Lipidomics: Volume 1 (Methods in Molecular Biology, v579)

Methods

in

Molecular Biology™

Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK



For other titles published in this series, go to www.springer.com/series/7651

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Lipidomics Volume 1: Methods and Protocols

Edited by

Donald Armstrong University at Buffalo, Buffalo, NY, USA University of Florida, Gainesville, FL, USA

Editor Donald Armstrong University of Buffalo Buffalo, NY, USA and University of Florida Gainesville, FL, USA

The cover image are overlapping snapshots of a single peroxidized lipid, taken from computer stimulations. Peroxidation mofidies the local conformational preferences of acyl chains and increases their mobility, with implications for structural and dynamic properties of the membrane. ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-60761-321-3 e-ISBN 978-1-60761-322-0 DOI 10.1007/978-1-60761-322-0 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009927725 © Humana Press, a part of Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or ­dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, ­neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Cover illustration: Snapshots of a single PLPC and a peroxidized analogue (13-tc), taken at 5 ns intervals. Molecules are oriented along the membrane and superimposition was done on the phosphorus and oxygen atoms. Images were obtained from Chapter 18, Vol. 1. Cover design: Karen Schulz Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

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Preface Lipidomics is a sub-discipline of metabolomics and is defined as the large-scale study of non-water-soluble metabolites (lipids and lipidome) that utilize system-level analysis to characterize lipids and their interacting moieties (1). A literature search at the end of 2008 showed there were 200 articles published on lipidomics encompassing glycerophospholipids, sphingolipids, polyunsaturated fatty acids, glycolipids, sterol lipids, and proteolipids. It has been predicted that the combination of these lipid classes totals between 1,000 and 2,000 molecules. Lipids can also act as s­econd messengers, or mitogens, and participate in profiling and signaling via specialized microdomains that have large amounts of lipids. When lipids are disturbed, their metabolites probably contribute to disease. In prostate cancer, for example, cyclooxygenase and lipooxygenase are upregulated reducing angiogenesis and tumor growth. Early separation and identification of lipids started with TLC, and as technology advanced, it progressed to the use of GC and HPLC. Technical improvements to HPLC include reversed-phase methods, ESI, evaporative light scattering, electrochemical detection, APCI, suppressed conductivity and multi-dimensional electrophoresis. Other ­technologies coupled to chromatographic methods, such as MS/MS-MSn/MALDI/ TOF, NMR, and MRM, provide a powerful approach to the global analysis of complex lipid mixtures, understanding structural changes through biophysical approaches and the effects of lipids on physiology, i.e. atherosclerosis. This has given us a clearer understanding of human and animal pathology, i.e. diabetes, cancer, neurodegeneration, and infectious disease. A new approach to measure oxidized lipids, referred to as “oxidative lipidomics,” has recently been described which provides methodology for separation and identification of these highly reactive lipids, especially in mitochondria. Many novel techniques are described in these volumes, including an imaging lipidomics approach. For another lipidomic approach of a lipid-derived radical technique, the reader is referred to Iwabashi, H., 2008. Advanced Protocols in Oxidative Stress I, volume 477, Chapter 6, Humana Press. In that same volume, a lipidomics technique for sphingolipids is also described, i.e. Wilder, AJ and Cowart, LA, Chapter 28. The present volumes have taken a “shotgun” approach and are divided into seven parts in order to include as many different varieties of technology as possible. Chapters by international experts present a wide variety of reviewed as well as unpublished data on ­isolation techniques, structural analysis, lipid rafts, lipid trafficking and profiling, biomarkers, lipid peroxidation, biostatistics applied to lipids, software tools, and bioinformatics. These studies range from simple systems, such as in yeast, to complex biological models. The ever increasing utilization of lipidomics will lead to more powerful technology, improved diagnostic–prognostic capabilities for medical disorders, and for the identification of new classes of lipids.

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I thank my son, Dennis Armstrong, and my grandson, David Armstrong of On-Staff Technology, Inc., for assistance with technical support, information technology, and multi-media services. Buffalo, NY Gainesville, FL

Donald Armstrong

Reference 1. Wenk, M.R. 2005. The emerging field of lipidomics. Nature Reviews 4: 595–610.





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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part I  Shotgun and Global Lipidomics   1  Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael A. Kiebish, Xianlin Han, and Thomas N. Seyfried   2  Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid by Shotgun Lipidomics . . . . . . . . . . . . . . . . . . . . . . . . Todd W. Mitchell   3  Global Analysis of Retina Lipids by Complementary Precursor Ion and Neutral Loss Mode Tandem Mass Spectrometry . . . . . . . . . . . Julia V. Busik, Gavin E. Reid, and Todd A. Lydic   4  Combining Lipidomics and Proteomics of Human Cerebrospinal Fluids . . . . . . . Alfred N. Fonteh and Rachel D. Fisher

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Part II Analytical Approaches   5  Lipid Profiling Using Two-Dimensional Heteronuclear Single Quantum Coherence NMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engy A. Mahrous, Robin B. Lee, and Richard E. Lee   6  Capabilities and Drawbacks of Phospholipid Analysis by MALDI-TOF Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beate Fuchs, Ariane Nimptsch, Rosmarie Süß, and Jürgen Schiller   7  Lipidomics of the Red Cell in Diagnosis of Human Disorders . . . . . . . . . . . . . . . Peter J. Quinn , Dominique Rainteau , and Claude Wolf   8  HPLC/MS/MS-Based Approaches for Detection and Quantification of Eicosanoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Susanna L. Lundström, Fabio L. D´Alexandri, Kasem Nithipatikom, Jesper Z. Haeggström Åsa M. Wheelock, and Craig E. Wheelock   9  Brain Phosphoinositide Extraction, Fractionation, and Analysis by MALDI-TOF MS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roy A. Johanson and Gerard T. Berry 10  Lipidomic Analysis of Biological Samples by Liquid Chromatography Coupled to Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . Giuseppe Astarita, Faizy Ahmed, and Daniele Piomelli 11  Lipidomic Analysis of Human Meibum Using HPLC–MS . . . . . . . . . . . . . . . . . . Igor A. Butovich

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12  Lipid Geographical Analysis of the Primate Macula by Imaging Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timothy J. Garrett and William W. Dawson 13  A Novel Role for Nutrition in the Alteration of Functional Microdomains on the Cell Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wooki Kim , Robert S. Chapkin, Rola Barhoumi, and David W.L. Ma 14  Lipidomic Analysis of Prostanoids by Liquid Chromatography–Electrospray Tandem Mass Spectrometry . . . . . . . . . . . . . . . . . Anna Nicolaou, Mojgan Masoodi, and Adnan Mir 15  Qualitative and Quantitative Analyses of Phospholipids by LC–MS for Lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hiroki Nakanishi, Hideo Ogiso, and Ryo Taguchi 16  Determination of Fatty Acid Profiles and TAGs in Vegetable Oils by MALDI-TOF/MS Fingerprinting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zeev Wiesman and Bishnu P. Chapagain 17  Dynamics of Adipose Tissue Development by 2 H 2O Labeling . . . . . . . . . . . . . . . Etienne Pouteau, Carine Beysen, Nabil Saad, and Scott Turner 18  Analysis of Lipid Particles from Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Melanie Connerth, Karlheinz Grillitsch, Harald Köfeler, and Günther Daum 19  Mammalian Fatty Acid Elongases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Donald B. Jump 20  Membrane Lipidomics and the Geometry of Unsaturated Fatty Acids : From Biomimetic Models to Biological Consequences . . . . . . . . . . . . . . . . . . . . . Carla Ferreri and Chryssostomos Chatgilialoglu 21  OnLine Ozonolysis Methods for the Determination of Double Bond Position in Unsaturated Lipids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael C. Thomas, Todd W. Mitchell, and Stephen J. Blanksby 22  Comprehensive Quantitative Analysis of Bioactive Sphingolipids High-Performance Liquid Chromatography-Tandem Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jacek Bielawski, Jason S. Pierce, Justin Snider, Barbara Rembiesa, Zdzislaw M. Szulc, and Alicja Bielawska 23  The Use of Charged Aerosol Detection with HPLC for the Measurement of Lipids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marc Plante, Bruce Bailey, and Ian Acworth

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Part III Lipid Maps 24  Non-invasive Mapping of Lipids in Plant Tissue Using Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Thomas Neuberger, Hardy Rolletschek, Andrew Webb, and Ljudmilla Borisjuk



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25  Mapping the Lipolytic Proteome of Adipose Tissue Using Fluorescent Suicide Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Maximilian Schicher, Manfred Kollroser, and Albin Hermetter 26  Identifying the Spatial Distribution of Vitamin E, Pulmonary Surfactant and Membrane Lipids in Cells and Tissue by Confocal Raman Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 J. Renwick Beattie and Bettina C. Schock Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

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Contributors Ian Acworth  •  ESA Biosciences, Inc., Chelmsford, MA, USA Faizy Ahmed  •  Agilent Technologies, Irvine/Agilent Analytical Discovery Facility, University of California – Irvine, Irvine, CA, USA Giuseppi Astarita  •  Department of Pharmacology, University of California – Irvine, Irvine, CA, USA Bruce Bailey  •  Applications Department, ESA Biosciences, Inc., Chelmsford, MA, USA Rola Barhoumi  •  Department of Nutritional Sciences, University of Toronto Faculty of Medicine, Toronto, ON, Canada; Center for Environmental and Rural Health, Texas A&M University, College Station, TX, USA J. Renwick Beattie  •  School of Medicine, Dentistry and Biomedical Sciences, Queen’s University, Belfast, UK Gerard T. Berry  •  Division of Genetics, Children’s Hospital, Boston, MA, USA Carine Beysen  •  KineMed, Inc., Emeryville, CA, USA Alicja Bielawska  •  Department of Biochemistry and Molecular Biology, Lipidomic Core Mass Spectrometry Lab, Medical University of South Carolina, Charleston, SC, USA Jacek Bielawski  •  Department of Biochemistry and Molecular Biology, Lipidomic Core Mass Spectrometry Lab, Medical University of South Carolina, Charleston, SC, USA Stephen J. Blanksby  •  School of Chemistry, University of Wollongong, Wollongong, NSW, Australia Ljudmilla Borisjuk  •  Leibniz-Institut fur Pflanzengenetik und Kulturpflanzenforschung (IPK), Gaterslaben, Germany Julia V. Busik  •  Department of Physiology, Michigan State University, East Lansing, MI, USA Igor A. Butovich  •  Department of Ophthalmology, University of Texas Southwestern Medical Center, Dallas, TX, USA Bishnu P. Chapagain  •  The Phyto-Lipid Biotechnology Laboratory, Department of Biotechnology Engineering, The Institutes for Applied Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel Robert S. Chapkin  •  Program in Integrative Nutrition & Complex Diseases, Genomics & Bioinformatics Facility Core Center for Environmental and Rural Health, Kieberg Biotechnology Center, Texas A&M University, College Station, TX, USA Chryssostomos Chatgilialoglu  •  ISOF, Consiglio Nazionale delle Ricerche, Bologna, Italy Melanie Connerth  •  Institute of Biochemistry, Graz University of Technology, Austria

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Fabio Luiz D’Alexandri  •  Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Stockholm, Sweden; Department of Parasitology, Department of Biochemical Sciences, University of San Paulo, San Paulo, Brazil Günther Daum  •  Institute of Biochemistry, Graz University of Technology, Graz, Austria William W. Dawson  •  Department of Ophthalmolgy, College of Medicine, University of Florida Health Sciences Center, Gainesville, FL, USA Carla Ferreri  •  ISOF-BioFree Radicals, Consiglio Nazionale delle Riceriche, Bologna, Italy Rachel D. Fisher  •  Scripps College, Claremont, CA, USA Alfred N. Fonteh  •  Molecular Neurology Program, Huntington Medical Research Institutes, Pasadena, CA, USA Beate Fuchs  •  Medical Department, Institute of Medical Physics and Biophysics, University of Leipzig, Leipzig, Germany Timothy J. Garrett  •  Department of Medicine, College of Medicine, University of Florida Health Science Center, Gainesville, FL, USA Karlheinz Grillitsch  •  Institute of Biochemistry, Graz University of Technology, Graz, Austria Jesper Z. Haeggström  •  Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Stockholm, Sweden Xianlin Han  •  Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA Albin Hermetter  •  Institute of Biochemistry, Graz University of Technology, Graz, Austria Roy A. Johanson  •  Department of Neurology, Thomas Jefferson University Medical Center, Philadelphia, PA, USA Donald B. Jump  •  Department of Nutrition and Exercise Sciences, The Linus Pauling Institute, Oregon State University, Corvallis, OR, USA Michael A. Kiebish  •  Biology Department, Boston College, MA, USA Wooki Kim  •  Department of Nutritional Sciences, University of Toronto Faculty of Medicine, Toronto, ON, Canada; Center for Environmental and Rural Health, Texas A&M University, College Station, TX, USA Harald Köfeler  •  Institute of Biochemistry, Graz University of Technology, Graz, Austria Manfred Kollroser  •  Institute of Forensic Medicine, Medical University of Graz, Graz, Austria Richard E. Lee  •  Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA Robin B. Lee  •  Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Sciences Center, Memphis, TN, USA Susanna L. Lundström  •  Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institute, Stockholm, Sweden Todd A. Lydic  •  Department of Physiology, Michigan State University, MI, USA David W.L. Ma  •  Department of Nutritional Sciences, University of Toronto Faculty of Medicine, Toronto, ON, Canada; Center for Environmental and Rural Health, Texas A&M University, College Station, TX, USA



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Engy A. Mahrous  •  Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Sciences Center, Memphis, TN, USA Mojgan Masoodi  •  Division Pharmaceutics and Pharmaceutical Chemistry, School of Pharmacy, Life Sciences, University of Bradford, West Yorkshire, UK Adnan Mir  •  Division Pharmaceutics and Pharmaceutical Chemistry, School of Pharmacy , Life Sciences, University of Bradford, West Yorkshire, UK Todd W. Mitchell  •  School of Health Sciences, University of Wollongong, Wollongong, NSW, Australia Hiroki Nakanishi  •  Department of Metabolome, Graduate School of Medicine, The University of Tokyo and Core Research for Evolutional Science and Technology, Saitama, Japan Thomas Neuberger  •  Department of Bioengineering, Pennsylvania State University, University Park, PA, USA Anna Nicolaou  •  Division of Pharmaceutics and Pharmacological Chemistry, School of Pharmacy, Life Sciences, University of Bradford, West Yorkshire, UK Ariane Nimptsch  •  Medical Department, Institute of Medical Physics and Biophysics, University of Leipzig, Leipzig, Germany Kasem Nithipatikom  •  Department of Pharmacology and Toxicology, Medical College of Wisconsin, WI, USA Hideo Ogiso  •  Department of Metabolome, Graduate School of Medicine, The University of Tokyo and Core Research for Evolutional Science and Technology, Saitama, Japan Jason S. Pierce  •  Department of Biochemistry and Molecular Biology, Lipidomic Core Mass Spectrometry Lab, Medical University of south Carolina, Charleston, SC, USA Daniele Piomelli  •  Department of Pharmacology, University of California – Irvine, Irvine, CA, USA Marc Plante  •  Applications Department , ESA Biosciences, Inc., Chelmsford, MA, USA Etienne Pouteau  •  Nutrition and Health Department, Nestle Research Center, Lausanne, Switzerland Paul J. Quinn  •  Life Sciences, King’s College, London, UK Dominique Rainteau  •  UMRS 893-Faculte de Medicine, Pierre et Marie Curie, CHU Saint Antoine, Paris, France Gavin E. Reid  •  Departments of Chemistry, Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA Barbara Rembiesa  •  Department of Biochemistry and Molecular Biology, Lipidomics Core Mass Spectrometry Lab, Medical University of South Carolina, Charleston, SC, USA Hardy Rolletschek  •  Leibniz-Institut fur Pflanzengenetik und Kulturpflanzenforschung ( IPK ), Gaterslaben, Germany Nabil Saad  •  KineMed, Inc., Emeryville, CA, USA Maximilian Schicher  •  Institute of Biochemistry, University of Graz, Graz, Austria Jürgen Schiller  •  University of Leipzig Medical Faculty, Leipzig, Germany Bettina C. Schock  •  School of Medicine, Dentistry and Biomedical Sciences, Queen’s University of Belfast, Belfast, UK

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Contributors

Thomas N. Seyfried  •  Department of Biology, Boston College, Chestnut Hill, MA, USA Justin Snider  •  Department of Biochemistry and Molecular Biology, Lipidomics Core Mass Spectrometry Lab, Medical University of South Carolina, Charleston, SC, USA Rosmarie Süß  •  Medical Department, Institute of Medical Physics and Biophysics, University of Leipzig, Leipzig, Germany Zdzislaw M. Szulc  •  Department of Biochemistry and Molecular Biology, Lipidomics Core Mass Spectrometry Lab, Medical University of South Carolina, Charleston, SC, USA Ryo Taguchi  •  Department of Metabolome, Graduate School of Medicine, The University of Tokyo and Core Research for Evolutional Science and Technology, Saitama, Japan Michael C. Thomas  •  School of Chemistry and Health Sciences, University of Wollongong, Wollongong, NSW, Australia Scott Turner  •  KineMed, Inc., Emeryville, CA, USA Andrew Webb  •  Department of Bioengineering, Pennsylvania State University, PA, USA Åsa M. Wheelock  •  Lung Research Lab L4:01, Respiratory Medicine Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden Craig E. Wheelock  •  Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Stockholm, Sweden Zeev Wiesman  •  Phyto-Lipid Biotechnology Lab, Department of Biotechnology Engineering, The Institutes for Applied Research, Ben Gurion University of the Negrev, Beer-Sheva, Israel Claude Wolf  •  HDR, Laboratoire de Spectrometrie de Masse, Faculte de Medicine, Pierre et Marie Curie, Universite Paris, Paris, France

Part I Shotgun and Global Lipidomics

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Chapter 1 Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics Michael A. Kiebish, Xianlin Han, and Thomas N. Seyfried Summary  Contamination from subcellular organelles and myelin has hindered attempts to characterize the lipidome of brain mitochondria. A high degree of mitochondrial purity is required for accurate measurements of the content and molecular species composition of mitochondrial lipids. We devised a discontinuous Ficoll and sucrose gradient procedure for the isolation and purification of brain mitochondria free from any detectable contamination. Shotgun lipidomics was used to analyze the lipid composition of the brain mitochondria. These procedures can be used to determine whether intrinsic lipid abnormalities underlie mitochondrial dysfunction associated with neurological and neurodegenerative diseases. Key words:  Nonsynaptic, Synaptic, Mitochondria, Brain, Lipidome

1. Introduction The brain contains two major populations of mitochondria that can be isolated by discontinuous gradients. These populations include the nonsynaptic (NS) mitochondria, which are largely derived from neuronal and glial cell bodies, and the synaptic (Syn) mitochondria, which originate from the synaptic nerve terminal of neurons (1, 2). These mitochondria populations differ in calcium homeostasis, enzymes involved in the TCA cycle, electron transport chain activities, and glutamate metabolism (2–5). Differences between NS and Syn mitochondria may underlie the complexity of neural metabolism (1). A better understanding of brain bioenergetics can be obtained with accurate information on the mitochondrial lipidome. In contrast to extensive research on mitochondria DNA and proteins in disease pathology, little attention has been focused on Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, DOI 10.1007/978-1-60761-322-0_1, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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the role of mitochondrial membrane lipids (6). Mitochondrial lipids can influence electron transport chain activities and create a membrane environment conducive for an efficient proton gradient (7). In addition, mitochondrial lipids regulate membrane fluidity and can affect the morphological structure of mitochondria (8, 9). Also, lipid composition can influence numerous mitochondrial enzyme activities to include creatine kinase, adenine nucleotide transporter, voltage dependent ion channel, and carnitine acyltransferase (7, 10, 11). Thus, the maintenance of the mitochondrial lipidome is critical for mitochondrial functionality. Shotgun lipidomics uses a high-throughput platform which allows for the rapid quantitation of lipid molecular species using multidimensional mass spectrometry (12). This lipidomic approach has the capabilities of detecting glycerophospholipid subclasses which contain alkyl ether, vinyl ether, or ester linkages (13–15). In addition, shotgun lipidomics has demonstrated increased detection sensitivity to quantitating cardiolipin, a mitochondrial specific phospholipid (16, 17). Cardiolipin is a complex phospholipid, containing four acyl chains, three glycerol moieties, and two phosphate groups (8). We also used shotgun lipidomics and tandem MS to measure all detectable molecular species of cardiolipin in the C57BL/6J mouse brain (1). Difficulty in removing subcellular contamination from brain mitochondrial populations has hindered the accurate analysis of mitochondrial lipid composition. This is especially the case for lipid rich myelin membranes, which can remain bound to brain mitochondria thus distorting analysis of lipid composition and content. Mitochondria can be isolated by Percoll, Nycodenz, metrizamide, Ficoll, and sucrose discontinuous gradients, however, the purity of these mitochondrial preparations differ depending on the intended use of these mitochondrial fractions (18–24). We used discontinuous Ficoll and sucrose gradients to isolate NS and Syn brain mitochondria free from detectable contamination. This procedure will be useful for analyzing differences in brain mitochondria in a variety of neurodegenerative and neurological disorders that involve alterations in energy metabolism.

2. Materials 2.1.Equipment (See Also Note 1)

  1. Ultracentrifuge.

2.2. Reagents

  1. Mitochondrial isolation buffer. 0.32 M sucrose, 10 mM Tris–HCl, and 1 mM EDTA–K (pH 7.4).

2.2.1. Mitochondrial Isolation

  2. Mass Spectrometer.

  2. Nanopure deionized water.



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3. TE buffer (1 mM EDTA–K and 10 mM Tris–HCl, pH 7.4). 4. 20% Ficoll stock solution made with MIB, 12% and 7.5% Ficoll working solutions. 5. 1.6 M sucrose stock containing 1 mM EDTA–K and 10 mM Tris–HCl (pH 7.4); 0.8 M, 1.0 M, 1.3 M, and 1.6 M sucrose gradient working solutions. 6. Bovine serum albumin (BSA). 7. Mitochondrial isolation buffer containing 0.5 mg/mL BSA. 8. 6 mM Tris–HCl (pH 8.1). 2.2.2. Mass Spectrometry

1. 1,2-dimyristoleoyl-sn-glycero-3-phosphocholine (14:1–14:1 PtdCho). 2. 1,2-dipalmitoleoyl-sn-glycero-3phosphoethanolamine (16:1–16:1 PtdEtn). 3. 1,2-dipentadecanoyl-sn-glycero-3-phosphoglycerol (15:0–15:0 PtdGro). 4. 1,2-dimyristoyl-sn-glycero-3-phosphoserine (14:0–14:0 PtdSer). 5. N-lauroryl sphingomyelin (N12:0 CerPCho). 6. 1,1¢,2,2¢-tetramyristoyl cardiolipin (T14:0 Ptd2Gro). 7. Heptadecanoyl ceramide (N17:0 Cer). 8. 1-heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine (17:0 LysoPtdCho). 9. Deuterated cholesterol (d6-chol). 10. Chloroform. 11. Methanol. 12. Lithium chloride.

3. Methods 3.1. Brain Mitochondrial Isolation

Mice of the C57BL/6 strain (4 months of age) were sacrificed by cervical dislocation and the cerebral cortex was dissected. Mitochondria were isolated in a cold room (4°C) and all reagents were kept on ice. The isolation procedure employed a combination of gradients and strategies as previously described (2–4, 25–28) (Fig. 1). The cerebral cortexes (a pool of 6 per sample) were diced on an ice-cold metal plate and then placed in 12 mL of mitochondria isolation buffer (MIB; 0.32 M sucrose, 10 mM Tris–HCl, and 1 mM EDTA–K (pH 7.4)). The pooled cerebral cortexes were homogenized using a Potter Elvehjem homogenizer with a Teflon coated pestle attached to a hand-held drill.

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Fig. 1. Procedure used for the isolation and purification of NS and Syn mitochondria from mouse cerebral cortex.



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Samples were homogenized using 15 up and down strokes at 500 rpm. The homogenate was centrifuged at 1,000 × g for 5 min. The supernatant was collected and the pellet was washed twice by centrifugation, collecting the supernatants each time. The supernatants were pooled and centrifuged at 1,000 × g for 5 min. The collected supernatant was then spun at 14,000 × g for 15 min. The supernatant was discarded and the pellet, which contained primarily NS mitochondria, synaptosomes, and myelin, was resuspended in 12 mL MIB and was layered on a 7.5%/12% discontinuous Ficoll gradient. Each Ficoll gradient layer contained 12 mL for a total volume of 36 mL. The Ficoll gradients were made from a 20% Ficoll stock with MIB. The gradient was centrifuged at 73,000 × g for 36 min (4°C) in a Sorvall SW 28 rotor with slow acceleration and deceleration (Optima L-90K Ultracentrifuge). The centrifugation time used permitted sufficient acceleration and deceleration to achieve maximum g force (28), and to prevent synaptosomal contamination of the mitochondrial fraction below the 12% Ficoll layer. Crude myelin collected at the MIB/7.5% Ficoll interface was discarded. Synaptosomes were collected at the 7.5%/12% interface and were resuspended in MIB and centrifuged at 16,000 × g for 15 min. The Ficoll gradient-purified NS mitochondria (FM) were collected as a pellet below 12% Ficoll. 3.2. Mitochondria Purification 3.2.1. Purification of NS Mitochondria

3.2.2. Purification of Syn Mitochondria

The FM pellet, containing NS mitochondria, was resuspended in MIB containing 0.5 mg/mL bovine serum albumin (BSA) and was centrifuged at 12,000 × g for 15 min. The resulting pellet was collected and resuspended in 6 mL of MIB. The resuspended FM pellet was layered on a discontinuous sucrose gradient containing 0.8 M/ 1.0 M/ 1.3 M/ 1.6 M sucrose. The volumes for the sucrose gradient were 6 mL/ 6 mL/ 10 mL/ 8 mL, respectively. The gradients were made from a 1.6 M sucrose stock containing 1 mM EDTA–K and 10 mM Tris–HCl (pH 7.4). The discontinuous sucrose gradient was centrifuged at 50,000 × g for 2 h (4°C) in a Sorvall SW 28 rotor using slow acceleration and deceleration to prevent disruption of the gradient. Purified NS mitochondria were collected at the interface of 1.3 M and 1.6 M sucrose. NS mitochondria were collected and resuspended in (1:3, v/v) TE buffer (1 mM EDTA–K and 10 mM Tris–HCl, pH 7.4) containing 0.5 mg/mL BSA and centrifuged at 18,000 × g for 15 min. The pellet was then resuspended in MIB and centrifuged at 12,000 × g for 10 min. The pellet was again resuspended in MIB and centrifuged at 8,200 × g for 10 min. Synaptosomes were burst by homogenization in 6 mM Tris–HCl (pH 8.1) using five up and down strokes. The homogenized synaptosomes were transferred to a 15 mL conical tube and then placed on a rocker for 1 h (4°C). The burst synaptosomes were

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centrifuged at 10,000 × g for 10 min. The pellet was resuspended in 6 mL of MIB. The resuspended pellet was layered on a discontinuous sucrose gradient and centrifuged following the same procedure as described above for NS mitochondria. 3.3. Mass Spectrometry 3.3.1. Sample Preparation for Mass Spectrometric Analysis

3.3.2. Instrumentation and Mass Spectrometry

An aliquot of the mitochondrial preparation was transferred to a disposable culture borosilicate glass tube (16 × 100 mm). Internal standards were added based on protein concentration and included 16:1–16:1 PtdEtn (100 nmol/mg protein), 14:1–14:1 PtdCho (45 nmol/mg protein), T14:0 Ptd2Gro (3 nmol/mg protein), 15:0–15:0 PtdGro (7.5 nmol/mg protein), 14:0–14:0 PtdSer (20 nmol/mg protein), 17:0 LysoPtdCho (1.5 nmol/mg protein), N12:0 CerPCho (20 nmol/mg protein), N17:0 Cer (5 nmol/mg protein). This allowed the final quantified lipid content to be normalized to the protein content and eliminated potential loss from the incomplete recovery. The molecular species of internal standards were selected because they represent < 0.1% of the endogenous cellular lipid mass as demonstrated by ESI/MS lipid analysis. A modified Bligh and Dyer procedure was used to extract lipids from each mitochondrial preparation as previously described (29). Each lipid extract was reconstituted with a volume of 500 mL/mg protein (which was based on the original protein content of the samples as determined from protein measurement) in CHCl3/MeOH (1:1, v/v). The lipid extracts were flushed with nitrogen, capped, and stored at –20°C for ESI/MS analysis. Each lipid solution was diluted approximately 50-fold immediately prior to infusion and lipid analysis. A triple-quadrupole mass spectrometer (Thermo Scientific TSQ Quantum Ultra, Plus, San Jose, CA, USA), equipped with an electrospray ion source and Xcalibur system software, was utilized as previously described (30). The first and third quadrupoles serve as independent mass analyzers using a mass resolution setting of peak width 0.7 Thomson while the second quadrupole serves as a collision cell for tandem MS. The diluted lipid extract was directly infused into the ESI source at a flow rate of 4 mL/min with a syringe pump. Lipid classes were analyzed in three different modes: negative-ion ESI, negative-ion ESI plus lithium hydroxide, and positive-ion ESI plus lithium hydroxide (Fig. 2). Typically, a 2-min period of signal averaging in the profile mode was employed for each mass spectrum. For tandem MS, a collision gas pressure was set at 1.0 mTorr, but the collision energy varied with the classes of lipids as described previously (14, 30). Typically, a 2- to 5-min period of signal averaging in the profile mode was employed for each tandem MS spectrum. All the mass spectra and tandem MS spectra were automatically acquired by a customized sequence subroutine operated under



Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics

9

Fig. 2. Schematic of the shotgun lipidomics procedure used for the analysis of the mitochondrial lipidome (modified from (12)).

Xcalibur software. Data processing of 2D MS analyses including ion peak selection, data transferring, peak intensity comparison, and quantitation was conducted using self-programmed MicroSoft Excel macros (30). 3.4. Results 3.4.1. Mitochondrial Purity

Ficoll as well as sucrose discontinuous gradients were used to purify NS and Syn mitochondria (Fig. 1). As Ficoll gradientpurified NS mitochondria (FM) contained markers for cytoskeletal (b-actin) and membrane (SNAP25, PCNA, tuberin, PLP, and calnexin) contamination, we further purified mitochondria using a discontinuous sucrose gradient (see also Note 2). None of these markers were present in the Ficoll and sucrose discontinuous gradient-purified mitochondria, which contained only mitochondrial-enriched markers representing the inner mitochondrial membrane (Complex IV, subunit IV) and the outer mitochondrial membrane (MAO-A) (Fig. 3a). Cholera toxin b immunostaining is a sensitive procedure for detecting gangliosides with the GM1a structure in cells and tissues (31). The toxin can have slight cross reactivity with GD1a. GM1a and a low amount of GD1a was found in the TH, My, and FM fractions, indicating the presence of myelin and microsomal membranes in these subcellular fractions (Fig. 3b). Only a trace amount of GM1a was detected in the NS mitochondria and no GM1a was detected in Syn mitochondria. These findings attest to the high degree of mitochondrial purity achieved with the isolation procedure.

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Kiebish, Han, and Seyfried

Fig. 3. Distribution of protein markers on Western blots (a) and of gangliosides on thin-layer chromatography (b) in subcellular fractions from mouse cerebral cortex. Subcellular fractions included total homogenate (TH), crude myelin (My), Ficoll gradient-purified NS mitochondria (FM), Ficoll and sucrose gradient purified nonsynaptic mitochondria (NS), and Ficoll and sucrose gradient-purified synaptic mitochondria (Syn). Western blots were performed to determine the distribution of specific protein markers for the inner mitochondria membrane (complex IV, subunit IV), outer mitochondrial membrane (monoamine oxidase A), myelin (proteolipid protein), synaptosomal membrane (SNAP25), cytoskeleton (β-actin), nuclear membrane (proliferating cell nuclear antigen), Golgi membrane (Tuberin), and microsomal membrane (calnexin). GM1a was visualized on TLC plates with cholera toxin b immunostaining as described in Methods. Std. is GM1a.

3.4.2. Mitochondrial Lipid Composition

We used shotgun lipidomics to evaluate lipid content and distribution of fatty acid molecular species in the NS and Syn mitochondria. The lipid classes were listed according to their relative abundance (Table 1) (see also Note 3). Although the content of most lipids was similar in the NS and Syn mitochondria, the content of Ptd2Gro was lower whereas the content of PtdSer and Cer were higher in the Syn mitochondria than in the NS mitochondria. The myelin-enriched lipids, sulfatides, and cerebrosides, were not detected in either NS or Syn mitochondria. No major changes in lipid molecular species were found between NS and Syn mitochondria (see also Note 4). The major molecular species (>2%) of anionic, weak anionic, and weak polar lipids are found in Tables 2–4.



Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics

11

Table 1  Lipid composition of C57BL/6J mouse brain mitochondria Lipid

Non-synaptic

Synaptic

Ethanolamine glycerophospholipids   Phosphatidylethanolamine   Plasmenylethanolamine

187.4 ± 12.1 164.9 ± 10.0 22.5 ± 2.2

211.7 ± 21.3 184.6 ± 20.3 27.0 ± 1.0

Choline glycerophospholipids   Phosphatidylcholine   Plasmenylcholine   Plasmanylcholine

129.9 ± 7.7 119.6 ± 5.3 1.2 ± 0.1 9.1 ± 3.2

156.3 ± 26.1 137.4 ± 17 2.4 ± 1.1 16.5 ± 8.5

Cholesterol

139.0 ± 46.7

126.7 ± 31.2

Cardiolipin

52.7 ± 4.5

39.9 ± 3.4

Phosphatidylinositol

9.4 ± 0.8

10.2 ± 0.9

Phosphatidylglycerol

7.1 ± 0.5

6.4 ± 0.7

Sphingomyelin

5.3 ± 1.2

6.5 ± 0.6

Phosphatidylserine

4.6 ± 1.5

14.1 ± 3.0

Lysophosphatidylcholine

2.7 ± 0.6

3.3 ± 0.4

Ceramide

0.7 ± 0.2

1.6 ± 0.2

Values are expressed as mean nmol/mg protein ±S.D (N = 3) Significantly different values from NS mitochondria at *: P < 0.02; **: P < 0.005 as determined from the two-tailed t-test

Table 2 Mass content of major molecular species of anionic lipids as detemined by shotgun lipidomics [M − 2H]−

Major species

Non-synaptic

Synaptic

713.0

18:2–18:1–18:1–16:1 18:1–18:1–18:1-16:2 18:2–18:1–18:0–16:2 18:2–18:2–18:0–16:1

0.71 ± 0.02

0.53 ± 0.04

714.0

18:1–18:1–18:1–16:1 18:2–18:1–18:1–16:0

1.47 ± 0.23

1.03 ± 0.12

715.0

18:1–18:1–18:1–16:0 8:0–18:1–18:1–16:1

0.68 ± 0.04

0.61 ± 0.08

724.0

20:4–18:2–18:1–16:1

0.95 ± 0.08

0.66 ± 0.08

Cardiolipin

(continued)

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Kiebish, Han, and Seyfried

Table 2  (continued) [M − 2H]−

Major species

Non-synaptic

Synaptic

725.0

20:4–18:2–18:1–16:0 20:4–18:1–18:1–16:1

2.22 ± 0.17

1.56 ± 0.22

726.0

20:4–18:1–18:1–16:0 20:3–18:1–18:1–16:1 20:3–18:1–18:1–16:1

1.74 ± 0.13

1.31 ± 0.09

727.0

18:2–18:1–18:1–18:1

2.48 ± 0.33

1.59 ± 0.18

728.0

18:1–18:1–18:1–18:1

3.72 ± 0.72

2.21 ± 0.15

735.0

20:4–20:4–18:2–16:1

0.56 ± 0.06

0.39 ± 0.02

736.0

20:4–20:4–18:1–16:1

1.41 ± 0.16

1.10 ± 0.12

737.0

20:4–20:4–18:1–16:0 22:6–18:1–18:1–16:1 22:6–18:2–18:1–16:0

2.11 ± 0.07

1.54 ± 0.17

738.0

20:4–18:2–18:1–18:1 22:6–18:1–18:1–16:0

2.68 ± 0.37

2.02 ± 0.12

739.0

20:4–18:1–18:1–18:1

3.30 ± 0.45

2.48 ± 0.23

740.0

20:4–18:1–18:1–18:0 20:3–18:1–18:1–18:1

1.07 ± 0.14

0.75 ± 0.03

748.0

20:4–20:4–18:2–18:2 20:4–20:4–20:4–16:0 22:6–20:4–18:1–16:1 22:6–22:6–16:0–16:0 22:6–18:2–18:2–18:2

1.38 ± 0.09

1.11 ± 0.17

749.0

20:4–20:4–18:2–18:1

1.99 ± 0.19

1.46 ± 0.20

750.0

20:4–20:4–18:1–18:1

3.24 ± 0.24

2.31 ± 0.28

751.0

22:6–18:1–18:1–18:1 20:4–20:3–18:1–18:1

2.74 ± 0.10

2.36 ± 0.26

752.0

22:6–18:1–18:1–18:0

0.54 ± 0.17

0.46 ± 0.07

760.0

22:6–20:4–18:2–18:2 22:6–20:4–20:4–16:0 22:6–22:6–18:1–16:1

0.76 ± 0.05

0.58 ± 0.09

761.0

22:6–20:4–18:2–18:1

1.72 ± 0.08

1.27 ± 0.05

762.0

22:6–20:4–18:1–18:1

2.94 ± 0.23

2.36 ± 0.13

763.0

22:6–20:4–18:1–18:0 22:6–20:3–18:1–18:1

1.03 ± 0.05

0.78 ± 0.07

773.0

22:6–20:4–20:4–18:1

1.42 ± 0.12

1.10 ± 0.10

Cardiolipin

(continued)



Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics

13

Table 2  (continued) [M − 2H]−

Major species

Non-synaptic

Synaptic

774.0

22:6–22:6–18:1–18:1 22:6–20:4–20:3–18:1

1.81 ± 0.09

1.56 ± 0.17

786.0

22:6–22:6–20:4–18:1 22:6–20:4–20:4–20:3

1.33 ± 0.08

1.11 ± 0.16

797.0

22:6–22:6–22:6–18:1 22:6–22:6–20:4–20:3

0.50 ± 0.04

0.50 ± 0.05

Cardiolipin

Phosphatidylinositol 857.5

16:0–20:4

1.12 ± 0.09

1.17 ± 0.11

881.5

18:2–20:4 16:0–22:6

0.23 ± 0.01

0.26 ± 0.06

885.5

18:0–20:4

6.68 ± 0.41

7.04 ± 0.69

909.5

18:0–22:6

0.37 ± 0.02

0.48 ± 0.03

Phosphatidylglycerol 719.5

16:0–16:1

0.29 ± 0.02

0.31 ± 0.02

721.5

16:0–16:0

0.49 ± 0.04

0.49 ± 0.03

743.5

16:1–18:2

0.22 ± 0.04

0.19 ± 0.04

745.5

16:0–18:2

0.54 ± 0.10

0.48 ± 0.05

747.5

16:0–18:1

3.41 ± 0.13

3.07 ± 0.43

769.5

18:2–18:2

0.82 ± 0.04

0.64 ± 0.10

771.5

18:1–18:2

0.29 ± 0.03

0.26 ± 0.02

773.5

18:1–18:1

0.47 ± 0.12

0.38 ± 0.02

775.5

18:0–18:1

0.55 ± 0.10

0.60 ± 0.14

Phosphatidylserine 786.5

18:0–18:2

0.29 ± 0.07

0.47 ± 0.11

788.5

18:0–18:1

0.24 ± 0.21

1.61 ± 0.77

810.5

18:0–20:4

0.10 ± 0.08

0.35 ± 0.15

834.5

18:0–22:6

3.13 ± 1.22

9.59 ± 1.60

836.5

18:0–22:5

0.22 ± 0.27

0.31 ± 0.20

838.6

20:0–20:4

0.28 ± 0.07

0.79 ± 0.04

18:0–22:4

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Kiebish, Han, and Seyfried

Table 3  Mass content of major molecular species of weak anionic lipids as detemined by shotgun lipidomics [M − H]−

