLB), 425 Hydrophobic proteins, 192, 313 Hydrophobicity, 21, 106, 133, 150, 184, 192, 209, 215, 216, 223, 227, 231– 233, 267, 268, 307, 367, 433, 435 Ion-exchange, 23, 101, 112, 148, 160, 167, 187, 200, 214, 215, 270, 291, 295, 296, 428 see also Anion-exchange and Cation-exchange chromatography (IEX, IEC) with reversed-phase, 166, 299 Ion suppression, 158 Immobilized metal ion afﬁnity chromatography (IMAC), 379 Information entropy, 21 theory (IT), 20, 36 Immobilized pH gradient (IPG), 83, 254 Instant release systems, 401 Ion mobility-mass spectrometry (IM-MS), 16 Ion-pair liquid chromatography (IPLC), 267, 300, 428 Ionic surfactants, 425, 428, 431, 434 Isocratic elution, 13, 109, 131, 135, 137, 139, 150, 158, 165, 166, 178, 191, 195, 196, 121, 215, 303, 304, 387, 398, 399, 407, 416, 433, 438 Isoelectric focusing (IEF), 2, 3, 11, 80, 96, 141, 224, 254, 347, 348, 351, 360, 366, 379, 388 Isoelectric point (pI) 2, 3, 11, 64, 81, 82, 84, 87, 117, 141, 222–6, 233–5, 270, 295, 308, 311, 347 Laser-induced ﬂuorescence (LIF), 100, 106, 354, 366, 379, 380 Leucine, see Amino acids, 332 Limit of detection (LOD), 26, 27, 262, 276, 280, 282, 284, 370 Liquid chromatography-mass spectrometry (LC-MS) 158 Liquid chromatography at critical conditions (LCCC) 135, 391, 393, 397, 398, 399, 402–405, 419 Liquid chromatography at the critical point of adsorption (LCCPA) see Liquid

451

chromatography at the critical condition (LCCC) Liquid chromatography-mass spectrometry (LC-MS) 17, 118, 158, 159, 198, 216, 228, 261–267, 273, 275–283, 294, 299, 300–308, 314, 324, 334 see also Detectors and Mass Spectrometry Liquid exclusion adsorption chromatography (LEAC), 398 Lyophilization, 189, 192, 193,195 Lysine, see Amino acids Macromolecules, see polymers MALDI peptide mass ﬁngerprinting (PMF), 229, 230, 236, 308, 309, 311 MALDI-TOF, 152, 186, 210, 214, 217, 229, 308, 325, 236, 379, 396 Malt beverages, 333 MascotTM, 230, 244, 247, 249, 254 Mass spectrometry, 1, 3, 6, 16, 17, 35, 79, 83, 88, 99, 100, 104, 110, 113, 114, 118, 119, 120, 132, 153, 158, 181, 182, 185–187, 192, 193, 198, 200, 207–210, 214, 216, 223, 228, 229, 237, 243, 244, 245, 248, 255, 261, 280, 292–294, 299, 300, 308, 324, 328, 334, 365, 366, 368, 379, 396, 402, 403, 405, 408, 431, 434, 443, see also Detectors data, 113, 114, 118, 119, 200, 302 data format, 113 electrospray ionization (ESI), 112, 153, 158, 167, 168, 186, 187, 228, 230, 236, 244–246, 293, 297, 299–301, 308, 311, 313, 328, 368–370 tandem mass spectrometry (MS/MS) analysis, 2, 96, 118, 208, 243–249, 251, 254, 255, 261, 262, 264, 265, 275, 276, 280–282, 284, 292, 294, 308, 311, 313, 314, 324, 368, 370, -based proteomics, 243 mass mapping, 221, 228, 230–234 signal intensity, 199, 200, 217, 237, 251, 252, 254 Matrix-assisted laser desorption/ionization (MALDI), 308 with Time-of-ﬂight (TOF) detection, 229