Major species

Non-synaptic

Synaptic

Ethanolamine Glycerophospholipids 716.5

D16:0–18:1

5.11 ± 1.14

4.82 ± 0.68

742.5

D18:0–18:2 D18:1–18:1 D16:0–20:2

5.07 ± 0.62

5.09 ± 0.88

746.5

P16:0–22:6 D18:0–18:0 P18:2–20:4

9.03 ± 1.39

9.89 ± 1.72

762.5

D16:0–22:6

9.39 ± 0.81

11.08 ± 1.00

764.5

D16:0–22:5 D18:1–20:4

8.78 ± 0.52

8.35 ± 0.86

766.5

D18:0–20:4 D16:0–22:4

37.49 ± 2.42

36.39 ± 4.86

768.6

D18:1–20:2 D16:0–22:3 D18:0–20:3

11.49 ± 2.19

16.42 ± 1.11

774.5

P18:0–22:6 P18:1–22:5 D18:0–20:0

10.37 ± 1.44

13.25 ± 1.63

776.6

P18:0–22:5 P18:1–22:4

7.29 ± 2.19

8.24 ± 2.24

788.5

D18:1–22:6

5.95 ± 0.44

6.47 ± 0.55

790.5

D18:0–22:6 D18:1–22:5

58.18 ± 1.87

58.66 ± 8.62

792.6

D18:1–22:4 D18:0–22:5

3.62 ± 0.29

4.90 ± 0.38

794.6

D20:0–20:4 D18:1–22:3 D18:0–22:4

6.53 ± 0.46

15.63 ± 2.58

796.6

D20:0–20:3 D18:0–22:3

6.85 ± 1.34

9.14 ± 0.48

564.5

N18:0

0.66 ± 0.15

1.54 ± 0.19

592.6

N20:0

0.04 ± 0.02

0.03 ± 0.00

646.6

N24:1

0.02 ± 0.02

0.04 ± 0.02

648.6

N24:0

0.02 ± 0.00

0.01 ± 0.01

Ceramide



Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics

15

Table 4  Mass content of major molecular species of weak polar lipids as detemined by shotgun lipidomics [M + Li]+

Major species

Non-synaptic

Synaptic

Choline glycerophospholipids 740.6

D16:0–16:0

10.00 ± 1.27

13.58 ± 1.32

766.6

D16:0–18:1

33.31 ± 1.31

37.34 ± 3.07

768.6

D16:0–18:0

2.47 ± 0.26

3.36 ± 0.93

782.7

A16:0–20:0

7.42 ± 2.81

13.18 ± 7.40

788.6

D18:2–18:2 D16:0–20:4

23.37 ± 1.28

23.13 ± 2.55

792.6

D18:0–18:2 D18:1–18:1

4.15 ± 0.42

3.89 ± 0.28

794.6

D18:0–18:1

4.39 ± 0.46

6.43 ± 0.99

812.6

D16:0–22:6 D18:2–20:4

13.94 ± 0.79

14.08 ± 1.45

814.6

D18:1–20:4 D16:0–22:5

5.66 ± 0.72

5.51 ± 1.07

816.6

D18:2–20:2 D18:0–20:4

11.36 ± 0.90

12.21 ± 2.03

840.6

D18:0–22:6 D20:2–20:4

2.07 ± 1.96

4.14 ± 1.02

709.6

N16:0

0.27 ± 0.32

0.27 ± 0.13

735.6

N18:1

0.26 ± 0.02

0.29 ± 0.08

737.6

N18:0

1.99 ± 0.38

2.76 ± 0.17

765.6

N20:0

2.33 ± 0.41

2.61 ± 0.32

793.7

N22:0

0.16 ± 0.27

0.32 ± 0.13

821.7

N24:0

0.20 ± 0.14

0.08 ± 0.02

Sphingomyelin

Lysophosphatidylcholine 490.3

14:0

0.06 ± 0.01

0.04 ± 0.02

504.3

A16:0

0.06 ± 0.03

0.04 ± 0.01

516.3

16:1

0.09 ± 0.04

0.13 ± 0.07

518.3

16:0

0.89 ± 0.21

1.22 ± 0.14

542.3

18:2

0.06 ± 0.02

0.03 ± 0.02

544.3

18:1

0.41 ± 0.13

0.54 ± 0.12 (continued)

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Kiebish, Han, and Seyfried

Table 4  (continued) [M + Li]+

Major species

Non-synaptic

Synaptic

Lysophosphatidylcholine 546.4

18:0

0.32 ± 0.08

0.44 ± 0.08

566.3

20:4

0.16 ± 0.09

0.16 ± 0.03

574.4

20:0

0.04 ± 0.02

0.07 ± 0.09

590.3

22:6

0.24 ± 0.12

0.27 ± 0.03

592.3

22:5

0.08 ± 0.03

0.09 ± 0.03

4. Notes 1. Additional equipments and supplies needed include a 4°C temperature-controlled room, Potter Elvehjem homogenizer with a Teflon coated pestle (10 mL and 25 mL), handheld drill (max speed 500 rpm), table top centrifuge, 1.5 mL Eppendorf tubes, 15 mL and 50 mL conical tubes, Sorvall SW 28 rotor, Ultra-Clear™ ultracentrifuge tubes (Part 344058 Beckman Coulter), and culture borosilicate glass tube (16 × 100 mm). The mass spectrometer and ultracentrifuge used were a triple-quadrupole mass spectrometer (Thermo Scientific TSQ Quantum Ultra, Plus, San Jose, CA, USA), equipped with an electrospray ion source and Xcalibur system software and an Optima L-90K ultracentrifuge, respectively. 2. The analysis of highly purified brain mitochondria by shotgun lipidomics presents an innovated high-throughput approach to analyzing the mitochondrial lipidome. The major obstacle that arises from analyzing brain mitochondria is obtaining a highly purified fraction free from contamination. Although numerous types of discontinuous gradients can be utilized, we found that a Ficoll gradient in addition to a sucrose discontinuous gradient could obtain a highly purified brain mitochondria preparation suitable for lipidomic analysis (1). 3. Differences can exist in content, molecular species, glycerophospholipid subclass, or total fatty acid distribution in mitochondrial lipids. These differences can be readily detected using a shotgun lipidomics platform with minimal sample processing (14). Changes in any or all aspects of the mitochondrial



Examination of the Brain Mitochondrial Lipidome Using Shotgun Lipidomics

17

lipidome will likely change membrane fluidity, efficiency of the proton gradient, regulation of specific enzyme activities, as well as the overall bioenergetic efficiency of mitochondria (7). Alterations in brain energy metabolism in neurological and neurodegenerative diseases is well established (32–34). Analysis of the brain mitochondrial lipidome in diseased tissues or mouse models can provide new insight into the role of mitochondria lipid alterations during disease pathogenesis. 4. The development of a high-throughput approach to analyze the brain mitochondrial lipidome is a new tool to study the cause(s) for altered energy metabolism in diseased brain (35). We have also used this approach to provide lipidomic evidence supporting the Warburg theory of cancer in a series of mouse brain tumors (36). It is our opinion that new insight on brain function and pathogenesis will be realized with further investigations of the brain mitochondrial lipidome. References 1. Kiebish M.A., Han X., Cheng H., Lunceford A., Clarke C.F., Moon H., Chuang J.H. and Seyfried T.N. (2008) Lipidomic analysis and electron transport chain activities in C57BL/6J mouse brain mitochondria. J Neurochem 106, 299–312. 2. Lai J.C., Walsh J.M., Dennis S.C. and Clark J.B. (1977) Synaptic and non-synaptic mitochondria from rat brain: isolation and characterization. J Neurochem 28, 625–631. 3. Brown M.R., Sullivan P.G. and Geddes J.W. (2006) Synaptic mitochondria are more susceptible to Ca2+overload than nonsynaptic mitochondria. J Biol Chem 281, 11658– 11668. 4. Dagani F., Gorini A., Polgatti M., Villa R.F. and Benzi G. (1983) Synaptic and non-synaptic mitochondria from rat cerebral cortex. Characterization and effect of pharmacological treatment on some enzyme activities related to energy transduction. Farmaco [Sci] 38, 584–594. 5. Villa R.F., Gorini A., Geroldi D., Lo Faro A. and Dell’Orbo C. (1989) Enzyme activities in perikaryal and synaptic mitochondrial fractions from rat hippocampus during development. Mech Ageing Dev 49, 211– 225. 6. Wallace D.C. (2001) A mitochondrial paradigm for degenerative diseases and ageing. Novartis Found Symp 235, 247–263; discussion 263–266. 7. Daum G. (1985) Lipids of mitochondria. Biochim Biophys Acta 822, 1–42.

8. Hoch F.L. (1992) Cardiolipins and biomembrane function. Biochim Biophys Acta 1113, 71–133. 9. Stuart J.A., Gillis T.E. and Ballantyne J.S. (1998) Remodeling of phospholipid fatty acids in mitochondrial membranes of estivating snails. Lipids 33, 787–793. 10. Rostovtseva T.K. and Bezrukov S.M. (2008) VDAC regulation: role of cytosolic proteins and mitochondrial lipids. J Bioenerg Biomembr 40, 163–170. 11. Campbell A.M. and Chan S.H. (2008) Mitochondrial membrane cholesterol, the voltage dependent anion channel (VDAC), and the Warburg effect. J Bioenerg Biomembr 40, 193–197. 12. Han X. and Gross R.W. (2005) Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass spectrometry reviews 24, 367–412. 13. Yang K., Zhao Z., Gross R.W. and Han X. (2007) Shotgun lipidomics identifies a paired rule for the presence of isomeric ether phospholipid molecular species. PLoS ONE 2, e1368. 14. Han X. and Gross R.W. (2005) Shotgun lipidomics: multidimensional MS analysis of cellular lipidomes. Expert review of proteomics 2, 253–264. 15. Han X., Holtzman D.M. and McKeel D.W., Jr. (2001) Plasmalogen deficiency in early Alzheimer’s disease subjects and in animal models: molecular characterization using electrospray

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ionization mass spectrometry. J Neurochem 77, 1168–1180. 16. Cheng H., Mancuso D.J., Jiang X., Guan S., Yang J., Yang K., Sun G., Gross R.W. and Han X. (2008) Shotgun lipidomics reveals the temporally dependent, highly diversified cardiolipin profile in the mammalian brain: temporally coordinated postnatal diversification of cardiolipin molecular species with neuronal remodeling. Biochemistry 47, 5869–5880. 17. Han X., Yang K., Yang J., Cheng H. and Gross R.W. (2006) Shotgun lipidomics of cardiolipin molecular species in lipid extracts of biological samples. J Lipid Res 47, 864–879. 18. Zischka H., Lichtmannegger J., Jagemann N., Jennen L., Hamoller D., Huber E., Walch A., Summer K.H. and Gottlicher M. (2008) Isolation of highly pure rat liver mitochondria with the aid of zone-electrophoresis in a free flow device (ZE-FFE). Methods Mol Biol 424, 333–348. 19. Sims N.R. (1990) Rapid isolation of metabolically active mitochondria from rat brain and subregions using Percoll density gradient centrifugation. J Neurochem 55, 698–707. 20. Sims N.R. and Anderson M.F. (2008) Isolation of mitochondria from rat brain using Percoll density gradient centrifugation. Nat Protoc 3, 1228–1239. 21. Dagani F., Zanada F., Marzatico F. and Benzi G. (1985) Free mitochondria and synaptosomes from single rat forebrain. A comparison between two known subfractionation techniques. J Neurochem 45, 653–656. 22. Taylor S.W., Warnock D.E., Glenn G.M., Zhang B., Fahy E., Gaucher S.P., Capaldi R.A., Gibson B.W. and Ghosh S.S. (2002) An alternative strategy to determine the mitochondrial proteome using sucrose gradient fractionation and 1D PAGE on highly purified human heart mitochondria. J Proteome Res 1, 451–458. 23. Stocco D.M. and Hutson J.C. (1980) Characteristics of mitochondria isolated by rate zonal centrifugation from normal liver and Novikoff hepatomas. Cancer Res 40, 1486–1492. 24. Graham J.M. (2001) Purification of a crude mitochondrial fraction by density-gradient centrifugation. Curr Protoc Cell Biol Chapter 3, Unit 3 4. 25. Lai J.C. and Clark J.B. (1976) Preparation and properties of mitochondria derived from synaptosomes. Biochem J 154, 423–432.

26. Mena E.E., Hoeser C.A. and Moore B.W. (1980) An improved method of preparing rat brain synaptic membranes. Elimination of a contaminating membrane containing 2´,3´-cyclic nucleotide 3´-phosphohydrolase activity. Brain Res 188, 207–s31. 27. Rendon A. and Masmoudi A. (1985) Purification of non-synaptic and synaptic mitochondria and plasma membranes from rat brain by a rapid Percoll gradient procedure. J Neurosci Methods 14, 41–51. 28. Battino M., Bertoli E., Formiggini G., Sassi S., Gorini A., Villa R.F. and Lenaz G. (1991) Structural and functional aspects of the respiratory chain of synaptic and nonsynaptic mitochondria derived from selected brain regions. J Bioenerg Biomembr 23, 345–363. 29. Cheng H., Guan S. and Han X. (2006) Abundance of triacylglycerols in ganglia and their depletion in diabetic mice: implications for the role of altered triacylglycerols in diabetic neuropathy. J Neurochem 97, 1288–300. 30. Han X., Yang J., Cheng H., Ye H. and Gross R.W. (2004) Toward fingerprinting cellular lipidomes directly from biological samples by two-dimensional electrospray ionization mass spectrometry. Anal Biochem 330, 317–331. 31. Brigande J.V., Platt F.M. and Seyfried T.N. (1998) Inhibition of glycosphingolipid biosynthesis does not impair growth or morphogenesis of the postimplantation mouse embryo. J Neurochem 70, 871–882. 32. Petrozzi L., Ricci G., Giglioli N.J., Siciliano G. and Mancuso M. (2007) Mitochondria and neurodegeneration. Biosci Rep 27, 87–104. 33. Beal M.F. (2005) Mitochondria take center stage in aging and neurodegeneration. Ann Neurol 58, 495–505. 34. Calabrese V., Scapagnini G., Giuffrida Stella A.M., Bates T.E. and Clark J.B. (2001) Mitochondrial involvement in brain function and dysfunction: relevance to aging, neurodegenerative disorders and longevity. Neurochem Res 26, 739–64. 35. Bowling A.C. and Beal M.F. (1995) Bioenergetic and oxidative stress in neurodegenerative diseases. Life Sciences 56, 1151–1171. 36. Kiebish M.A., Han X., Cheng H., Chuang J.H. and Seyfried T.N. (2008) Cardiolipin and electron transport chain abnormalities in mouse brain tumor mitochondria: Lipidomic evidence supporting the Warburg theory of cancer. J Lipid Res 49, 2545–2556

Chapter 2 Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid by Shotgun Lipidomics Todd W. Mitchell Summary Docosahexaenoic acid (DHA, 22:6 n-3) is an omega-3 fatty acid with a 22 carbon acyl chain containing six cis double bonds and is predominantly found in membrane glycerophospholipids. Dietary consumption of DHA has been positively linked with the prevention of numerous pathologies and consequently, it has been the focus of extensive research over the last four decades. Nevertheless, our understanding of its molecular mode of action is not well understood. One likely mechanism is through DHA’s influence on cell membranes and the proteins embedded within them. This influence may be altered depending on the glycerophospholipid head group DHA is esterified to and its fatty acid partner, i.e., the specific glycerophospholipid molecule. Accordingly, an understanding of the exact glycerophospholipid distribution of DHA within a tissue is important if we wish to gain further insight into its role in the prevention of disease. In this chapter a rapid, shotgun lipidomic approach for identifying the molecular glycerophospholipid distribution of DHA is described. Key words:  Docosahexaenoic acid, ESI–MS, Phospholipid, Shotgun lipidomics, Lipid

1. Introduction Docosahexaenoic acid (DHA, 22:6 n-3) is a long-chain polyunsaturated omega-3 fatty acid with a 22 carbon acyl chain that contains six cis double bonds (Fig. 1). It is found in most animals, particularly in glycerophospholipids (GPLs) and is abundant in fish. DHA has been the focus of a large amount of research over the last few decades with interest in this essential fatty acid initiated by the famous work of Bang and Dyerberg in the 1970s (1). From this pioneering work, a link between the consumption of omega-3 fatty acids, in particular DHA and eicospentaenoic acid Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, DOI 10.1007/978-1-60761-322-0_2, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Fig. 1. Structure of dochosahexaenoic acid.

(EPA, 20:5 n-3) and a reduced risk of cardiovascular disease was identified. Since that time research has implicated DHA in the prevention of several prevalent chronic diseases affecting modern society, e.g., cardiovascular disease (2), depression (3), type 2 diabetes (4, 5), obesity (6), and cancer (7). In fact, dietary consumption of this simple lipid has been associated with the prevention of a myriad of pathologies (8). Although DHA’s mechanism/s of action are not well known, its high preference for phospholipids has ensured that its effect on membrane physics is of particular interest. The high number of possible structural conformations of DHA (9, 10) leads to a reduction in membrane packing and stability (11). DHA is also known to increase membrane permeability (12) and propensity for fusion (13). With such an extensive influence on the physical properties of membranes it is not surprising that phospholipid DHA content has been linked to the activity of numerous membrane proteins, e.g., Na + K + ATPase (14). Nevertheless, studies on protein kinase C suggest that, at least in some cases, DHA’s precise molecular glycerophospholipid distribution may be of more importance than its influence on bulk membrane properties (15, 16). In 2000, Farkas and co workers were able to identify several phosphatidylcholine (GPCho) and phosphatidylethanolamine (GPEtn) molecules containing DHA in vertebrate brains (17). Although successful, the method employed for tracking the molecular GPL distribution of DHA was extremely laborious, requiring numerous chromatographic and derivatization steps (18). More recently, the relatively simple approach of shotgun lipidomics has been applied to the identification of DHA-containing glycerophospholipids in various tissues from mice and naked mole rats (19). Shotgun lipidomics describes the ability to determine the lipid content, from classes to subclasses and even individual molecules directly from a crude lipid extract by electrospray-ionization mass spectrometry (ESI–MS) without need for prior separation or derivatization (20–22). This technique is highly sensitive, specific, and can produce quantitative data with the addition of appropriate internal standards to the lipid extract (23). In this technique, GPLs are initially separated by their charge polarity, i.e., by selecting either the positive or negative ion mode. These molecules are easily viewed using a simple MS scan, in



Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid

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which all GPLs carrying a common charge are selected from a lipid extract. The power in ESI–MS is unveiled in its ability to select one molecular ion, and through collision-induced dissociation (CID), observe the compositional fragments of that ion. From these fragments the phospholipid head group as well as the carbon chain length and unsaturation of each fatty acid (FA) can be determined (24). The production of these characteristic fragments forms the basis of the separation methods exploited by the shotgun lipidomics approach. A triple quadrupole or a quadrupole time of flight mass spectrometer (with enhanced duty cycle) permits precursor ion scanning allowing the analysis of specific phospholipids based on a characteristic fragment ion. An example of such is the glycerol backbone of GPLs at m/z 153.0 that identifies phosphtidylserine (GPSer), Phosphatidic acid (GPA), phosphatidylglycerol (GPGro), phosphatidylinositol (GPIns), and cardiolipin in negative ions, or a FA scan to separate out GPLs based on their FA moieties, e.g., DHA (20, 25). Alternatively, triple quadrupole mass spectrometers also provide the ability to perform neutral loss scans to identify a common neutral fragment, such as the loss of didehydroalanine (87 Da) from GPSer in negative ions or phosphoethanolamine (141 Da) from GPEtn in positive ions. Such scans focusing on the head group fragments reduce the incidence of isobaric interference from other GPLs and are used for the quantification of GPLs by comparing to an internal standard, and varying the collision energy depending on the headgroup (25). In this chapter, a step-by-step description of how to utilize these various mass spectrometric scan techniques to identify the molecular glycerophospholipid distribution of DHA within tissues will be presented.

2. Materials 2.1. Equipment

1. Glass–glass homogenizers. 2. Tube rotator. 3. Vortex mixer. 4. UV 1601 spectrophotometer (Shimadzu Scientific Instruments, Colombia, U.S.A.). 5. Waters QuattroMicro™ triple quadrupole mass spectrometer (Waters, Manchester, U.K.).

2.2. Reagents and Solvents

All solvents must be of minimum HPLC grade. 1. Ammonium acetate.

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2. Ammonium molybdate. 3. Butylated hydroxyltoluene (BHT). 4. Chloroform. 5. Hydrochloric acid. 6. Methanol. 7. Phospholipid Standards (all purchased from Avanti Polar Lipids):

(a)  Dinonadecanoyl phosphatidylcholine, GPCho (19:0/ 19:0).



(b) Diheptadecanoyl phosphatidylserine, GPSer (17:0/17:0).



(c) Diheptadecanoyl phosphatidylethanolamine, GPEtn (17:0/17:0).



(d) Diheptadecanoyl phosphatidylglycerol, GPGro (17:0/ 17:0).



(e) Diheptadecanoyl phosphatidic acid, GPA (17:0/17:0).



(f) Heptadecanoyl eicosatetraenoyl phosphatidylinositol, GPIns (17:0/5Z,8Z,11Z,14Z-20:4).

8. Potassium dihydrogen phosphate. 9. Perchloric acid. 10. Stannous chloride. 2.3. Supplies

1. Pasteur pipettes. 2. Pyrex screw cap 15 mL test tubes. 3. Plastic cuvettes.

3. Methods A workflow outlining the procedures required for the identification and relative quantification of glycerophospholipids containing DHA is shown in Fig. 2. 3.1. Total Lipid Extraction

Total lipids are extracted from tissues according to traditional methods (26) with slight modifications to enhance the compatibility of the extracts with mass spectrometric analysis as described previously (27). In detail: 1. Weigh tissue and homogenize in 2 mL methanol:chlorofrorm (1:2 v/v) containing 0.01% butylated hydroxytoluene (BHT) using a glass–glass homogenizer. 2. Add internal standard mixture at 4 mL /g tissue (see Note 1).



Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid

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Fig. 2. Workflow for tracking the glycerophospholipid distribution of docosahexaenoic acid.

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3. Add further methanol:chlorofrorm (1:2 v/v) containing 0.01% BHT to ensure that the total methanol:chlorofrorm volume is 20× the tissue weight. 4. Vortex and mix in a test tube rotator for a minimum of 4 h or preferably overnight at 4°C. 5. Add 500 mL 0.15 M ammonium acetate and vortex for at least 15 s. 6. Centrifuge for 5 min at 2,000 × g. There should be two phases with chloroform at the bottom containing the lipids. Proteins float in the water/methanol phase or interface. 7. Aspirate a glass pipette with a small amount of chloroform and expel. Insert glass pipette gently to the bottom of the tube and take out the chloroform without removing any of the water/methanol phase or proteins. Expel the chloroform/ lipid mixture into a new test tube. 8. Add 2 mL methanol:chloroform (1:2 v/v) to original homogenate tube and vortex for at least 15 s. 9. Centrifuge for 5 min at 2,000 × g. 10. Aspirate glass pipette with a small amount of chloroform and expel. Insert glass pipette gently to the bottom of the tube and take out the chloroform without removing any of the water/methanol phase or proteins, combine with first chloroform extract. 11. Add 500 mL of 0.15 M ammonium acetate to the combined chloroform extract and vortex for at least 15 s. 12. Centrifuge for 5 min at 2,000 × g. 13. Insert glass pipette gently into the aqueous phase without removing any of the organic phase, and discard this aqueous phase and protein layer as waste. 14. Dry down under nitrogen at 37°C. 15. Resuspend phospholipids in 2 mL methanol:chloroform (2:1 v/v) and vortex. 16. Store in glass vial at −80°C. 3.2. Phosphorus Assay

The total phospholipid concentration of lipid extracts is determined by phosphorous assay (28). The concentration of phosphorous in the lipid extract is quantified by comparison of absorbance values (680 nm) with a standard reference curve (see Note 2). 1. Take 100 mL aliquots of each lipid extract (in duplicate), dry under nitrogen and resuspend in 0.8 mL of 72% (w/v) perchloric acid. 2. Heat at 190°C for 45 min. 3. Place on ice and add 5 mL of water and 500 mL each of ammonium molybdate (8%, w/v) and stannous chloride (0.005% dilution of 40% (w/v) SnCl2 in HCl).



Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid

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4. Make up to 10 mL by the addition of water and allow color to develop for 10 min. 5. Measure absorbance at 680 nm. 6. Calculate phospholipid content using the following equation:

Phospholipid content =

µg phosphorous × 780 , 30.97

where 780 is the assumed average mass of phospholipids in grams and 30.97 is the molecular weight of phosphorous in grams. 3.3. Mass Spectrometry 3.3.1. Instrumentation 3.3.2. Identification of Phospholipids Containing DHA

The following discussion is based on the use of a Waters QuattroMicro™ triple quadrupole mass spectrometer (Waters, Manchester, U.K.) equipped with a z-spray electrospray ion source and controlled by Micromass Masslynx version 4.0 software. Performing this step will identify all phospholipids containing a DHA moiety, providing a targeted approach for later quantitative analysis. To identify anionic glycerophospholipids (GPA, GPGro, GPSer, GPIns, and GPEtn; see Note 3) containing DHA by precursor ion scanning in negative ion mode: 1. Dilute lipid extracts to a final phospholipid concentration of 40 mM with the addition of methanol:chloroform (2:1 v/v). 2. Set capillary voltage to 3,000 V, source temperature to 80°C, desolvation temperature to 120°C and Cone voltage to 50 V. Nitrogen drying gas is used at a flow rate of 320 L/h. 3. Infuse samples into the electrospray ion source at a flow rate of 10 mL/min using the instrument’s on-board syringe pump. 4. Set the argon collision gas at a pressure of 3 mTorr and accelerate the ions at a collision energy offset of 35 eV 5. Set quadrupole 3 (Q3) to m/z 327.3 and scan quadrupole 1 (Q1) over a mass range of m/z 740–920 For the identification of GPCho containing DHA by neutral loss scanning in positive ion mode: 1. Dilute lipid extracts to a final phospholipid concentration of 40 mM with the addition of methanol:chloroform (2:1 v/v) and add aqueous lithium acetate (to a final concentration of 200 mM). 2. Set capillary voltage to 3,000 V, source temperature 80 °C, desolvation temperature 120°C and Cone voltage to 35 V. Nitrogen drying gas is used at a flow rate of 320 L/h. 3. Infuse samples into the electrospray ion source at a flow rate of 10 mL/min using the instrument’s on-board syringe pump. 4. Set the argon collision gas at a pressure of 3 mTorr and accelerate the ions at a collision energy offset of 35 eV

Fig. 3. (a) A precursor ion scan for anionic glycerophospholipids containing DHA (m/z 327.3) and (b) a neutral loss scan for GPCho containing DHA (387.3 Da; GPCho – trimethylamine – DHA) in total lipids extracts from mouse skeletal muscle. GPA phosphatidic acid, GPEtn phosphatidylglycerol, GPSer phosphatidylserine, GPCho phosphatidylcholine, DHA docosahexaenoic acid.

26 Mitchell



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5. Scan Q1 over a mass range of m/z 750–850 and scan Q3 at an m/z offset of Q1–387.3. This offset corresponds to the loss of both trimethylamine and DHA (see Note 4). 3.4. Results 3.4.1. Structural Characterization of DHA Containing Glycerophospholipids

The above scans provide spectra of all anionic phospholipid and GPCho molecules containing DHA as shown in Fig. 3a, b respectively. Product ion spectra can now be obtained from each of the DHAcontaining phospholipid molecules to complete their structural characterization. These spectra are obtained using the same instrument settings and sample preparations as described for both positive and negative ion analysis above with the exception that: 1. Q1 is set to the m/z of the deprotonated or lithiated molecular ion, and 2. Q3 is scanned over an appropriate range to identify all fatty acid moieties present, i.e., m/z 200–350 in negative ion mode for deprotonated ions or an m/z range that is between 400 and 200 Da less than the lithiated molecular ion in positive ion mode. If GPEtn or GPCho ethers are identified as containing DHA, their identity can be confirmed by the presence of ions indicative of the loss of the ether-linked acyl chain as an alcohol from lithiated ions as described by Hsu and Turk (29, 30). Alternatively, the identity of ether lipids can be confirmed by ozone-induced dissociation (OzID) (31) that is described in detail in Chapter 21.

3.4.2. Quantification of DHA Containing Glycerophospholipids

In order to remove isobaric interference across glycerophospholipid classes, quantification of molecular phospholipids is performed using precursor ion and neutral loss scans of head group-specific fragments (25). It is unlikely that all GPL classes will contain DHA and therefore the specific head group scans required are determined by the previous identification of molecular GPLs containing DHA. GPA, GPGro, and GPIns specific scans are performed in negative ion mode: 1. Dilute lipid extracts to a final phospholipid concentration of 40 mM with the addition of methanol:chloroform (2:1 v/v). 2. Set capillary voltage to 3,000 V, source temperature 80°C, desolvation temperature 120°C and Cone voltage to 50 V. Nitrogen drying gas is used at a flow rate of 320 L/h. 3. Infuse Samples into the electrospray ion source at a flow rate of 10 mL/min using the instrument’s on-board syringe pump. 4. Set the argon collision gas at a pressure of 3 mTorr. 5. Q1 and Q3 settings and collision energies are set as listed in Table 1. GPCho, GPSer, and GPEtn specific scans are performed in positive ion mode:

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Table 1 Mass spectrometer scan parameters used for the relative quantification of GPA, GPGro, and GPIns GPL class

Scan type

Q1 scan range (m/z) Q2

Collision energy offset (eV)

GPA/GPGro Precursor ion 650–830

m/z 153.0 50

GPIns

m/z 241.0 45

Precursor ion 860–920

GPL glycerophospholipid, GPA phosphatidic acid, GPGro phosphatidylglycerol, GPIns phosphatidylinositol, m/z mass-to-charge ratio

Table 2 Mass spectrometry scan parameters used for the relative quantification of GPCho, GPEtn, and GPSer GPL class Scan type

Q1 scan range (m/z) Q2

Collision energy offset (eV)

GPCho

Precursor ion 780–840

m/z 184.1

35

GPEtn

Neutral loss

700–800

Q1–141.5 Da 25

GPSer

Neutral loss

750–840

Q1–185.4 Da 22

GPL glycerophospholipid, GPCho phosphatidylcholine, GPEtn phosphatidylethanolamine, GPSer phosphatidylserine; m/z, mass-to-charge ratio

1. Dilute lipid extracts to a final phospholipid concentration of 40 mM with the addition of methanol:chloroform (2:1 v/v). The formation of protonated GPSer and GPEtn ions can be enhanced by the addition aqueous ammonium acetate (to a final concentration of approximately 50 mM) (32). 2. Set capillary voltage to 3,000 V, source temperature 80°C, desolvation temperature 120°C, and Cone voltage to 35 V. Nitrogen drying gas is used at a flow rate of 320 L/h. 3. Infuse samples into the electrospray ion source at a flow rate of 10 mL/min using the instrument’s on-board syringe pump. 4. Set the argon collision gas at a pressure of 3 mTorr. 5. Q1 and Q3 settings and collision energies are set as listed in Table 2. Glycerophospholipids are then quantified by comparing their peak areas, obtained from averaging a minimum of 100 scans with the appropriate internal standard for each class after ­correction



Tracking the Glycerophospholipid Distribution of Docosahexaenoic Acid

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Table 3 Molecular glycerophospholipids containing DHA identified in mouse skeletal muscle using shotgun lipidomics GPL

Proportion of Total GPL (%)

GPCho (16:0/22:6)

16.5 ± 0.4

GPCho (16:1/22:6)

0.6 ± 0.1

GPCho (18:0/22:6)

2.2 ± 0.2

GPCho (18:1/22:6)

0.9 ± 0.1

GPCho (18:2/22:6)

0.6 ± 0.1

GPEtn (16:0/22:6)

4.6 ± 0.3

GPEtn (18:0/22:6)

8.3 ± 0.5

GPEtn (18:1/22:6)

2.1 ± 0.1

GPEtn (18:2/22:6)

0.8 ± 0.1

GPSer (18:0/22:6)

3.6 ± 1.1

GPA (18:0/22:6)

0.2 ± 0.0

Total DHA-containing GPL

40.5 ± 1.3

GPL glycerophospholipid, GPCho phosphatidylcholine, GPEtn phosphatidylethanolamine, GPSer phosphatidylserine, GPA phosphatidic acid. Data are presented as mean ± SE (n = 4)

for isotope contributions as described by Deeley et al. (27) (see Note 5). To achieve this, the isotopic ion distribution of each phospholipid can be calculated from isotope models and the area of the monoisotopic peak multiplied by the calculated correction factor. This calculation must start with the smallest observed phospholipid so that any contribution of its isotope peaks to the area of phospholipids of greater m/z can be subtracted before subsequent isotope calculations are performed. Where two or more isomeric phospholipids are identified the relative abundance of each isomer can be determined from comparison of the combined abundances of the two fatty acid carboxylate ions arising from each lipid with the combined peak area of all carboxylate anions present in the product ion spectrum. ­Neither the relative position of the acyl chains on the glycerol backbone (33) (often called the sn-position) nor the position of double bonds (31, 34, 35) can be rigorously assigned from these data. A list of DHA-containing glycerophospholipids detected in mouse skeletal muscle using this technique is shown in Table 3.

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4. Notes 1. An internal standard mixture in methanol:chloroform (2:1, v/v) should be prepared with the concentration of each internal standard reflecting the concentration of each phospholipid class within the tissue, e.g., for skeletal muscle use GPCho (19:0/19:0), 250 mM; GPEtn (17:0/17:0), 188 mM; GPSer (17:0/17:0), 125 mM; GPA (17:0/17:0), 25 mM; GPGro (17:0/17:0), 25 mM, and GPIns (17:0/20:4), 25 mM. 2. The standard used for the phosphorous assay is potassium dihydrogen phosphate (KH2PO4) at 20 mg/mL and the curve constructed using 1, 2, 5, and 10 mg of phosphorous. 3. GPEtn is technically a zwitterionic phospholipid, however it can be easily deprotonated (particularly at elevated pH) to form an anion. 4. Other ions characteristic of the neutral loss of DHA are also produced by the collision-induced dissociation of lithated GPCho; however, the neutral loss described here produces the most abundant of these ions under the described conditions. 5. Isotope corrections are required to account for (a) The greater contribution of isotopic ions to the total abundance of larger phospholipids. (b) The contribution of isotope peaks of one phospholipid to the area of the monoisotopic peak of a larger one. References   1. Dyerberg J, Bang HO, Hjorne N. (1975) Fatty acid composition of the plasma lipids in Greenland Eskimos. Am J Clin Nutr. 28(9):958–66.   2. McLennan P, Abeywardena M. (2005) Membrane basis for fish oil effects on the heart: Linking natural hibernators to prevention of human sudden cardiac death. J Membr Biol. 206(2):102.   3. Hibbeln JR, Salem N, Jr. (1995) Dietary polyunsaturated fatty acids and depression: when cholesterol does not satisfy. Am J Clin Nutr. 62(1):1–9.   4. Andersen G, Harnack K, Erbersdobler HF, Somoza V. (2008) Dietary eicosapentaenoic acid and docosahexaenoic acid are more effective than alpha-linolenic acid in improving insulin sensitivity in rats. Ann Nutr Metab. 52(3):250–6.   5. Carpentier YA, Portois L, Malaisse WJ. (2006) n-3 Fatty acids and the metabolic syndrome. Am J Clin Nutr. 83(6):S1499–504.   6. Li J-J, Huang CJ, Xie D. (2008) Anti-obesity effects of conjugated linoleic acid, docosahex-

aenoic acid, and eicosapentaenoic acid. Mol Nutr Food Res. 52(6):631–45.   7. Chapkin RS, Seo J, McMurray DN, Lupton JR. (2008) Mechanisms by which docosahexaenoic acid and related fatty acids reduce colon cancer risk and inflammatory disorders of the intestine. Chem Phys Lipids. 153(1):14–23.   8. Stillwell W. (2008) Docosahexaenoic acid: a most unusual fatty acid. Chem Phys Lipids. 153(1):1–2.   9. Feller SE. (2008) Acyl chain conformations in phospholipid bilayers: a comparative study of docosahexaenoic acid and saturated fatty acids. Chem Phys Lipids. 153(1):76–80. 10. Feller SE, Gawrisch K, MacKerell AD, Jr. (2002) Polyunsaturated fatty acids in lipid bilayers: intrinsic and environmental contributions to their unique physical properties. J Am Chem Soc. 124(2):318–26. 11. Stillwell W, Wassall SR. (2003) Docosahexaenoic acid: membrane properties of a unique fatty acid. Chem Phys Lipids. 126(1):1–27.



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12. Huster D, Arnold K, Gawrisch K. (1998) Influence of Docosahexaenoic Acid and Cholesterol on Lateral Lipid Organization in Phospholipid Mixtures. Biochemistry. 37(49):17299–308. 13. Ehringer W, Belcher D, Wassall SR, Stillwell W. (1990) A comparison of the effects of linolenic (18:3[Omega]3) and docosahexaenoic (22:6[Omega]3) acids on phospholipid bilayers. Chem Phys Lipids. 54(2):79–88. 14. Turner N, Else PL, Hulbert AJ. (2003) Docosahexaenoic acid (DHA) content of membranes determines molecular activity of the sodium pump: implications for disease states and metabolism. Naturwissenschaften. 90(11):521–3. 15. Giorgione JR, Kraayenhof R, Epand RM. (1998) Interfacial membrane properties modulate Protein Kinase C Activation: Role of the Position of Acyl Chain Unsaturation. Biochemistry. 37(31):10956–60. 16. Slater SJ, Kelly MB, Taddeo FJ, Ho C, Rubin E, Stubbs CD. (1994) The modulation of protein kinase C activity by membrane lipid bilayer structure. J Biol Chem. 269(7):4866–71. 17. Farkas T, Kitajka K, Fodor E, et  al. (2000) Docosahexaenoic acid-containing phospholipid molecular species in brains of vertebrates. Proc Natl Acad Sci USA. 97(12):6362–6. 18. Takamura H, Kito M. (1991) A highly sensitive method for quantitative analysis of phospholipid molecular species by high-performance liquid chromatography. J Biochem. 109:436–9. 19. Mitchell TW, Buffenstein R, Hulbert AJ. (2007) Membrane phospholipid composition may contribute to exceptional longevity of the naked mole-rat (Heterocephalus glaber); a comparative study using shotgun lipidomics. Exp Gerontol. 42(11):1053–62. 20. Ekroos K, Chernushevich IV, Simons K, Shevchenko A. (2002) Quantitative profiling of phospholipids by multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer. Anal Chem. 74:941–9. 21. Han X, Gross RW. (2003) Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res. 44(6): 1071–9. 22. Han X, Gross RW. (2005) Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom Rev. 24:367–412. 23. Han X, Yang K, Yang J, Fikes KN, Cheng H, Gross RW. (2006) Factors influencing the electrospray intrasource separation and selective ionization of glycerophospholipids. J Am Soc Mass Spectrom. 17(2):264–74.

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24. Pulfer M, Murphy RC. (2003) Electrospray mass spectrometry of phospholipids. Mass Spectrom Rev. 22:332–64. 25. Brugger B, Erben G, Sandhoff R, Wieland FT, Lehmann WD. (1997) Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionisation tandem mass spectrometry. Proc Natl Acad Sci U S A. 94:2339–44. 26. Folch J, Lees M, Sloane-Stanley GH. (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 226:497–509. 27. Deeley JM, Mitchell TW, Nealon JR, et  al. (2008) Human lens lipids differ markedly from those of commonly used experimental animals. Biochim Biophys Acta. 1781(6–7):288–98. 28. Mills GL, Lane PA, Weech PK. (1984) A guide to lipoprotein technique. In: Burdon RH, Kippenberg PHv, eds. Laboratory Techniques in Biochemistry and Molecular Biology. Elsevier Science, New York, pp. 240–1. 29. Hsu FF, Turk J. (2000) Characterization of phosphatidylethanolamine as a lithiated adduct by triple quadrupole tandem mass spectrometry with electrospray ionization. J Mass Spectrom. 35(5):595–606. 30. Hsu FF, Turk J, Thukkani AK, Messner MC, Wildsmith KR, Ford DA. (2003) Characterization of alkylacyl, alk-1-enylacyl and lyso subclasses of glycerophosphocholine by tandem quadrupole mass spectrometry with electrospray ionization. J Mass Spectrom. 38(7): 752–63. 31. Thomas MC, Mitchell TW, Harman DG, Deeley JM, Nealon JR, Blanksby SJ. (2008) Ozone-induced dissociation: Elucidation of double bond position within mass-selected lipid ions. Anal Chem. 80(1):303–11. 32. Thai TP, Rodemer C, Worsch J, Hunziker A, Gorgas K, Just WW. (1999) Synthesis of plasmalogens in eye lens epithelial cells. FEBS Lett. 456(2):263–8. 33. Ekroos K, Ejsing Christer S, Bahr U, Karas M, Simons K, Shevchenko A. (2003) Charting molecular composition of phosphatidylcholines by fatty acid scanning and ion trap MS3 fragmentation. J Lipid Res. 44(11):2181–92. 34. Thomas MC, Mitchell TW, Blanksby SJ. (2006) Ozonolysis of phospholipid double bonds during electrospray ionization: A new tool for structure determination. J Am Chem Soc. 128(1):58–9. 35. Thomas MC, Mitchell TW, Harman DG, Deeley JM, Murphy RC, Blanksby SJ. (2007) Elucidation of double bond position in unsaturated lipids by ozone electrospray ionization mass spectrometry. Anal Chem. 79(13):5013–22.