452

INDEX

Maximum entropy, 228, 301 MEKC buffer solutions, 106 Melt transesteriﬁcation process, 415 Membrane proteins, 120, 291, 294, 313 Mesoprop, 335 Metabolomics, 6, 28, 59, 133 Metastasis-associated protein proﬁles, 233 Methacrylate-based monolithic columns, 149, 150 Methacryloyl-terminated polyethylene oxides, 398 Methionine, see Amino acids, 230, 311, 330, 332 Methyltrimethoxysilane (MTMS), 154, 155 Method development, 20, 30, 95, 127, 128, 130, 133, 135, 139, 144, 433 cardinal rules 132 Micellar electrokinetic chromatography (MEKC or MECC), 20, 21, 29, 106, 107, 350–352, 355, 358, 360, 379 Microchip electrophoresis, 380 Microchip fabrication, 152 Microdialysis interfaces, 105 Microfabricated devices, 348 Microﬂuidics-based chromatography columns, 29 Molar-mass analysis, 392 distribution, 408 Microfraction collector, 375 Molecular topology, 392 Molecular weight search score (MOWSE score), 230 Monolithic columns acrylamide, 150 chromatographic properties of, 156 columns, 149,150, 157, 160, 171 methacrylate-based monolithic columns, 149, 150 polymer columns, 148–152 structure, 150 properties of, 151 silica columns, 149, 154–162 Monosodium glutamate (MSG), 333 Multichannel data, 118 Multicomponent 2D maps, 60, 68, 62, 72, 74

Multidimensional protein identiﬁcation technology (MudPIT), 96, 244, 246, 251–254 mzXML 114 Networks of separators, 30 Nonionic surfactants, 395, 425, 428, 429, 431, 433–435 Nonporous silica (NPS) column, 226–228, 435 Normal-phase liquid chromatography (NPLC), 100, 108, 133, 141, 429, 431, 434–443 amino NPLC column, 432, 433, 436, 437, 438 cyano column, 161, 325–327, 338 chromatograms, 441 silica column, 435, 441 separations, 432 Octylphenol ethoxylate oligomers, 431 Octylphenol ethoxylate sulphate, 428–430 Octylphenoxy terminated PEO, 396, 397 Off-line SCX-online RP/MS/MS, 244, 247, 248 Off-line MDLC, 4, 115, 148, 152, 188, 189, 191, 275–277, 282, 393, 434 Oligomers, 100, 395, 397, 398, 405, 406, 408–410, 414–417, 419, 428–431, 433, 435, 438, 440 see also Polymers On-chip multidimensional analysis, 105, 130 One-dimensional data vectors, 113 method(s), 60, 128, 130, 365 resolution, 17 separation, 35, 224, 429, 435 techniques, 22, 428, 437 theory, 41, 190 Open-tubular splitter capillary, 194 Optically gated interfaces (OGI), 104, 105 Optimization, 132 ﬁrst dimension, 141 Orthogonal chromatography, 12, 19, 263, 292, 390 separations, 12, 191, 200, 203, 214, 267, 268, 272, 293, 295, 365, 377, 380, 428, 435

INDEX

Ovarian surface epithelium (OSE), 231, 232 Overloading effects, 77, 283, 296 PAH compounds, 150, 151, 161 Paper chromatography, 1 Parallel-column chromatography systems, 103, 184 Parallel factor analysis (PARAFAC), 28 Partition free energy, 63, 64 Pattern recognition, 28, 237 Peak capacity, 3, 5, 13–17, 19–23, 27, 35– 50, 60, 72, 74, 93, 108, 129, 130, 152, 155, 158–160, 163, 165, 167, 170, 171, 177, 180–182, 185–187, 189–191, 196, 198, 200, 202, 203, 109, 215, 255, 262–264267–269, 271, 272, 274–276, 280, 283, 284, 295, 299, 300, 303–307, 312, 319, 347, 348, 365, 372, 379, 387, 388 derived from mass spectrometry, 16–17, 35, 284 Peak counting, 69 clustering and ordering 23, 24, 39, 62, 69, 70, 78, 84, 85 overlap, 5, 16, 21–23, 28, 37–39, 41, 74, 83, 88, 134, 272, see also Statistical Model of Overlap (SMO) plotting, 116, 117, 183 Pearson correlation coefﬁcient, 231 Pentabromobenzyloxy propyl-silyl-bonded (PBB), 162 Peptides, 16, 20, 104, 109, 150, 151, 152, 158, 185, 207–217, 243–248311, 312, 349, 351, 352, 365–370, 377, 378, 407, 429 concentrations, 208, 209, 217 mass ﬁngerprint (PMF) analysis, 229, 308 -protein identiﬁcation, 276 resolution of, 261 two-dimensional separations of, 180, 181, 183, 184, 203, 243, 261–284, 366, 372 tryptic peptides, 23, 116, 153, 160, 188, 203, 229, 243, 264, 268, 294, 379, 380 Peptidomics, 207, 208 Phenoxyalkanoic herbicides, 335

453

Phenylalanine, see Amino acids Phenylpropanolamine, 338 Phenylthiocarbamoyl derivatives, 334 Phosphopeptide 248, 254 pI distribution, 84 see also Isoelectric point Pindolol enantiomers, 324 Pipecolic acid, 328 Planar techniques, 3, 11, 388 Plasma, 141, 208, 210, 211, 214, 216, 217, 323, 282, 283, 292 analysis of enantiomers, 319, 323, 328, 334 human 84, 120, 209, 248 Plotting software, 117 Poisson distribution, 11, 12, 272 law, 65, 72 statistics, 41 Polar organic mode, 328 Polyacrylamide gel electrophoresis (PAGE), 60, 79, 80, 96, 141, 222, 347, 348, 350 Polyamides, 407, 408, 412, 413 Polycondensation polymers, 406, 407 Polydispersity indices, 406 Polyester, 406, 407, 414, 415, 417 Polyether polyols, 403 Polyethylene glycol (PEG), 138, 154, 398, 399, 400, 405, 427, 428, 432, 433, 435, 436, 440, 443 Polyethylene oxide (PEO), 23, 395–398, 410, 402, 403, 405, 406, 425–428 Polylactides, 417, 419 Polymer monoliths see Monolithic columns Polymers, aliphatic polyesters, 407, 417 block copolymers, 389, 403–406 complex, 390, 392 condensation polymers, 406, 407 copolymers, 100, 135, 387, 389 EO-PO, 402, 403 EO-VA, 401–403 triblock, 403–406 two-block copolymers, 389 cyclic oligomers, 395, 406, 408, 410, 414–417, 419 macromolecules, 395 macromonomers, 387

454

INDEX

Polymers (Continued) octylphenol ethoxylate, 428–431, 440 octylphenol ethoxylate sulphate, 430 polylactides, 417, 419 polyamides, 406–408,, 410–414, propionic amide-propionic amide polyamides, 412 polycondensation polymers, 406, 407 polyester, 415 polyether polyols, 403 polyethylene glycol (PEG), 427 polyethylene oxide (PEO), 395 methacryloyl-terminated, 398 octylphenoxy terminated, 396, 397 polypropylene oxide (PPO), 405, 426 Polymeric packing materials, 148, 167, 212 Polypropylene oxide (PPO), 405, 426 Poppe plot, 16, 128, 129 Pore-size distribution, 148, 152, 211, 390, 431 Porosity, 148, 149, 151–153, 155–1157 Post-translational modiﬁcation (PTM), 84, 85, 87, 237, 311 Postcolumn derivatization, 322, 328 Product rule, 15 Propionic amide-propionic amide polyamides, 412 Proteins 2, 296, 313, 348, 352 analysis methods, 284 cellular protein homogenates, 350 chromatography, 292, 296, 312 expression, 3, 207, 222, 228, 230–233, 237 heat shock proteins (HSPs), 236, 237 homogenates, 348, 350–352, 355 hydrophobic proteins, 192, 313 indentiﬁcation, 3, 223 intact mass of, 229, 230, 308, 311 immobilized monoliths, 158, 295 mass spectrometric analysis of, 313, 314 membrane proteins, 120, 209, 291, 294, 313 mixed-mode interactions, 296 -peptide workﬂows, 314 proﬁling, 222, 233 ribosomal proteins, 200, 295, 299–303, 305–309, 311 samples, 360