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Chapter 3 Global Analysis of Retina Lipids by Complementary Precursor Ion and Neutral Loss Mode Tandem Mass Spectrometry Julia V. Busik, Gavin E. Reid, and Todd A. Lydic Summary Despite an increasing recognition of the causative and diagnostic role of lipids in the onset and progression of retinal disease, information on the global lipid profile of the normal retina is quite limited. Here, a “shotgun” tandem mass spectrometry approach involving the use of multiple lipid class-specific precursor ion and neutral loss scan mode experiments has been employed to analyze lipid extracts from normal rat retina, obtained with minimal sample handling prior to analysis. Redundant information for the identification and characterization of molecular species in each lipid class was obtained by complementary analysis of their protonated or deprotonated precursor ions, or by analysis of their various ionic adducts (e.g., Na+, NH4+, Cl–, CH3OCO2–). Notably, “alternative” precursor ion or neutral loss scan mode MS/ MS experiments are introduced that were used to identify rat retina lipid molecular species that were not detected using “conventional” scan types typically employed in large-scale lipid-profiling experiments. This chapter outlines the principles and advantages of utilizing complementary/redundant identification of lipid species as a strategy to overcome inherent challenges and limitations of shotgun lipid analysis, and provides examples of the application of this strategy in the analysis of the retina lipidome. Key words: Retina, Lipidomics, Lipid analysis, Electrospray ionization, Tandem mass spectrometry

1. Introduction Retina has a unique fatty acid profile with the highest levels of long chain polyunsaturated fatty acids (LCPUFA), including docosahexaenoic acid (DHA22:6n3), and arachidonic acid (ARA20:4n6), observed in the body (1–5). Of these, DHA22:6n3 is the most abundant fatty acid in both neural and vascular elements of the retina (2), retinal pigment epithelial cells (6, 7) and retinal

Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, DOI 10.1007/978-1-60761-322-0_3, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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photoreceptor outer segment disc membranes (8, 9). Extensive studies clearly demonstrate the important role of lipids in retinal health and disease (5). However, most studies to date have focused on the role of LCPUFA’s, and DHA in particular, measuring total fatty acid levels without obtaining structural information. The reason for this is likely methodological – LCPUFA are very abundant in the retina and relatively easy to measure by well developed high-performance liquid chromatography (HPLC) or gas chromatography (GC) techniques. The limitations of the traditional techniques have precluded comprehensive complex lipid analysis from the limited amount of retinal material that can be obtained from animal models such as rats and mice. Thus, there is surprisingly little information available regarding the lipid composition of the normal retina, and only limited information describing changes in global lipid profiles between normal and diseased retinal tissue (See Note 1). Recent advances in the application of electrospray ionization (ESI) and matrix assisted laser desorption/ionization (MALDI) (10–20) techniques, coupled with the use of tandem mass spectrometry methods employing selective precursor ion and neutral loss scan mode analysis strategies, have enabled the development of “shotgun” lipidomics approaches for rapid and sensitive monitoring of the molecular compositions and abundances of individual lipid species in unfractionated lipid extracts (10–17). While shotgun approaches allow for high-throughput analysis of multiple lipid classes without prior chromatographic separation of lipid analytes, the large number of lipid molecular species present in crude extracts presents a significant challenge for the analyst. In addition to possible overlap of the molecular ions and the 13C isotope peaks of numerous lipid species, even greater extract complexity can arise due to the presence of individual lipid species in multiple ionic forms, via adduction with a variety of cationic (e.g., +H+, +Na+, +NH4+) or anionic (e.g., −H–, +Cl–) species in positive or negative ion modes, respectively, that may be present in small amounts following extraction of lipids from tissues or cells. Additional complexity may also be observed for certain lipids due to the presence of adducts formed by reaction of the solvents and buffer additives that are commonly employed for sample analysis. For example, phosphocholine-containing lipids are readily adducted with methylcarbonate (CH3OCO2–) anions (21), formed by the in vitro reaction of hydroxide or bicarbonate salts (22, 23) with methanol, effectively increasing the number of lipid species observed in negative ion mode analysis. In our studies, relatively limited tissue availability has prompted us to develop strategies for attaining a thorough accounting of the global lipid composition of retina without requirement for multiple sample fractionation or processing steps, such as chromatographic separation, or destruction of glycerophospholipids



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for enhanced sphingolipid analysis. Utilizing a triple quadrupole mass spectrometer to perform multiple precursor ion and neutral loss scan mode MS/MS experiments, we have found that complementary/redundant detection of a given lipid class based on the unique fragmentation behaviors of various ionic forms (e.g.,[M + H]+, [M − H]−, [M + Na]+, [M + NH4]+, [M + Cl]−, [M + CH3OCO2]−) of various lipid classes facilitates (a) the ability to identify molecular lipid species that may not be detected when only one scan mode is used, (b) a more thorough accounting of the various lipid species that may be present in multiple ionic forms when “absolute quantification” is desired, and (c) simplification of relative quantification by selection of an MS/MS scan mode in which lipid species are present in only one ionic form. Furthermore, the ability to identify lipids in more than one ionic form greatly increases the confidence for peak identification, even for ions observed at low (<5%) relative abundance. Here, we outline the general approach and the results obtained by performing complementary precursor ion and neutral loss scan mode tandem mass spectrometry analyses. Although presented within the context of defining the normal rat retina lipidome, the analytical principles could benefit the analysis of virtually any tissue or cell lipid extract. In this chapter, our discussion will be limited to analysis of rat retina sphingomyelin (SM), glycerophosphatidylcholine (GPCho), glycerophosphatidylethanolamine (GPEtn), glycerophosphatidylinositol (GPIns), glycerophosphatidylserine (GPSer), and diacylglycerol (DG) and triacylglycerol (TG) molecular lipid species. Complementary sets of precursor ion and neutral loss scan mode MS/MS experiments will be described for the analysis of these lipid classes. Analogous sets of complementary precursor ion and neutral loss experiments will also be suggested for additional lipid classes including ceramides, cholesteryl esters, glycerophosphatidylglycerol (GPGro), glycerophosphatidic acid (GPA), and monoacylglycerols. The results of the analyses for these additional lipid classes in rat retina lipid extracts may be obtained upon request from the authors.

2. Materials 2.1. Equipment

1. Teflon/glass tissue homogenizer (Glas-Col®, Sigma-Aldrich St. Louis, MO). 2. Vortex. 3. Centrifuge with swinging bucket rotor (Sorvall RC 5B Plus, Thermo Fisher Scientific Inc., Waltham, MA).

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4. Evaporating Unit (Reacti-Vap, Pierce, Thermo Fisher Scientific Inc., Waltham, MA). 5. SpeedVac SC110 (Savant, Thermo Fisher Scientific Inc., Waltham, MA). 6. Chip-based nano-electrospray ionization (nESI) source (Advion NanoMate, Ithaca, NY). 7. Thermo Scientific model TSQ Quantum Ultra triple quadrupole mass spectrometer (San Jose, CA). 8. Anesthesia System Isoflurane Vapor 19.1 (G.A.S. Inc., Bridgeville). 2 .2. Reagents and Chemicals

1. Methanol (MeOH), HPLC grade, J.T. Baker (Phillipsburg, NJ). 2. Deionized water, HPLC grade, J.T. Baker (Phillipsburg, NJ). 3. Sodium chloride (NaCl), HPLC grade, J.T. Baker (Phillipsburg, NJ). 4. Ammonium hydroxide (NH4OH), EMD Chemicals (Gibbstown, NJ). 5. Chloroform (CHCl3), EMD Chemicals (Gibbstown, NJ). 6. Isopropanol, Fisher Scientific (Pittsburgh, PA). 7. Lipid standards from Avanti Polar Lipids (Alabaster, AL).

2.3. Animals

Male Wistar rats were maintained on Harlan-Teklad laboratory chow (#8640) and water ad libitum. At 12 weeks of age, rats were sacrificed under anesthesia (isoflurane/Vapomatic), and eyeballs were removed and used for retinal preparations. All procedures for the use and care of animals for laboratory research were approved by the All University Committee for Animal Use and Care at Michigan State University.

3. Methods 3 .1. Lipid Extraction from Rat Retinal Tissue

Lipids were extracted using a modified method of Folch (24). Whole retinas (~12 mg tissue) from 12-week old male Wistar rats were placed in an ice-chilled Teflon/glass tissue grinder and homogenized on ice in 50 mL/mg tissue of 40% MeOH in H2O. Retina homogenates were extracted with 200 mL/mg tissue of CHCl3:MeOH (2:1 v/v) by vortexing for 1.0 min. After centrifugation at 3,000 × g for 10 min, the lower organic phase was recovered and transferred to a new glass tube. The aqueous upper phase was re-extracted with 200 mL/mg tissue of CHCl3, then vortexed and centrifuged as before. The lower organic phase was collected and combined with the lower phase from the first extraction. The pooled organic phases were evaporated under



Global Analysis of Retina Lipids

37

nitrogen and further dried in a speedvac overnight. Dried lipid extracts were resuspended in 50 mL/mg tissue of isopropanol: methanol:chloroform (4:2:1v/v/v) and stored under nitrogen in glass vials in the dark at −80°C until further use. 3 .2. nESI-MS and MS/ MS Conditions

Immediately prior to analysis, aliquots of retina lipid extracts were diluted 1:20 in a solution of isopropanol: methanol: chloroform (4: 2: 1 v/v/v) containing 20 mM NH4OH. Synthetic lipid standards were diluted in the same solution to a final concentration of 2 mM. For some experiments, samples were analyzed in the presence of 0.5 mM NaCl. All samples were centrifuged, loaded into Whatman Multichem 96-well plates (Fisher Scientific, Pittsburgh, PA) and sealed with Teflon Ultra-Thin Sealing Tape (Analytical Sales and Services, Pompton Plains, NJ). Lipids were then introduced to a Thermo Scientific model TSQ Quantum Ultra triple quadrupole mass spectrometer (San Jose, CA) via a chip-based nano-electrospray ionization (nESI) source (Advion NanoMate, Ithaca, NY) operating in infusion mode, using an ESI HD_A chip, a spray voltage of 1.4 kV, a gas pressure of 0.3 psi, and an air gap of 2 mL. The ion transfer tube of the mass spectrometer was maintained at 150°C. All MS and MS/MS spectra were acquired using methods controlled by the Xcalibur software (Thermo, San Jose, CA). In MS mode, the Q1 peak width was maintained at 0.5 u. For neutral loss and precursor ion MS/MS scans, the peak widths of both Q1 and Q3 were maintained at 0.5 u. For product ion scan mode MS/MS experiments, Q1 and Q3 were operated with peak widths of 1.0 u. The Q2 collision gas pressure was set at 0.5 mTorr. MS and MS/ MS scans were typically acquired using scan rates of 500m/z/s. Collision energies were individually optimized for each product ion, neutral loss, or precursor ion scan mode MS/MS experiment using commercially available lipid standards. The MS/MS spectra shown were typically averaged over 100–300 scans.

3 .3. Identification of Lipid Molecular Species

Peak finding and correction for 13C isotope effects was performed on complementary sets of precursor ion and/or neutral loss scan mode MS/MS data using the Lipid Mass Spectrum Analysis (LIMSA) v.1.0 software (25) peak model fit algorithm in conjunction with an expanded user-defined database of hypothetical lipid compounds. Assignment of glycerophospholipid acyl chain constituents was achieved by precursor ion scans to monitor for the formation of specific m/z product ions corresponding to deprotonated fatty acid anions in negative ion mode; these scans identified glycerophospholipids as [M − H]−, [M + Cl]−, and [M + CH3OCO2]− ions. Assingment of diacylglycerol and triacylglycerol acyl chain constituents was achieved by neutral loss scans to monitor for the loss of m/z values corresponding to the loss of a fatty acid + NH3 from DG and TG [M + NH4]+ ions in positive ion mode.

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3 .4. Application of Complementary Precursor Ion and Neutral Loss Mode Tandem Mass Spectrometry to Analysis of Rat Retina Lipids 3 .4.1. Mass Spectrum of a Crude Rat Retina Lipid Extract

The use of an animal model, such as the rat, for studying changes in global retina lipid profiles as a function of the onset and progression of disease is attractive for several reasons. These include the ability to control or systematically evaluate the effect of a number of variables, such as genetic background, environment, age, and diet that are known to alter the presence and abundance of particular lipid species at any given time. In order to obtain an initial “snapshot” of the global lipid profile of the normal rat retina, a crude lipid extract isolated from a whole retina was subjected to analysis by using nanoelectrospray ionization coupled to a triple quadrupole mass spectrometer in both positive and negative ionization modes. The mass spectra obtained from these analyses are shown in Fig. 1a, b respectively. Analysis of lipid extracts from three separate rat retinas resulted in essentially identical spectra to those shown in Fig. 1a, b, indicating that minimal biological variability was observed between these animals (data not shown). This was expected given that these samples were obtained from animals with identical genetic backgrounds that were maintained under essentially identical conditions. The resolution and mass accuracy associated with the mass spectra shown in Fig. 1 precluded the identification of individual lipid species on the basis of their masses alone. Indeed, in many cases, the product ion scan mode MS/MS experiments and the precursor ion and neutral loss scan mode MS/MS experiments discussed below indicated that multiple lipid species having the same nominal mass values, as well as multiple lipids with isobaric masses, were present at a given m/z value (see Notes 2 and 3). Furthermore, the presence and the identities of low abundance

Fig. 1. Mass spectra obtained from unfractionated rat retina lipid extract by scanning from m/z 400 to m/z 1,200 in (a) positive ion or (b) negative ion mode.



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lipid species, such as mono-, di-, and tri-acylglycerols, ceramides, and cholesteryl esters that were present at or below the level of chemical noise in these spectra could not be determined. 3 .4.2. Complementary Sets of Precursor Ion and Neutral Loss Scans for Global Lipid Analysis

In order to obtain a more comprehensive profile of the lipid composition of the crude rat retina lipid extract, a series of 27 precursor ions and neutral loss scan mode MS/MS experiments were performed in both positive and negative ionization modes (11, 13, 14, 21, 26–30). A complete description of these scans, including the specific lipid class to be identified, the precursor ion type selected for each scan (e.g., [M + H]+, [M + Na]+, [M + NH4]+, [M − H]−, [M + Cl]− and [M + CH3OCO2]−), the mass of the specific product ion or the neutral loss that was detected, and their identities, is given in Table 1 (see Notes 4, 5, and 6). Specific examples of these experiments, for determining the presence of SM, GPCho, GPEtn, GPIns, GPSer, DG, and TG lipids, are discussed below. In each case, multiple precursor ion and neutral loss scan mode experiments were used to identify and characterize the individual components of a particular lipid class. The use of multiple scans enabled the compositions of the overlapping ions present at a particular m/z value in either the positive or negative ionization mode mass spectra to be more fully elucidated, and allowed the list of spectral features (i.e., molecular ions and their various adducts) that were observed in both the positive and negative ionization mode mass spectra to be significantly expanded. Particularly noteworthy was an observation that the use of complementary precursor ion and neutral loss scans enabled the identification and characterization of molecular species that were not detected using the ‘typical’ scan types that have been most commonly employed previously in the literature for lipid-profiling experiments.

3 .4.3. Complementary Precursor Ion and Neutral Loss Scan Mode Analysis of Rat Retina SM and GPCho Lipids

The identification of abundant SM and GPCho species in negative ion mode is useful when it is necessary to (a) identify spectral features of a negative ion mass spectrum; (b) perform fatty acid analysis of lipid species by negative ion product ion tandem mass spectrometry or precursor ion scanning of m/z corresponding to deprotonated fatty acyls; and (c) identify m/z at which one or more lipid species may be present in a negative ion mass spectrum or tandem mass spectrum. Analysis of these two lipid classes is often complicated by overlap of a GPCho first 13C isotope peak with a putative SM molecular ion. This can be overcome by destruction of GPCho (and all glyerophospholipids) by alkaline hydrolysis (29). This approach is not useful, however, when analysis of both SM and GPCho species are needed from limited amounts of tissue. While algorithms to correct for 13 C isotope abundances have been used to distinguish putative

3.4.3.1. Negative Ion Analysis of SM and GPCho

Neutral phosphocholine

NLS 183

+

[M + Na] [M + H]+, M + Na]+ [M − H] [M − H]− [M + NH4]+ [M + H]+, [M + Na]+ [M + Na]+ [M − H]−

  7 SM, GPCho

  8 GPEtn

  9 GPEtn

10 GPIns

11 GPIns

12 GPSer

13 GPSer

14 GPSer

−

Sodium cholinephosphate

NLS 205

[M + Na]+

  6 GPCho

NLS 87

PIS 208

NLS 185

NLS 277

Serine

Sodium phosphoserine

Phosphoserine

Phosphoinositol+NH3

Dehydrated phosphoinositol

Glycerol phosphoethanolamine derivative

PIS 196 PIS 241

Phosphoethanolamine

NLS 141

Sodium cyclophosphane

PIS 147

[M + Na]

  5 SM, GPCho

Phosphocholine

[M + H]+

  4 SM, GPCho

PIS 184

CH3OCO2+(CH3)3 NCHCH2)

NLS 161

[M + CH3OCO2]

  3 SM, GPCho

+

CH3OCO2H+(CH3)3N

NLS 135

−

CH3Cl

[M + CH3OCO2]−

NLS 50

MS/MS scan type Product ion / Neutral loss

  2 SM, GPCho

Precursor ion [M + Cl]−

Lipid class

  1 SM,GPCho

#

(13)

–

(11)

(38)

(13)

(13)

(14)

(37)

(13)

(37)

(13)

(21)

(21)

(21)

Ref

Table 1 Description of the precursor ion and neutral loss scan mode MS/MS experiments employed for the class-specific identification of lipid molecular species

40 Busik, Reid, and Lydic

Cyclic glycerophosphate derivative Sphinganine derivative Sphingosine derivative Sphingadienine derivative 16:0 aldehyde 16:1 aldehyde

NLS 115 PIS 153 PIS 266 PIS 264 PIS 262 NLS 258 NLS 256

+

[M + NH4] [M − H]− +

[M + H]+ +

[M + H] [M − H]− −

[M + H]

[M − H] [M − H]−

20 GPA

21 GPGro, GPA

22 18:0-based ceramides

23 18:1-based ceramides

24 18:2-based ceramides

25 18:0-based ceramides

26 18:1-based ceramides

27 18:2-based ceramides

PIS parent ion scan, NLS neutral loss scan

NH3+H3PO4

NLS 189

[M + NH4]+

19 GPGro

NLS 254

Phosphoglycerol+NH3

PIS (m/z varies)

16:2 aldehyde

Fatty acyl anion

H2O+NH3

[M − H] , [M + Cl] , [M + CH3OCO2] −

18 Glycerophospholipids −

NLS 35

−

[M + NH4]+

17 Mono-/ Diacylglycerols

Cholestane cation

PIS 369

[M + NH4]

Fatty acid+NH3

NLS (m/z varies)

16 Cholesteryl esters +

[M + NH4]+

15 Di-/Triacylglycerols, Cholesteryl esters

(30)

(30)

(30)

(27)

(29)

(29)

(13)

–

(38)

(13)

–

(13)

(28)

Global Analysis of Retina Lipids 41

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Busik, Reid, and Lydic

SM molecular ions from GPCho first 13C isotope peaks (25, 31), the extremely low abundance of retinal SM molecular ions detected by typical positive ion analysis (see Subheading 3.3.2) has precluded the successful reliance upon deisotoping algorithms alone for thorough analysis of rat retina SM species. The ability of SM and GPCho species to form adducts with chloride (Cl−) and methyl carbonate (CH3OCO2−) anions enabled a convenient method for negative ion mode detection of SM and GPCho molecular species. Chloride ions are often present in small amounts in lipid extracts. While Cl− ions may be minimized by multiple sample washing and desalting steps, we have found the presence of Cl− to be beneficial for negative ion analysis of SM and GPCho. Figure 2a demonstrated negative ion mode collisionally induced dissociation (CID) of the [M + Cl]− ion of a synthetic GPCho(14:0/14:0) species in the presence of 0.5 mM NaCl. Collisionally induced dissociation of chloride adducts of GPCho species led to an abundant neutral loss of 50 Da, corresponding to loss of methyl chloride. Dissociation of this species also resulted in ions corresponding to the loss of the GPCho 14:0 fatty acid moieties as neutral ketene species, observed at m/z 452, as well as abundant ions at m/z 227 corresponding to the deprotonated fatty acids. By contrast, CID of chloride-adducted synthetic SM(d18:1/12:0) revealed the abundant neutral loss of 50 Da was the primary product ion observed

Fig. 2. Negative ion mode product ion MS/MS spectra of [M + Cl]− ions of synthetic GPCho(14:0/14:0) (a), SM(d18:1/12:0) (b), and [M + CH3OCO2]− ions of synthetic GPCho(14:0/14:0) (c), and SM(d18:1/12:0) (d).



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43

over a wide range of collision energies (Fig. 2b). Dissociation of GPCho(14:0/14:0) [M + CH3OCO2]− ions resulted in the spectra shown in Fig. 2c. Abundant neutral losses of 76 Da (loss of CH3OCO2 + H), 135 Da (loss of CH3OCO2+ (CH3)3N), and 161 Da (loss of CH3OCO2+ (CH3)3 NCHCH2) were observed, as well as ions representing the loss of the neutral 14:0 fatty acid (m/z 391) and the fatty acid anion (m/z 227). Fragmentation of the methyl carbonate adduct of synthetic SM(d18:1/12:0) by CID Fig. (2d) revealed phosphocholine-derived ions similar to those observed for GPCho, however at significantly different abundances. Abundant neutral losses of 76, 135, and 161 Da were all observed, yet here the loss of 161 Da dominated, as opposed to the loss of 135 Da that was seen for GPCho. The negative ion mode gas-phase fragmentation behavior of SM and GPCho species, as demonstrated in Fig. 2, enable the use of neutral loss scan mode MS/MS for class-specific detection of SM and GPCho. Performing the conventionally used neutral loss scan (NLS) of 50 Da (scan number 1 in Table 1) in rat retina lipid extracts in the presence of 0.5 mM chloride resulted in detection of both SM and GPCho species (Fig. 3a). From the NLS

Fig. 3. Complementary negative ion mode MS/MS analysis of rat retina SM and GPCho species. Bold text indicates m/z of identified SM species. (a) Detection of SM and GPCho as [M + Cl]− ions by neutral loss scanning of 50 Da in the presence of 0.5 mM NaCl. (b) Detection of SM and GPCho as [M + CH3OCOO]− ions by neutral loss scanning of 135 Da. (c) Detection of SM and GPCho as [M + CH3OCOO]− ions by neutral loss scanning of 161 Da.

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50 scan a total of eight SM peaks, highlighted with bold text in Fig. 3a, were readily discernable from GPCho species. As the neutral loss of 135 Da was the most abundant loss observed for GPCho [M + CH3OCO2]− ions across a wide range of collision energies, it enabled negative ion detection of rat retina GPCho species as methyl carbonate adducts by performing NLS 135 (Fig. 3b, scan number 2 in Table 1) when ammonium hydroxide was added to the rat retina lipid extract at a concentration of 20 mM. Five low abundance SM peaks, highlighted with bold text in Fig. 3b, were also detected with this scan. We next employed the neutral loss of 161 Da (scan number 3 in Table 1) to detect SM and GPCho [M + CH3OCO2]− ions in a rat retina lipid extract, which resulted in the spectrum shown in Fig. 3c. Here, seven additional SM peaks could be detected that were not observed in the NLS 135 scan, as well as four SM peaks not detected by NLS 50. Additionally, the relative abundances of SM species at m/z 777 and 805 (corresponding to SM(d18:1/16:0) and SM(d18:1/18:0) [M + CH3OCO2]− ions) were increased approximately tenfold and 20-fold, respectively, compared to their relative abundances in the NLS 135 scan. The differential fragmentation behavior of methylcarbonate-adducted anions of SM and GPCho was thus exploited to preferentially detect either a predominance of GPCho ions via the neutral loss of 135 Da, or a predominance of SM ions via the neutral loss of 161 Da. Both chloride and methylcarbonate adducts of GPCho species were suitable for performing negative ion mode product ion scans to confirm GPCho fatty acid assignments. Likewise, GPCho species were detected as both chloride and methylcarbonate-adducted anions when precursor ion scanning of m/z corresponding to deprotonated fatty acyls was performed in rat retina lipid extracts (scan number 18 in Table 1, data not shown.) Therefore, it was advantageous to assess rat retinal GPCho content by utilizing both NLS 135 and NLS 50 for detection of GPCho species as [M + CH3OCO2]− and [M + Cl]− ions, respectively. Performing the additional NLS 161 for detection of SM [M + CH3OCO2]− ions allowed for the most thorough identification of rat retinal SM species. Under the conditions employed here, negligible formate or acetate adducts of PC and SM lipids were observed (11, 32). 3 .4.3.2. Positive Ion Analysis of SM and GPCho

Collisionally induced dissociation of GPCho and SM [M + H]+ ions yields an abundant characteristic product ion at m/z 184 corresponding to the protonated phosphocholine headgroup. Product ion spectra of synthetic GPCho(14:0/14:0) and SM(d18:1/12:0) are shown in Fig. 4a, b, respectively. As observed for chloride and methylcarbonate adducts of GPCho and SM, product ion spectra of synthetic GPCho(14:0/14:0) and SM(d18:1/12:0) [M + Na]+ ions yielded similar product ions, though at differing relative abundances (Fig. 4b, c). ­Abundant



Global Analysis of Retina Lipids

45

Fig. 4. Positive ion mode product ion MS/MS spectra of [M + H]+ ions of synthetic GPCho(14:0/14:0) (a) and SM(d18:1/12:0) (b) and [M + Na]+ ions of synthetic GPCho(14:0/14:0) (c) and SM(d18:1/12:0) (d).

ions corresponding to loss of 59 Da (N(CH3)3), 183 Da (neutral phosphocholine), and an ion corresponding to sodiated cyclophosphane at m/z 147 were observed in the product ion spectra of both GPCho and SM. In the case of GPCho, the ion at m/z 147 was the most abundant ion observed in the product ion spectrum. In the case of SM, however, the neutral losses of 183 and 59 Da were more abundant than the ion at m/z 147. Additionally, the loss of 205 Da (corresponding to the neutral loss of sodium choline phosphate) was abundant from dissociation of GPCho [M + Na]+ ions, yet was present at less than 1% relative abundance upon dissociation of SM [M + Na]+ ions. The spectra obtained from various positive ionization mode precursor ion and neutral loss scan mode MS/MS experiments for the identification of SM and GPCho lipid ions from crude whole rat retina lipid extract are shown in Fig. 5. The spectrum obtained from the conventional precursor ion scan (PIS) mode MS/MS experiment (scan number 4 in Table 1), to monitor for the formation of the characteristic phosphocholine product ion at m/z 184 from dissociation of SM and GPCho [M + H]+ ions, is shown in Fig. 5a. After isotopic correction, only four SM peaks were identified (m/z 703, 731, 759, and 815, corresponding to d18:1/16:0, d18:1/18:0, d18:1/20:0, and d18:1/24:0 species of SM, respectively) at low (<5%) relative abundance. However, PIS 184 provided excellent sensitivity for GPCho detection, as it provided the highest signal/noise observed for all methods of GPCho detection.

46

Busik, Reid, and Lydic

Fig. 5. Complementary positive ion mode MS/MS analysis of rat retina SM and GPCho species. Bold text indicates m/z of identified SM species. (a) Detection of SM and GPCho as [M + H]+ ions by precursor ion scanning of m/z 184. (b) Detection of SM and GPCho as [M + Na]+ ions by precursor ion scanning of m/z 147. (c) Detection of GPCho as [M + Na]+ ions by neutral loss scanning of 205 Da. (d) Detection of SM and GPCho as [M + Na]+ ions by neutral loss scanning of 183 Da.

As sodium (Na+) ions are often present in lipid extracts due to carryover from the aqueous phase during lipid extraction, sample washing steps, and the mass spectrometer matrix, additional precursor ion and neutral loss scan mode MS/MS analyses of SM and GPCho [M + Na]+ ions were also performed (see Fig. 5b–d; scan numbers 5–7 in Table 1.) Notably, each scan type varied greatly in terms of utility for SM analysis, consistent with the analysis of product ion spectra from GPCho and SM [M + Na]+ ions. We found that PIS 147 (Fig. 5b, scan number 5 in Table 1), resulted in relative abundances of SM and GPCho species that were overall very similar to those obtained by the PIS 184 scan; however, only two SM species could be identified with this MS/ MS scan. Figure 5c demonstrates the virtually exclusive detection of GPCho species as [M + Na]+ ions with no detection of SM [M + Na]+ ions. This was achieved by utilizing the constant neutral loss of 205 Da (scan number 6 in Table 1). Exclusive detection



Global Analysis of Retina Lipids

47

of GPCho species by NLS 205 was consistent with the expected result based on the product ion spectra of GPCho and SM [M + Na]+ ions. Conversely, the product ion spectra of GPCho and SM [M + Na]+ ions suggested that the neutral loss of 183 Da could allow enhanced detection of SM ions relative to GPCho ions. The use of NLS 183 in rat retina lipid extracts resulted in the spectrum shown in Fig. 5d. Here, four SM species could be detected, and the relative abundances of SM and GPCho were similar to those observed for PIS 184. The same results were obtained when NLS 183 was performed across a wide range of collision energies, which implies that this MS/MS scan mode does not afford a significant improvement over PIS 184 in terms of detection of SM species. Additionally, the ion at m/z 856 (corresponding to GPCho(18:0/22:6) was observed at greater relative abundance in NLS 183 relative to the other MS/ MS scans utilized, which may implicate an acyl chain contribution to the fragmentation behavior of these ions. All of the scans used to detect SM and GPCho as [M + Na]+ ions were equally useful in providing redundant identification of GPCho molecular species, yet none of these scans matched the PIS 184 MS/MS scan in terms of overall sensitivity. As observed for negative ion analysis, complementary precursor ion and neutral loss scan mode MS/MS analysis of SM and GPCho in the positive ion mode enabled redundant detection of SM and GPCho species, and the use of NLS 205 enabled specific detection of only GPCho without lipid extract fractionation prior to analysis. The ability to redundantly detect multiple molecular species, especially in the case of GPCho, by using multiple lipid class-specific product ions increased confidence in peak identification for low abundance ions. It should be noted, however, that negative ion analysis of SM species as either [M + CH3OCO2]− or [M + Cl]− ions proved more useful than positive ion analysis in terms of readily identifying the greatest number of distinct SM molecular species. In contrast, positive ion analysis was more effective for identification of GPCho species in terms of overall sensitivity (signal/noise); however, negative ion analysis of GPCho was useful for determining fatty acid substituents by product ion scanning and/or precursor ion scanning of deprotonated fatty acyl species. If “absolute quantitation” of retinal SM and GPCho were to be attempted here, it would be necessary to analyze all ionic forms of SM and GPCho relative to internal standards and sum the contributions of each ionic form of SM or GPCho molecular species. Relative comparisons of lipid species across numerous samples could be achieved by selecting optimal scan modes for the desired lipid classes (i.e., NLS 161 for analysis of SM and PIS 184 for analysis of GPCho). The MS/MS spectra collected for analysis of SM and GPCho species described here were analyzed between m/z 700–1,200 for

48

Busik, Reid, and Lydic

positive ion mode, and m/z 700–1,300 for negative ion mode. To allow these spectra to be viewed in greater detail, smaller mass ranges are shown in Figs. 3 and 5. However, it should be noted that complementary analysis of rat retina SM and GPCho species verified the presence of GPCho species containing 32:6 and 34:6 very long chain polyunsaturated fatty acids; in each case, a 22:6 fatty acid was identified at the remaining glycerol position of the GPCho species. These species were observed as abundant ions in the positive ion MS spectrum Fig. (1a) at m/z 1,018 and 1,046, respectively. These species were identified by all positive ion MS/MS scan modes employed here for the analysis of SM and GPCho. These high molecular weight GPCho species were also identified by NLS 50 as [M + Cl]− ions, which subsequently were used for product ion mode MS/MS analysis to verify the identities of the fatty acid moieties. However, these GPCho species were not detected as methylcarbonate adducts by either NLS 135 or NLS 161. By combining both positive and negative ion mode shotgun MS/MS analyses of all phosphocholine-containing lipids, identification of 13 SM and 29 GPCho lipids was achieved in rat retina crude lipid extracts with a significant degree of redundancy (see Note 7). A summary of the SM and GPCho molecular species detected by all MS/MS scan modes described above is provided in Table 2. 3 .4.4. Complementary Precursor Ion and Neutral Loss Scan Mode Analysis of Rat Retina GPEtn and GPIns Lipids

Pairs of complementary neutral loss and precursor ion scans were also employed for the identification and characterization of GPEtn and GPIns lipid species. The detection of GPEtn lipids has typically been achieved by monitoring for the neutral loss of 141 Da (i.e., phosphoethanolamine) from their [M + H]+ precursor ions in positive ion mode MS/MS analysis (scan number 8 in Table 1). The spectrum obtained from analysis of the crude rat retina lipid extracts using this scan is shown in Fig. 6a. After correction for 13C isotope contributions, a total of 20 GPEtn lipid species were identified in this experiment. The use of a complementary precursor ion scan to monitor for the presence of a characteristic m/z 196 product ion, corresponding to a glycerol phosphoethanolamine derivative Fig. (6b) from the [M − H]− precursor ions of GPEtn (scan number 9 in Table 1), enabled the detection of three additional GPEtn lipids. The observed discrepancy in identified GPEtn lipids between the two scan types was due, in part, to the fragmentation behavior of GPEtn alkenyl species. While collisionally induced dissociation of GPEtn alkenyl [M − H]− ions gives rise to the GPEtn-specific fragment at m/z 196, the neutral loss of 141 Da is virtually absent from CID of GPEtn alkenyl [M + H]+ ions (38). GPEtn alkenyl species detected by the negative ion PIS 196 included those at m/z 748, 750, and 774 in Fig. 6b (see Table 3). Although scanning for the constant neutral loss of 141 Da affords the most

Scan #1 NLS 50 (m/z)

–

737

738

–

763

765

766

768

–

–

–

–

–

793

794

796

–

Lipid speciesa

SM(d18:1/16:1)

SM(d18:1/16:0)

GPCho(14:1/16:0)

GPCho(14:0/16:0)

SM(d18:1/18:1)

SM(d18:1/18:0)

GPCho(16:0/16:1)

GPCho(16:0/16:0)

SM(d18:1/19:0)

GPCho(O-18:0/16:0)

SM(d18:1/20:1)

GPCho(16:1/18:1)

GPCho(16:0/18:2)

SM(d18:1/20:0)

GPCho(16:0/18:1)

GPCho(16:0/18:0)

GPCho(O-18:0/18:0)

–

836

834

833

832

832

–

–

–

808

806

805

–

–

–

777

–

Scan #2 NLS 135 (m/z)

Table 2 Rat retina SM and GPCho molecular species

–

836

834

833

832

832

831

820

819

808

806

805

803

780

778

777

775

Scan #3 NLS 161 (m/z)

774

762

760

759

758

758

–

746

–

734

732

731

–

706

704

703

–

Scan #4 PIS 184 (m/z)

–

784

782

–

780

780

–

–

–

756

754

753

–

728

–

725

–

Scan #5 PIS 147 (m/z)

–

784

782

–

–

780

–

–

–

756

–

–

–

–

–

–

–

Scan #6 NLS 205 (m/z)

– (continued)

784

782

781

780

780

–

–

–

756

754

753

751

–

726

725

–

Scan #7 NLS 183 (m/z)

Global Analysis of Retina Lipids 49

Scan #1 NLS 50 (m/z)

816

–

–

820

820

822

824

840

840

841

–

844

845

847

849

–

–

Lipid speciesa

GPCho(16:0/20:4)

SM(d18:1/22:2)

SM(d18:1/22:1)

GPCho(18:1/18:1)

GPCho(18:0/18:2)

GPCho(18:0/18:1)

GPCho(18:0/18:0)

GPCho(16:0/22:6)

GPCho(16:1/22:5)

SM(d18:1/24:4)

GPCho(38:5)

GPCho(18:0/20:4)

SM(d18:1/24:2)

SM(d18:1/24:1)

SM(d18:1/24:0)

GPCho(18:1/20:0)

GPCho(18:1/22:6)

Table 2 (continued)

906

–

–

–

–

884

–

881

880

880

864

862

860

860

–

857

856

Scan #2 NLS 135 (m/z)

–

890

889

887

–

884

–

881

880

880

864

862

860

860

859

857

856

Scan #3 NLS 161 (m/z)

832

816

815

–

–

810

808

–

806

806

790

788

786

786

–

–

782

Scan #4 PIS 184 (m/z)

854

–

–

–

–

832

–

–

828

828

812

810

808

808

–

–

804

Scan #5 PIS 147 (m/z)

854

–

–

–

–

832

830

–

828

828

812

810

808

808

–

–

804

Scan #6 NLS 205 (m/z)

854

–

–

–

–

832

830

–

828

828

812

810

808

808

–

–

804

Scan #7 NLS 183 (m/z)

50 Busik, Reid, and Lydic

868

868

–

–

–

912

1,052

1,080

GPCho(18:0/22:6)

GPCho(18:1/22:5)

GPCho(40:4)

GPCho(42:10)

GPCho(42:4)

GPCho(22:6/22:6)

GPCho(22:6/32:6)

GPCho(22:6/34:6)

–

–

952

–

–

–

908

908

–

–

952

–

–

–

908

908

1,046

1,018

878

866

854

838

834

834

1,068

1,040

900

–

876

860

856

856

1,068

1,040

900

–

876

860

856

856

1,068

1,040

900

–

876

860

856

856

Global Analysis of Retina Lipids 51

52

Busik, Reid, and Lydic

Fig. 6. Complementary positive and negative ion mode MS/MS analysis of rat retina GPEtn and GPIns species. (a) Detection of GPEtn as [M + H]+ and [M + Na]+ ions by positive ion mode neutral loss scanning of 141 Da. (b) Detection of GPEtn as [M − H]− ions by precursor ion scanning of m/z 196. (c) Detection of GPIns as [M − H]− ions by negative ion mode precursor ion scanning of m/z 241. (d) Detection of GPIns as [M + NH4]+ ions by positive ion mode neutral loss scanning of 277 Da.

sensitivity for detection of GPEtn species in terms of signal/noise ratio and absolute ion abundance (data not shown), the utility of the positive ion NLS 141 is effectively limited to detection of diacyl GPEtn species. Furthermore, GPEtn [M + Na]+ ions, if present, are also detected by NLS 141, further complicating spectrum interpretation and quantification against an internal standard. m/z 814 and 858 in Fig. 6a represent abundant ions corresponding to sodium adducts of GPEtn(18:0/22:6) and GPEtn(22:6/22:6), respectively. It is possible to decrease the abundance of [M + Na]+ ions in this scan mode by using low collision energy CID (<30 V under the conditions employed); however, the use of lower collision energies in the analysis of rat retina GPEtn lipids resulted in underrepresentation of GPEtn species containing 22:6 fatty acid constituents due to the lower fragmentation efficiency of these species. While the negative ion precursor of m/z 196 scan affords detection of both diacyl and alkyl/acyl GPEtn species, this scan mode may also detect GPEtn [M + Cl]− ions formed when chloride is present in a lipid extract (m/z 826 and 870 in Fig. 6b). It should be noted that the signal-to-noise associated with the spectrum obtained by negative ion mode PIS 196 was approximately two orders of magnitude lower than that of the positive ion mode neutral loss scan, thereby placing a limitation on the utility of this scan mode



Global Analysis of Retina Lipids

53

Table 3 Rat retina GPEtn species detected by complementary positive and negative ion mode MS/MS analysis Lipid speciesa

Scan #8 NLS 141 (m/z)

Scan #9 PIS 196 (m/z)

GPEtn(16:0/18:1)

718

716

GPEtn(16:0/18:0)

720

718

GPEtn(O-16:1/20:4)

–

722

GPEtn(16:0/20:4)

740

738

GPEtn(18:1/18:1)

744

–

GPEtn(18:0/18:1)

746

744

GPEtn(O-16:1/22:6)

–

746

GPEtn(O-16:0/22:6)

750

748

GPEtn(O-18:0/20:4)

752

750

GPEtn(16:0/22:6)

764

762

GPEtn(18:1/20:4)

766

764

GPEtn(18:0/20:4)

768

766

GPEtn(O-18:2/22:6)

774

772

GPEtn(O-18:1/22:6)

776

774

GPEtn(O-18:1/22:5) GPEtn(O-18:0/22:6)

778

776

GPEtn(O-20:1/20:4)

–

778

GPEtn(18:2/22:6)

788

786

GPEtn(18:1/22:6)

790

788

GPEtn(18:0/22:6)

792

790

GPEtn(20:0/20:4)

796

794

GPEtn(20:4/22:6)

812

810

GPEtn(22:6/22:6)

836

834

GPEtn(22:4:/22:6)

840

838

a  GPEtn fatty acid substituents were determined by negative ion mode MS/MS experiments as described in Subheading 3. Alkenyl species are denoted by O- preceding the acyl chain. A dash indicates a species that was not detected.

for the detection of additional low abundance GPEtn lipid ions. Due to the potential complications arising from the presence of various ionic adducts, as well as the differential fragmentation

54

Busik, Reid, and Lydic

behavior of alkenyl GPEtn species in positive vs. negative ionization mode, the use of both of these MS/MS scan modes to redundantly detect GPEtn lipids allowed for more thorough evaluation of GPEtn lipids and greater confidence in peak identification (see Note 2). The GPEtn lipids identified in rat retina are summarized in Table 3. Fatty acid constituents were analyzed in negative ion mode as described in Subheading 3.3. In this analysis, a total of 24 GPEtn species were identified in rat retina after accounting for isobaric species, with redundant identification achieved for all diacyl species. The detection of GPIns lipids is typically achieved using a negative ion precursor ion scan mode MS/MS experiment, by monitoring for the formation of a characteristic product ion at m/z 241 (corresponding to a dehydrated phosphoinositol) from their [M − H]− ions (scan number 10 in Table 1). The spectrum resulting from this scan, shown in Fig. 6c, enabled the identification of 14 GPIns lipids. The use of a complementary scan (number 11 in Table 1) to monitor for the characteristic neutral loss of 277 Da (corresponding to the combined losses of phosphoinositol + NH3) from the [M + NH4]+ adducts of GPIns Fig. (6d) enabled nine of the lipids observed in the negative ion mode precursor ion scan experiment to be confirmed. This was especially useful in confirming ions of relatively low abundance. For example, the ions at m/z 909 and 929 in the spectrum shown in Fig. 6c represent putative GPIns [M − H]− (Total carbon: total double bond) 40:6 and 42:10 species, respectively. These ions were confirmed as likely GPIns species by the presence of their corresponding [M + NH4]+ ions after conducting NLS 277 (m/z 928 and 948 in Fig. 6d). Further complementary negative ion mode fatty acid analysis by scanning for precursor ions corresponding to deprotonated fatty acyl groups (scan number 18 in Table 1) confirmed the fatty acid constituents of these GPIns species as diacyl 18:0/22:6 and 20:4/22:6, respectively (data not shown.) Taken together, these complementary sets of data enabled confident identification of the ions in question despite their low relative abundance. A compilation of all rat retina GPIns species identified by positive and negative ion mode analysis is provided in Table 4. 3 .4.5. Complementary Precursor Ion and Neutral Loss Scan Mode Analysis of Rat Retina GPSer Lipids

Detection of glycerophosphatidylserine (GPSer) lipid species is typically achieved in either positive or negative ion mode by the use of neutral loss scan mode MS/MS experiments. Figure 7a demonstrates the collisionally induced dissociation of the [M + H]+ ion of synthetic GPSer(14:0/14:0) at m/z 680. The fragmentation of this ion is dominated by the neutral loss of 185 Da corresponding to loss of the phosphoserine headgroup. Figure 7b shows the spectrum obtained from collisionally induced dissociation of the [M + Na]+ ion of the same



Global Analysis of Retina Lipids

55

Table 4 Rat retina GPIns species detected by complementary positive and negative ion mode MS/MS analysis Lipid speciesa

Scan #10 PIS 241 (m/z)

Scan #11 NLS 277 (m/z)

GPIns(16:0/18:1)

835

–

GPIns(16:1/20:4)

855

874

GPIns(16:0/20:4)

857

876

GPIns(18:2/20:4)

881

900

GPIns(16:0/22:6)

881

900

GPIns(18:1/20:4)

883

902

GPIns(18:0/20:4)

885

904

GPIns(18:2/22:6)

905

–

GPIns(18:1/22:6)

907

–

GPIns(18:0/22:6)

909

928

GPIns(20:0/20:4)

913

932

GPIns(20:4/22:6)

929

948

GPIns(20:4/22:6)

931

–

GPIns(22:6/22:6)

953

–

a  GPIns fatty acid substituents were determined by negative ion mode MS/MS experiments as described in Subheading 3. A dash indicates a species that was not detected.