separations with LC/MS, 314 sequencing, 368 SDS complexes, 349, 350 Protein Prospector, 230 Protein TrawlerTM, 229 Proteomics, 6, 16, 20, 22, 27, 28, 59, 79, 81, 85, 114, 117, 118, 160, 186, 191, 203, 207, 208, 210, 223, 229, 230, 237, 243, 244, 251, 254, 255, 261, 264, 291–294, 311–313, 348 bottom-up proteomics, 118, 203 top-down, 118 Quantitative peak intensity, 301 Random access media-strong cationexchange (RAM-SCX), 212, 214, 215, 216, 217 Random distributions, 39, 152, Randomness, 62–65, 67, 68, 78 Refractive index (RI), 100, 109, 110, 398 Rejection algorithm, 81 Resolution in 2D chromatography, 17, 68, 143 peak-valley method, 17 Retention-structure relationships, 62 Retention-time coordinates, 36–38, 50 Retention pattern, 62, 65–70, 72, 74, 77, 88 Reversed-phase liquid chromatography (RPLC), 3, 13, 20, 21, 23, 100, 104, 106, 108, 109, 117, 130, 132, 133, 139–142, 164, 180–188, 192, 193, 195, 196, 198, 199, 202, 216, 244, 265, 276, 280, 293, 294, 312, 313, 333, 336, 368, 370–375, 380, 408, 426, 428, 429, 433–442 column, 226, 254 gradient, 193 random-access media (RAM), 211 Ribosomal proteins, 306, 307, 300, 302 Roach equation, 42, 47, 49 S-ketorolac enantiomer, 323 Sample loops, 24–26, 29, 30, 94, 95, 99– 103, 105, 110–112, 131, 132, 134, 137, 139, 161, 178, 193, see also Storage loops Sampling, see Zone sampling Saturation factor, 72, 74, 77,

INDEX

Scanning electron micrographs (SEM), 149, 154, 155 Schultz–Flory distribution, 406 SDS-polyacrylamide gel electrophoresis, see Sodium dodecyl sulfate polyacrylamide gel electrophoresis SDS-protein complexes, 350 Second-dimension column(s), 97, 103, 104, 111, 133, 139, 144 efﬂuent, 297 elution process, 112 elution time, 132, 137 pumping system, 297 Separation efﬁciency, 166 extent, 70, 77 impedance, 157 parameters, 68, 81, 84, 85, 231, 267 techniques, 1, 2, 13, 60, 96, 143, 187, 224, 248, 261, 385, 428 time, 13, 155, 161, 163, 165, 170, 203, 215, 227, 306, 356, 399 Serum, 98, 99, 210, 222, 234, 247, 261, 282, 283, 284, 292, 324, 328, 367, 370, 377 Serum-free conditioned media, 234 Sheath-ﬂow cuvette, 353, 354 Signal processing, 22, 88, 110, 119 Silica-based restricted access materials (RAM), 210 Single-dimension methods, 388 Single ﬁxed cell, 358 Six-port valve, 94–96, 98, 99, 104, 108, 161, 162, 193, 371 Size exclusion chromatography (SEC), 4, 98–100, 104, 108, 109, 130, 133, 137–139, 148, 160, 178, 179, 181, 183–188, 212, 248, 265–268, 273–276, 293, 352, 379, 389–394, 397, 398, 402, 403, 405–407, 426 columns, 178, 181 with reversed-phase, 183 process, 212 separations, 178, 185 Small-molecule metabolite proﬁles, 6 Sodium dodecyl sulfate (SDS), 2, 141, 347 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS/PAGE), 80–82, 141, 347–350

455

Sol-gel processes, 148 Solid phase extraction (SPE), 229, 322, 333, 369, 370, Speed-resolution trade-off, 143 Split injection/ﬂow system, 373 Spot overlapping degree, 67, 82, 85 Statistical degree of overlapping (SDO), 81–84 Statistical model of overlap (SMO), 40, 47, 65, 68, 71–73, 81–83, 85, 88 Poisson statistics, 68 Step-gradient chromatography, 304, 305, 311, Stopped-ﬂow method, 130 Storage loops, 178, 181, 183, 184, 194, 196 see also Sample Loops Strong cation-exchange (SCX), see Cationexchange Supercritical ﬂuid chromatography (SFC), 5, 20, 21, 338 Surfactants, 2, 6, 23, 100, 102, 108, 109, 143, 296, 349, 360, 395, 398, 425–429, 431–435 alcohol ethoxylate surfactant, 23, 426 BrijTM 398, 427, 432, 433, 435, 438–440, 443 ionic, 349, 360, 426, 428 NeodolTM surfactants, 438 nonionic, 395, 425, 428, 429, 431, 433–435, PEG, 427 TritonTM, 225, 426, 431, 432 zwitterionic, 4, 426, 434 SwissProt (SWISS-2D-PAGE database), 2, 81, 84, 229 Synovial ﬂuid, 208 System optimization, 218 Tablet coating, 400, 401 Tandem mass spectrometry (MS/MS), 2, 96, 116, 203, 208, 243–249, 251, 254,, 255, 261–265, 275, 276, 280– 282, 284, 292, 294, 308, 311, 313, 314, 324, 368, 370, 434 Temperature-programmed gas chromatography, 14 Ten-port valve, 94, 96, 99, 100, 102, 161, 297 t-butyl methacrylate, 389

456

INDEX

Tetraethoxysilane (TEOS), 154 Tetrahydrofuran (THF), 138, 152, 164– 166, 394, 402, 414, 427, 431 Tetramethoxysilane (TMOS), 154, 155, 157 Thin-layer chromatography (TLC), 11, 12, 17, 96, 388, 389 Three-dimensional (3D) separation, 4, 19, 65, 218, 262 Time-of-Flight (TOF) mass spectrometry, 16, 83, 152, 167, 168, 186, 192, 194, 210, 214, 217, 228–230, 235, 236, 247, 249, 254, 293, 308, 311, 314, 379 396, 398, 403, 408, 410–414, 419 see also Detectors Tissue lysate preparation, 225 Transistor-transistor logic, 111 Trapping column, 160, 216, 217, 246, 248, 251, 254, 292, 297–299, 305, 322, 323, 398 Triblock copolymer(s), 403, 404, 405 Triﬂuoroacetic acid (TFA), 136, 142, 153, 228, 229, 267, 300, 301, 338, 376, 378 Triphasic column, 246, 254 Tryptic peptides, 153, 229, 264–271, 274, 294, 379 Twelve-port valve, 102, 103 Two-block copolymers, 389 Two-dimensional (2D) gel electrophoresis, 2, 209, 347, 348 electrophoresis, 141, 355, 358–360 chromatography, 127, 254, 392 gas chromatography (2DGC), 36, 118 HPLC/CE separations, 379 -ion mobility mass spectrometry (2D-IMS), 16 liquid mass mapping, 221, 230 planar techniques, 96

separation space, 306 theory of peak overlap, 28, 40 see also Statistical Model of Overlap (SMO) -thin-layer chromatography (2DTLC), 11, 96 Ultrahigh pressure liquid chromatography (UHPLC), 147, 160, 171, 190–192, 196, 202, 203 Ultraviolet (UV) detection, 3, 109, 186, 196, 197, 212, 226, 322, 376, 430, 434 Urine, 208, 214, 217, 319, 323, 324, 326, 328, 334, 374, 375 Valves eight-port, 100, 102, 179 four-port, 100, 183, 194 four-position, 111 microﬂuidic, 105 sequencing, 111 six-port valves, 94–96, 98, 99, 104, 108, 161, 162, 193, 371 ten-port, 94, 96, 99, 100, 102, 161, 297 twelve-port, 102, 103 valve-free gating interface, 380 Van Deemter equation, 150, 156, 190 Vinyl acetate (VAc), 401, 402 Void volume, 265, 277 Yeast alcohol dehydrogenase, (ADH), 265 Zirconia material, 133 Zone dilution, 26–27, 108 sampling, 15, 24–26, 129, 132–136, 144 visualization, 115, 117 Zwitterionic detergents, 4

FIGURE 1.2 2D liquid protein expression map of the HCT-116 human colon adenocarcinoma cell line. (See text for full caption.)