GPSer(14:0/14:0) species. While a neutral loss of 185 Da was observed, the fragmentation of the GPSer(14:0/14:0) [M + Na]+ ion was dominated by the formation of the ion at m/z 208, corresponding to a sodium phosphoserine cation. Additionally, loss of neutral sodium phosphoserine (207 Da) was observed at m/z 495. Figure 7c demonstrates CID of the GPSer(14:0/14:0) [M − H]− ion observed at m/z 678. The most abundant ion observed in this spectrum results from the loss of 87 Da, corresponding to a serine derivative. Additional abundant ions were observed at m/z 381 and 363, corresponding to losses of the 14:0 fatty acids as ketene and neutral species, respectively; m/z 227, corresponding to the deprotonated fatty acid ions; and m/z 153, corresponding to a cyclic glycerolphosphate derivative. As the neutral loss of 185 Da was observed from dissociation of both GPSer [M + H]+ and [M + Na]+ ions, performing the typical positive ion mode analysis of rat retina GPSer by utilizing NLS 185 (scan number 12 in Table 1), resulted in

56

Busik, Reid, and Lydic

Fig. 7. Product ion mode MS/MS spectrum of synthetic GPSer(14:0/14:0) [M + H]+ ions (a), [M + Na]+ ions (b), and [M − H]− ions (c).

detection of both GPSer [M + H]+ and [M + Na]+ ions (see Fig. 8a). As observed in the positive ion analysis of GPEtn lipids, the presence of GPSer species in two different ionic forms within the same spectrum complicated interpretation of the spectrum, as ions that differ by 22 mass units could represent a difference of an adducted sodium ion, or the mass difference represented by the addition of two fatty acid methylene groups and three double bonds. The spectrum in Fig. 8a contains several sets of ions that differ by 22 mass units, such as m/z 790 and m/z 812; m/z 812 and m/z 834; m/z 836 and m/z 858; m/z 858 and m/z 880; and m/z 880 and m/z 902. In each case, the ion observed at an increase of 22 mass units could represent either a sodium-adducted species of the ion at the lower nominal mass, or a unique GPSer molecular species with one or more fatty acids that contain additional carbons and double bonds relative to the GPSer species at the lower nominal mass. Additionally, the spectrum obtained by NLS 185 contained several ions of very low relative abundance (denoted by bold type), including m/z 762, m/z 790, and m/z 936. In order to provide confirmation of low abundance rat retina GPSer species and clarify the identities and ionic forms of ions observed in the NLS 185 spectrum, we employed two additional complementary



Global Analysis of Retina Lipids

57

Fig. 8. Complementary positive and negative ion mode MS/MS analysis of rat retina GPSer species. Bold text indicates m/z of species at very low relative abundance that were redundantly detected in multiple ion forms. (a) Detection of GPSer as [M + H]+ and [M + Na]+ ions by positive ion mode neutral loss scanning of 185 Da. (b) Detection of GPSer as [M + Na]+ ions by positive ion mode precursor ion scanning of m/z 208. (c) Detection of GPSer as [M − H]− ions by negative ion mode neutral loss scanning of 87 Da.

GPSer-specific MS/MS scans. Figure 8b demonstrates use of a precursor ion scan for m/z 208 to detect formation of the sodium phosphoserine ion from GPSer [M + Na]+ ions (scan number 13 in Table 1.) The spectrum obtained by performing this scan contained ions of only a single ionic form and elucidated m/z at which sodium-adducted GPSer species contributed to total ion current of the NLS 185 spectrum. The use of this complementary precursor ion scan would be useful in any positive ion mode analysis of GPSer in order to confirm the presence or absence of GPSer [M + Na]+ ions, or to simplify relative quantitation of GPSer species against an internal standard. Furthermore, the use of PIS 208 enabled redundant detection of the low abundance ions observed in the spectrum obtained by scanning for the neutral loss of 185 Da. The ions in bold in Fig. 8b at m/z 784, m/z 812, and m/z 958 represent sodiated adducts of the protonated GPSer ions observed at m/z 762, m/z 790, and m/z 936, respectively, detected by the neutral loss of 185 Da.

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Busik, Reid, and Lydic

Negative ion mode analysis of rat retina GPSer was achieved by the commonly used neutral loss of 87 Da (scan number 14 in Table 1). The spectrum obtained from this neutral loss mode MS/MS experiment is shown in Fig. 8c. Performing NLS 87 results in detection of GPSer molecular species in only one ionic form (i.e., [M − H]−), which offers the same advantage as the positive ion mode PIS 208 in terms of spectrum simplification and ease of relative quantification against an internal standard. The detection of GPSer [M − H]− ions by NLS 87 offers an additional advantage in that ions identified by this MS/ MS scan may in turn be used for direct analysis of esterified fatty acid constituents by product ion mode MS/MS experiments, or by precursor ion scanning of individual fatty acid anions. The use of NLS 87 to detect rat retina GPSer also provided another means of confirming the presence of low abundance ions detected by positive ion GPSer analysis. The ions in bold in Fig. 8c at m/z 760, m/z 788, and m/z 934 represent deprotonated equivalents of the low-abundance ions detected by NLS 185 and PIS 208. Subsequent analysis of the fatty acid constituents of these GPSer species from their deprotonated ions revealed that the identities of these low abundance ions were diacyl GPSer(16:0/18:1), GPSer(18:0/18:1), and GPSer(24:6/24:6). Notably, an abundant ion at m/z 810 was observed in the spectrum obtained from the negative ion NLS 87 MS/MS experiment. This ion represents the deprotonated form of the protonated ion observed at m/z 812 in the NLS 185 MS/MS experiment. However, an ion at m/z 812 was also observed in the PIS 208 MS/MS experiment, indicating that a small contribution of the ion current for m/z 812 in the NLS 185 spectrum was made by a GPSer sodium adduct of the ion at m/z 790. The quantification of m/z 812 in the NLS 185 spectrum against an internal standard would necessitate accommodation for the presence of underlying the GPSer [M + Na]+ ion; however, the presence of the underlying GPSer [M + Na]+ ion would not have been detected without performing the PIS 208 MS/MS scan. Furthermore, complementary positive and negative ion mode analysis of GPSer utilizing only NLS 185 and NLS 87, respectively, would have falsely confirmed that the ion detected at m/z 812 in the NLS 185 MS/MS scan consisted entirely of GPSer [M + H]+ species. This analysis underscores the advantages of employing lipid class-specific MS/MS detection methods in which lipid species are detected as only one ionic form. The ability to redundantly detect low abundance rat retina GPSer species in multiple ionic forms enabled increased confidence in peak calling and subsequent inclusion of these ions in analysis of esterified fatty acid groups. The ability to identify both protonated and sodium-adducted GPSer molecular ions in positive ion mode MS/MS analysis expanded the number of spectral features



Global Analysis of Retina Lipids

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that could be assigned in the spectrum obtained from the initial MS analysis. Importantly, the use of a precursor ion scan of m/z 208 to identify GPSer [M + Na]+ ions simplified positive ion GPSer analysis by clarifying the identities of lipid species at m/z where potential overlap existed between protonated and sodiated GPSer ions of differing fatty acid composition. Use of the negative ion NLS 87 scan for detection of GPSer enabled the identification of spectral features of the negative ion mode MS spectra, and provided additional confirmation of GPSer species observed at very low abundance. Relative quantification of GPSer would be most straightforward when using either the negative ion NLS 87 MS/MS experiment, or the positive ion PIS 208 MS/MS experiment when known amounts of exogenous salts have been added to a lipid extract; this scan is also useful for determining the presence of endogenous salts that may be present following lipid extraction. After taking into account the redundant lipid species identified in both the negative ion scan mode neutral loss experiment and the positive ion scan mode neutral loss and precursor ion experiments, a total of 29 GPSer molecular species could be identified in rat retina, with 22 redundant identifications. See Table 5 for a detailed catalog of all identified rat retina GPSer species. This example clearly highlights the utility of alternative or complementary MS/MS scans that are not commonly employed in lipidome-profiling studies to provide more detailed insights into the composition of complex lipid mixtures. 3 .4.6. Complementary Neutral Loss Scan Mode Analysis of Rat Retina DG and TG Lipids

Nonpolar lipids, such as diacylglycerols (DG) and triacylglycerols (TG), have typically required derivatization and/or chromatographic separation prior to ESI-MS analysis (33–36). This limits the ability to analyze both polar and nonpolar lipid classes from limited sample amounts. We have applied the principle of complementary neutral loss scan mode MS/MS to the analysis of DG and TG species in the same unfractionated rat retina extract that was used for analysis of the polar lipid classes described above. The addition of ammonium hydroxide (other other ammonium salts) to lipid extracts prior to mass spectrometry analysis provides abundant ammonium cations that readily adduct neutral lipids such as DG’s and TG’s. Redundant detection of DG and TG [M + NH4]+ ions was achieved by neutral loss scanning for multiple m/z corresponding to the loss of a neutral fatty acid + NH3 (scan number 15 in Table 1.) By comparing spectra obtained from neutral loss scans corresponding to multiple naturally occurring fatty acids, the fatty acid moieties esterified to the glycerol backbone of species at each m/z were elucidated. Additionally, we have found that mono- and diacylglycerol [M + NH4]+ ions are readily identified by neutral loss scanning of 35 Da, corresponding to an abundant loss of NH3 + H2O (scan number 17 in Table 1.) This neutral loss scan was advantageous when

Table 5 Rat retina GPSer molecular species detected by complementary positive and negative ion mode MS/MS analysis Lipid species a

Scan #12 NLS 185 (m/z)

Scan #13 PIS 208 (m/z)

Scan #14 NLS 87 (m/z)

GPSer(16:0/18:1)

762

784

760

GPSer(16:1/18:0)

762

784

760

GPSer(16:0/18:0)

–

–

762

GPSer(18:0/18:1)

790

812

788

GPSer(16:1/20:4)

–

–

808

GPSer(16:0/20:4)

812

834

810

GPSer(16:0/22:0)

–

842

818

GPSer(18:0/20:0)

–

842

818

GPSer(O-18:0/22:6)

–

842

818

GPSer(18:1/22:6)

834

856

832

GPSer(18:0/22:6)

836

858

834

GPSer(18:0/22:5)

838

860

836

GPSer(18:0/22:4)

840

862

838

GPSer(20:0/20:4)

840

862

838

GPSer(20:4/22:6)

856

878

854

GPSer(20:4/22:5)

858

–

856

GPSer(20:4/22:4)

–

–

858

GPSer(20:0/22:6)

–

886

862

GPSer(20:0/22:5)

866

–

864

GPSer(22:6/22:6)

880

902

878

GPSer(22:5/22:6)

882

904

880

GPSer(22:4/22:6)

884

906

882

GPSer(22:3/22:6)

–

908

884

GPSer(22:6/24:6)

908

930

906

GPSer(22:5/24:6)

910

932

908

GPSer(22:4/24:6)

912

934

910

GPSer(22:3/24:6)

–

–

912

GPSer(24:6/24:6)

936

958

934

GPSer(24:5/24:6)

938

–

936

a  GPSer fatty acid substituents were determined by negative ion mode MS/MS experiments as described in the Subheading 3. Alkenyl species are denoted by O-preceding the acyl chain. A dash indicates a species that was not detected.



Global Analysis of Retina Lipids

61

simultaneous comparison of all MG/DG species to an internal standard was desired. Use of the NLS 35 MS/MS experiment was also useful for identifying DG species present in the ammoniumadducted fatty acid neutral loss spectra, as scanning for loss of a fatty acid + NH3 may also detect cholesteryl esters present at very low abundance in rat retina. Thus, NLS 35 enabled differentiation between putative DG and cholesteryl ester ions detected by positive ion mode fatty acid analysis of neutral lipid species. This neutral loss mode MS/MS scan also provided another means of verifying low abundance putative DG peaks identified in one or more fatty acid neutral loss scan. The limitation of NLS 35 was that DG species were only identified in terms of total carbons: total double bonds, which could be deduced from the nominal mass of a given species. Figure 9a shows the spectrum obtained from simultaneous analysis of all abundant rat retina DG species by scanning for a neutral loss of 35 Da between m/z 580 and 700. m/z 614 was detected as a putative diglyceride in this scan, consistent with the mass of a 34:0 (TC:TDB) diglyceride as an [M + NH4]+ ion. The fatty acid compositions of DG and TG species were then determined by scanning for the neutral loss of a 16:0 fatty acid + NH3 (Fig. 9b), loss of an 18:0 fatty acid + NH3 (Fig. 9c), and the loss of a 22:6 fatty acid + NH3 (Fig. 9d), over a wider mass range (m/z 580–980). m/z 614 was redundantly detected in scans for the neutral loss of 16:0 and 18:0 fatty acids, confirming the identification of this m/z as DG(16:0/18:0). Analysis of the high mass range of the spectra shown in Fig. 9b–d also revealed the presence of m/z 924 in all three fatty acid neutral loss scans. This led to the identification of the TG species as TG(16:0/18:0/22:6). However, an ion at low relative abundance was observed at m/z 670 in the spectrum obtained from scanning for a neutral loss of an 18:0 fatty acid (Fig. 9c) that was not observed in the NLS 35 MS/MS experiment used to identify DG species. While m/z 670 is within the expected mass range for putative DG molecular species, the lack of an ion at m/z 670 in the NLS 35 spectrum cast doubt that this particular ion represented an 18:0 fatty acid-containing diglyceride. Direct product ion MS/MS analysis of m/z 670 was precluded by the exceedingly low abundance of this ion relative to polar lipid ion abundances in the unfractionated rat retina lipid extract. As the expected mass range for cholesteryl esters is similar to the expected mass range of diglycerides, we conducted an additional precursor ion scan of m/z 369 (scan number 16 in Table 1) to detect cholesteryl esters as [M + NH4]+ ions. A peak at m/z 670 was observed in the PIS 369 spectrum, corresponding to an 18:0 cholesteryl ester (data not shown). This was in agreement with the original observation of m/z 670 in a neutral loss scan of 18:0 fatty acid neutral species. Hence, the use of complementary precursor ion and neutral loss scan mode MS/MS experiments enabled detailed

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Fig. 9. Complementary positive ion mode MS/MS analysis of rat retina DG and TG species. (a) Simultaneous detection of all abundant DG species as [M + NH4]+ ions by neutral loss scanning of 35 Da. (b) Detection of all 16:0-containing DG and TG as [M + NH4]+ ions by neutral loss scanning of 273 Da. (c) Detection of all 18:0-containing DG and TG as [M + NH4]+ ions by neutral loss scanning of 301 Da. (d) Detection of all 22:6-containing DG and TG as [M + NH4]+ ions by neutral loss scanning of 345 Da.

analysis and redundant confirmation of low abundance ions corresponding to rat retina neutral lipids without chromatographic separation of polar and nonpolar analytes. Note that the fatty acids selected for analysis by neutral loss scanning presented here were for demonstrative purposes only; a thorough analysis was conducted by scanning for neutral losses of all naturally occurring fatty acids. From this set of complementary analyses, we delineated a list of 29 diacylglycerol and 111 triacylglycerol distinct molecular species (see Table 6).

Lipid species

DG(16:0/16:1)

DG(16:0/16:0)

DG(16:0/18:1)

DG(16:1/18:0)

DG(16:0/18:0)

DG(16:0/20:4)

DG(18:1/18:1)

DG(16:1/20:0)

DG(18:0/18:1)

DG(18:0/18:0)

DG(16:0/20:0)

DG(16:0/22:6)

DG(18:1/20:4)

DG(16:0/22:5)

DG(18:0/20:4)

DG(18:0/20:3)

m/z

584

586

612

612

614

634

638

640

640

642

642

658

660

660

662

664

848

846

846

846

846

844

844

844

844

824

824

822

822

820

820

820

m/z

TG(14:0/18:1/18:1)

TG(18:1/14:1/18:1)

TG(18:1/14:0/18:2)

TG(16:1/16:1/18:1)

TG(16:0/18:2/16:1)

TG(18:2/14:0/18:2)

TG(18:1/18:2/14:1)

TG(16:1/16:1/18:2)

TG(16:0/16:1/18:3)

TG(16:0/16:0/16:0)

TG(14:0/16:0/18:0)

TG(16:0/16:0/16:1)

TG(14:0/16:0/18:1)

TG(16:0/16:1/16:1)

TG(14:1/16:1/18:0)

TG(14:0/16:0/18:2)

Lipid species

898

898

898

898

896

896

896

894

894

894

894

880

878

878

876

876

m/z

TG(16:0/20:3/18:2)

TG(16:0/18:2/20:3)

TG(16:0/18:1/20:4)

TG(16:0/16:0/22:5)

TG(18:2/18:2/18:2)

TG(16:0/18:2/20:4)

TG(16:0/16:0/22:6)

TG(18:1/14:1/22:5)

TG(18:0/14:1/22:6)

TG(16:1/18:3/20:3)

TG(16:0/22:6/16:1)

TG(16:0/18:0/18:0)

TG(16:1/16:0/20:0)

TG(16:1/18:0/18:1)

TG(16:1/16:1/20:0)

TG(16:0/18:0/18:1)

Lipid species

956

952

950

950

948

948

946

944

928

926

926

926

924

924

924

922

m/z

(continued)

TG(18:0/20:0/20:4)

TG(18:0/18:0/22:6)

TG(18:0/18:2/22:5)

TG(18:0/18:1/22:6)

TG(18:1/18:1/22:6)

TG(18:0/18:2/22:6)

TG(18:1/20:4/20:4)

TG(18:1/22:6/18:3)

TG(18:0/18:0/20:4)

TG(18:0/18:1/20:4)

TG(16:0/20:1/20:4)

TG(16:0/18:0/22:5)

TG(16:1/18:0/22:5)

TG(16:0/18:0/22:6)

TG(16:0/16:0/24:6)

TG(18:1/20:4/18:2)

Lipid species

Table 6 Rat retina DG and TG molecular species detected as [M + NH4]+ ions by complementary positive ion mode neutral loss scanning of m/z corresponding to the mass of naturally occurring fatty acids+NH3 (scan #15)

Global Analysis of Retina Lipids 63

Lipid species

DG(18:2/20:0)

DG(18:1/20:0)

DG(18:0/20:0)

DG(20:4/20:4)

DG(18:1/22:6)

DG(18:0/22:6)

DG(18:0/22:5)

DG(20:0/20:4)

DG(20:0/20:0)

DG(20:4/22:6)

DG(20:0/22:6)

DG(20:0/22:3)

DG(22:6/22:6)

TG(14:0/14:0/16:0)

TG(14:0/14:0/18:1)

TG(14:0/14:1/18:0)

m/z

666

668

670

682

684

686

688

690

698

706

714

720

730

768

794

794

Table 6 (continued)

872

872

TG(16:1/18:0/18:3)

TG(16:0/20:3/16:1)

TG(16:0/18:2/18:2)

TG(16:0/18:1/18:3)

872 872

TG(16:0/16:0/20:4)

TG(18:2/18:3/16:0)

TG(16:1/18:3/18:1)

TG(16:1/18:2/18:2)

TG(16:1/16:1/20:3)

TG(16:0/20:4/16:1)

TG(16:0/16:0/18:0)

TG(18:0/14:0/18:1)

TG(16:0/18:0/16:1)

TG(16:0/16:0/18:1)

TG(18:1/14:0/18:1)

TG(16:0/16:1/18:1)

TG(16:0/16:0/18:2)

Lipid species

872

870

870

870

870

870

852

850

850

850

848

848

848

m/z

906

906

906

904

904

904

902

902

902

900

900

900

900

900

898

898

898

m/z

TG(18:0/18:0/18:1)

TG(16:0/18:0/20:1)

TG(16:0/18:1/20:0)

TG(18:0/18:1/18:1)

TG(18:0/18:0/18:2)

TG(16:0/20:1/18:1)

TG(18:2/20:1/16:0)

TG(18:1/18:2/18:0)

TG(18:1/18:1/18:1)

TG(18:1/18:1/18:2)

TG(18:0/18:2/18:2)

TG(18:0/18:1/18:3)

TG(16:0/18:1/20:3)

TG(16:0/18:0/20:4)

TG(18:2/18:2/18:1)

TG(16:1/20:3/18:1)

TG(16:1/20:1/18:3)

Lipid species

978

974

972

970

968

968

968

966

m/z

TG(18:2/22:5/20:0)

TG(18:0/20:4/22:5)

TG(18:0/20:4/22:6)

TG(16:1/22:5/22:5)

TG(22:6/22:6/16:0)

TG(18:3/20:3/22:6)

TG(18:2/22:6/20:4)

TG(20:4/20:4/20:5)

Lipid species

64 Busik, Reid, and Lydic

TG(14:0/16:1/18:2)

TG(16:1/14:1/18:1)

TG(16:1/16:1/16:1)

TG(14:0/16:0/18:2)

TG(14:0/16:1/18:1)

818

818

818

820

820

876

876

874

874

874

872

TG(16:0/18:1/18:1)

TG(16:0/18:0/18:2)

TG(16:1/18:1/18:1)

TG(16:1/18:0/18:2)

TG(16:0/18:1/18:2)

TG(16:1/18:1/18:2)

Stereochemistry of fatty acids has not been verified

TG(14:0/16:1/16:0)

794

922

922

922

922

922

922

TG(16:1/18:1/22:5)

TG(16:1/20:2/20:4)

TG(16:1/18:0/22:6)

TG(16:0/16:1/24:6)

TG(16:0/20:3/20:4)

TG(16:0/18:1/22:6)

Global Analysis of Retina Lipids 65

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4. Notes 1. Although disruption of lipid metabolism is known to play a key role in the onset and progression of a variety of retinal diseases, the use of retinal lipid profiles has not been sufficiently explored. This lack of knowledge is largely due to the diversity of lipids and to limits in the sensitivity of previously available analytical methods. The methodology described in this chapter for lipid profile analysis of normal retina will be further applied to various diseased retinal states to aid in developing effective therapeutic strategies for retinal disorders. 2. From the data described above, it is clear that crude lipid extracts from whole rat retinas contain an extremely complex mixture of molecular species, representing a wide array of lipid classes at varying abundances. The observation of multiple ionic adducts of a given lipid class in both positive (e.g., [M + H]+, [M + Na]+, and [M + NH4]+) and negative (e.g., [M − H]−, [M + Cl]− and [M + CH3OCO2]−) ionization modes adds further complexity, effectively increasing the number of “spectral features” to the resultant mass spectra obtained by analysis of these extracts. However, the ability to identify specific lipid species in both positive and negative ionization modes, as well as in their multiple adducted forms, enables unambiguous lipid identification, particularly important when lipids were present at low abundance. 3. In many cases, multiple lipid species were found to be present at a single nominal m/z value. For example, m/z 834 in the positive ion mode MS spectrum shown in Fig. 1a, was found to contain diacyl GPCho(18:0/22:6) [M + H]+, GPSer (18:1/22:6) [M + H]+, and GPSer(18:0/20:4) [M + Na]+ ions. Likewise, m/z 834 in the negative ionization mode spectrum shown in Fig. 1b was found to consist of [M − H]− ions of GPSer(18:0/22:6) and GPEtn(22:6/22:6), as well as the methyl carbonate adduct ([M + CH3OCO2]−) of GPCho(16:0/18:1). Detection of lipid classes in multiple ionization forms was necessary to fully elucidate the identities of ions present in both positive and negative ion mass spectra at numerous m/z. This point becomes especially important when comparisons must be made across numerous samples, such as comparisons between normal and disease states, in which case any observed change in ion abundance between two or more groups of samples must be accurately characterized. 4. To assist in the implementation of complementary analysis of major lipid classes within the lipidome, we have assembled multiple pairings of possible precursor ion and neutral loss scan mode MS/MS experiments to enable redundant detec-



Global Analysis of Retina Lipids

67

tion of each lipid class. The advantages and disadvantages of many of these scan types have been discussed, and in several cases, the use of redundant detection of a lipid class minimized the shortcomings of one or more MS/MS experiments. For example, positive ion analysis of GPEtn [M + H]+ ions by neutral loss scan mode MS/MS was found to be more sensitive than negative ion mode precursor ion analysis of GPEtn [M − H]− ions in terms of signal/noise and absolute ion abundance. The more sensitive positive ion neutral loss scan is therefore optimal for analysis of low abundance GPEtn ions. However, the additional presence of [M + Na]+ ions, even at very low abundance, complicates analysis of the resultant positive ion MS/MS spectra and necessitates complementary analysis of GPEtn [M − H]− ions for clarity. Negative ion MS/ MS analysis of GPEtn enables the identification of alkenyl species that are not observed in positive ion GPEtn MS/MS analysis; yet the significantly lower sensitivity of the negative ion mode GPEtn precursor ion scan precludes analysis of lipid species that may be present at relatively low abundance. 5. Most of the individual MS/MS experiments we have paired for use in complementary analysis of rat retina lipid classes were obtained from the available literature. In several cases we have employed novel precursor ion and neutral loss scans derived from previously published gas-phase fragmentation pathways to detect lipid classes in various ionic forms or modification states. To our knowledge, this analysis of the rat retina lipidome was the first analysis to include the negative ion neutral loss of 135 Da for detection of SM and GPCho species as methylcarbonate adducts; likewise, it was the first analysis to explore differential detection of SM and GPCho as methylcarbonate adducts by use of the negative ion neutral loss of 161 Da for preferential detection of SM species; and it was the first analysis to include detection of sodiated GPCho and GPSer species by positive ion mode precursior ion scanning of m/z 147 and m/z 208, respectively. Additionally, we introduced the positive ion mode neutral loss of 35 Da for simultaneous detection of diglyceride species as ammonium adducts. In this chapter we have provided a preliminary outline of the possible benefits and shortcomings of each of these novel MS/MS experiments, although a more rigorous examination of each proposed scan would be beneficial for assessment of the viability of these scans for use in large-scale lipid-profiling experiments. 6. We have provided, as a reference, pairings of MS/MS experiments in Table 1 that can be used in the complementary analysis of lipid classes that were not discussed in this chapter, including cholesteryl esters, glycerophosphatidic acid (GPA), glycerophosphatidylglycerol (GPGro), and ceramides. As these lipid classes are of low abundance in many tissues, and

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especially in rat retina, their analysis certainly benefits from complementary analysis of multiple ion forms where possible, or by redundant detection of the same ion form by multiple scans. The pairings suggested for complementary analysis of lipid classes are certainly not exhaustive, and other sets of complementary pairs could be substituted for analysis of various lipid classes. Furthermore, as the understanding of gasphase fragmentation pathways of lipid classes and their ionic adducts progresses, it will expand the ability to perform complementary analysis to lipid classes that have not been addressed in this chapter. 7. Clearly, the use of redundant identification of rat retina lipid classes from multiple precursor ion types enabled the identification of molecular species that would not have been observed if only one MS/MS scan mode had been employed in the analysis of each lipid class. This was especially true in the analysis of SM and GPCho species, in which only four rat retina SM species could be readily detected by the conventional positive ion precursor ion scan of m/z 184; four SM species were detected by neutral loss of 183 Da from sodiated SM ions; two SM species were detected by PIS 147 from sodiated SM ions; and 12 SM species were detected by the negative ion neutral loss of 161 Da from SM methylcarbonate adducts.

References 1. Anderson RE. Lipids of ocular tissues. IV. A comparison of the phospholipids from the retina of six mammalian species. Exp Eye Res 1970;10(2):339–44. 2. Fliesler SJ, Anderson RE. Chemistry and metabolism of lipids in the vertebrate retina. Prog Lipid Res 1983;22(2):79–131. 3. Lecomte M, Paget C, Ruggiero D, Wiernsperger N, Lagarde M. Docosahexaenoic acid is a major n-3 polyunsaturated fatty acid in bovine retinal microvessels. J Neurochem 1996;66(5):2160–7. 4. O’Brien JS, Sampson EL. Fatty acid and fatty aldehyde composition of the major brain lipids in normal human gray matter, white matter, and myelin. J Lipid Res 1965;6(4):545–51. 5. SanGiovanni JP, Chew EY. The role of omega-3 long-chain polyunsaturated fatty acids in health and disease of the retina. Prog Retin Eye Res 2005;24(1):87–138. 6. Wang N, Anderson RE. Synthesis of docosahexaenoic acid by retina and retinal pigment epithelium. Biochem (Mosc) 1993;32(49): 13703–9.

7. Wang N, Anderson RE. Transport of 22:6n-3 in the plasma and uptake into retinal pigment epithelium and retina. Exp Eye Res 1993; 57(2):225–33. 8. Soubias O, Teague WE, Gawrisch K. Evidence for specificity in lipid–rhodopsin interactions. J Biol Chem 2006;281(44):33233–41. 9. Suh M, Wierzbicki AA, Clandinin MT. Dietary fat alters membrane composition in rod outer segments in normal and diabetic rats: impact on content of very-long-chain (C > or = 24) polyenoic fatty acids. Biochim biophys Acta 1994;1214(1):54–62. 10. Watson AD. Lipidomics: a global approach to lipid analysis in biological systems. J Lipid Res 2006;47(10):2101–11. 11. Brugger B, Erben G, Sandhoff R, Wieland FT, Lehmann WD. Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. Proc Natl Acad Sci U S A 1997;94(6):2339–44. 12. Ejsing CS, Duchoslav E, Sampaio J, et  al. Automated identification and quantification

of glycerophospholipid molecular species by multiple precursor ion scanning. Anal Chem 2006;78(17):6202–14. 13. Han X, Gross RW. Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom Rev 2005;24(3):367–412. 14. Pulfer M, Murphy RC. Electrospray mass spetrometry of phospholipids. Mass Spectrom Rev 2003;22(5):332–64. 15. Schwudke D, Oegema J, Burton L, et al. Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Anal Chem 2006;78(2):585–95. 16. Welti R, Wang X, Williams TD. Electrospray ionization tandem mass spectrometry scan modes for plant chloroplast lipids. Anal Biochem 2003;314(1):149–52. 17. Welti R, Shah J, LeVine S, Esch SW, Williams TD, Wang X. High throughput lipid profiling to identify and characterize genes involved in lipid metabolism, signaling, and stress response. In: L. F, G.D. P, eds. Functional Lipidomics. New York: Marcel Dekker; 2005. 18. Jones JJ, Batoy SM, Wilkins CL. A comprehensive and comparative analysis for MALDI FTMS lipid and phospholipid profiles from biological samples. Comput Biol Chem 2005; 29(4):294–302. 19. Jones JJ, Borgmann S, Wilkins CL, O’Brien RM. Characterizing the phospholipid profiles in mammalian tissues by MALDI FTMS. Anal Chem 2006;78(9):3062–71. 20. Jones JJ, Stump MJ, Fleming RC, Lay JO, Jr., Wilkins CL. Strategies and data analysis techniques for lipid and phospholipid chemistry elucidation by intact cell MALDI-FTMS. J Am Soc Mass Spectrom 2004;15(11):1665– 74. 21. Zhang X, Reid GE. Multistage tandem mass spectrometry of anionic phosphatidylcholine lipid adducts reveals novel dissociation pathways. Int J Mass Spectrom 2006;252(3):242–55. 22. Koivusalo M, Haimi P, Heikinheimo L, Kostiainen R, Somerharju P. Quantitative determination of phospholipid compositions by ESI-MS: effects of acyl chain length, unsaturation, and lipid concentration on instrument response. J Lipid Res 2001;42(4):663–72. 23. Zacarias A, Bolanowski D, Bhatnagar A. Comparative measurements of multicomponent phospholipid mixtures by electrospray mass spectroscopy: relating ion intensity to concentration. Anal Biochem 2002;308(1):152–9. 24. Folch J, Lees M, Sloane Stanley GH. A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 1957;226(1):497–509.

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25. Haimi P, Uphoff A, Hermansson M, Somerharju P. Software tools for analysis of mass spectrometric lipidome data. Anal Chem 2006; 78(24):8324–31. 26. Callender HL, Forrester JS, Ivanova P, Preininger A, Milne S, Brown HA. Quantification of diacylglycerol species from cellular extracts by electrospray ionization mass spectrometry using a linear regression algorithm. Anal Chem 2007;79(1):263–72. 27. Colsch B, Afonso C, Popa I, et al. Characterization of the ceramide moieties of sphingoglycolipids from mouse brain by ESI-MS/MS: identification of ceramides containing sphingadienine. J Lipid Res 2004;45(2):281–6. 28. Murphy RC, James PF, McAnoy AM, Krank J, Duchoslav E, Barkley RM. Detection of the abundance of diacylglycerol and triacylglycerol molecular species in cells using neutral loss mass spectrometry. Anal Biochem 2007;366(1): 59–70. 29. Merrill AH, Sullards MC, Allegood JC, Kelly S, Wang E. Sphingolipidomics: High-throughput, structure-specific, and quantitative analysis of sphingolipids by liquid chromatography tandem mass spectrometry. Methods 2005; 36(2):207–24. 30. Han X. Characterization and direct quantitation of ceramide molecular species from lipid extracts of biological samples by electrospray ionization tandem mass spectrometry. Anal Biochem 2002;302(2):199–212. 31. Song H, Hsu F, Ladenson J, Turk J. Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching. J Am Soc Mass Spectrom 2007;18:1848–58. 32. Houjou T, Yamatani K, Nakanishi H, Imagawa M, Shimizu T, Taguchi R. Rapid and selective identification of molecular species in phosphatidylcholine and sphingomyelin by conditional neutral loss scanning and MS3. Rapid Commun Mass Spectrom 2004;18(24):3123– 30. 33. Liebisch G, Binder M, Schifferer R, Langmann T, Schulz B, Schmitz G. High throughput quantification of cholesterol and cholesteryl ester by electrospray ionization tandem mass spectrometry (ESI-MS/MS). Biochim Biophys Acta 2006;1761(1):121–8. 34. Hvattum E. Analysis of triacylglycerols with non-aqueous reversed-phase liquid chromatography and positive ion electrospray tandem mass spectrometry. Rapid Commun Mass Spectrom 2001;15(3):187–90. 35. Li YL, Su X, Stahl PD, Gross ML. Quantification of diacylglycerol molecular species in biological samples by electrospray ionization mass

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37. Han XL, Gross RW. Structural determination of picomole amounts of phospholipids via electrospray ionization tandem mass spectrometry. J Am Soc Mass Spectr 1995;6(12):1202–10. 38. Taguchi R, Houjou T, Nakanishi H, et  al. Focused lipidomics by tandem mass spectrometry. J Chromatogr 2005;823(1):26–36.