FIGURE 4.1 2D polyacrylamide gel electrophoresis (2D-PAGE) maps of protein mixtures. (See text for full caption.)

FIGURE 4.3 Example of 1D separations as the result of five superimposed homologous series having (a) constant phase (red arrow in inset) and (b) constant frequency (blue arrow in inset).

FIGURE 4.4 Example of 2D separations of five homologous series having (a) constant phase (red vector in inset) and uncorrelated frequencies and (b) constant frequency (blue vector in inset) and uncorrelated phases.

FIGURE 4.13 Identification of a train of spots by the 2D autocovariance function method. (See text for full caption.)

FIGURE 5.16 Mass spectrometry data and the corresponding mass spectrum of a selected spot. Figure courtesy of Kroungold Analytical (2007).

FIGURE 8.14 2D chromatograms from AEX RPLC separations of an E. coli lysate using different anion-exchange gradient lengths. The scale at the right-hand side of the figure represents the signal intensity, as measured by the total ion current (TIC) from the mass spectrometer. Parts (a), (b), and (c) represent anion exchange gradient lengths of 30, 60 and 120 min, respectively, all plotted using the same intensity scale range. Part (d) is the same chromatogram as Part (c), except that the intensity scale range has been altered to enhance peak visibility (see the text for explanation). Chromatograms have been cropped to show only the separation space in which proteins were found to elute.

FIGURE 10.2 2D map of a whole cell lysate (top) along with an illustration of the reproducibility of the pI versus hydrophobicity profiling technique (bottom) using the Beckman PF2D automated instrument and software.

FIGURE 10.6 2D differential display of CF fractions from M4A4 and NM2C5 secreted samples. These fractions ranged in pH from 5.6 to 5.4. The differential display map was created using point-by-point subtraction of the areas of the deconvoluted peaks in the TIC.

FIGURE 12.2 full caption.)

Normalized retention time plots for investigated 2DLC systems. (See text for

FIGURE 13.8 Duplicate 2DLC(SAX/RP)/MS analyses of an E. coli cytosolic fraction were conducted using either a linear (Panel a) or step gradient (Panel b) IEX gradient first-dimension. (See text for full caption.)

FIGURE 17.2 Schematic flow sheet of a 2D separation using a HPLC system in the first dimension and a SEC system in the second dimension (Kilz and Pasch, 2000).

FIGURE 17.4 Configuration of an automatic fraction transfer valve.

FIGURE 17.7 Contour plot of the two-dimensional separation of an octylphenoxy terminated PEO sample (reprinted from Adrian et al., 1998, with permission of Advanstar Communications, UK).

FIGURE 17.13 LCCC analysis of PEO and PEO-g-PVA, stationary phase: Nucleosil C18, mobile phase: MeOH:H2O 82.5:17.5% by volume, samples: PEO (black), copolymer 1 (red) and 2 (blue).

FIGURE 17.14 2D separation of PEO-g-PVA copolymer 2, first dimension: LCCC, second dimension: SEC (reprinted from Gutzler et al., 2005, with permission of Wiley-VCH Publishers, Germany).

FIGURE 17.16 2DLC separation of a PEO–PPO–PEO triblock copolymer, first dimension: LCCC, second dimension: SEC.

FIGURE 17.19 2D contour plot of a modified polyamide 6, first dimension: LCCC, second dimension: SEC. Area 1: unmodified chains, area 2: modified chains, area 3: cyclic oligomers.

FIGURE 17.21 2DLC contour plot of a modified polyamide 6.6, first dimension: LCCC, second dimension: SEC, experimental conditions; see discussion of Fig. 17.19.

FIGURE 17.23 2D contour plot of a propionic acid modified polyamide 6.6 (assignments according to Table 17.1), first dimension: LCCC, second dimension: MALDI–TOF.

FIGURE 17.24 2DLC plots of a virgin polycarbonate (a) and a hydrolytically degraded polycarbonate (b) first dimension: LCCC, second dimension: SEC (from Coulier et al., 2005; with permission from Elsevier).