Chapter 4 Combining Lipidomics and Proteomics of Human Cerebrospinal Fluids Alfred N. Fonteh and Rachel D. Fisher Summary  Lipids from dietary sources or from de novo synthesis are transported while bound to proteins to other tissues where they are used for cell membrane synthesis or stored for energy generation. In cell membranes or in plasma, lipids can undergo several modifications that are important in cell function. Several proteins orchestrate the transport, biosynthesis, and modification of lipids. Thus, the intersection of lipids and proteins is important in human metabolic pathways. Recent advances in mass spectrometry and bioinformatics have made it possible to obtain compositional (structural and functional) data of lipid molecular species and proteins in biological samples. This combination of lipidomics and proteomics is advantageous because it allows us to better define biochemical pathways, discover new drug targets, and better understand the pathophysiology of several diseases. Key words:  Lipidomics, Proteomics, Shotgun sequencing, Mass spectrometry, Liquid chromatography, Isotope dilution, Cerebrospinal fluid

1. Introduction Lipid metabolic pathways are complex, comprising diverse substrates and products (1). Generally, most unsaturated lipids are obtained from dietary sources while de novo fatty acid synthesis accounts for saturated fats. Thus, there are several enzymes involved in the uptake, transport, biosynthesis, and modification of lipids (Fig. 1). Once ingested, lipids may be subjected to lipolysis before adsorption in the gut. Once absorbed, lipids may be modified in the liver and transported to other parts of the body while bound to albumin or to apolipoprotein particles of different sizes (2–4). Lipids are incorporated into cellular membranes or are used as a major storage form of energy in adipose tissue. Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, DOI 10.1007/978-1-60761-322-0_4, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Fig. 1. The intersection of lipidomics and proteomics – Lipids from diets are subjected to lipolysis before adsorption in the gut. Absorbed lipids are modified in the liver and transported to other parts of the body bound to several transport proteins. In other tissues, lipids are incorporated into cellular membranes or are stored in adipose tissues as a major form of energy. Several proteins can modify plasma or cell membrane-bound lipids. Modified lipids often signify physiologic changes characteristic of inflammatory, immune, or other cell responses. We also identify four major classes of proteins that intersect with lipids that are listed at the bottom of Fig. 1.

In cell membranes or when bound to plasma proteins, lipids and several proteins interact, resulting in the modifications of lipids. Oxidative, inflammatory, or immune response usually results in the modification of lipids (5–14). The most studied pathway involves the initial digestion of lipids by lipases (phospholipase A2,



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phospholipase C, phospholipase D, lipases) (15–37). A combination of lipase and oxygenase activities results in the generation of bioactive lipids. For example, PLA2 activity on phospholipids results in the release of fatty acids and lysophospholipids. When the fatty acid is arachidonic acid (AA, 20;4, n-6), it is further metabolized by cyclooxygenase, lipoxygenase, or epoxygenases to form lipid mediators of inflammation collectively known as eicosanoids (38–43). The lysophospholipid generated by the same PLA2 action can also be acetylated to a potent lipid mediator of inflammation known as platelet-activating factor (44–46). From this example, it is clear that lipids intersect with proteins and this intersection can have ramifications on cellular function. Thus, a better understanding of how lipids are modified in diseases should encompass a targeted study of proteins which modify these lipids. Currently, understandings of these biochemical pathways involve metabolic labeling experiments in combination with chromatographic separations to quantify changes in lipid substrates or products. Detection of enzyme expression involves activity assays using fluorescent or colorimetric substrates in combination with antibody-based assays (western analysis, ELISA) or pharmacologic assays that employ different inhibitors (47–49). While these methods have increased our knowledge of lipid biochemical pathways, only a limited number of substrates, products or proteins can be measured in the same assay. In cases where sample amounts are limiting, a comprehensive approach involving lipidomic and proteomic approaches will allow us to elucidate biochemical pathways important in regulating lipid metabolism (1). Our approach is to use liquid chromatography in combination with mass spectrometry to catalog lipids and proteins involved in the pathophysiology of brain diseases.

2. Materials 2.1. Equipment 2.1.1. Equipment for Lipidomics

1. Borosilicate glass tubes (clean with chloroform and heat at 100°C for at least 1 h). 2. HPLC system (HP 1100, Hewlett Packard) fitted with a Binary pump, autosampler and column heater. 3. HPLC column – (Luna Silica, 150 × 3.0 mm) from Phenomenex (Torrance, CA). 4. Triple quadrupole mass spectrometer (TSQ Quantum, Thermo Fisher, San Jose, CA). 5. XCaliber acquisition and Quan Browser software (Thermo Fisher, San Jose, CA).

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2.1.2. Equipment for Proteomics

1. Heat block at 57°C. 2. Aluminum foil. 3. 5 kDa Vivaspin tubes (Millipore, Bedford, MA). 4. Centrifuge (Maximum force: ~12,000 × g). 5. Water bath maintained at 37°C. 6. Vacuum centrifuge. 7. HPLC systems. For ion trap studies, we use a Surveyor system fitted with a pump and autosampler (Thermo Fisher, San Jose, CA). For quantitative proteomic studies, we use an HP 1100 system (Hewlett Packard, Foster City, CA) fitted with a Binary pump, autosampler and column heater (40°C). 8. HPLC columns. For ion trap protein sequencing studies, we use a Luna C18 column (1 mm × 250 mm) from Phenomenex (Torrance, CA). For quantitative proteomic studies, we use a hydrophilic interaction liquid chromatography column (TSK-Gel Amide-80, 2 × 150 mm from Tosoh Biosciences, Montgomeryville, PA). 9. Ion trap mass spectrometer (LCQ, Thermo Fisher, San Jose, CA). 10. Triple quadrupole mass spectrometer (TSQ Quantum, Thermo Fisher, San Jose, CA). 11. XCaliber acquisition and Quan Browser software (Thermo Fisher, San Jose, CA).

2.2. Reagents 2.2.1. Reagents for Lipidomics

1. Solvents, chloroform, methanol (VWR). Aqueous ammonium hydroxide (28%) and glacial formic acid were purchased from Sigma (St Louis, MO). Make a 9% aqueous solution of formic acid using double-distilled deionized water (VWR, West Chester, PA). 2. Phospholipid standards (phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA), phosphatidylglycerol (PG) and internal standards (C11 PC, C11 PE, C11 PA)) from Avanti Polar Lipids (Alabaster, AL). Soy bean PI, ceramide (CM), sphingomyelin (SPM), 1-palmitoyl-2-sn-arachidonoyl glycerophoscholine, 1-palmitoyl-2-sn-docosahexaenoyl glycerophoscholine and 1-palmitoyl-2-sn-eicosatetraenoyl glycerophoscholine were purchased from Sigma. 3. HPLC solvents: Solvent A. CHCl3:CH3OH:NH4OH (80:19.5: 0.5, v/v/v); Solvent B: CHCl3:CH3OH:H2O:NH4OH (60:34: 5.5:0.5, v/v/v/v). HPLC sample buffer (Hexane:isopropanol:water, 40:50:3, v/v/v) 4. Make a solution (5 mg/mL) of butylated hydroxyl toluene (BHT) in methanol.



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1. 100 mM ammonium bicarbonate, pH 8.0. 2. Rapigest™ [1% (w/v)] (Waters, Milford, MA) stock in ammonium bicarbonate. Rapigest™ is stable for 1 week at 2–8°C. 3. Urea (add salt directly to CSF or peptide standard solutions to make an 8 M solution). 4. Dithiothreitol (DTT, 100 mg/mL) in ammonium bicarbonate. 5. Iodoacetamide (30 mg/mL) in ammonium bicarbonate made fresh each time and protected from light. 6. Sequencing grade modified porcine trypsin (Princeton Separations, Aldelphia, NJ) in water, stored in a 4°C refrigerator or on ice until use. 7. Glacial formic acid. 8. HPLC solvents. Solvent A: H2O with 0.1% (v/v) formic acid and 5 mM ammonium bicarbonate. Solvent B: Acetonitrile with 0.1% (v/v) formic acid and 5 mM ammonium bicarbonate. 9. Isotope-labeled peptides used as internal standards synthesized with AQUA peptide synthesis (Dr. John Rush, Cell Signaling Technologies, Boston, MA).

3. Methods 3.1. Extraction of CSF Lipids

1. Add BHT solution to samples to a final concentration of 1 mg/mL (see Note 1). 2. Extract lipids using a modified method of Bligh and Dyer previously described in detail in Methods in Molecular Biology (50, 51). 3. Dry lipid extracts under a stream of N2 and resuspend in 100 mL HPLC sample buffer.

3.2. Detection and Analyses of CSF Lipids

1. Inject lipid extract onto the HPLC column maintained at 40°C. 2. Start with 50% HPLC solvent B for 14 min, then increase to 100% B over 10 min, maintain at 100% B for 15 min and then return to 50% B. 3. Equilibrate column for 15 min (5 times column volume) before next sample injection. 4. Directly interphase LC eluent to the electrospray ionization (ESI) source of the triple quadrupole mass spectrometer (TSQ Quantum, Thermo Fisher, San Jose, CA). 5. For positive ions, tune the mass spectrometer for parent ion scanning of 184.14, 141.32, 264.27 for PC, PE, CM, and SPM, respectively. Tune for neutral ion lose of 86.86 for PS (see Note 2).

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6. For negative ions, tune the mass spectrometer for parent ion scanning of 240.961, 196.31, 171.14, 168.17, and 153.05/96.99 for PI, PE, PG, SPM and all glycerophospholipids/phosphate, respectively. Tune the mass spectrometer for neutral ion loss of 86.86 and 50.1 for PS and PC, respectively (see Note 2). 7. Tune instrument for collision energy (CE) and collisioninduced dissociation (CID) for each phospholipid class and program a segment for each phospholipid using the XCalibur acquisition software (Thermo Fisher, San Jose, CA). 8. Acquire data and process using Quan Browser software (Thermo Fisher, San Jose, CA) (see Note 3). 3.3. ShotgunSequencing of CSF Proteins

1. Add 45 mL of CSF into 1.7 mL microtube and vortex briefly (see Note 4) 2. Add 5 mL of Rapigest™ stock a sample (see Note 5) 3. Add 0.67 mL of DTT stock a sample and vortex briefly 4. Briefly centrifuge (30 s, 1,000 × g) and incubate samples on a heat block at 56°C for 30 min 5. Add 2.67 mL of freshly made iodoacetamide stock to each sample and centrifuge briefly (30 s, 1,000 × g). Protect samples from light by wrapping with aluminum foil 6. Leave at room temperature in the dark for 30 min 7. Clean-up excess iodoactamide by adding 2.67 mL of DTT, vortex briefly and centrifuge for 30 s 8. Leave at room temperature for 30 min 9. Remove excess denaturing solution by adding the entire solution to a 500 mL Vivaspin (5 kDa cutoff) tube. Centrifuge at 8,000 x g until all solution has passed through the membrane filter 10. Add 50–100 mL of ammonium bicarbonate to the Vivaspin tube and centrifuge at 8,000 x g until all solution has passed through the membrane filter 11. For on filter digestion, add 100 mL of ammonium bicarbonate to the CSF concentrate and add 5 mL of trypsin stock (peptide/protein to trypsin ratio should be ~50:1) (see Note 6). 12. Incubate in a water bath overnight at 37°C 13. Stop all reactions by adding 5 mL of glacial formic acid 14. Centrifuge on-filter digest at 8,000 x g until all solution has passed through the membrane filter and use the filtrate for LC/MS2 studies



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3.4. Liquid Chromatography of Peptides

1. Start with 0% HPLC solvent B for 30 min (Surveyor MS pump), then increase to 60% B at 110 min, and then increase to 80% B at 114 min. Maintain at 80% B until 128 min and then return to 0% B at 132 min. Equilibrate column until 162 min before injecting another sample (see Note 7).

3.5. Mass Spectrometry of Peptides

1. Using an LCQ ion trap mass spectrometer (Thermo Fisher, San Jose, CA), set the acquisition time to 160 min with four different scan events. 2. Program the mass spectrometer for positive ions of four scan events. For scan event 1 (300.0–1,600.0); scan event 2 (Dependent MS/MS of most intense ion from scan event 1); scan event 3 (Dependent MS/MS of second most intense ion from scan event 1); scan event 4 (Dependent MS/MS of third most intense ion from scan event 1). Use default charge state of +2, isolation width of 2.0, normalized collision energy of 35.0, activation time of 30, and a minimum signal of 1,000 (see Note 8). For global data dependent acquisition, set exclusion mass widths to 1.0 and 1.5 for low and high masses, respectively.

3.6. Data Search and Analyses

1. Search MS spectra against a SwissProt database (release 7459 or later release) using the Sequest algorithm implementation of BioWorks 3.1 (Thermo Fisher, San Jose, CA) (52). 2. Use the Washburn criteria (53) for identification of CSF proteins and export protein list to an Excel spreadsheet. 3. Search the spreadsheet using key words for lipids to identify all lipid-metabolizing proteins.

3.7. Quantification of Lipid-Metabolizing Proteins in CSF

Several metabolic-labeling methods can be used to quantify changes in proteins in biological samples (54, 55). We favor isotope dilution tandem mass spectrometry for our quantitative proteomics (56). In this method, peptides identified from shotgun sequencing or mechanistically determined to be important in lipid metabolism are designed and synthesized with stable isotopes (see Note 8). Samples are spiked with known amounts (50 pmol) of each stable isotope standard and then processed as described below.

3.7.1. Denaturation and Tryptic Digestion of CSF Proteins for Isotope Dilution Studies

1. Add 45 mL of CSF into 1.7 mL microtube and vortex briefly. 2. Add 5 mL of Rapigest™ stock a sample containing 50 pmol stable isotope-labeled peptide internal standards (see Note 5). 3. Add 0.67 mL of DTT stock a sample and vortex briefly, and then centrifuge (30 s, 1,000 × g). 4. Leave in heat block at 56°C for 30 min.

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5. Add 2.67 mL of freshly made iodoacetamide mL to each sample. Wrap the tubes in aluminum foil to keep in the dark and mix using Vortexer, and centrifuge for 30 s 6. Leave at room temperature in the dark for 30 min. 7. Add 2.67 mL of DTT and vortex briefly, and centrifuge for 30 s. 8. Leave at room temperature for 30 min. 9. Add entire solution to 500 mL Vivaspin tube. Centrifuge at 8,000 x g until all solution has passed membrane. 10. Add 50 mL of ammonium bicarbonate to the 5 kDa Vivaspin tube and centrifuge at 8,000 x g until all solution has passed membrane. 11. Make a stock solutions containing 50 pmol of each stable isotope ([15N],[13C]) labeled internal standard a sample. Dry in vacuum centrifuge and resuspend in 50 mL of ammonium bicarbonate solution. 12. On-filter digestion. Add 100 mL of ammonium bicarbonate on the filter. Add stable isotope standards to filter and add 5 mL of trypsin stock solution. 13. Incubate in heat block overnight at 37°C. 14. On day 2, stop all reactions with 5 mL of formic acid. 15. Centrifuge on-filter digestion at 8,000 x g until all solution has passed membrane and use the filtrate for LC/MS2 studies. 3.7.2. Quantification of Peptides by LC/MS

1. Dry samples and resuspend in 85% acetonitrile with 15% H2O and 0.1% formic acid. 2. Condition the column at 100% solvent A. Run the samples at flow rate 0.3 mL/min (HP 1100 HPLC system) using the following sequence (%A/min): 100/0.0, 100/5.0, 0/37.5, 0/45.0, 100/47.5, 100/65.0. 3. Perform MS2 using a triple quadrupole mass spectrometer (TSQ-Quantum, Thermo Fisher, San Jose, CA). Isolate parent ions for each peptide and perform tandem MS to obtain the most abundant product ion. Verify products ions using Mass prospector (http://prospector.ucsf.edu/). 4. Optimize parent ion-product ion transitions for collision energy and tube lens voltage and perform multiple reaction monitoring (MRM) of unlabeled and stably labeled peptides of interest (see Note 8).

3.7.3. Data Analyses for Isotope Dilution LC/MS Studies

1. For isotope dilution studies, serially dilute (0–100 pmol) unlabeled peptides or protein standards if available. Recombinant proteins from several manufacturers can be used.



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2. Spike unlabeled peptides/proteins with 50 pmol [13C],[15N]labeled peptide. 3. Once MRM are performed, analyze data using Quan Browser of the XCalibur software (Thermo Fisher). 4. Also spike CSF samples with 50 pmol [13C],[15N]-labeled peptide standards, denature, digest, and subject to LC tandem mass spectrometry. 5. Determine amounts of proteins in biological samples using standard curves obtained by plotting concentrations of [12C]peptide against the ratio of peak intensity [12C]-peptide to peak intensity of [13C],[15N]-labeled peptide. 3.8. Results 3.8.1. Design and Synthesis of Stable Isotope Peptide Standards

1. Select peptides of interest from an extensive LTQ database of CSF peptides, or design peptides in silico using Peptidemass (http://www.expasy.org/tools/peptide-mass.html). UniProt Knowledgebase names of some lipid-metabolizing proteins (Table 1) of interest can be used to get tryptic peptides. An example of Peptidemass digest of apolipoprotein A1 (APOA1_HUMAN) is shown on Table 2. 2. The major criteria for peptide selection included size (500– 1,500 Da for m/z = 1, or 1,000–3,000 Da for m/z = 2) and the presence of amino acids that can be substituted with a stable isotopes ([13C9],[15N]-Phe, ([13C6],[15N]-Leu, [13C5],[15N]Val, [13C5],[15N]-Pro, [13C9],[15N]-Tyr). 3. Perform BLAST (http://www.expasy.ch/tools/blast/) on all peptides to confirm structure and specificity before AQUA peptide synthesis by Dr. John Rush (Cell Signaling Technologies, Boston, MA). 4. An advantage of isotope dilution studies is that post-translational modifications (PTMs) of proteins can be studied. For example, a peptide with a phosphorylation site can be synthesized with phosphorylated stable isotopes and used to quantify phosphorylation of that specific site. Similarly, variants can be studied by synthesizing peptide standards containing the modifications found in the variants. Several variants of APOA1_HUMAN (Table 2) can be studies by synthesizing peptides with the substitutions found in these variants.

3.9. Conclusions and Future Directions

Using LC/MS, we can catalog most lipid species in biological samples (1) (see Note 2–3). Shotgun sequencing allows us to determine the composition of proteins in CSF (52, 57). Several lipid-metabolizing proteins are detected in human CSF. These include proteins that bind and transport lipids, and proteins that synthesize or break down lipids. Once these proteins are detected in CSF, quantitative proteomics using isotope dilution methodologies can be applied to determine the levels of several lipid-metabolizing enzymes.

UniProt Knowledgebase name of human proteins APOA_HUMAN, APOA1_HUMAN, AIBP_HUMAN, A6XGK9_HUMAN, Q5T3I3_HUMAN, Q5T3I4_HUMAN, A6XGL0_HUMAN, COT2_HUMAN, APOA2_HUMAN, Q76EI7_HUMAN, APOA4_HUMAN,APOA5_HUMAN, B0YIV9_HUMAN, B0YIW1_HUMAN, APOB_HUMAN, AB48R_HUMAN, Q9NS13_HUMAN, ABEC1_HUMAN, B0QYD2_HUMAN, B0QYD3_HUMAN, B2CML4_HUMAN, ABC3F_HUMAN, B0QYD4_HUMAN, Q05JX5_HUMAN, ABC3H_HUMAN, B0QYP0_HUMAN, B0QYP1_HUMAN, ABEC4_HUMAN, APOC1_HUMAN, APOC2_HUMAN, APOC3_HUMAN, B0YIW2_HUMAN, A3KPE2_HUMAN, APOC4_HUMAN, A5YAK2_HUMAN, APOD_HUMAN, APOE_HUMAN, LRP1_HUMAN, LRP8_HUMAN, B1AMT6_HUMAN, B1AMT7_HUMAN, B1AMT8_HUMAN, APOF_HUMAN, APOH_HUMAN, CLUS_HUMAN, APOL1_HUMAN, A5PLQ4_HUMAN, APOL2_HUMAN, B0QYK7_HUMAN, APOL3_HUMAN, B1AHI4_HUMAN, B1AHI5_HUMAN, B1AHI6_HUMAN, B1AHI7_HUMAN, Q5U5N4_HUMAN, APOL4_HUMAN, B1AHJ3_HUMAN, Q6ICI8_HUMAN, Q9BRG6_HUMAN, APOL5_HUMAN, APOL6_HUMAN, APLD1_HUMAN, A0AVN6_HUMAN, APOM_HUMAN, Q5SRP5_HUMAN, Q5SRP4_HUMAN, B0UX98_HUMAN, APOO_HUMAN, APOOL_HUMAN, LPAL2_HUMAN, Q6ZU85_HUMAN AT8A1_HUMAN, AT8A2_HUMAN, AT8B1_HUMAN, AT8B2_HUMAN, AT11B_HUMAN, AT11C_HUMAN, AT11A_HUMAN, AT8B3_HUMAN, AT8B4_HUMAN, ATP9A_HUMAN, ATP9B_HUMAN, AT10A_HUMAN, AT10B_HUMAN, AT10D_HUMAN, AT93_HUMAN CETP_HUMAN, GLTD1_HUMAN, GLTD2_HUMAN, GLTP_HUMAN, LCAT_HUMAN, PLTP_HUMAN DGKA_HUMAN, DGKB_HUMAN, A4D117_HUMAN, A4D116_HUMAN, DGKD_HUMAN, DGKE_HUMAN, A1L4Q0_HUMAN, DGKG_HUMAN, Q2M1H4_HUMAN, DGKH_HUMAN, DGKI_HUMAN, DGKK_HUMAN, DGKQ_HUMAN, Q6P3W4_HUMAN, DGKZ_HUMAN, Q8IVW9_HUMAN FAS_HUMAN, SAST_HUMAN, ACBG1_HUMAN, ACBG2_HUMAN, ACSL1_HUMAN, ACSL3_HUMAN, ACSL4_HUMAN, ACSL5_HUMAN, ACSL6_HUMAN, S27A2_HUMAN, S27A5_HUMAN, A6GV77_HUMAN

Protein group

Apolipoproteins

Phospholipid transporting ATPase

Other lipid transfer proteins

Diacylgycerol kinases

Fatty Acid snythetases

Table 1  List of lipid-metabolizing proteins of interest from UniProt Knowledgebase (Swiss-Prot: http://www.expasy.ch/tools/)

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PLCB1_HUMAN, Q17RQ6_HUMAN, PLCB2_HUMAN, Q9BVT6_HUMAN, PLCB3_HUMAN, A5PKZ6_HUMAN, PLCB4_HUMAN, PLCD1_HUMAN, PLCD3_HUMAN, PLCD4_HUMAN, PLCE1_HUMAN, Q5VWL4_HUMAN, PLCG1_HUMAN, A2A284_HUMAN, PLCG2_HUMAN, PLCH1_HUMAN, PLCH2_HUMAN, Q86YW0_HUMAN, NSMA_HUMAN, PLCX1_HUMAN, PLCX2_HUMAN, PLCX3_HUMAN, PLCL2_HUMAN, PLCL1_HUMAN, SOCS7_HUMAN PLD1_HUMAN, PLD2_HUMAN, PLD3_HUMAN, PLD4_HUMAN, PLD5_HUMAN, ENPP2_HUMAN, PHLD_HUMAN, NAPEP_HUMAN, DDHD1_HUMAN, DDHD2_HUMAN, YQ008_HUMAN PGH1_HUMAN, PGH2_HUMAN, Q5T7T7_HUMAN, Q5T7T8_HUMAN, Q3HY30_HUMAN, Q3HY29_HUMAN, Q3HY28_HUMAN, Q6ZYK7_HUMAN, Q8IZA9_HUMAN, PD2R_HUMAN, PTGD2_HUMAN, PTGES_HUMAN, PGES2_HUMAN, A6NHH0_HUMAN, FEM1A_HUMAN, CBR1_HUMAN, PE2R1_HUMAN, PE2R2_HUMAN, PE2R3_HUMAN, PE2R4_HUMAN, Q6TTN3_HUMAN, Q5CZ60_HUMAN, Q5CZ62_HUMAN, Q5CZ57_HUMAN, O00325_HUMAN, TEBP_HUMAN, AK1C3_HUMAN, PF2R_HUMAN, FPRP_HUMAN, Q6RYQ6_HUMAN, PGDH_HUMAN, PGH1_HUMAN, PGH2_HUMAN, Q5T7T7_HUMAN, Q5T7T8_HUMAN, PTGDS_HUMAN, PTGIS_HUMAN, PI2R_HUMAN, SO2A1_HUMAN LOXE3_HUMAN, LOX5_HUMAN, AL5AP_HUMAN, LOXP_HUMAN, LX12B_HUMAN, LOX1_HUMAN, LX15B_HUMAN, LOXH1_HUMAN

Phospholipase C

Phospholipase D

Prostaglandin enzymes

Leukotrienes

Protein groups identified in human CSF by shotgun sequencing were searched using the UniProt Knowledgebase (UPK). All human proteins in the UPK were listed. Each name can be used for the in silico design of tryptic peptides (http://www.expasy.ch/tools/peptide-mass.html). These peptides can be used for quantitative proteomics using isotope dilution MS. Other protein groups not included in this list include proteins involved with sphingomyelin/ceramide metabolism and the cytochrome P450 enzymes involved in cholesterol and polyunsaturated fatty acid metabolism.

PA21B_HUMAN, PA2GA_HUMAN, PA2GC_HUMAN, PA2GD_HUMAN, B1AEL9_HUMAN, PA2GE_HUMAN, PA2GF_HUMAN, PA2G3_HUMAN, PA24A_HUMAN, B1AKG4_HUMAN, Q14064_HUMAN, PA24B_HUMAN, Q19KD6_HUMAN, Q19KD5_HUMAN, PA24C_HUMAN, PA24D_HUMAN, PA24E_HUMAN, PA24F_HUMAN, A6H8K5_HUMAN, A5PKZ7_HUMAN, PA2G5_HUMAN, PLPL2_HUMAN, PLPL8_HUMAN, ADPN_HUMAN, PRDX6_HUMAN, B0QYE8_HUMAN, PA2G6_HUMAN, PA2GX_HUMAN, Q14DU3_HUMAN, PG12A_HUMAN, PG12B_HUMAN, PAFA_HUMAN, PAFA2_HUMAN, Q6DN23_HUMAN, OC90_HUMAN, PLB1_HUMAN, PLA2R_HUMAN, B2RTU9_HUMAN, PLAP_HUMAN, ANXA1_HUMAN, LYPA2_HUMAN, Q5QPN9_HUMAN, Q5QPQ0_HUMAN, Q5QPQ2_HUMAN, Q5QPQ3_HUMAN, LYPA3_HUMAN

Phospholipase A2

Combining Lipidomics and Proteomics of Human Cerebrospinal Fluids 81

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Table 2 Peptidemass digest of apolipoprotein A1 (APOA1_HUMAN) Position

Peptide sequence

M/z = 1

m/z = 2

Variants

25–34

DEPPQSPWDR

1,226.5436

613.775

27:P->H(in Munster-3C). 27: 28:P->R(in Munster-3B). 34:

37–47

DLATVYVDVLK

1,235.6881

618.348

52–64

DYVSQFEGSALGK

1,400.6692

700.838

65–69

QLNLK

615.3824

308.195

70–83

LLDNWDSVTSTFSK

1,612.7853

806.896

86–101

EQLGPVTQEFWDNLEK

1,932.9337

966.971

102–107

ETEGLR

704.3573

352.682

108–112

QEMSK

622.2865

311.647

113–118

DLEEVK

732.3774

366.692

113:

121–130

VQPYLDDFQK

1,252.6208

626.814

126: 127:

132–140

WQEEMELYR

1,283.5725

642.29

132: 134:E->K(in Fukuoka).

143–147

VEPLR

613.3668

307.187

148–155

AELQEGAR

873.4424

437.225

158–164

LHELQEK

896.4836

448.745

160:E->K(in Norway). 163:

165–173

LSPLGEEMR

1,031.519

516.263

167: 168:L->R(in Zaragoza). 171:

178–184

AHVDALR

781.4315

391.219

180: 184:R->P(indbSNP

185–195

THLAPYSDELR

1,301.6484

651.328

189:P->R.

202–206

LEALK

573.3606

287.184

207–212

ENGGAR

603.2845

302.146

213–219

LAEYHAK

831.4359

416.222

220–230

ATEHLSTLSEK

1,215.6215

608.314

231–239

AKPALEDLR

1,012.5785

506.793

240–250

QGLLPVLESFK

1,230.7092

615.858

251–262

VSFLSALEEYTK

1,386.7151

693.861

61:A->T

92:

222:

APOA1_HUMAN was subjected to tryptic digestion using Peptidemass (http://www.expasy.org/ tools/peptide-mass.html). The number of missed cleavages was set at 0 and all cysteines were treated with iodoacetamide to form carbamidomethyl-cysteine. Methionines were oxidized to methionine sufoxide and only peptide masses >500 Da are displayed. Monoisotopic masses ([M + H+], m/z = 1) and the masses with a charge state (m/z) of 2 (default for the ion trap mass spectrometer) are shown. All known variants are also shown.



Combining Lipidomics and Proteomics of Human Cerebrospinal Fluids

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This approach allows for the complete understanding of biological pathways and should be useful in detecting changes in protein expression for brain diseases. Any change in lipids in combination with proteins should better illuminate strategies for biomarker discovery or molecular targets for drugs.

4. Notes 1. Lipids with polyunsaturated fatty acids are susceptible to oxidation. Enzymes at room temperature may also rapidly degrade some lipid mediators and signaling lipids. It is important to protect lipids from oxidation by adding BHT and by storing samples under inert conditions. We flush air out of our sample tubes before storage at −80°C. Once lipids are extracted, they are similarly stored in organic solvents until required for LC/MS2. In cases where specific enzyme inhibitors are known, these can be incorporated into the samples to avoid degradation of lipids. 2. While ESI is normally used for lipids, others have reported efficient use of other ionization methods. These include chemical ionization or photo ionization under atmospheric conditions (58). We have used the triple quadrupole mass spectrometer for most of our studies. However, iontrap mass spectrometers and high resolution linear FT/MS have also been used for lipidomic studies (59). 3. Several databases can be used to search mass spectrometer data of lipids. So far, these are proprietary and not universally available. These may also be instrument- and/or method-specific especially when attempts are made to identify molecular species of lipids. With accurate mass detection and increase in sensitivity of instruments, it may be possible to link individual lipidomic studies with the lipid maps database (http://www. lipidmaps.org/) for identification of lipid molecular species. 4. High-abundant proteins may be removed from samples to increase the dynamic range. This allows detection of the less abundant proteins in CSF. 5. Sample preparation is a key step in shotgun sequencing of proteins. For denaturation, urea or guanidine HCl can be used in place of Rapigest™. However, these have to be removed before mass spectrometry since these compounds may reduce the ionization of peptides of interest. 6. Other proteolytic enzymes and other buffer conditions can also be used instead of, or in combination, with trypsin. Such combinations may increase sequence coverage and increase the number of proteins detected in a sequencing experiment (52).

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7. 2-Dimensional liquid chromatography can be used to improve the separation and detection of peptides. Normally, the first dimension is a cation exchange resin that is followed by a reverse phase column. The Pepfinder kit (ThermoFisher, San Jose, CA) has been effectively used to improve extraction of peptides, removal of salts, and thus enables detection of thousands of proteins in human CSF without prior removal of abundant proteins (52). 8. The default charge state of the ion trap mass spectrometer is 2. Ions of charge state 1 or 2 may appear when a full scan is performed on the triple quadrupole mass spectrometer. For some peptides, the major charge state may be 1 or it may be 2 for others. We use the more intense ion for MS/MS and MRM studies.

Acknowlegments We thank Susan Onami and Elena Oborina for technical help. We appreciate the support and critical discussion with Drs. Michael Harrington, Andreas Hulmer, and Roger Biringer. This work was supported in part by NIH grants RO1# NS43295; institutional support (HMRI), the Norris, Jamison and Glide Foundations and donations from the Dunlevey, Hezlep and Posthuma families. References 1. Fonteh, A. N., Harrington0, R. J., Huhmer, A. F., Biringer, R. G., Riggins, J. N., and Harrington, M. G. (2006) Identification of disease markers in human cerebrospinal fluid using lipidomic and proteomic methods. Dis Markers 22(1–2), 247–272. 2. Albers, J. J. (1978) Effect of human plasma apolipoproteins on the activity of purified lecithin:cholesterol acyltransferase. Scand J Clin Lab Invest Suppl 150, 48–52. 3. Beffert, U., Danik, M., Krzywkowski, P., Ramassamy, C., Berrada, F., and Poirier, J. (1998) The neurobiology of apolipoproteins and their receptors in the CNS and Alzheimer’s disease. Brain Res Brain Res Rev 27(2), 119–142. 4. Haffner, S., Applebaum-Bowden, D., Wahl, P. W.et al. (1985) Epidemiological correlates of high density lipoprotein subfractions, apolipoproteins A-I, A-II, and D, and lecithin cholesterol acyltransferase. Effects of smoking, alcohol, and adiposity. Arteriosclerosis 5(2), 169–177.

5. Bazan, N. G., and Allan, G. (1996) Plateletactivating factor is both a modulator of synaptic function and a mediator of cerebral injury and inflammation. Adv Neurol 71, 475–482. 6. Brady, H. R., and Serhan, C. N. (1996) Lipoxins: putative braking signals in host defense, inflammation and hypersensitivity. Curr Opin Nephrol Hypertens 5(1), 20–27. 7. Fonteh,vv A. N., and Harrington, M. G. (2004) Remodeling of arachidonate and other polyunsaturated fatty acids in Alzheimer’s disease. In: Fonteh AN, Wykle RL, editors. Arachidonate Remodeling and Inflammation. Birkhauser Verlag 145–168. 8. Ford-Hutchinson, A. W. (1990) Leukotriene B4 in inflammation. Crit Rev Immunol 10(1), 1–12. 9. Lefkowith, J. B. (1988) Essential fatty acid deficiency inhibits the in  vivo generation of leukotriene B4 and suppresses levels of resident and elicited leukocytes in acute inflammation. J Immunol 140(1), 228–233.



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10. O’Banion, M. K. (1999) COX-2 and Alzheimer’s disease: potential roles in inflammation and neurodegeneration. Expert Opin Investig Drugs 8(10), 1521–1536. 11. Peroutka, S. J. (2005) Neurogenic inflammation and migraine: implications for the therapeutics. Mol Interv 5(5), 304–311. 12. Pruzanski, W., and Vadas, P. (1989) Phospholipase A2 and inflammation. Ann Rheum Dis 48(11), 962–963. 13. Serhan, C. N. (1996) Inflammation. Signalling the fat controller. Nature 384(6604), 23–24. 14. Zurier, R. B. (1993) Fatty acids, inflammation and immune responses. Prostaglandins Leukot Essent Fatty Acids 48(1), 57–62. 15. Axelrod, J. (1995) Phospholipase A2 and G proteins. Trends Neurosci 18(2), 64–65. 16. Bazan, N. G., Allan, G., and Rodriguez de Turco, E. B. (1993) Role of phospholipase A2 and membrane-derived lipid second messengers in membrane function and transcriptional activation of genes: implications in cerebral ischemia and neuronal excitability. Prog Brain Res  96, 247–257. 17. Bonventre, J. V. (1992) Phospholipase A2 and signal transduction. J Am Soc Nephrol 3(2), 128–150. 18. Clark, J. D., Schievella, A. R., Nalefski, E. A., and Lin, L. L. (1995) Cytosolic phospholipase A2. J Lipid Mediat Cell Signal 12(2–3), 83–117. 19. Dennis, E. A. (2000) Phospholipase A2 in eicosanoid generation. Am J Respir Crit Care Med 161(2 Pt 2), S32–S35. 20. Diez, E., Chilton, F. H., Stroup, G., Mayer, R. J., Winkler, J. D., and Fonteh, A. N. (1994) Fatty acid and phospholipid selectivity of different phospholipase A2 enzymes studied by using a mammalian membrane as substrate. Biochem J 301(Pt 3), 721–726. 21. Farooqui, A. A., Antony, P., Ong, W. Y., Horrocks, L. A., and Freysz, L. (2004) Retinoic acid-mediated phospholipase A2 signaling in the nucleus. Brain Res Brain Res Rev 45(3), 179–195. 22. Fonteh, A. N., Bass, D. A., Marshall, L. A., Seeds, M., Samet, J. M., and Chilton, F. H. (1994) Evidence that secretory phospholipase A2 plays a role in arachidonic acid release and eicosanoid biosynthesis by mast cells. J Immunol 152(11), 5438–5446. 23 Kramer, R. M., Stephenson, D. T., Roberts, E.F., and Clemens, J. A. (1996) Cytosolic phospholipase A2 (cPLA2) and lipid mediator release in the brain. J Lipid Mediat Cell Signal 14(1–3), 3–7.

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24. Kudo, I., and Murakami, M. (2002) Phospholipase A2 enzymes. Prostaglandins Other Lipid Mediat 68–69, 3–58. 25. Cocco, L., Martelli, A. M., Gilmour, R. S., Rhee, S. G., and Manzoli, F. A. (2001) Nuclear phospholipase C and signaling. Biochim Biophys Acta 1530(1), 1–14. 26. Cockcroft, S. (2006) The latest phospholipase C, PLCeta, is implicated in neuronal function. Trends Biochem Sci 31(1), 4–7. 27. Manzoli, L., Billi, A. M., Martelli, A. M., and Cocco, L. (2004) Regulation of nuclear phospholipase C activity. Acta Biochim Pol 51(2), 391–395. 28. Matsushima, H., Shimohama, S., Kawamata, J., Fujimoto, S., Takenawa, T., and Kimura, J. (1998) Reduction of platelet phospholipase C-delta1 activity in Alzheimer’s disease associated with a specific apolipoprotein E genotype (epsilon3/epsilon3). Int J Mol Med 1(1), 91–93. 29. Minke, B. (2001) The TRP channel and phospholipase C-mediated signaling. Cell Mol Neurobiol 21(6), 629–643. 30. Nakahara, M., Shimozawa, M., Nakamura, Y., et  al. (2005) A novel phospholipase C, PLC(eta)2, is a neuron-specific isozyme. J Biol Chem 280(32), 29128–29134. 31. Rhee, S. G. (2001) Regulation of phosphoinositide-specific phospholipase C. Annu Rev Biochem 70, 281–312. 32. Shimohama, S., Sasaki, Y., Fujimoto, S.et  al. (1998) Phospholipase C isozymes in the human brain and their changes in Alzheimer’s disease. Neuroscience 82(4), 999–1007. 33. Stewart, A. J., Mukherjee, J., Roberts, S. J., Lester, D., and Farquharson, C. (2005) Identification of a novel class of mammalian phosphoinositol-specific phospholipase C enzymes. Int J Mol Med 15(1), 117–121. 34. Cockcroft, S. (2001) Signalling roles of mammalian phospholipase D1 and D2. Cell Mol Life Sci 58(11), 1674–1687. 35. Jenkins, G. M., and Frohman, M. A. (2005) Phospholipase D: a lipid centric review. Cell Mol Life Sci 62(19–20), 2305–2316. 36. Klein, J. (2005) Functions and pathophysiological roles of phospholipase D in the brain. J Neurochem 94(6), 1473–1487. 37. Xie, Z., Ho, W. T., Spellman, R., Cai, S., and Exton, J. H. (2002) Mechanisms of regulation of phospholipase D1 and D2 by the heterotrimeric G proteins G13 and Gq. J Biol Chem 277(14), 11979–11986. 38. Balazy, M. (2004) Eicosanomics: targeted lipidomics of eicosanoids in biological systems. Prostaglandins Other Lipid Mediat 73(3–4), 173–180.

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39. Fitzpatrick, F. A., and Murphy, R. C. (1988) Cytochrome P-450 metabolism of arachidonic acid: formation and biological actions of “epoxygenase”-derived eicosanoids. Pharmacol Rev  40(4), 229–241. 40. Lands, W. E. (1993) Eicosanoids and health. Ann N Y Acad Sci 676, 46–59. 41. Murphy, R. C. (2001) Free-radical-induced oxidation of arachidonoyl plasmalogen phospholipids: antioxidant mechanism and precursor pathway for bioactive eicosanoids. Chem Res Toxicol 14(5), 463–472. 42. Serhan, C. N., Lu, Y., Hong, S., and Yang, R. (2007) Mediator lipidomics: search algorithms for eicosanoids, resolvins, and protectins. Methods Enzymol 432, 275–317. 43. Smith, W. L. (1989) The eicosanoids and their biochemical mechanisms of action. Biochem J 259(2), 315–324. 44. Bazan, N. G., Squinto, S. P., Braquet, P., Panetta, T., and Marcheselli, V. L. (1991) Platelet-activating factor and polyunsaturated fatty acids in cerebral ischemia or convulsions: intracellular PAF-binding sites and activation of a fos/jun/AP-1 transcriptional signaling system. Lipids 26(12), 1236–1242. 45. Benveniste, J., Chignard, M., Le Couedic, J. P., and Vargaftig, B. B. (1982) Biosynthesis of platelet-activating factor (PAF-ACETHER). II. Involvement of phospholipase A2 in the formation of PAF-ACETHER and lyso-PAFACETHER from rabbit platelets. Thromb Res 25(5), 375–385. 46. Snyder, F. (1994) Metabolic processing of PAF. Clin Rev Allergy 12(4), 309–327. 47. Farooqui, A. A., Litsky, M. L., Farooqui, T., and Horrocks, L. A. (1999) Inhibitors of intracellular phospholipase A2 activity: their neurochemical effects and therapeutical importance for neurological disorders. Brain Res Bull 49(3), 139–153. 48. Berenbaum, F. (1995) Phospholipase A2 inhibitors: a challenge for the future. Rev Rhum Engl Ed 62(6), 409–414. 49. Glaser, K. B., Lock, Y. W., and Chang, J. Y. (1991) PAF and LTB4 biosynthesis in the human neutrophil: effects of putative inhibitors of phospholipase A2 and specific inhibitors of 5- lipoxygenase. Agents Actions 34(1–2), 89–92. 50. Bligh, E. A., and Dyer, W. T. (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37, 911–917.

51. Fonteh, A. N. (1999) Assessment of arachidonic acid distribution into phospholipids of inflammatory cells. Methods Mol Biol 120, 77–89. 52. Biringer, R. G., Amato, H., Harrington, M. G., Fonteh, A. N., Riggins, J. N., and Huhmer, A. F. (2006) Enhanced sequence coverage of proteins in human cerebrospinal fluid using multiple enzymatic digestion and linear ion trap LC-MS/MS. Brief Funct Genomic Proteomic 5(2), 144–153. 53. Wolters, D. A., Washburn, M. P., and Yates, III J.R. (2001) An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem 73(23), 5683–5690. 54. Amanchy, R., Kalume, D. E., and Pandey, A. (2005) Stable isotope labeling with amino acids in cell culture (SILAC) for studying dynamics of protein abundance and posttranslational modifications. Sci STKE 267, l2. 55. Romijn, E. P., Christis, C., Wieffer, M. et al. (2005) Expression clustering reveals detailed co-expression patterns of functionally related proteins during B cell differentiation: a proteomic study using a combination of onedimensional gel electrophoresis, LC-MS/MS, and stable isotope labeling by amino acids in cell culture (SILAC). Mol Cell Proteomics 4(9), 1297–1310. 56. Fonteh, A. N., Biringer, R. G., Huhmer, A. F., Rush, J. D., and Harrington, M. G. (2005) Use of [13C],[15N]-peptide standards to quantify enzymes of the Cyclooxygenase and Lipoxygenase pathways in human cerebrospinal fluids. American Society for Mass Spectrometry: Conference Proceeding 5–6(http://www. asms.org/asms05pdf/A052054.pdf). 57. Huhmer, A. F., Biringer, R. G., Amato, H., Fonteh, A. N., and Harrington, M. G. (2006) Protein analysis in human cerebrospinal fluid: Physiological aspects, current progress and future challenges. Dis Markers 22(1–2), 211–234. 58. Lee, S. H., and Blair, I. A. (2007) Targeted chiral lipidomics analysis by liquid chromatography electron capture atmospheric pressure chemical ionization mass spectrometry (LC-ECAPCI/ MS). Methods Enzymol 433, 159–174. 59. Schwudke, D., Liebisch, G., Herzog, R., Schmitz, G., and Shevchenko, A. (2007) Shotgun lipidomics by tandem mass spectrometry under data-dependent acquisition control. Methods Enzymol 433, 175–191.

Part II Analytical Approaches

sdfsdf

Chapter 5 Lipid Profiling Using Two-Dimensional Heteronuclear Single Quantum Coherence NMR Engy A. Mahrous, Robin B. Lee, and Richard E. Lee Summary  The use of NMR spectroscopy in lipid research has been traditionally reserved for the analysis and structural elucidation of discrete lipid molecules. Although NMR analysis of organic molecules provides a plethora of structural information that is normally unattainable by most other techniques, its use for global analysis of mixed lipid pools has been hampered by its relatively low sensitivity and overlapping of signals in the spectrum. However, the last few decades have witnessed great advancements in NMR spectroscopy that generally resulted in greater sensitivity and offered more flexibility in sampling techniques. The method discussed in this chapter describes the use of NMR for global lipidome analysis. This methodology benefits from the quantitative nature of this technique together with the abundance of the structural information it can offer, while partially overcoming the problems of low sensitivity and overlapping signals through isotope-enrichment and the use of multidimensional NMR, respectively. We have applied this method successfully to the mycobacterial lipidome as an example of an organism with a very complex and chemically diverse lipid pool. The same concept is applicable to a wide range of prokaryotes that can grow in the laboratory in well-defined growth media. Key words:  NMR spectroscopy, Mycobacteria, 2D-HSQC, Cell wall lipids, Polyketides, Glycolipids

1. Introduction The genus Mycobacteria is characterized by a unique cell wall rich in complex lipids, polyketides, and polysaccharides. Historically, numerous studies of individual mycobacterial lipids and the way they participate in pathogenesis and virulence have been conducted (1–3). Early attempts for global analysis of the mycobacterial lipidome began in the 1960s as a search for a taxonomical tool to assign newly discovered species in their right taxonomical order and to help identify clinical isolates at Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, DOI 10.1007/978-1-60761-322-0_5, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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the species level (4–7). A major breakthrough in this field was achieved by Dobson and Minnikin who reported five 2D-TLC systems in the 1980s to analyze the mycobacterial lipidome (8). Despite its wide application the methodology remained less than satisfactory. Problems associated with poor staining, low sensitivity, and limited resolution on one hand and the recent advancement of bioanalytical tools on the other, have fueled a research effort for new methodologies to study the mycobacterial lipidome. Recently, two new methodologies based on mass spectroscopy have been described. The first uses Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) (9). The second uses prior separation with HPLC followed by analysis with electrospray ionization HPLC/ ESI-MS (10). These analytical techniques are highly sensitive, however, they require significant sample manipulation to overcome the phenomena of ion suppression within lipid mixtures thus are best used for the analysis of readily ionizable molecules (9, 10). The method discussed in this chapter focuses on the application of NMR spectroscopy as a complementary procedure that can be utilized to rapidly analyze the composition of the mycobacterial lipid pool directly from crude lipid extracts (11). It offers a great tool for investigating the mode of action of antituberculosis drugs, determining gene function, understanding the stringent response of Mycobacteria under stress conditions that resemble the in  vivo environment, and investigating differences between species or strains of Mycobacteria. To maximize its benefit, this lipid-profiling technique can be used in a complementary fashion with other genomic and proteomic methods. As it is the case with other “omics” techniques, the great utility of lipidomics methods is that they provide a wealth of information regarding the total composition of the lipid pools, from which a small number of candidate molecules can then be defined for more comprehensive follow-up studies through purification and isolation of such molecules of interest. For introduction of this method, we applied the technique to the lipid pool of Mycobacterium tuberculosis, the etiological agent of tuberculosis, which currently infects one-third of the world population and claimed 1.7 million lives worldwide in 2006 (12). The cell wall of M. tuberculosis, with its associated lipids, is believed to play an important role in its pathogenesis and virulence. The currently accepted structural and functional model of the mycobacterial cell wall describes it as a three-layer structure (13). The innermost layer consists of the plasma membrane, followed to the outside by the peptidoglycan layer, which is then covalently attached to the outer mycolyl arabinogalactan complex. The unusual long chain mycolic acids form a ­permeability barrier where a mixture of lipids, proteins, and glycolipids are loosely bound to the outside of the cell wall. Some of these molecules are surface



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exposed and are free to interact with the host immune system (14–16), which may contribute to virulence and pathogenicity. This population of lipids can be easily extracted and analyzed by NMR spectroscopy. As NMR measures properties of nuclei rather than the physical property of the entire molecule, NMR signals are less likely to be affected by the different physicochemical properties exhibited by different classes of lipids (molecular weight, polarity, ionization potential, etc.). This uniformity of response is advantageous because it limits artifacts and confers quantitative representation of all lipids regardless of their structure. However, several challenges exist for using NMR to study complex pools of metabolites. First is the low sensitivity associated with NMR analysis. This can be partially overcome by enriching the cell lipidome with 13 C isotope through the replacement of the carbon source in the growth media with 13C-labeled precursors. Second, is the complexity and frequent overlapping of the NMR signals in the onedimensional spectrum. This problem can be addressed through the use of two- and three-dimensional NMR. Two-dimensional 1 H-13C heteronuclear single quantum coherence (HSQC) NMR can be used to rapidly generate lipid profiles, while more sophisticated three-dimensional experiments can be used to resolve the lipid profile and help assign signals when necessary. These experiments require no specialized equipment and can be performed using a standard high-field NMR spectrometer. Third, data analysis of such complex data requires extra consideration and efforts. To ensure data quality, signals in the NMR need to be referenced in both their chemical shift and relative intensity to an internal reference that produces a discrete signal in the NMR spectrum. To enhance reproducibility, external references are required to calibrate the NMR magnet and account for any observed variation due to instability of the magnet overtime. The data must then be normalized based on these internal and external standards using NMR software that confers robust analysis of large data sets. Specific NMR acquisition and data processing methods will not be discussed here as they are highly dependent on the instrumentation and software available. The following sections describe methods for sample preparation and analysis of mycobacterial lipid profiles using 2D NMR spectroscopy.

2. Materials 2.1. Equipment

1. High-field nuclear magnetic resonance spectrometer. 2. NMR processing software.

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2.2. Reagents and Supplies 2.2.1. Cell Culture

1. Sterile solution of Difco Middlebrook 7H9 Becton Dickinson and company, Sparks, MD. 2. Sterile 10× ADG supplement (2% glucose, 2% glycerol, 0.8% sodium chloride, 5% Bovine serum albumin (BSA) fraction V, Calbiochem, La Jolla, CA). 3. Sterile 10× solution of 5% Bovine serum albumin/0.8% sodium chloride. 4. Sterile solution of 10% 13C6 glucose Cambridge Isotope Laboratories, Andover, MA. 5. Sterile solution of 10% 13C3 glycerol Cambridge Isotope Laboratories, Andover, MA. 6. Sterile solution of 10% Tween-80, Calbiochem, La Jolla, CA.

2.2.2. Lipid Extraction

1. Deuterium oxide D2O 99% Cambridge Isotope Laboratories, Andover, MA. 2. Deuterated chloroform CDCl3 99% Cambridge Isotope Laboratories, Andover, MA. 3. Deuterated methanol CD3OD Cambridge Isotope Laboratories, Andover, MA.

2.2.3. Mycolic Acid Extraction

1. 1 M methanolic solution of KOH. 2. Diethyl ether. 3. Concentrated HCl.

3. Methods 3.1. Cell Culture and Preparation of Cell Pellet

To generate a good 2D HSQC spectrum in a short acquisition time, it is necessary to enrich the cell lipidome with 13C isotope. This can be achieved through the replacement of the carbon source in the growth media with 13C-labeled lipid precursors such as U-13C2 acetate, U-13C6 glucose, or U-13C3 glycerol. To attain a well-enriched sample, it is best to use a defined medium and limit any carbon sources to 13C as much as possible. Culture can also be passed in 13 C medium to further increase enrichment. Although the procedure discussed here focuses mainly on profiling the overall free cell wall lipid populations, the method can be easily modified to target a specific lipid population. The global 13C-enrichement strategy using 13C glucose and glycerol can be replaced by a biosynthetically guided approach that uses specific 13C-labeled precursors in order to enrich the signals for certain lipid targets. Cells can be grown or treated under various conditions to probe the response of the bacteria to different environmental conditions such as



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nutrient starvation, drug treatment, pH, oxidative stress, or media composition. The time 13C label is incorporated can be varied based on the conditions tested. For example, studies involving the response of bacteria to drug treatment may use either cells fully labeled prior to drug treatment to observe changes in the complete lipidome or the 13C precursors may be added at the time of drug treatment to observe incorporation of label, and hence determine which lipids are synthesized in the presence of the drug. After cells have been grown under the desired conditions, the cell pellet must be washed with D2O, prior to extraction of the lipids, to ensure the removal of water and media components that may interfere with signals in the NMR spectrum. The methods below describe the standard protocol for the preparation of a mycobacterial sample (see Note 1). Generally, a 20–50 ml culture grown to an OD600 of 0.4–0.6 is sufficient to generate a good lipid profile. 1. 50 ml of 13C-labeled Middlebrook 7H9 media is inoculated from a midlog culture to an OD600 = 0.05 for slow growing species or OD600 = 0.005 for fast growing species (see Notes 1–3). 2. The cells are incubated at 37°C with continuous shaking at 250 rpm until an OD600 of 0.4–0.6 is reached. The cells are harvested by centrifugation at 3,700 × g for 10 min. 3. The supernatant is carefully discarded and the cells are washed twice with a volume D2O equal to 20% of the original culture volume (10 ml for a 50 ml culture). Centrifuge at 3,700 × g for 10 min and discard the supernatant. 4. Use 1 ml of D2O to resuspend the pellet and transfer the sample to a preweighed microcentrifuge tube. Centrifuge the cells at 3,700 × g for 10 min. 5. Discard the supernatant and carefully remove all remaining D2O from the cell pellet. Determine the weight of the wet pellet. 3.2. Extraction and Analysis of the Total Free Lipid Pool

In these experiments, the use of deuterated solvents is necessary to suppress solvent peaks and optimize the signal to noise ratio. The lipid pool of mycobacterial cells are extracted directly from the washed cell pellet by a 2:1 mixture of CDCl3:CD3OD, a solvent system commonly used to extract surface lipids of mycobacteria. Internal standards are incorporated into the extraction mixture to facilitate NMR data processing and allow normalization of data collected at different times. Ideally, compounds are selected that provide two sets of discrete internal reference peaks located in an uncongested area of the spectrum. For mycobacterial lipid profiling, dimethyl sulfoxide (DMSO) and/or 1,3,5 trimethyl benzene can be added to the CDCl3:CD3OD mixture prior to extraction. A lipid profile is generated directly from the

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crude extract using a 2D-HSQC pulse sequence to resolve the signals into two dimensions (the F1 proton dimension and the F2 carbon dimension). This inverse detection sequence helps maximize sensitivity to heteronuclei and increase signal to noise ratio. The isotope enrichment strategy greatly enhances the signal intensity and confers a good spectrum in a relatively short acquisition time. A good quality 2D spectrum is attainable in 20 min. However, observation of some less abundant lipid species benefits from a longer acquisition time of 90 min. Importantly, such maps are not attainable from normally grown (unlabeled) cells using larger quantities of crude lipid in the same volume as these lipids precipitate at high concentrations in the NMR solvents. After generation of a baseline lipid profile, to facilitate interpretation of the data, it is necessary to define biomarker signals of known lipid species using purified standards. In the lipid profile, each molecule is represented by multiple signals. Although many signals in the 2D spectrum still overlap, each lipid molecule generally has at least one discrete signal present. These discrete signals are designated as biomarker peaks. Therefore, the presence or absence and the quantity of a certain molecule can be readily determined by the identification of these discrete and diagnostic biomarker peaks within the 2D spectrum. When possible it is useful to identify and utilize multiple biomarker signals for molecules of interest to confer a greater degree of confidence in the data interpretation and hence limit the possibility of artifacts from unidentified overlapping species. Due to the complexity of the spectra and incomplete spectral assignments in the literature, it is important to purify a set of standards and analyze them under the same conditions as the lipid profile, in order to assign biomarkers effectively. For some molecules, their abundance in M. tuberculosis is not sufficient to purify enough sample. This can often be resolved by utilizing the increased expression of the targeted lipids in other mycobacterial species, from which they can then be isolated in suitable amounts. If necessary 3D HCCH TOCSY and 3D HCCH COSY can then be used to confirm assignment of biomarker signals in the total lipid profile. This is especially useful for confirming the shift of any signals that may vary slightly from standards that were purified from other mycobacterial species, which occurs due to slight variations in structural composition or substitution. After biomarkers have been assigned in the total lipid profile, spectral sets can be compared and lipids quantified by processing the data using software such as Felix or NMR Pipe. 1. Add 2 ml of CDCl3 and 1 ml of CD3OD per 1 mg of wet pellet weight. Mix the sample to homogeneity and extract at 37°C with continuous shaking at 250 rpm for 90 min.



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2. After extraction, add an extra 1 ml of CD3OD for every milligram of original pellet weight and centrifuge at 3,700 × g for 10 min. Due to the water released from the cells during lysis, the additional CD3OD is necessary to allow the cell debris to pellet and to form a single phase solution. If the solution is still biphasic after centrifugation, CD3OD can be added dropwise with mixing until a single phase is formed after centrifugation. 3. Remove the supernatant to a clean microcentrifuge tube, being careful not to disrupt the pellet. Recentrifuge at 3,700 × g for 10 min and carefully transfer the supernatant to a fresh tube. 4. Transfer an appropriate volume of the supernatant to a clean dry NMR tube and acquire a 2D HSQC spectrum according to the NMR spectrometer used (Notes 4 and 5). 3.3. Preparation of the Mycolic Acid Pool

One important population of lipids in Mycobacteria is the mycolic acids. These unusual long chain fatty acids form a pseudomembrane layer where a mixture of free lipids, proteins, and glycolipids are embedded in a parallel inclusion. They are tethered to the cell wall by the arabinogalactan and therefore additional steps are required to isolate them from the cell pellet to complete the lipid profile in global lipid analysis studies. 13C enrichment significantly reduces the amount of sample required and they can easily be isolated from the left over cell debris after extraction of the free lipids. 1. The remaining pellet left after free lipid extraction is washed with 1 ml of D2O, which is subsequently removed by centrifugation and decantation. 2. A solution of 1 M methanolic KOH is added to the washed pellet (2 ml per 100 mg of original weight of the wet cell pellet). The methanolic solution is then transferred to a glass screw capped tubes and stirred for 16 h at 80°C. 3. The methanolysate is then cooled down and acidified by adding concentrated HCl dropwise to reach pH 4, to precipitate the free mycolic acids. The acidified methanolic solution is then diluted 3× with deionized water and extracted with one volume of diethyl ether. The top ether layer is carefully removed and transferred to another tube. 4. The extraction step is repeated and the ether extract is pooled and washed collectively with one volume of deionized water. The water is finally removed and the ether layer is dried under an atmosphere of inert gas. 5. The lipid film produced upon dryness is then dissolved in 1:1 solution of CD3OD: CDCl3 and transferred to a standard NMR tube.

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3.4. Alternative Extraction Methods

Although it is advantageous to monitor the whole cell lipidome at once, in some cases lipids of low abundance are better observed in a more limited subpopulation. The general extraction method discussed previously in Subheading 3.2 can be easily modified, through the incorporation of simple extraction or fractionation processes, to focus on specific or less abundant subpopulations. For example, methods include successive extraction of cells with deuterated solvents of increasing polarity, use of scavenger resins to pull down and fractionate anionic lipids and cationic lipids (17, 18), and the use of solvent partitions to remove very abundant signals or to separate the polar and apolar lipid pools. Separation of the polar and apolar lipids can be easily achieved through a simple modification to this method. As water is released from cells during extraction with 2:1 CDCl3:CD3OD, the relative ratio of chloroform to methanol to water in the solvent mixture results in partitioning of the sample into two phases as described in Subheading 3.2. The two phases can simply be collected and analyzed separately rather than adding more CD3OD to achieve a monophasic solution. 1. Add 2 ml of CDCl3 and 1 ml of CD3OD per 1 mg of wet pellet weight. Mix the sample to homogeneity and extract at 37°C with continuous shaking at 250 rpm for 1 h, 30 min. After extraction, centrifuge the sample at 3,700 × g for 10 min. A 2-layer suspension will be formed with the cellular debris at the interface. 2. Remove the aqueous upper layer carefully and transfer to a clean tube. This constitutes the bulk of the polar cell wall lipids. 3. Transfer the lower organic to a clean microcentrifuge tube. Add 2 ml of CDCl3 per original cell pellet weight to the extraction mixture. Vortex the tube for 30 s, then centrifuge the mixture at 3,700 × g for 10 min. 4. Transfer the chloroformic extract (lower layer) to a clean NMR tube. This constitutes the apolar lipid pool.

3.5. Results

1. Figure 1 shows the 2D HSQC lipid profile of M. bovis BCG. Signals in the HSQC spectrum can be categorized into three main regions; the most up-field region (d1H,–0.5–3.0 ppm) representing mainly the aliphatic chain of the lipid molecules, the far down-field region (d1H, 5.2–8.5 ppm) representing unsaturated and aromatic substructures and the middle region (d1H, 3.2–5.4 ppm), representing signals from sugars attached to glycolipids and phospholipids. Two sets of biomarker signals for menaquinone and trehalose dimycolates are labeled on the HSQC spectrum for illustration.



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Fig. 1. 2D HSQC lipid profile of M. bovis BCG: 1H-13C HSQC spectrum of the 2:1 v/v CDCl3:CD3OD extract of M. bovis BCG. The extract was obtained as described in the text Subheading 3.2. For illustration, three biomarker signals for menaquinones are shown (signals Ia–c), and two biomarker signals are shown for trehalose dimycolate (signals IIa,b) including the diastereotopic CH2. Signal III indicates the signal for DMSO, which was included as an internal standard during the extraction. The total sample preparation time including extraction was 2 h. Total NMR acquisition time was 1 h and 40 min (ni = 256, nt = 10).

2. Figure 2 demonstrates the utility of this technique to probe gene function by detecting changes in the lipid pool as a result of gene knockout. In this experiment the lipid profiles of the hypervirulent M. tuberculosis HN-878 was compared to that of HN878-▵Dpks, a mutant strain where the pks 1–15 gene responsible for the production of the virulence factor phenolicglycolipid (PGL) was effectively disrupted. The three biomarker signals corresponding to M. tuberculosis PGL can be observed in the lipid profile of the clinical isolate HN-878, but are missing in the lipid profile of the mutant strain as a result of pks1–15 gene knockout. PGL is generally a difficult molecule to isolate from M. tuberculosis due to its low abundance, which makes application of this technique particularly useful for PGL studies. Indeed, this method was found to be applicable for rapidly screening clinical isolates for the presence or absence of PGL expression. 3. Figure 3 shows the 2D 1H-13C HSQC spectrum of the mycolic acid pool extracted from M. bovis BCG grown in Middlebrook 7H9 supplemented with 13C glucose and 13C glycerol under normal growth conditions. Each class of

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a

c

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Fig. 2. Demonstration of lipid profiling to probe gene function. Biomarker signals for phenolicglycolipid (PGL) were defined using the 1H-13C HSQC spectrum acquired from a sample of PGL purified from M. liflandii [shown as circled cross peaks in spectra (a) and (b)]. These signals were readily detected in extracts from wild type M tuberculosis: HN878 (c) and (d) but not in the knockout strain of HN878 ▵∆ pks (e) and (f). For simplicity, relevant smaller regions are shown from the whole HSQC spectra: two biomarker aromatic signals are shown in solid circles in panels (a), (c), (e) (dH 6.7–7.4, dC 115–133) and the benzylic CH2 signal is shown in dashed circles in panels (b), (d), (f) (dH 2.3–3.4, dC 33.8–49.4). Due to differences in the glycosidation pattern between PGL molecules produced by M. tuberculosis and M. liflandii, a small difference in the chemical shift of the aromatic protons at the ortho-position to the glycosidic linkage was observed in their respective 2D-HSQC spectrum. Adapted from (11) with permission.

mycolic acids exhibit unique biomarker signals, which can be used to detect the relative abundance of a certain class in the mycolic acid pool. 4. Figure 4 demonstrates the differences in the polar and apolar lipid profiles of M. bovis BCG compared to the total lipid profile: total lipids A, apolar lipids B, polar lipids C. By acquiring the 2D HSQC spectra of the polar and apolar lipid fractions separately, some overlapping signals were effectively resolved. Noticeably, the abundant triglycerides and phosphatidyl inositol mannosides signals were eliminated from the polar lipid pool allowing the detection of many glycolipids of relatively low abundance.



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Fig. 3. 2D-HSQC mycolic acid profiles for M. bovis BCG. 1H-13C HSQC spectrum of mycolic acids, extracted from M. bovis BCG as described in text Subheading 3.3. Examples for signals common to all mycolic acid are shown by arrow [(a), bCH–OH (b), aCH–COOH)]. Signals indicative to specific classes of mycolic acids are circled in dashed lines.

4. Notes 1. The handling of mycobacterial species should follow the biosafety recommendation for each specific species. Mycobacterium tuberculosis must be handled at Biosafety Level 3. Handling of extracts should be performed in accordance with institutional guidelines. The sterilization ability of the extraction methods should be verified before samples are removed from the BSL3 laboratory. In this case, it was found that extraction of 200 mg of cells with 600 ml of a 2:1 mixture of CDCl3:CD3OD for 90 min followed by two centrifugation steps at 3,700 × g for 10 min resulted in a sterile sample. Samples were prepared in triplicate, dried under sterile conditions, resuspended in 7H9 and the entire contents of a single preparation were plated on 7H11 media or inoculated into 7H9 broth. After 6 weeks, no growth on plates or in broth was observed. 2. Composition of 13C-7H9 Media (a) 45 ml Sterile Middlebrook 7H9 media (7H9, 0.05% Tween 80) (b) 5 ml Filter sterilized solution of BSA (5%) and NaCl (0.8%) (c) 1 ml Filter sterilized solution of 13C-glucose 0.1 gm/ml (d) 1 ml Filter sterilized solution of 13C-glycerol 0.1 gm/ml (e) the growth

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Fig. 4. Separation of the polar and apolar lipid profiles of M. bovis BCG. 1H-13C HSQC spectra generated from M. bovis BCG grown in 13C-labeled 7H9 (a) total lipid, (b) apolar lipids, and (c) polar lipids. Expanded views showing detail of the biomarkers for mycobactins, mycoside B, (dashed box, dH 6.68–7.28, dC 113–132.3) and glycerol-containing phospholipids (solid box, dH 5.0–5.44, dC 67–78.8) have been shown in these spectra as examples of how lipids can be separated through simple fractionation. Only mycoside B is present in the apolar lipid spectrum while mycobactin signals are observed in the polar spectrum only. The glycerol-containing phospholipids are overlapping in the total lipid pool, however, when the abundant triglyceride and PIMs are removed into the apolar solvent, less-abundant species were better presented in the polar lipid spectrum.

rate of bacteria is not affected by the use of a labeled carbon source. 3. Examples of fast growing species include the saprophytic mycobacterial species M. smegmatis and M. phlei. Examples of slow growing species include M. tuberculosis, M. bovis, and M avium.



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4. For the NMR data presented in this chapter, a Varian Inova 500 MHz NMR spectrometer was used. The spectrometer was equipped with a triple resonance trpfg 5 mm probe, which was tuned, matched, and shimmed for each individual sample. The 1H-13C HSQC spectra were acquired with a standard pulse sequence with 10 or 12 transients and 256 increments. 5. Extraction of lipids from a 20–50 ml culture usually generates approximately 0.5–0.7 ml of extract, which is appropriate for use with 5 mm sample probe and standard 5 mm NMR tubes. However, microsampling inserts and other low volume NMR probes require far less sample volume and may be used if analysis of smaller samples is required.

Acknowledgments We thank Dr Clifton Barry III, (National Institutes of Health) for providing M. tuberculosis HN-878 and HN878-▵D pks strains, Dr. Pamela L.C. Small, (University of Tennessee, Knoxville) for providing both M. marinum and M. liflindii strains, and Dr. Wei Li (University of Tennessee, Memphis) for technical assistance. We acknowledge financial support for this work from National Institutes of Health grant AI076938. References 1. Goren, M. B., O. Brokl, and W. B. Schaefer. 1974. Lipids of putative relevance to virulence in Mycobacterium tuberculosis: phthiocerol dimycocerosate and the attenuation indicator lipid. Infection and immunity 9:150–158. 2. Goren, M. B., O. Brokl, and W. B. Schaefer. 1974. Lipids of putative relevance to virulence in Mycobacterium tuberculosis: correlation of virulence with elaboration of sulfatides and strongly acidic lipids. Infection and immunity 9:142–149. 3. Kato, M., M. Kusunose, K. Miki, K. Matsunaga, and Y. Yamamura. 1959. The mechanism of the toxicity of cord factor. The American review of respiratory disease 80:240–248. 4. Etemadi, A. H. 1967. [Mycolic acids. Structure, biogenesis and phylogenetic value]. Exposes annuels de biochimie medicale 28:77–109. 5. Etemadi, A. H. 1967. [Structural and biogenetic correlations of mycolic acids in relation to the phylogenesis of various genera

of Actinomycetales]. Bulletin de la Societe de chimie biologique 49:695–706. 6. Lechevalier, M. P., A. C. Horan, and H. Lechevalier. 1971. Lipid composition in the classification of nocardiae and mycobacteria. Journal of bacteriology 105:313–318. 7. Minnikin, D. E., L. Alshamaony, and M. Goodfellow. 1975. Differentiation of Mycobacterium, Nocardia, and related taxa by thin-layer chromatographic analysis of wholeorganism methanolysates. Journal of general microbiology 88:200–204. 8. Dobson, G., D. E. Minnikin, S. M. Minnikin, M. Parlett, M. Goodfellow, M. Ridell, and M. Magnusson. 1985. Systematic analysis of complex mycobacterial lipids. In Chemical methods in bacterial systematics. M. Goodfellow, D. E. Minnikin(eds). Academic, London. 237–2654. 9. Jain, M., C. J. Petzold, M. W. Schelle, M. D. Leavell, J. D. Mougous, C. R. Bertozzi, J. A. Leary, and J. S. Cox. 2007. Lipidomics reveals control of Mycobacterium tuberculosis

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virulence lipids via metabolic coupling. Proceedings of the National Academy of Sciences of the United States of America 104:5133–5138. 10. Shui, G., A. K. Bendt, K. Pethe, T. Dick, and M. R. Wenk. 2007. Sensitive profiling of chemically diverse bioactive lipids. Journal of lipid research 48:1976–1984. 11. Mahrous, E. A., R. B. Lee, and R. E. Lee. 2008. A rapid approach to lipid profiling of mycobacteria using 2D HSQC NMR maps. Journal of lipid research 49:455–463. 12. 2008. WHO report 2008 Global tuberculosis control surveillance, planning, financing. WHO. 17–20. 13. Brennan, P. J. and H. Nikaido. 1995. The envelope of mycobacteria. Annual review of biochemistry 64:29–63. 14. Puzo, G. 1990. The carbohydrate- and lipidcontaining cell wall of mycobacteria, phenolic glycolipids: structure and immunological properties. Critical reviews in microbiology 17:305–327. 15. Karakousis, P. C., W. R. Bishai, and S. E. Dorman. 2004. Mycobacterium tuberculosis cell envelope

lipids and the host immune response. Cellular microbiology 6:105–116. 16. Ortalo-Magne, A., A. Lemassu, M. A. Laneelle, F. Bardou, G. Silve, P. Gounon, G. Marchal, and M. Daffe. 1996. Identification of the surface-exposed lipids on the cell envelopes of Mycobacterium tuberculosis and other mycobacterial species. Journal of bacteriology 178:456–461. 17. Villeneuve, C., G. Etienne, V. Abadie, H. Montrozier, C. Bordier, F. Laval, M. Daffe, I. Maridonneau-Parini, and C. Astarie-Dequeker. 2003. Surface-exposed glycopeptidolipids of Mycobacterium smegmatis specifically inhibit the phagocytosis of mycobacteria by human macrophages. Identification of a novel family of glycopeptidolipids. The Journal of biological chemistry 278:51291–51300. 18. Villeneuve, C., M. Gilleron, I. MaridonneauParini, M. Daffe, C. Astarie-Dequeker, and G. Etienne. 2005. Mycobacteria use their surface-exposed glycolipids to infect human macrophages through a receptor-dependent process. Journal of lipid research 46:475–483.

Chapter 6 Capabilities and Drawbacks of Phospholipid Analysis by MALDI-TOF Mass Spectrometry

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Beate Fuchs, Ariane Nimptsch, Rosmarie Süß, and Jürgen Schiller

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Summary

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The important roles of lipids particularly certain phospholipids in signal transduction processes and as important disease markers are becoming increasingly evident. Unfortunately, however, sensitive methods of lipid analysis are established to a much lesser extent than, e.g., methods of protein analysis. Mass spectrometry (MS) is an increasingly used technique of lipid analysis and electrospray ionization (ESI) MS is the so far most established ionization method. Although matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) was so far primarily used for protein analysis, however, this method has itself proven to be very useful in the field of lipid analysis, too. This chapter gives an overview of methodological aspects of MALDI-TOF MS in lipid research and summarizes the specific advantages and drawbacks of this soft-ionization method. In particular, suppression effects of some lipid classes, especially those with quaternary ammonia groups such as phosphatidylcholine, will be highlighted and possible ways to overcome this problem (use of different matrices, separation of the relevant lipid mixture prior to analysis) will be discussed on the example of an organic liver extract.

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Key words: Glycerophospholipids, Lipid analysis, MALDI-TOF MS, Lipid extracts, Liver, Matrix, Thin-layer chromatography, Mass spectrometry

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1. Introduction

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Over decades lipids were primarily considered as the cellular “packing material” of more important contents, for instance, enzymes, nucleic acids, etc., and as energy-rich “fuel” in nutrition (1). Nowadays, however, lipids such as diacylglycerols and particularly phospholipids (PLs) such as phosphatidic acids or ­phosphoinositides are known to represent important secondmessenger molecules involved in cellular communication (2). Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, doi 10.1007/978-1-60761-322-0_6, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Additionally, lipids such as lysophosphatidylcholines (LPCs) were also recognized as important disease markers, for instance, in atherosclerosis or rheumatoid arthritis (3, 4). Accordingly, in addition to terms as “proteomics” or “genomics”, the term “lipidomics” (5, 6) was also recently introduced. In order to make a long story short: The interest in lipids and their analysis has significantly increased during the last decade. Surprisingly, however, in comparison to the analysis of proteins, there are currently only much less developed protocols of lipid analysis. One potential reason is the considerable diversity of lipids as “lipid” – in the most general definition – relates to all compounds that may be isolated from body fluids or biological tissues by extraction with organic solvents due to their apolar character (1). This diversity of phospholipids is not only coming from differences in the headgroup (e.g., choline or ethanolamine) but also from the linkage type between the apolar alkyl chains and the glycerol (diacyl, alkyl-acyl-, or alkenyl-acyl) and, finally, the large variability of potential fatty acyl residues (7). Therefore, complex lipid patterns with thousands of different lipid species can be expected if crude organic extracts from biological samples are analyzed. The best analytical approach to assess this diversity is still an open issue (8): Although chromatographic techniques are highly established, they have the disadvantage – equally if liquid chromatography, in particular, high-performance liquid chromatography (HPLC) (9) or thin-layer chromatography (TLC) (10) is used – that several runs are normally required in order to obtain complete compositional information: Normal phase chromatography is needed for the separation of lipids according to their headgroups, whereas reversed phase chromatography is usually used for the differentiation of lipids according to their fatty acyl compositions (8). Another problem is the detection of the lipids within the obtained fractions. If, for instance, a UV detector is used, lipids with unsaturated fatty acyl residues are primarily detected, whereas this kind of detector is not suitable for the detection of lipids with saturated fatty acyl residues as they lack the UV absorption of olefinic residues (1). Mass spectrometry (MS) is increasingly used for the analysis of complex lipid mixtures (11), in particular, for lipidomic studies (5). Although methods such as atmospheric pressure chemical ionization (APCI) (12) or electrospray ionization (ESI) (13) are nowadays considered to be the methods of choice for lipids, there is growing evidence that matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) is also capable of providing important information about the composition of an unknown mixture. The particular advantages of MALDI MS are the simple performance and the high sensitivity (14) down to a few attomoles of the analyte. The use



1.1. Advantages and Disadvantages of MALDI-TOF MS in the Lipid Field

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of MALDI-TOF MS for lipid and phospholipid analysis has been recently reviewed (15, 16) and compared with other methods of lipid analysis (17).

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Although a comprehensive treatise of the fundamentals of MALDI-TOF MS is clearly outside the scope of this chapter (for a detailed description see (18)), some important aspects representing the most important differences between MALDI MS and other MS methods have to be shortly discussed: 1. A solid sample is used in MALDI-TOF MS. The lipid sample is mixed directly on the sample plate (the “target”) or prior to its deposition with the matrix solution and is subsequently allowed to crystallize (15). The matrix is required to absorb the energy emitted from the laser and enables the ionization of the analyte, even if this analyte does not exhibit a major absorption at the laser wavelength (often 337 nm). Although there are UV- and IR lasers in use (18), the majority of commercially available MALDI devices are equipped with UV lasers and, therefore, IR lasers will not be considered here to a larger extent. However, completely different matrix compounds in comparison to UV lasers are required if IR lasers are used. One common IR MALDI matrix is, for instance, glycerol that provides intense IR absorption (19). Equally if IR or UV MALDI is used: The actual analyte is not the sample as such but cocrystals between the matrix and the sample of interest. As these cocrystals are never completely homogenous, the reproducibility of MALDI measurements is often doubted (20). However, as many laser shots, each leading to an individual mass spectrum, are averaged for the final mass spectrum, these inhomogeneities may be significantly minimized. Additionally, the matrix, that normally comprises an aromatic, rather hydrophobic residue, as well as the lipid are both readily soluble in organic solvents and there is no need to use water as additional solvent (as, for instance, in the case of water-soluble analytes). Therefore, the homogeneity of matrix/lipid crystals is higher than that of matrix/protein crystals (21). Finally, the use of solid samples enables to record spatially resolved mass spectra. This “mass spectrometric imaging” is an important topic of modern research and attracts currently significant interest in medical diagnosis, for instance, in order to differentiate different types of cancer (22).

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2. Soft ionization MS techniques as MALDI provide – in contrast to traditional electron impact (EI) MS – no radical cations by the abstraction of an electron from the analyte of interest (cf. Fig. 1), but “quasimolecular” ions. Thereby the analyte is cationized by the addition of a proton or an alkali metal ion (positive ions) or the loss of a proton (negative ions) (23). Protons are normally provided by the matrix that is often an

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Fig. 1. Schema showing the events of ion formation upon electron impact ionization and quasimolecular ion generation that is most relevant to the MALDI ionization process. Please note that radical cations are generated upon EI ionization. 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148

organic acid, whereas alkali metal ions are present as such in virtually all biological samples. The tendency of the analyte to add or loose a proton depends, however, on the basicity of the analyte in comparison to the acidity of the used matrix or vice versa. Although 2,5-dihydroxybenzoic acid (DHB) is the most common matrix for the analysis of lipids (24), it should be noted that for special problems, other matrices as paranitroaniline (PNA) can be advantageously used (25). This will be illustrated below in more detail. A survey of the characteristics of electron impact ionization and positive as well as negative quasimolecular ion formation is given in Fig. 1. In the light of these aspects it is evident that providing information on the detection limits of MALDI-TOF MS makes sense only if the applied matrix is simultaneously mentioned (26). 3. Although MALDI is relatively robust against impurities of the sample as salts or buffer components (23), great care is needed if detergents such as Triton or Brij, for instance, are used for the extraction of lipids from the biological material as these molecules are also rather apolar and cannot be easily separated from the lipids of interest. The use of commercially available “detergent removal” kits normally fails in the case of lipids because these kits were primarily developed for the purification of proteins that are normally more polar in comparison to the applied detergent. Due to that problem, the use of organic solvents or mixtures is recommended for lipid



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extraction. Additionally, the extraction with organic solvents removes simultaneously salts and other low molecular compounds of high polarity that might affect the ion yield.

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4. MALDI-TOF MS offers an extremely high sensitivity and detection limits in the atto- (10−18) and even low zeptomole (10−21) range were already reported in the case of lipids – in particular for lyso-phosphatidylcholine (14). Although these detection limits seem only realistic for special samples in combination with highly sophisticated matrices, sensitivities in the femtomole (10−15) range can be easily achieved by standard MALDI-TOF MS. This means that 1 ml of a 1 mg/ml lipid sample is absolutely sufficient to detect the lipid of interest (26). This high sensitivity clearly requires the use of extremely pure chemicals: It is much simpler to detect very small amounts of the lipid of interest than – vice versa – NOT to detect impurities in the solvents, the matrix compounds, etc.

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2. Materials 2.1. Equipment and Supplies (See Also Note 1)

2.2. Reagents

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166

1. Bruker “Autoflex” MALDI-TOF mass spectrometer (Bremen, Germany) equipped with reflectron, “delayed extraction facility” and N2 laser emitting at 337 nm. The capability to record positive and negative ion spectra should be available.

167 168

2. MALDI targets made from stainless steel or from aluminum with gold-coated surface (see Note 2).

171

3. Micropipetts (see Note 3).

173

4. Glass (Hamilton) syringes of different sizes.

174

5. Small glass vessels for single use for mixing matrix and sample or for diluting stock solutions of lipids (available, for instance, from Knauer, Berlin, Germany).

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6. TLC silica gel 60 plates (Merck, Darmstadt, Germany) and TLC trough (CAMAG, Muttenz, Switzerland).

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7. Heat gun or common hair dryer (see Note 4).

180

8. Standard laboratory centrifuge.

181

1. Some selected phospholipid samples of known composition and with known concentration should be used as the first samples. As palmitoyl and oleoyl residues are highly abundant in samples of biological origin (16), it is recommended to use stock solutions of 1-palmitoyl-2-oleoyl-sn-phosphatidylcholine, -ethanolamine, and -glycerol. These are available from Avanti Polar Lipids, Alabaster, AL, USA. These compounds

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are normally abbreviated by POPC, POPE, and POPG. A stock concentration of 1 mg/ml lipid in chloroform is recommended for best performance and to optimize instrumental parameters.

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2. 2,5-Dihydroxybenzoic acid (DHB) and para-nitroaniline (PNA) of highest available purity (see Note 5).

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3. Chloroform, methanol, ethanol, triethylamine, and distilled water of highest commercially available quality (see Note 6).

197

4. A small piece of fresh bovine liver that is best obtained from a local butcher or slaughterhouse.

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3. Methods 3.1. Sample Processing 3.1.1. Artificial Lipid Samples of Known Composition

1. If sufficient amounts of samples are available, the use of stock solutions in the mg/ml concentration range is recommended. Therefore, dilute the available lipid samples to about 1 mg/ml with CHCl3. Mix one equivalent of the lipid standard solution with one equivalent of DHB matrix solution (0.5 mol/l corresponding to about 77 mg/ml in CH3OH). Trifluoroacetic acid (TFA) is often suggested as an additive because it enhances the yield of H+ adducts due to its considerable acidity (23). However, TFA may also have deleterious effects because lipids containing alkenyl ether groups (“plasmalogens”) are readily hydrolyzed in the presence of TFA (27, 28). Therefore, the use of TFA is discouraged – particularly if unknown lipid mixtures are to be analyzed. Prepare the same samples in the presence of PNA (0.17 mol/l in chloroform/methanol (2:1, v/v)). 2. Apply the prepared samples to the MALDI target. Do not be surprised if a very large spot is formed and the sample “spreads” out over the sample plate: In contrast to water, organic solvents possess only a rather small surface tension (H2O: ~73 × 10−3 N/m; CH3OH ~23 × 10−3 N/m). This prevents the formation of a small spot on the metal surface of the sample plate. Do not be worried! Simply leave some space between the individual samples in order to avoid mixing of the different samples during deposition onto the MALDI target. Avoid touching the MALDI target with the pipette tip or the needle of the Hamilton syringe in order not to affect homogeneous crystallization. The MALDI target should also not be touched with the fingers. 3. Evaporate the solvent quickly by drying the sample plate with a hair dryer and load the prepared sample plate directly into the mass spectrometer. Avoid long-term exposition of the



3.1.2. Preparation of the Liver Extract

3.1.3. Separation of the Crude Lipid Extract by Thin-Layer Chromatography

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TLC plate to air: Due to the large surface of the lipid film on the MALDI target, lipids may be easily oxidized by air. This is particularly a problem if diluted samples are analyzed. It has also been shown that the content of residual water in the organic solvents influences the quality of the mass spectra (29). This may be surprising as completely “dry” samples are investigated by MALDI-TOF MS under high vacuum conditions. However, there is increasing evidence that the solvent or solvent mixture determines the homogeneity of the crystallization between the matrix and the analyte. Therefore, the quality of mass spectra depends dramatically on the used solvent system.

231

1. Cut the liver tissue into small pieces with a scalpel. Add the 20-fold amount (by weight) of Bligh & Dyer solvent (30) mixture (CHCl3:CH3OH:H2O = 1:1:0.9) and stir or vortex the resulting mix vigorously for a few minutes. Subsequently centrifuge the sample (10 min, ca. 1000 g, 20°C) in order to improve the separation of the organic (bottom) and the H2O/CH3OH phase (see also Note 7).

243

2. Remove the organic (lower) phase by a Hamilton syringe and transfer it to another unused vial. Do not try to get the complete organic phase in order to avoid the introduction of impurities from the aqueous layer. This organic phase is used for all further experiments and the lipid concentration is of the order of 6–8 mg/ml. The lipid yield can be easily determined by evaporation of the solvent under reduced pressure and subsequent weighing of the residual material. If needed, a bovine liver extract is also commercially available from AVANTI Polar Lipids, Alabaster, AL, USA. Another established method to determine the PL concentration is the classical phosphate determination according to Bartlett (31).

250

3. The obtained lipid extract may be directly used for MALDITOF MS by simply diluting the organic solution with the prepared matrix solution (1:10, v/v).

262 263

1. For the separation of phospholipids according to differences of the headgroup normal phase chromatography is used, whereas reversed phase chromatography is used for the separation according to differences in the fatty acyl compositions (8). Use HPTLC silica gel 60 plates (10 cm × 10 cm in size with aluminum or glass backs) (Merck, Darmstadt, Germany). Prerun the plates with the needed solvent mixture in order to remove impurities. Dry the plate carefully.

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2. Apply 0.5–1 ml (corresponding to a total lipid amount of 4–8 mg) of the liver extract by using a standard Hamilton syringe as small spots with 1 cm space between the individual spots and

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at a distance of 1.5 cm from the bottom edge of the TLC plate. Also apply a lipid mix of known composition (about 1–2 mg per PL). Dry the plate carefully prior to development.

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3. Develop the plates in a commercially available vertical ascending TLC chamber (CAMAG, Switzerland) with chamber saturation using chloroform, ethanol, water, triethylamine (30:35:7:35, v/v/v/v) as solvent mixture (32). This should result in good separation quality of the individual PL classes. The required time of development is about 35 min: The total length of the run is about 6 cm under these conditions and is a good compromise between resolution and the required time. The separation is performed at room temperature (22 ± 2°C) and 50 ± 5% relative humidity.

279 280 281 282 283 284 285 286 287 288

4. Subsequent to development, TLC plates are dried under a stream of warm air and the lipids are visualized by spraying with a solution of primuline (Direct Yellow 59) in acetone according to (33). Upon irradiation by UV light individual PLs become detectable as violet spots and can be easily identified by comparison with the reference mixture. The binding between the dye and the lipid is noncovalent and does not affect the quality of the mass spectra. The monoisotopic mass of primuline is 475.0 and, therefore, a very small signal is sometimes detected at m/z = 498 (Na+ adduct) in the positive ion mass spectra.

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5. Mark the spots of interest (under UV light) by circling with a soft pencil.

300 301

6. The spots of interest are carefully scratched off from the TLC plate into different small glass vessels and the PLs are eluted from the silica gel by the addition of a mixture of 75 ml CHC13, 75 ml methanol, and 75 ml 0.9% NaCl in water and intense vortexing (see Note 8). Afterward, samples are centrifuged (ca. 1000 g) as described above to enhance phase separation. The organic layers are evaporated to dryness and the residual material redissolved in 20 ml matrix solution (DHB or PNA) and directly used for MALDI-TOF MS.

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3.2. Recording MALDI-TOF Mass Spectra

1. Due to the large number of MALDI devices that are available from different suppliers, it is impossible to describe the necessary experimental parameters in detail. Therefore, please consult the manual of your device for a suitable data file to start with. You should start with “delayed extraction conditions” (DE) and use the reflectron of the device. This results in higher resolution and mass accuracy than the common linear mode that provides, however, higher sensitivity. 2. It is recommended to start in all cases with the analysis of a known sample of known concentration in order to check if the



3.3. Quantitative Data Analysis

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device is properly working and all parameters are adequately set. This known sample may also be used to check the mass accuracy, i.e., the quality of the applied – often default – calibration, as well as the resolution achievable by the used instrumental settings. Please note, however, that the applied laser intensity has the most pronounced effect on spectral quality.

321

3. Always take care that the MALDI target is carefully dried before it is inserted into the mass spectrometer in order to avoid a significant decrease of the vacuum – and the delay for waiting until high vacuum is re-established.

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4. It is an incorrect assumption that spectral quality, in particular the signal-to-noise ratio, may be enhanced by increasing the laser intensity. Although absolute signal intensities may be enhanced at elevated laser intensities, the quality of the baseline simultaneously gets poor and the achievable resolution is simultaneously diminished. Therefore, always set the laser intensity as high as needed but as low as possible.

331 332

5. Try to move the laser randomly over the sample plate in order to average nonhomogeneous spots resulting from the sample preparation. It is, however, not possible to improve the signal-to-noise ratio by averaging a larger number of individual laser shots (19) because the level of unspecific chemical background noise forms the limiting criterion.

338 339

6. Try to optimize the required parameters always with a wellknown sample and use these parameters afterward for the unknown samples, i.e., the organic liver extract in our case.

344

7. DHB is completely stable under high vacuum conditions, while PNA tends to sublime at longer times in vacuum (25). Therefore, it is advisable to perform measurements with the PNA matrix relatively quickly. If no signal is detectable, try to add somewhat additional PNA matrix.

347

1. Quantitative evaluation of MALDI mass spectra is still a challenging task. However this does not only hold for MALDI MS but also for other soft-ionization techniques: The ion yield in EI mass spectra primarily depends on the ionization potential of the functional groups present in the analyte of interest. The ionization energy is rather constant for e.g., carbonyl groups – independent of their chemical environment. Therefore, the concentration of different compounds containing carbonyl groups can be easily determined by using the signal intensities. The comparison of different lipid classes with different acidities is particularly tricky if lipid mixtures are analyzed as the ion yield may be very different for the individual lipid classes (26). The very best way to obtain quantitative compositional information is normally the separation of the individual lipid

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353 354 355 356 357 358 359 360 361 362 363 364 365

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classes and to add an internal standard to each fraction. As the structure of this internal standard should be as similar as possible to the analyte of interest, deuterated lipids are the reference compounds of choice (34). Unfortunately, however, this approach requires some prior knowledge about the composition of the sample to warrant that the standard is added in a suitable amount and particularly to avoid the addition of an excess of the standard that might result in severe signal suppression. Due to these problems, other simpler methods of quantitative analysis were also suggested.

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2. Absolute peak intensities can hardly be used as quantitative concentration measures in MALDI-TOF mass spectra because they are affected by many different parameters, for instance, the applied laser intensity, the matrix to analyte ratio, the presence of impurities, etc.

376 377 378 379 380

3. Comparison of the intensity of the peak of interest to a defined matrix peak. Although common matrices have normally much lower masses than lipids, nearly all matrices, at least the ionic ones, tend to undergo photochemical reactions upon laser irradiation in the gas phase leading to peaks at higher m/z ratios (24). Although some applications of this method to apolar lipids as triacylglycerols (16) were described, the applicability of this method seems quite limited because the matrix peak pattern is influenced by many parameters.

381 382 383 384 385 386 387 388 389

4. Using the signal-to-noise (S/N) ratio. The achievable S/N ratio increases with the lipid concentration over a large concentration range. Only by using very large amounts of lipids, i.e., an inappropriate ratio between lipid and matrix, the S/N ratio decreases. Although this method seems to be applicable for all substances, it was so far only used for rather polar species, in particular lysophospholipids (35) and phosphoinositides (36). The majority of MALDI devices calculates the S/N ratio directly from the obtained spectra. Therefore, this is an easily accessible parameter.

390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411

3.4. Results

1. A coarse overview about the shape of the positive (left) and negative (right) ion MALDI-TOF mass spectra of different phospholipids (as well as their characteristic headgroups) in the presence of different matrix compounds is shown in Fig. 2. 1-Palmitoyl-2-oleoyl-sn-phosphatidylcholine (POPC, (2a,2e)), -phosphatidylethanolamine (POPE, (2b, 2f)), and -phosphatidylglycerol (POPG, (2c, 2g)) were chosen as defined PLs because they are rather abundant in biological materials but differ in their charge states (1): POPC and POPE are zwitterionic PLs, whereas POPG is a negatively charged compound at physiological pH. In (2d, 2h) the spectra of a 1:1:1 mixture of these three PLs are shown in order to illustrate the problems of mixture analysis by



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Fig. 2. Positive (left) and negative ion (right) MALDI-TOF mass spectra of 1-palmitoyl-2-oleoyl-sn-phosphatidylcholine (a), (e), 1-palmitoyl-2-oleoyl-sn-phosphatidylethanolamine (b), (f), 1-palmitoyl-2-oleoyl-sn-phosphatidylglycerol (c), (g) and a 1:1:1 mixture of these three compounds (d), (h). Positive ion spectra were recorded with a 0.5 M solution of DHB in methanol, whereas a 0.17 M solution of PNA (in CHCl3 and CH3OH) was used as matrix for the negative ion spectra. Lipid sample solutions (1 mg/ml) were diluted 1:1 (v/v) with the corresponding matrix and afterwards spotted onto the MALDI target. All peaks are marked according to their m/z ratios and the structures of both matrices are shown in the figure. Please note that the PC can not be detected as negative ion and the presence of a characteristic fragment ion in the POPE and POPG spectra (b, c).

MALDI-TOF MS. It is evident that the spectra differ significantly: POPC gives – in the presence of DHB – two signals at m/z = 760.6 and 782.6 in the positive ion mode according to the generation of the H+ and the Na+ adduct (1a) (21). Due to the permanent positive charge of the quaternary ammonia group (37), PC cannot be detected as negative ion under the applied conditions (1e).

Please note that the PC does not give any fragmentation products under these conditions – even the loss of the choline moiety may be prevented by careful control of the laser intensity (38). In contrast, the PE (1b) as well as the PG (1c) gives significant yields of a fragment ion that corresponds to the loss of the polar headgroup. As the fatty acyl composition is the same, both compounds give the same fragment at m/z = 577.5 (38). The structure of this fragment is also shown in (2c). Additionally, it is evident that the POPE with the monoisotopic mass of 717.5 g/mol gives only rather small yields of the H+ adduct (m/z = 718.5) but significant amounts of the Na+ adduct (m/z = 740.5) as well

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as the Na+ adduct subsequent to the exchange of one H+ by one Na+ (m/z = 762.5). This is caused by the exchangeable protons of the –NH3+ group. The reason why POPE and POPG give more pronounced fragmentation than POPC is not yet known. However, it might be possible that the stability of the H+ adduct of POPE is not high enough to allow these ions to reach the detector. A similar observation was already made in the case of triacylgylcerols (39), where no H+ adducts were detectable at all. Using PNA as an alkaline matrix, POPE is also detectable as negative ion at m/z = 716.5 (2f). Please note that the detectability of POPE as negative ion (data not shown) in the presence of DHB would be extremely weak due to the acidic properties of this matrix. POPG can be detected as negative ion in the presence of DHB (data not shown) as well as PNA (2g, m/z = 747.5) as negative ion because of its enhanced acidity in comparison to POPE. Regarding mixture analysis significant differences are obtained between the positive and the negative ion spectra: In the positive ion mode (2d), the spectrum of the mixture is clearly dominated by the POPC. The POPE is only detectable with low intensity although it is present in the same amount as the POPC. POPG is not detectable at all under these conditions. Therefore, the presence of PCs prevents the detection of other PL species in mixtures (37). Due to this problem, a simple method to remove PC from lipid mixtures has been recently suggested (40). In contrast, the POPC is completely absent in the negative ion spectrum (2h) and only POPE and POPG are detectable in the mixture. Although this is indeed a very simple example, it is evident that mixture analysis by MALDI-TOF MS must be regarded with great caution. 2. The problem of signal suppression is still more evident if the organic liver extract is investigated (Fig. 3). The liver extract was chosen as an educational example because it can be easily prepared with good reproducibility and is even commercially available. The left hand spectra represent the positive ion spectra, while the spectra at the right hand represent the negative ion spectra. Spectra (3a) and (3c) were recorded with DHB as matrix, while PNA was used for the spectra shown in (3b) and (3d). It is obvious that the positive ion spectra are, beside minor intensity differences of the individual adducts, virtually identical equally if DHB or PNA is used as matrix (3a, 3b). Both spectra are dominated by different PC species and a more detailed assignment of the individual peaks is given in Table 1. The negative spectra, however, differ significantly and it is evident at the first glance that the spectrum recorded with the DHB matrix (3c) exhibits rather poor quality. Although peaks of a few PI species (the signal at m/z = 885.5, for instance, corresponds to PI 18:0/20:4) can be easily identified, there are no further lipids detectable.

Fig. 3. Positive (left) and negative ion (right) MALDI-TOF mass spectra of an organic bovine liver extract. Traces (a) and (c) were recorded with DHB, whereas (b) and (d) were recorded in the presence of PNA. Due to the large variety of individual lipid classes in the extract, a relatively concentrated lipid solution was used (about 6 mg/ml) and diluted 1:10 with the matrix. All peaks are marked according to their m/z ratios. Note the poor quality of the negative ion spectrum recorded with DHB as matrix.

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Table 1 Overview of the positive ions detected in the MALDI-TOF mass spectra of the liver extract and the corresponding assignments Peak position (m/z)

Assignment of molecular mass

577.5

Fragment of PE 16:0/18:1/PG 16:0/18:1

603.5

Fragment of PE 16:0/18:1

702.5

Oxidation product of PS 18:0/18:1 (+H+)

703.6

SM 16:0 (+H+)

715.0

4 DHB - 2 H+ + Na+ + 2 K+

721.0

4 DHB - 3 H+ + 3 Na+ + K+

721.5

Oxidation product of PI 18:0/20:4 (+H+)

725.6

SM 16:0 (+Na+)

727.0

4 DHB - 4 H+ + 5 Na+

743.5

Oxidation product of PI 18:0/20:4 (+Na+)

753.5

Oxidation product of PI 18:0/18:1

760.6

PC 16:0/18:1 (+H+)

766.5

PE 18:0/18:2 (+Na+)

768.5

PE 18:0/18:1 (+Na+)

771.5

PG 16:0/18:1 (+H+)

777.5

Oxidation product of PI 18:0/18:1 + H+

782.6

PC 16:0/18:1 (+Na+)

786.6

PC 18:0/18:2 (+H+)

788.5

PE 18:0/18:2 (−H+ + 2 Na+)

788.6

PC 18:0/18:1 (+H+) or PE 18:1/20:4 (+Na+)

790.5

PE 18:0/20:4 (+Na+)

793.5

PG 16:0/18:1 (+Na+)

808.6

PC 18:0/18:2 (+Na+)

810.6

PC 18:0/18:1 (+Na+) and PC 18:0/20:4 (+H+) or PS 18:0/18:2 (+H+)

812.5

PE 18:0/20:4 (−H+ + 2Na+)

812.6

PS 18:0/18:1 (+H+) and PC 18:0/20:3 (+H+)

832.6

PC 18:0/20:4 (+Na+)

834.6

PC 16:0/22:6 (+H+) or PS 18:0/18:1 (+ Na+)

(continued)



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Table 1 (continued) Peak position (m/z)

Assignment of molecular mass

856.6

PC 16:0/22:6 (+Na+) or PS 18:0/18:1 (−H+ + 2 Na+)

885.5

PI 18:0/18:2 (+ H+)

887.5

PI 18:0/18:1 (+ H+)

909.5

PI 18:0/20:4 (+ H+) and PI 18:0/18:1 (+ Na+)

911.5

PI 18:0/20:3 (+ H+)

931.5

PI 18:0/20:4 (+ Na+)

933.5

PI 18:0/20:3 (+ Na+)

PC phosphatidylcholine, PI phosphatidylinositol, PS phosphatidylserine, PE phosphatidylethanolamine Please note that all discussed PL– besides PC – contain functional groups showing exchange with the solvents and/or ions of the matrix solution leading to complex peak patterns. Please note that in the case of acidic PL (PS and PI) the corresponding sodium salt is considered as the neutral molecule.

The peaks between about m/z = 720 and 780 are stemming from the applied DHB matrix (24). The spectrum recorded in the presence of PNA (3d) is characterized by a much higher quality and provides the additional advantage that PE species are detectable simultaneously with the PI species. The additional signals at m/z = 742.5 and 766.5 correspond to PE 18:0/18:2 and PE 18:0/20:4. Please note that in the case of mixtures, negative ions have the considerable advantage that their assignment is less problematic as there is no superposition between the different proton and sodium adducts and differences of the fatty acyl composition. Each PE species results in a single peak. Of course, the differentiation of isobaric ions cannot be made by the provided simple mass spectra (MS1). For instance, the signal at m/z = 742.5 might not only represent PE 18:0/18:2 but also PE 18:1/18:1 that have the same masses. However, this differentiation can be easily made by recording the corresponding “postsource decay” (PSD) mass spectra, i.e., by observing the characteristic fragments generated during the travel of the ions from the source to the detector. Under these conditions the released fatty acyl residues can be observed as negative ions allowing the unequivocal differentiation of the above-mentioned compounds: The anion of stearic acid would be detected at m/z = 283, while oleic acid and linoleic acid are detected at m/z = 281 and 279, respectively (41). Three different PL species (PC, PE, and PI) may, therefore, already be identified from the analysis of the total extract without separation into the individual fractions. Nevertheless, for the

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identification of minor components a previous separation is indispensable and can be performed by TLC or HPLC. We have chosen TLC as it is a quite simple and often used method of PL separation. 3. In Fig. 4 a typical TLC plate of the separated liver PLs subsequent to primuline staining is shown. The left lane (4a) corresponds to a standard mixture of known composition and the right lane (4b) corresponds to the liver extract. From the TLC it is evident that the liver extract consists of five lipid classes, namely sphingomyelin (SM), phosphatidylcholine (PC), phosphatidylethanolamine (PE), quite small amounts of phosphatidylinositol (PI), and very small amounts of phosphatidylserine (PS). Please note that the observed slight differences in the migration properties between the standard and the liver extract are caused by differences in the fatty acyl compositions of the individual lipid classes. After visual inspection of the TLC plate, the identified lipid classes were scraped off from the TLC plate, the lipids eluted from the silica gel, and the obtained fractions individually characterized by MALDITOF MS.

Fig. 4. Videoimage of a typical HPTLC plate of a reference PL mixture (a) and the liver extract (b) subsequent to primuline staining (33). 1.69 mg of each PL were applied onto the TLC plate as standard (a) and the total amount of liver lipids was 6 mg (b). The detailed reasons why the PS results in a relatively diffuse spot are yet unknown. LPC lyso-Phosphatidylcholine, PC phosphatidylcholine, PI phosphatidylinositol, PE phosphatidylethanolamine, PS phosphatidylserine, SM sphingomyelin.



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4. The obtained spectra are shown in Fig. 5. It is evident that the quality of these mass spectra differs significantly. The best spectra regarding the signal-to-noise ratio are stemming from the SM (5a), PC (5b), and PE (5f). The qualities of the PI (5d,5e; negative and positive ion mode, respectively) and particularly the PS fraction (5c) are much worse. This is not only caused by their rather small contribution to the total amounts of lipids, but also by their high polarity and negative charge. Nevertheless it should be noted that the PS could not be identified neither in the positive nor the negative ion spectra of the total liver extract. This is a clear indication that separation is necessary if minor species of a mixture are of interest. All assignments are additionally provided in Table 1. Although the identification of an unknown compound is normally simpler as negative ion because the related adduct pattern is less complicated, the contribution of DHB matrix signals is much more pronounced in the negative ion spectrum (5d) and all the peaks at m/z = 845, 851, and 857 are stemming from the applied DHB matrix (24). This may lead to the suppression of minor species and is also the reason why no convincing negative ion spectrum of PS could be obtained. Please note that in some fractions oxidation of lipids is obvious. For instance the peak at m/z = 863.5 (5d) is caused by PI 18:0/18:1 and the intense peak at m/z = 753.5 corresponds to an oxidation product of that PI at the double bond of the oleoyl residue under generation of the corresponding aldehyde that is schematically shown in Fig. 5. The same mechanism is also valid in the case of the PS, where the peak at m/z = 702.5 is derived from the oxidation of PS 18:0/18:1 at m/z = 812.6. It is also not surprising that the minor fractions are most sensitive to oxidation. Details of this oxidation process are available in (16). Although oxidation is normally an unwanted process, it should be noted that these fragmentation products are sometimes also helpful because they allow the assignments of the positions of the double bonds. Of course, there are still open issues: First, the question of the most suitable matrix for lipid analysis by MALDI-TOF MS is not yet sufficiently answered. We have shown here that the matrix has a tremendous influence on spectral quality as well as the detectabilities of the individual lipid classes. Therefore, it is a strong hope that other still more suitable matrix compounds for the analysis of lipids will be discovered – maybe compounds that allow the detection of all lipid classes even in crude mixtures with higher sensitivity (14), lower matrix background (42), and improved reproducibility (43). Second, some caution is necessary if spectra are recorded from lipid fractions that were purified by means of chromatography (44). There is evidence that lipids differing in their fatty acyl compositions are not released from the TLC plate to the same extent and

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Fig. 5. Typical MALDI-TOF mass spectra of the lipid extract shown in Fig. 3 subsequent to TLC separation into the individual lipid classes. Subsequent to the TLC run, the individual fractions were scraped off and re-eluted from the silica gel of the TLC plate for MALDI-TOF MS. Traces correspond to the following lipid classes: SM (a), PC (b), PS (c), PI (d), (e) and PE (f). All spectra are positive ion spectra, the only negative ion spectrum was obtained from the PI fraction (trace (5d)). Please note that the contribution of matrix peaks (marked by asterisks) is much higher in the negative than the positive ion mode. The most common oxidation pathway of lipids leading to characteristic fragmentation at the position of the double of an oleoyl residue is also shown.



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saturated or moderately unsaturated lipid “stick” more tightly to the TLC plate. This is confirmed by the slight intensity differences of the PC species in trace (3a) and (5b). Therefore, some lipids may be lost during the extraction process. However, this problem may be overcome by the direct “scanning” of the TLC plate by MALDI-TOF MS without previous extraction of the separated fractions. This method has been recently reported by two different approaches (19, 32) and it is expected that it will develop significantly in the future. There are strong indications that this technique will also be applicable to complex lipids from, e.g., stem cells and other important cell lines (45).

4. Notes

573 574 575 576 577 578 579 580 581 582 583

584

1. Of course all commercially available MALDI-TOF devices can be used independent of the manufacturer. If product or company names are given here, this only means that the indicated products were used for performing the presented experiments. This is sometimes important as different nomenclatures may be used. For instance, “delayed extraction” and “energy lag focusing” are synonyms and mean the same. However, MALDI devices that are exclusively capable of recording linear mode spectra (23), are less suitable for lipid analysis because resolution as well as mass accuracy is reduced in comparison to reflector mode spectra. Isotopic resolution is, however, often important because this spectral feature enables the estimation of the molecular formula – at least in the case of smaller molecules.

585

2. Under the conditions described here, the matrix and the analyte give a relatively “thick” layer of hundreds of mm on the target. Only the upper layers are actually relevant for laser ablation. Therefore, the MALDI target material is less important than by using very thin layers. We are using by default gold-coated targets as they are expected to have a lower content of catalytically active transition metals than stainless steel (16).

598

3. Normally, the use of plastic pipettes (as well as other plastic material) is strongly discouraged because CHCl3 is a rather aggressive solvent that releases impurities as plasticers from the plastic material that may interfere with the signals of the analyte of interest (23). However according to our experience “grey” original Eppendorf pipette tips (up to a volume of 20 ml) may be used without problems. It is, however, recommended to check the potential contribution of impurities by using a sample of known composition and known concentration. Unfortunately, although CHCl 3 is

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a hazardous solvent, all attempts to replace it, for instance, by hexane and isopropanol, were not very successful so far in lipid research. 4. Different solvents are normally required to solubilize matrix and lipid. In the majority of cases, the lipid stock solutions will be prepared in CHCl3 or CHCl3/CH3OH mixtures. Unfortunately, the most common MALDI matrix – DHB – is nearly insoluble in CHCl3, but highly soluble in CH3OH. Due to the different volatilities of both solvents (the boiling point of CHCl3 is 61.2°C and that of CH3OH 64.5°C), the lipid will crystallize prior to the DHB resulting in rather inhomogeneous cocrystals. This problem can be minimized by drying the native matrix/sample mixtures rapidly under a warm stream of air. The extent of lipid oxidation induced by air-drying of the sample is practically negligible. 5. Special attention should be paid to the salt content of the matrix as well as the solvents. Changes of the salt content may lead to changes of the peak patterns and affect the ratio between H+ and Na+ adducts. Additionally, the peaks stemming from the DHB matrix are also influenced by changes of the salt content (37). Please note that a spectrum of DHB crystallized from CH3OH differs from that in the presence of salts. 6. All used solvents should be of highest quality! Due to the high sensitivity of MS even very minor impurities are detected: The detection of analytes in small amounts is often a minor problem than NOT detecting impurities stemming from the solvents or the used reagents. 7. It is not implied that under the used experimental conditions all lipids are completely extracted: Some lipids may also stick to the proteins that precipitate at the interphase between the aqueous and the organic layer. If protein-rich samples are investigated, higher ionic strength, i.e., a high salt concentration is recommended in order to reduce the loss of lipids due to the binding to the protein. Complete extraction of lipids from biological tissues is a science of its own and the extraction must be optimized in dependence on the tissue or body fluid of interest. Under the recommended conditions of centrifugation using moderate g-values, special centrifuge glasses are not absolutely required, but common test tubes may also be used. 8. The addition of salt (NaCl) is recommended in order to enhance the dissociation of the lipids from the silica gel. The use of distilled water instead of NaCl solution may lead to the loss of lipids. This particularly holds for negatively charged lipids that are otherwise lost.



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Abbreviations and Acronyms APCI Atmospheric Pressure Chemical Ionization DE Delayed Extraction DHB 2,5-Dihydroxybenzoic Acid EI Electron Impact ESI Electrospray Ionisation HPLC High-Performance Liquid Chromatography IR Infrared LPC Lyso-Phosphatidylcholine MALDI Matrix-Assisted Laser Desorption and Ionization MS Mass Spectrometry m/z mass over charge PC Phosphatidylcholine PE Phosphatidylethanolamine PG Phosphatidylglycerol PI Phosphatidylinositol PL Phospholipid PNA Para-Nitroaniline PO Palmitoyl-oleoylPS Phosphatidylserine PSD Post Source Decay rpm rotations per minute S/N Signal to Noise SM Sphingomyelin sn Stereospecific Numbering TFA Trifluoroacetic Acid TLC Thin-Layer Chromatography TOF Time-of-Flight UV Ultraviolet

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This work was supported by the German Research Council (DFG Schi 476/5–1 and FU 771/1-1) and the Federal Ministry of Education and Research (Grant BMBF 0313836). The kind and helpful advice of Dr. Suckau and Dr. Schürenberg (Bruker Daltonics, Bremen) is particularly gratefully acknowledged.

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18. Hillenkamp, F. and Peter-Katalinic, J. (2007) MALDI MS – A Practical Guide to Instrumentation. Methods and Application. Wiley, Weinheim. 19. Rohlfing, A., Müthing, J., Pohlentz, G., Distler, U., Peter-Katalinić, J., Berkenkamp, S. and Dreisewerd, K. (2007) IR-MALDI-MS analysis of HPTLC-separated phospholipid mixtures directly from the TLC plate. Anal. Chem. 79, 5793–5808. 20. Cohen, L. H. and Gusev, A. I. (2002) Small molecule analysis by MALDI mass spectrometry. Anal. Bioanal. Chem. 373, 571–586. 21. Schiller, J., Arnhold, J., Benard, S., Müller, M., Reichl, S. and Arnold, K. (1999) Lipid analysis by matrix-assisted laser desorption and ionization mass spectrometry: A methodological approach. Anal.Biochem. 267, 46–56. 22. McDonnell, L. A. and Heeren, R. M. (2007) Imaging mass spectrometry. Mass Spectrom. Rev. 26, 606–643. 23. Fuchs, B., Arnold, K. and Schiller, J. (2008) Mass spectrometry of biological molecules. In: Encyclopedia of Analytical Chemistry (Meyers, R. A., ed.), Wiley, Chichester, pp. 1–39. 24. Schiller, J., Süß, R., Fuchs, B., Müller, M., Petkovic, M., Zschörnig, O. and Waschipky, H. (2007) The suitability of different DHB isomers as matrices for the MALDI-TOF MS analysis of phospholipids: which isomer for what purpose? Eur. Biophys. J. 36, 517–527. 25. Estrada, R. and Yappert, M. C. (2004) Alternative approaches for the detection of various phospholipid classes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom. 39, 412–422. 26. Gellermann, G. P., Appel, T. R., Davies, P. and Diekmann, S. (2006) Paired helical filaments contain small amounts of cholesterol, phosphatidylcholine and sphingolipids. Biol. Chem. 387, 1267–1274. 27. Murphy, R.C. (2002) Mass spectrometry of phospholipids: Tables of molecular and product ions. Illuminati Press, Denver. 28. Fuchs, B., Müller, K., Göritz, F., Blottner, S. and Schiller, J. (2007) Characteristic oxidation products of choline plasmalogens are detectable in cattle and roe deer spermatozoa by MALDI-TOF mass spectrometry. Lipids 42, 991–998. 29. Harvey, D. J. (1995) Matrix-assisted laser desorption/ionization mass spectrometry of phospholipids. J. Mass Spectrom. 30, 1333–1346. 30. Bligh, E. G. and Dyer, W. J. (1959) A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 3, 911–917.



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31. Bartlett, G. R. (1959) Phosphorus assay in column chromatography. J. Biol. Chem. 234, 466–468. 32. Fuchs, B., Schiller, J., Süß, R., Schürenberg, M. and Suckau, D. (2007) A direct and simple method of coupling matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) to thin-layer chromatography (TLC) for the analysis of phospholipids from egg yolk. Anal. Bioanal. Chem. 389, 827–834. 33. White, T., Bursten, S., Frederighi, D., Lewis, R. A. and Nudelman, E. (1998) High-resolution separation and quantification of neutral lipid and phospholipid species in mammalian cells and sera by multi-one-dimensional thin-layer chromatography. Anal. Biochem. 10, 109–117. 34. Zschörnig, O., Richter, V., Rassoul, F., Süß, R., Arnold, K. and Schiller, J. (2006) Analysis of human blood plasma by MALDI-TOF MS – Evaluation of critical parameters. Anal. Lett. 39, 1101–1113. 35. Petkovic, M., Schiller, J., Müller, J., Müller, M., Arnold, K. and Arnhold, J. (2001) The signalto-noise ratio as the measure for the quantification of lysophospholipids by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Analyst 126, 1042–1050. 36. Müller, M., Schiller, J., Petkovic, M., Oehrl, W., Heinze, R., Wetzker, R., Arnold, K. and Arnhold, J. (2001) Limits for the detection of (poly-)phosphoinositides by matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS). Chem. Phys. Lipids 110, 151–164. 37. Petkovic, M., Schiller, J., Müller, M., Benard, S., Reichl, S., Arnold, K. and Arnhold, J. (2001) Detection of individual phospholipids in lipid mixtures by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry: phosphatidylcholine prevents the detection of further species. Anal. Biochem. 289, 202–216. 38. Al-Saad, K. A., Zabrouskov, V., Siems, W. F., Knowles, N. R., Hannan, R. M. and Hill, H.

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Chapter 7 Lipidomics of the Red Cell in Diagnosis of Human Disorders

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2 3

Peter J. Quinn, D. Rainteau, and C. Wolf

4

Summary

5

Applications of tandem mass spectrometry in the field of lipid clinical chemistry are considered. Haemato­ logical and biochemical advantages are presented favoring the choice of red blood cell membranes as a starting material in a wide variety of biomedical fields. Practical considerations are discussed with respect to methods of sampling, storage, and lipid extraction of red blood cells. The chapter describes the capabilities of a direct infusion of raw lipid extracts in the electro-spray ionization source compared with the more sophisticated method of high-performance liquid chromatography coupled with hybrid tandem mass spectrometry. Both methods have been evaluated and have been shown to be suitable for diagnosis and/or monitoring for a variety of human disorders.

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Key words: Lipidomics, Diagnosis, Erythrocyte, Membrane, Essential fatty acid deficit, Peroxisomal defects, Plasmalogen, Oxidized phosphatidylethanolamines

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1. Introduction

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The human red blood cell (RBC) membrane is an object pathological investigation because it can provide a convenient and reliable indica­ tion of a range of pathological conditions. This work is devoted to the description of the development of routine methodologies for lipid profiling of the human red cell membrane. One develop­ ment is the routine profiling of the fatty acid substituents of the complex lipids of the erythrocyte membrane. Due to the rigorous and precise homeostatic regulatory mechanisms that operate in the membrane it is possible, for example, to detect essential fatty acid (EFA) deficit in children (i.e., exocrine pancreatic insufficiency of mucoviscidosis) as well as in adults (i.e. extensive enterectomy in advanced inflammatory bowel diseases). Other more subtle Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, doi 10.1007/978-1-60761-322-0_7, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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alterations in the molecular species of RBC phospholipids are also investigated for long-term nutritional consequences of sup­ plemented diets or biosynthetic defects of ether–lipids associated with peroxisomal enzyme blockade. The biosynthetic defects of ether–lipids together with very long chain fatty acid accumula­ tion are needed for the diagnosis of 12 out of 17 neurological conditions associated with one of the multiple forms of peroxisomal deficit (1). The complexity of the clinical diagnosis of peroxi­ somal disorders in children is an incentive for more widespread applications of lipidomic analysis. The analysis is also recommended to monitor the outcomes of diet and supplementation treatments because EFA profiling of the RBC membrane is a much more reliable index of deficit than profiling fatty acids of serum lipoproteins. This is because of a relatively slow turnover of fatty acyl components of complex membrane lipids as compared to renewal of neutral lipids and phos­ pholipids circulating in serum. Serum lipolytic enzymes (secretory PLA2, Lecithin-Cholesterol Acyl Transferase, Lipoprotein Lipase) are relatively active compared with low activities of phospholipase or acyltransferase in human erythrocyte membranes and the cells remain in circulation with a normal lifetime of 120 days. As a result of the relatively stable RBC fatty acid composition variations of a few percent of fatty acid molar ratios can be a significant indica­ tor of EFA deficit. For example, normal proportions of 20:4n-6 is 11–14% but values less than 10% may be considered as a deficit. The original analytic protocols of fatty acid profiling were based on analysis of hydrolyzed lipids and production of volatile methyl ester derivatives (FAME) using gas–liquid chromatography. The introduction of liquid chromatography coupled with mass spec­ trometry allows the identification of molecular species of mem­ brane phospholipids with relatively slow turnover rates thereby improving precision in indexing the EFA profile. Phosphatidylcho­ lines, for example, are subject to a different turnover rate compared with phosphatidylethanolamines populating the inner leaflet of the RBC membranes which normally are not accessible to circulating enzymes. However, phosphatidylethanolamines are subjected to enzymes from the inner side of the membrane. In addition phos­ phatidylcholine species are comprised of lower proportions of EFA than phosphatidylethanolamines which represent a reservoir enriched in EFA which expands under conditions of nutritional supplementation. Phosphatidyserines and phosphatidylinositols are enriched in the, so called, “distal” EFA such as 20:4n-6 and 22:6n3. These particular EFA are generated as the end products of bio­ synthetic pathways in the liver. The pathways comprised of acyl chain elongation and unsaturation lead to the interconversion of shorter chain and less unsaturated precursor EFA, such as 18:2n-6 and 18:3n-3, respectively. It is anticipated that future develop­ ments will enable distinction between nutritional deficits of EFAs from those derived by hepatic interconversion. Besides global liver



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f­ailure it is expected that specific elongase/desaturase steps are crit­ ically impaired in pathology such as insulin resistance and diabetes, obesity, and hepatosteatosis (2). New therapeutic developments are aimed to remedy these defects. The measurement of kinetics of refilling with EFA into particular molecular species after enriched diets may also provide useful clinical information. Other applica­ tions include immunomodulation correlated with a shift in the bal­ ance of n-3/n-6 EFA. The assumption that inflammation may be prompted by an inappropriate eicosanoid synthesis in which there is obvious medical interest has not been supported yet by total FA methyl ester profiling studies. However, it is anticipated that with application of recent lipidomics methods described in this Chap­ ter evidence of a significant linkage will be established between abnormalities in the FA composition of a few molecular species of precursor phospholipids and the inflammatory condition. Current lipidomic methods using mass spectrometry are able to resolve the molecular species composition of the five major phosphol­ ipid classes present in human erythrocyte membranes: phosphatidyl­ cholines (PC), sphingomyelins (SM), phosphatidylethanolamines (PE), phosphatidylserines (PS), and phosphatidylinositols (PI). Cho­ lesterol and detailed glycolipid composition can also be determined but discussion of this is outside the scope of this presentation.

2. Materials Lipidomics studies of patient erythrocyte membranes were conducted at CHU Saint Antoine. The MS facility at the hospital center includes two complementary tandem MS2 equipments (a) a hybrid tandem QTrap 2000 (Applied Biosystems/MDS SCIEX) is coupled with high-pressure liquid chromatography (HPLC Agilent series 1100). (b) The triple quadrupole (TQ) API3000 (Applied Biosystems/MDS SCIEX) system is presently used for direct infusion (Harvard Apparatus syringe pump 11 Plus). Mass spectra are quantified after translation of the propri­ etary file format.wiff (Analyst, Applied Biosystems) to .cdf format using the translation module included in the software. Then mass spectra are computed for peak identification and deisotopisation using the software LIMSA (3).

3. Methods 3.1. The Use of RBC Membranes for Medical Investigations

As a tissue indicator of metabolic or nutritional disturbance or disease states, the human erythrocyte offers a number of advan­ tages. The cells are easily obtained without inordinate invasion

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and can be drawn at suitable intervals for monitoring purposes. They are nondividing cells with a single, relatively stable cell membrane. This section details the processes of haematopoiesis and the origin and turnover of the constituent lipids of the mem­ brane. This is followed by a consideration of sampling, storage, and processing of lipids for lipidomic analysis. 3.1.1. Haematopoiesis and RBC Lifetime

Haematopoietic stem cells of the bone marrow give rise to all the cellular components of blood. The pool of these multipotent cells is maintained by retention of multipotency by a propor­ tion of the daughter cells. Differentiation of the stem cells takes place in a stepwise manner in which changes in gene expression that place constraints on cell destiny move the cells closer to toward a mature erythrocyte. These changes are reflected in the proteins detected on the cell surface. The differentiation of erythrocytes from myeloid progenitor cells is augmented by hematopoietic growth factors and principally, erythropoi­ etin, produced in the kidneys. Prior to and immediately after leaving the bone marrow, the differentiating erythrocytes pass through a reticulocyte stage. Reticulocytes represent about 1% of circulating red blood cells. This proportion of reticulocytes is commonly determined to be used as a measurement of RBC turnover rate. In turn, the percentage is an indication of RBC lifetime with consequences on membranogenesis and the lipid incorporation rate at the time of measurement. Erythrocytes develop from committed stem cells through reticulocytes to mature erythrocytes in about 7 days and survive in circulation for a total of about 120 days. Ageing erythrocytes become damaged due to irreversible changes in the cell membrane, some of them related to mem­ brane lipids such as the outer exposure of phosphatidylserines and phosphatidylethanolamines, which are recognized by macrophages in the spleen, bone marrow, and liver and are removed from the circulation by phagocytosis. Much of the important breakdown products (iron) are then recirculated in the body but little is known of recirculation of lipids and EFA. Some physiological conditions (e.g., gender and altitude acclimation) influence turnover of RBCs and this, in turn may interfere with the lipid composition of the membranes. These changes have not been yet detailed by the recent lipidomic methods described below. Adult humans have approximately 2–3 × 1013 red blood cells in circulation at any given time (women have about 4–5 × 106 erythrocytes/mL of blood and men about 5–6 × 106). Eryth­ rocytes from adults and children with reduced vitamin E intake and with low vitamin E serum levels that are relatively sensitive to oxidants in vitro have a reduced lifetime. The lifetime of eryth­ rocytes in premature infants is also shorter than those in normal infants (4).



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Polycythemias are characterized by an increased concentra­ tion of circulating erythrocytes, some such as polycythemia vera is an acquired stem cell disease and others are congenital and are caused by mutations of the Erythropoitin-receptor gene, haemo­ globin variants, 2,3-bisphosphoglycerate mutase deficiency, or by disturbances in renal oxygen sensing. Acquired polycythemias can occur secondary to hypoxia at high altitudes, or primarily through acquired mutations in the EPO-receptor signaling system (JAK2 mutations). Alternatively they may be caused by pulmonary or renal diseases which perturb fatty acid composition of RBC with numerous intricate causes. Because they are relatively long lived in the circulation, their membrane lipid composition is more representative of the longer term lipid homeostatic situation than is the lipids of the plasma. The latter varies in lipid composition according to short-term dietary and physiological circumstances. 3.1.2. Regulation of Phospholipid Synthesis During Membrane Biogenesis

The cell membrane of the mature erythrocyte is not synthesized de novo but it is transformed from its progenitor cells by a proc­ ess of membrane differentiation. The primary site of synthesis of membrane lipids and proteins is the endoplasmic reticulum where most of the enzymes required for these tasks are located. The biosynthesis of membrane phospholipids and their incorpo­ ration into the membrane takes place on the cytosolic monolayer of the progenitor endoplasmic reticulum. Phospholipid flippases are needed to translocate newly synthesized phospholipids to the opposite leaflet of the membrane (luminal in the progenitor to become extracellular in the mature RBC). Phosphatidylcho­ line transferase operates to concentrate molecular species of this phospholipid class to the protoplasmic leaflet of the membrane but there are no transferase specific for PE, PS, and PI, so these phospholipid classes are predominant in the cytoplasmic leaflet (to become intracellular in the mature RBC). This asymmetry is preserved when transition vesicles derived from the endoplas­ mic reticulum fuse with membranes of other organelles of the endomembrane system including, ultimately, the plasma membrane. In addition, soluble lipid exchange proteins which are specific for particular membrane lipids, such as glucosylceramides, are present which translocate these lipids from the site of synthesis to the cytoplasmic surface of the plasma membrane. During passage of membrane through the endomembrane system of precursor RBC cell, the lipid composition changes as it is transformed into different morphologically distinct membrane. This process involves retailoring the molecular species of lipids that comprises the bilayer matrix. This process can involve both complete turnover of the lipid where the lipid is removed from the membrane and replaced by another molecular species of the same lipid class and partial turnover, when only a component of

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the lipid is exchanged for another. Complete turnover of lipids is mediated by phospholipase C-type enzymes in which the products of hydrolysis no longer possess an amphipathic character (diacylg­ lycerols and water-soluble phosphate esters). Partial turnover is of two types. The first is where the fatty acyl chains are exchanged one for another to remodel the molecular species of phospholipid. These types of reaction involve phospholipase A-type enzymes with an intermediate lysophospholipid product that is strongly amphipathic in character. The hydrolysis of fatty acids and reacyla­ tion of the lysophospholipid is known as the Lands cycle. The sec­ ond type of partial turnover is catalyzed by phospholipase D-type enzymes in which the intermediate product is phosphatidic acid. The reaction involves an exchange of bases on phosphatidic acid and its significance is concerned mainly with the biosynthesis of phosphatidylserine from phosphatidylethanolamine. The partial turnover of molecular species of phosphatidylinositols may also be achieved by this mechanism. During differentiation of reticulocytes into mature eryth­ rocytes fatty acids provided from diet and presented from the digestive system and/or after interconversion in the liver are incorporated into membrane lipids. This process takes place during the many steps of cellular differentiation from the early erythroid progenitors (burst-forming-units-erythroid) to late erythroid progenitors (colony-forming units-erythroid) and to morphologically recognizable erythroid precursors. Ultimately the endomembrane system including the nucleus is lost and the mature erythrocyte is no longer in possession of the complete machinery for producing membrane lipids. The lipid composition of the erythrocyte membrane is thereon dependent on removal of lipids by metabolic turnover or the limited remodeling of the existing membrane lipids using substrates and circulating enzymes that are provided from the plasma (secretory PLA2, LCAT, etc.). 3.1.3. Incorporation of Fatty Acids into Membrane Phospholipids

The key building block of membrane phospholipids is diacylg­ lycerol. The fatty acids of the glycerides reflect, to a large extent, the nutritional and dietary circumstances of the patient and are modified by pathological conditions. There are two principal pathways for de novo phospholipid biosynthesis. The fatty acid of triacylglycerol at the C3 position is hydrolyzed to form 1, 2-diacylglycerol. This may react with CDP derivatives of choline or ethanolamine to form PC and PE, respectively. Alternatively, 1, 2-diacylglycerol may be phosphorylated at the sn-3 position by the action of diacylglycerol kinase to form phosphatidic acid. Phosphatidic acid, in turn, reacts with CTP to form CDP-dig­ lyceride. The product can react directly with serine or inositol for PS and PI, respectively. Thus the origin of the fatty acids origi­ nates from the metabolic stores of triacylglycerol. In the case of sphingolipid biosynthesis from sphingosine bases, the fatty acids



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amide bonded to the precursor to form ceramide are distinctive from those acylated to the phospholipid molecular species. These particularly long-chain, more saturated fatty acids are preferentially utilized for biosynthesis of the sphingolipids. 3.1.4. Remodeling of Phospholipids by a Deacylation/ Reacylation Cycle

As indicated above, remodeling of membrane phospholipids via acyl exchange reactions in the Lands cycle is an active pathway in most tissues. The molecular species of membrane phospholipids is a reflection of the nature of the fatty acid substrates and the physical signals generated by the bilayer structure which govern the type of fatty acids that can be incorporated. The Lands cycle is highly active and plays an important role in membrane differ­ entiation. Operation of the cycle requires phospholipase A2/CoA ligase/acyltransferase enzyme components. The activity of these enzymes in mature erythrocytes, however, is very low compared with those in other tissues such as the liver. Moreover, the rela­ tively high concentrations of serum albumin (>30 g/L) strongly bind lysoderivatives formed in the RBC membranes. This means that in erythrocytes the turnover of molecular species of phos­ pholipids is relatively slow so that reliable long-term homeostasis can be characterized from a lipidomic analysis of the erythrocyte membrane. Recently the apoptosis-associated changes in the glyc­ erophospholipid composition of hematopoietic progenitor cells have been monitored by lipidomic methods (MALDI-TOF and 31 P-NMR) (5). The study has shown that withdrawal of growth factor induces an increase in the ratio of ether-linked glycero­ phospholipids to diacyl-glycerophospholipids during apoptosis as well as the relative decrease in the membrane of diacyl-phosphati­ dylcholine (PC) levels.

3.1.5. Membrane Lipids in Human Hemoglobinopathies

The lipid molecular species composition of the red blood cell is preserved within relatively narrow limits throughout the lifetime of the cell. Retailoring of the constituent lipids is accomplished by uptake of fatty acids from the serum which, in turn, reflects the intake of lipids in the diet. This intake can be highly variable so that the process of lipid turnover must be selective in that the fatty acids incorporated into complex membrane lipids are tightly regulated. The biophysical sensors of membrane lipid composi­ tion are, as yet, unknown but modulation of the processes of acyl chain turnover must take place. Only a few members of the enzyme pathways involved have so far been identified, and little is known about how regulation of catalysis is achieved through interaction of the enzymes with their lipid environment that would clarify the mechanisms that govern this selectivity. Never­ theless, changes in membrane lipids provide useful indicators of a range of hemoglobinopathies (6). It may be expected that phospholipid turnover and repair is higher in hemoglobinopathies because damage to the mem­

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brane resulting from free radical reactions is often a feature of these conditions. A number of reports indicate evidence of lipid oxidation in RBCs from sickle cell or thalassemia patients, ((7, 8)) suggesting that phospholipid repair is not efficient enough to maintain the proper lipid molecular species composition in these cells. Analysis of the lipid molecular species of density fraction­ ated sickle cells reveals a complex picture. Fractions of greater density which are comprised of more saturated molecular species of phospholipids have more variable proportions of unsaturated phospholipids comparing one patient with another. This obser­ vation infers that the processes responsible for turnover and repair of membrane phospholipids differ from one cell to another because all cells share the same serum substrate pool. It is note­ worthy that when the exchange of phospholipids between RBCs is facilitated by lipid exchange, the densest fractions are those that undergo the greatest change of density (9). Erythrocytes are unable to replace proteins that are irrevers­ ibly damaged by reactive oxygen species. Thus, incorporation of fatty acids in erythrocyte membrane phospholipids is affected when cell suspensions are oxidatively stressed in  vitro. Since complex pathways are employed in lipid repair, turnover dam­ age to any one member of these pathways has repercussions in membrane lipid composition. The relative sensitivity of the indi­ vidual proteins toward oxidant stress must vary but not much is known of where the vulnerabilities occur. Other than direct oxidative damage of these proteins, alterations in the ionic balance of the cytosol may also play a role. The recently identified acylCoA:Lysophosphatidylcholine acyltransferase (LPCAT) family, for example, which exhibits an EF-hand motif at the C-terminus, was shown to be modulated by calcium and magnesium (10). Since both cytosolic calcium and oxidant stress are altered in sub­ populations of sickle RBCs, this suggests that LPCAT may act differently in subpopulations of sickle RBCs, leading to insuf­ ficient or miss repair of membrane lipids. Overall, oxidant stress and alterations in the cytosol of erythrocytes from patients with hemoglobinopathies will lead to the inability to maintain a proper lipid composition, which in turn leads to alterations in membrane function, including ion and water transport across the bilayer and altered red cell density distributions. The alteration in the sickle cell membrane lipids may also be understood as a consequence of the shorter erythrocyte lifespan and accelerated membranogenesis in the progenitor cells. 3.2. Practical Considerations for Lipidomic Analysis of Human Erythrocytes

Blood are the most convenient liquid “tissue” for analysis because obtaining samples is less invasive and samples can be taken at frequent intervals for monitoring purposes. The physiological state of the subject must be standardized because blood lipids vary accordingly between subpopulations. Erythrocytes must be



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i­solated from other blood cells and serum lipoproteins by differen­ tial centrifugation methods under conditions where lipid changes to the erythrocyte membrane are minimized. Once isolated, the erythrocytes can be stored before lipid extraction and analysis or alternatively the extraction of lipids may proceed immediately on isolation of the cells and the lipid extract stored until examined. This section deals with the practical aspects of sample handling prior to lipidomic analysis. 3.2.1. Blood Sample Collection and Erythrocyte Storage

The first question to be decided is what volume of blood needs to be collected and under what conditions to preserve the integrity of blood cells? Drawing of a 1 ml sample of blood is likely to be sufficient for lipidomic analysis. This volume can be reduced to 0.2 ml in new born babies without compromising the analysis. It is advisable to adapt the blood volume and the tube capacity to minimize the air in the container in contact with the sample. Collection of the sample into a vacutainer on a chelator like EDTA is recommended to reduce the availability of metal cations (Fe2+, Cu2+, etc.) activating lipid peroxidation. This prevents the chance of reactive oxygen species oxidizing unsaturated molecular species of lipid. The blood sample should not be shaken vigorously to minimize the damage to the erythrocytes; vesicles are known to be released from stored erythrocyte membranes after prolonged shaking and can be recovered in the plasma after centrifugation. Because the lipid composition of the released membranes is very different from the plasma the plasma composition is corrupted by minor contamination due to the presence of fragmented membrane vesicles. Likewise, the composition of membrane lipids can be compromised by contamination by lipoproteins adsorbed from plasma. For this reason it is good practice to “wash” the pelleted RBC with five volumes of isotonic sodium chloride, especially if the sample is of a small volume (under 1 ml). The storage conditions before analysis are important. Freezing whole blood causes extensive membrane fragmentation and the resulting plasma/RBC contamination after hemolysis. Freezing RBC only after separation of plasma from the blood cells is rec­ ommended in order to avoid exchange protein and enzyme activ­ ities which alter RBC membrane lipids. The activity of the serum enzymes is slow in EDTA-containing samples and a time of 2–4 h for transportation after drawing the sample is reasonable. If samples are to be stored for longer periods of time this should be at −20°C rather than −70°/−80°C. Storage at 4°C is not suffi­ cient to inhibit all interfering enzyme activities: the sharp decrease in the unesterified membrane cholesterol content under the influ­ ence of the enzyme L.C.A.T. upon storage for 21 days at −4°C under the standard blood bank conditions is an example. Unlike storage of samples for proteomic studies there is no advantage in storage at the lower temperatures in maintaining lipid integrity.

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The major cause of lipid deterioration is peroxidation and oxy­ gen gas is more soluble at low temperatures. Also oxygen-derived radicals are more stable at low temperatures, so very cold tempera­ tures are of no advantage. The oxygen should be chased from the sample by gassing with a dense inert gas like argon; nitrogen is a less satisfactory substitute because, being less dense, it leaks out of the tube unless the container has been flame-sealed. Nitrogen is also less effective in displacing oxygen than argon. An example of LCMS2 method to trace the oxidized phosphatidylethanolamines is given in the last paragraph of this chapter. 3.2.2. Lipid Extraction

Lipidomics studies often refer to studies of crude lipid extracts inferring that there is no requirement for the complete separation of protein, sugar, and lipid classes in the sample subject to analysis. Tandem mass spectrometry has considerably simplified the preanalytical steps as compared to relatively “low tech” procedures such as thin-layer chromatography which are more demanding in terms of purity. This is explained by the high specificity of MS and relatively weak influence of the matrix for monitoring the characteristic transition of a parent molecular ion to a fragment prod­ uct (tandem MS). Electrospray (ESI) with intrasource separation has led to strategies using 2D mass spectrometry in, so called, shotgun lipidomics and quantitation of cellular lipidomes directly from “crude” extracts of biological samples (11). The intrasource separation procedure is based on the activity of an external electric field to induce separation of cations from negative ions in the infusate while different ionization of molecular species that possess differential electrical propensities can be induced in either the positive- or negative-ion mode during the electrospray ioniza­ tion process. Intrasource separation and selective ionization are expected to simplify lipid purification prior to MS and result in greater accuracy of the analysis (12). Preanalytical separation steps and enrichment of lipid sample, however, are still required in many cases prior lipidomic final analyses. Ion suppression is a major complication with phospholipids. It can be viewed as the competition for ionization and transfer of molecules from the solvent droplets forming the spray to the gas phase. Because many different molecules are introduced simulta­ neously in the ESI source and because the volume of the drop­ lets is extremely small the positive or negative electrical charge in excess are scarce and will be captured by more affine molecular species. Similarly the surface of the droplet is extremely limited and surfactant molecules such as phospholipids are competing to be transferred to the gas phase and enter the mass spectrometer interface orifice. Phospholipids have a high propensity to suppress ionization of coeluting molecules such as drugs (13) or ganglio­ sides (14). In biological samples, abundant phospholipid molecu­ lar species compete with less abundant species and examination



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of diluted samples is recommended for characterization of minor species of phospholipids in the presence of abundant species. Moreover the ionization and transfer to the gas phase is dependent on the saturation and acyl chain length of phospholipid molecular species. This dependence and other ion suppression phenomenon are all increased as a function of the total lipid concentration (15). Inclusion in the infusate of ammonia or solvent-soluble trimethylamine (TEA), diisopropyl-amine (DIPA), or piperidine enhances phospholipid ionization in the negative mode and displaces Na+ and K+ counterions in the positive mode. However ion pairing of PC and SM with amines TEA or DIPA (+101) should be taken into account in the quantitation of the molecular species. To maintain equipment performance during long sampling lists, it is recommended that extracts are clarified. Indeed, insoluble lipid precipitates are frequently observed in solvent mixtures used for chromatography or infusion where phospholipid high concentra­ tion and limited solubility occurs in the solvent system required for HPLC separation. Precipitation of nonsoluble phospholipids can be reduced by heating the sample vial, connection tubes, and column with heating tape and in a thermostated sampler main­ tained at around 30–45°C. It should be noted that temperature may profoundly alter the performances of chromatography sys­ tems and modifies the retention times of components eluted from columns. On a reverse stationary phase grafted with octadecyl (C18) acyl chains the temperature reduces the troublesome ionic interactions and helps to maintain peak symmetry, so reducing the need for ionic pairing (16). On the other hand, retention is delayed on polar stationary phases (e.g., diol-silica) and useful degrees of resolution of phospholipid classes can be achieved at the expense of long analysis time. In erythrocyte membranes the proteins are associated with the lipids by both hydrophobic interactions and polar bonds. At the lipid/water interface hydrogen- and ionic-binding between headgroups are prevalent. Inside the bilayer core hydropho­ bic binding is responsible for the compactness and ordering of acyl chains and sterol rings. Therefore both polar and nonpolar solvents should be used for extraction of lipids bound to pro­ teins. Quantitative extraction requires a gradual procedure to separate the lipids from proteins by a method that avoids the rapid denaturation of the proteins and the trapping of lipids sur­ rounded by aggregated proteins. The clumping into aggregates of denaturated protein reduces lipid extractability into nonpolar nonwater-soluble solvents. A comparison of solvent activities and a description of the most common lipid extraction procedures can be found in the Cyberlipid website: (http://www.cyberlipid. org). Briefly, an aqueous suspension of lipid-containing biologi­ cal material is first mixed with a polar water-soluble solvent such as methanol (or isopropanol or ether). In a second step the less-

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polar nonwater-soluble solvent (chloroform, hexane) is added in a proportion which forms a single phase for the ternary system (water/water-soluble solvent/nonwater-soluble solvent). In this environment the proteins form a fine precipitate which does not tend to clump and does hinder lipid extraction from RBCs. Lipid­ omics studies by MS may be conducted directly from this ternary solvent mixture. However, a subsequent step is usually the parti­ tioning of the filtered previous extract mixture into two immis­ cible layers (17, 18) after the addition of a large extra volume of aqueous buffer. This partition helps to separate water-soluble constituents acting as an ESI ion-suppressor (proteins, sugar, salts) into an upper layer phase separated from the lower chlo­ roform layer containing the water-insoluble lipids. Because ionic strength and acidic buffer pH favors the desorption of anionic lipids (e.g., PIPx, gangliosides) bound to basic protein residues it is recommended for extraction protocols with partitioning of the polar lipids into the chloroform layer. Anionic lipids, however, tend to partition into the aqueous-alcoholic layer at pH greater than 4. Excessive acidification results in the cleavage of vinyl ether bond of plasmalogens. In tissues such as the red blood cell mem­ branes where both acid-resistant and plasmenyl lipids are to be examined, two separate extracts (one extraction protocol with acid and the other without acid addition) can be prepared. In solvent extraction mixtures with chloroform most of the lipids are finally extracted into the lower dense layer which separates from the aqueous-alcoholic upper layer. Proteins accumulate at the aqueous/chloroform layer interface with unfolded sequences oriented upward or downward according to their hydrophilic/ hydrophobic affinities. Indeed a turbid suspension is observed if abundant proteins or low ionic strength or other amphiphiles of the biological preparation stabilize the interface of chloroform droplets. When emulsions are formed (e.g., in some lipid extracts prepared in the presence of high bile acid concentrations), the separation of the upper/lower phases is long lasting and diffi­ cult. Protocols derived from the Folch procedure (17) have been devised to speed up the separation of layers such as centrifugation, cooling, alteration of chloroform/methanol ratio, or addition of saline buffer. When the lower chloroform layer is turbid it is rec­ ommended that the extract is percolated through a dehydrating agent to remove water. Barium oxide or anhydrous sodium sul­ fate is often employed for this purpose. Contamination by water jeopardizes the integrity of lipid extracts after solvent evaporation because a residue of acidic water remains which decomposes vinyl ether bonds. Lipid oxidation is also enhanced at the air interface because evaporation of water takes a long time. To minimize degradation and set up automatic preanalytical facilities for a simultaneous treatment of numerous samples, methods have been described using an automat robot with Solid



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Phase Extraction (SPE) protocols in which there is no need for partition the solvent/water mixture into two superposed layers. Extraction by SPE reduces the volume of solvent required. The principles of lipid SPE have been reviewed elsewhere (19). Station­ ary phases are designed for quantitative extraction of phospholip­ ids on reversed phase (20), or separation of neutral/phospholipid on straight phase (21). The recovery of anionic lipids has to be assessed against standards for polar stationary phases. A recent innovation for lipid extraction suited to highthroughput lipidomics using methyl tert-butyl ether has been described by Matyash et al. (22). Rigorous testing demonstrated that the protocol delivers similar or improved recoveries of spe­ cies of most of all major lipid classes compared with the ”goldstandard” Folch or Bligh and Dyer recipes. Moreover, because methyl tert-butyl ether forms the upper, less dense layer where lipids partition after separation, it allows faster and cleaner lipid recovery and is well suited for automated shotgun profiling using pipettage robots. 3.2.3. Storage of Lipid Extracts

Liquid chromatography–mass spectrometry (LC–MS) analysis has a high sample throughput relative to the methods described previously for the lipid extraction. Samples are pooled after extraction and stored before analysis as a series. The storage of lipids in organic solvent is critical if not protected against the chemical alteration by water and traces of oxygen. Intrinsic pro­ tective mechanisms and association with natural compounds act in living organism to defend the vulnerable double bonds of vinyl ether and polyunsaturated fatty acids against attack by reactive oxygen species. The removal of these lines of defense by isola­ tion and extraction of lipids requires addition of antioxidants. It is therefore usual to add a radical scavenger such as butylated hydroxy-toluene (BHT, 2,6-di-tert-butyl-p-cresol) and/or toco­ pherol to the extract. Being soluble in solvents of low polarity they can be easily added to the chloroform extract and their oxi­ dation can act as a potential index of damage to the biological lipids. Nevertheless, it is necessary to ensure that the concen­ tration of such agents does not exceed a threshold of 0.0001 (w/w) when antioxidants like BHT have been shown to act as prooxidants at higher concentrations. Oxidation can also be exac­ erbated by contamination with metal cations such as copper or/ and iron which catalyze production of free radicals in Fentontype reactions. Contamination can be avoided by using single-use glass vessels with PTFE fittings and it is recommended for all preanalytical steps in Lipidomics. Brown glass is recommended to prevent exposure of lipid extracts to UV light, for example, when performing operations on a UV-illuminated sterile bench, which can be particularly deleterious. Elevated temperatures also favor double-bond migration and diene conjugation. The isomeriza­

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tion proceeds after the initial abstraction of an electron from an intermediary − CH2 methylene group intercalated between two double-bonds. Odd electron radicals derived of lipids induce an extensive peroxidation to other lipids especially in concentrated solutions. It is ideally recommended that lipid extracts are stored at low temperatures (−20°C) diluted in a solvent solution contained in an opaque (brown glass) vessel purged with argon gas and sealed by PTFE tape. The anionic phospholipids such as PI and PS present in RBC membrane extracts are protected in the long term from the head-group and fatty acid cleavage in the form of ammonium salts. Evaporation of the solvent prior to analysis is performed under a stream of oxygen-free dry nitrogen at a temperature not exceeding 40°C. SpeedVac is an effective alternative to this method since numerous samples can be evapo­ rated simultaneously in vials which can be subsequently trans­ ferred to an automatic sampler module of an HPLC system. Up to 40 samples can be evaporated simultaneously before they are loaded into the cooled sampler module. Cooling of the injec­ tion module prevents evaporation of solvent after the septum is perforated by the sampling needle which allows the reinjection of sample for further analysis using different data acquisition modes. Caution should be exercised, however, as temperatures lower than about 10°C may result in precipitation of the fraction of the most insoluble lipids concentrated in the type of solvent system used for HPLC (heating instead of cooling the sampler may be an alternative if sphingomyelins of RBC membranes are studied). Insoluble lipid aggregation results in the splitting of the eluted peaks into micellar and monomeric compounds. More efficient solvents for lipid solubilization like chloroform/methanol binary mixtures (1/1 or 2/1) can interfere with chromatography selec­ tive retention, the metal tubing of the equipment may deteriorate due to exposure to HCl formed from chloroform and finally the transfer of molecules from the ESI spray to the gas phase can be decreased. Using double layered PTFE-sealed vials and extensive purg­ ing with argon extracts containing highly polyunsaturated glyc­ erophospholipids such as human RBC membranes were found to remain unchanged for up to 6 months storage at subzero tem­ peratures. Sphingolipids are expected to be stable for much longer periods of time since they are generally more saturated. Sterols, steroids, and bile acids are also innately resistant to chemical altera­ tion as indicated by analyses of paleofecal specimens (23). By con­ trast, compounds such as 7-dehydrocholesterol or ergosterol with conjugated double bonds in the B-ring require more cautious handling among which is recommended low illumination, inert atmosphere, and analysis as soon as possible after isolation. The last paragraph in the chapter shows the measurement of oxidized phosphatidylethanolamines by LCMS2 after long-term storage.



3.3. Results 3.3.1. Analysis of RBC Phospholipids by Tandem MS2

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In this section the use of simple tandem MS2 to analyze most of the phospholipids extracted from human RBC membrane following a straightforward protocol will be described. Examination of the raw lipid extracts obtained by the method of Rose-Oklander (24) and methyl tert butyl ether (22) offers many advantages in terms of time and manpower per sample. The method developed for basic research can be now automated and fits appropriately within the context of biomedical applications. The utility of the method can also be compared to methods requiring more sophisticated equipment such as hybrid tandem MS2, Q-Trap, and Q-TOF, coupled with HPLC which are described below. To illustrate the method, results obtained after the direct infusion of the total extract of RBC membrane phospholipids into the ElectroSpray Ionisation (ESI) source of a triple quad­ rupole (TQ) spectrometer are presented. More comprehensive analyses are obtained if an HPLC separation of phospholipid class and subclass is conducted prior to MS2. However, incorporation of a chromatography step extends the time required for analysis by the length of the chromatographic run. Furthermore, comple­ mentary HPLC equipment requires pumps, filters, and seals that must be carefully maintained for running long series of samples in clinical applications. Examining RBC extracts for some particular medical applications may require a resolution not achieved by a direct infusion setup. An example of such demanding applica­ tion is the separation of diacyl/ether phospholipids described in below. The direct infusion of raw phospholipid extracts into ESI–MS has replaced the multiple steps previously used for the separa­ tion of phospholipid classes and fatty acid composition: recovery of individual phospholipids from TLC plates; phosphorus assay, and fatty acid analysis by gas chromatography. The MS-based method allows detailed analysis of lipid species in the field of biomedicine. The principle applied in tandem MS2 is the cleavage of a spe­ cific product ion from the phospholipid parent ion, e.g., serine from phosphatidylserine (PS) or phosphorylcholine from phos­ phatidylcholine and sphingomyelin (SM). Figure 1 shows the specific product ions resulting of collision-induced dissociation (CID) in the tandem MS2. As compared to electron impact fragmentography (energy = 70 eV) the collision of accelerated parent ions through a quadru­ pole filled with argon or nitrogen has a relatively low energy (<20 eV). Only a few bonds are cleaved in the parent ion. The yield and position of the fragmentation depends on the excess internal energy and its distribution in the excited molecule. The fragmen­ tation yield varies widely among the phospholipid classes with product ion m/z = +184 (PC and SM) being the most abun­ dant in the RBC membrane lipid extract. The ionization of other

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Fig. 1. Specific cleavage of the head-group is produced by collision-induced dissociation to characterize the phospholipid class. Specific ions and neutral fragments derived from the head-group moiety are obtained from ionized phospholipid in the positive or negative mode. Ester-bonded fatty acyl groups are also cleaved from the negative phospholipid parent ions in the form of acyl carboxylates at a higher CID energy. The fatty acid composition which is derived characterizes the phospholipid molecular species.



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phospholipid classes can, to some extent, compensate for their low fragmentation yield. For instance, positively charged parent ions M+H+. are favored in acidified extracts. Formic or acetic acid are volatile acids used for this purpose. The small molecules do not compete with phospholipids spread on the surface of the spray droplets for transfer to the gas phase, a critical step before entering the MS entrance orifice. Indeed, the addition of vola­ tile acids at 0.1–2% (vol/vol) to the infusate helps to create the excess of protons favorable to M + H+ adducts in the ESI spray droplets. By contrast, negatively charged parent phospholipid ions (M − H)− are favored by addition of volatile basic amines which trap protons and shift the balance to (M − H)− ions. Tri­ ethylamine (TEA), pyridine, and di-isopropylamine (DIPA) are volatile amine bases used at low concentrations (<2% vol/vol) to improve the low yield of ionization in ESI source for negatively charged phospholipid ions (M − H)−. Unfortunately the organic amine base can also participate in the pollution of the source and interface (25). The modifier activity of ammonia for phospholi­ pids is of interest for both of the positive and negative ioniza­ tion modes but its activity depends on the water content of the spray which alters the balance of NH3/NH4+ (base/acid). Both NH4OH aqueous solution or NH3 dissolved in solvents are available as modifiers. The addition of TEA or DIPA (MW = 101) has the disadvantage of splitting mass peaks of PC + H+ (Fig. 2) and PE + H+ (not shown) into multiple adducts (M + 101), which may jeopardize the quantification protocol. The addition of formic acid improves the ionization yield of positively charged phospholipids. Acid modifiers (H+ and R3NH+) also displace sodium and potassium cations abundant in the biological tissues which bind to phospholipids. For instance, the adduct ion (M + Na)+ is m/z +22 relative to (M + H)+. This +22 shift is confusing since a molecular species having an acyl chain length with two extra methylene groups and three double bonds is also shifted to +22 (M + 28 − 6). Quantification requires dei­ sotopization of the mass to resolve the superimposed mass peaks of the isobaric compounds. Substitution of isotonic sodium chloride or potassium chlo­ ride in the original Folch procedure with acidified water (acetic or formic acid 1/1,000, vol/vol) is recommended to avoid this problem. However, caution is required because acidic treatment can result in difficulties in separation of emulsified solvents and aqueous phases of the Folch mixture. Moreover, acidic condi­ tions can also degrade plasmalogens present in the RBC extract. LiCl has been used as a substitute for NaCl or KCl to beneficial effect. It is also good practice to split the raw extract into two aliquots, one added with the base for optimization of negative ionization and the other with formic acid to measure the positive ions. Measurements from two such infusions of the extract greatly

Fig. 2. Mass spectrum of phosphatidylcholine and sphingomyelin in an extract of RBC membrane. Each M + 1 (M + H+) adduct is associated with a supplementary adduct M + 101 (M + TEA)+ formed in the presence of triethylamine (MW = 101). The TEA base was added previously into the infused membrane lipid extracts to improve detection of negatively charged phospholipids, e.g., PE (detected with a low sensitivity as a precursor of the negative product ion 196). The addition of triethylamine 0.5% (vol/vol) creates abundant M + 101 adducts labeled in the spectrum as SM-C16:0 +101 (m/z 804) and PC 32:0 +101 (m/z 835).

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improve sensitivity of the method. Addition of ammonia (NH3/ NH4+) and water to change (NH3/NH4+) is also an option. Mass spectra of erythrocyte PE and PC accumulated during 2 min in the presence of triethylamine or formic acid, respectively, are shown in Fig. 3a, b. It shows 25–30 different molecular spe­ cies for each class in the RBC membrane, which is considered a sufficient resolution for many applications conducted with such a straightforward protocol and without a chromatographic step. 3.3.2. Analysis of RBC Phospholipids by Tandem Hybrid MS/MS

Tandem hybrid MS2 uses either an ion trap (Q-Trap) or a time-of-flight (Q-TOF) analyzer as the second mass filter. This achieves an enhanced resolution of ion products to less than width »0.3–0.1 amu for mass around m/z 700 compared to the mass analysis conducted in a quadrupolar filter (peak width at 50% »0.7 amu). The enhanced resolution of Q-Trap (QT) com­ pared to triple quadrupole spectrometers (TQ) has the advantage of a great selectivity for the assignment of structure from the mass spectrum. A particular advantage can be exploited in the case of phospholipid analysis by QT because as the phospholip­ ids are eluted from HPLC step ion-trap analysis is triggered for an “enhanced resolution” analysis during the data acquisition (Information Dependent Acquisition). When the CID mode of the parent ion is activated the product ions formed in the collision cell fill the trap to a preset appropriate optimum load. 20–50 ms are usual filling times to prevent the space charge effect in the ion trap (an excessive charge effect is minimized in a linear ion trap with a large volume). Then, an enhanced product ion (EPI) analysis is obtained. When the CID mode is off, the parent ions are filtered by the first quadrupole and the analysis of parent ions is repeated in the enhanced resolution (ER) mode in the ion trap. Triggering of the “enhanced mode” analysis of selected parent ions is depend­ ent on the signal intensity which is continuously monitored in the “standard” resolution mode. The criteria used by informa­ tion system for triggering the ER mode can include or exclude 13 C isotopes. An application of the enhanced mass resolution mode for QT coupled with HPLC is shown in Fig. 4a, b for phosphati­ dylethanolamines from the human red blood cell membrane. An important feature of the application is the discrimination of ether lipids which can be used to investigate peroxisomal defects in children with a neurological condition and with malformation syndrome. From a clinical viewpoint such results provide a ready means of diagnosis and for treatment monitoring. The uppermost panel in Fig. 4a, b shows the Total Ionisation Current (TIC) obtained by monitoring the loss of the neutral fragment 141 (phosphorylethanolamine) from the positively charged parent ions of phosphatidylethanolamines. Many different

Fig. 3. (a) Mass spectrum of phosphatidylethanolamine species accumulated after infusion for 2 min. 1/50 of the raw extract prepared from erythrocyte membranes is infused (0.5 ml RBC pellet is extracted). Thirty distinct molecular species of PE are detected as (M − H)– precursors of the negative product ion −196. The structure assignments indicated in the spectrum are obtained after CID products analysis (fatty acids and the lysoderivative moiety). In the negative mode the plasmalogen PEs (designated as p-PE) are detected with a

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Fig. 3. (continued) high sensitivity. (b) Mass spectrum of phosphatidylcholines and sphingomyelins accumulated after the infusion for 2 min of 1/50 of the raw extract prepared from 0.5 ml pelleted RBC. Thirty distinct molecular species are detected as (M + H)+ precursors of the positive product ion +184. The structure assignments indicated in the spectrum are obtained after CID product analysis in the negative mode. The TEA adducts (+101) are also detected in the sample.

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Fig. 4. (a) Q-Trap analysis of phosphatidylethanolamines extracted from human erythrocyte membranes. Phosphatidylethanolamines are eluted (50 µl/min) from the HPLC silica-diol column in the ESI source. The ternary solvent system (hexane/isopropanol/water) is added with ionization modifiers (formic acid and di-isopropylamine). Upper panel shows the Total Ion Current monitoring the neutral loss of 141 from the positively charged parent ions. At intervals the Information Dependent Acquisition (IDA) software triggers the high resolution analysis of negatively charged parent ions (middle panel shows Enhanced Resolution mass spectra triggered at 20.59 min for the overlapping plasmenyl species m/z 722 (pPE 16:0/20:4), m/z 750 (pPE 18:0/20:4), and m/z 748 (pPE 16:0/22:4). Structure assignments are obtained by product ion analysis after collision-induced dissociation (four lower panels: see text).

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Fig. 4. (continued) (b) Analysis of diacyl species of phosphatidylethanolamines extracted from human erythrocyte membranes. Upper panel shows the HPLC separation of plasmenyl(RT » 20min) and diacyl-(RT » 24 min) species. At interval the Information Dependent Acquisition (IDA) software triggers the high resolution analysis of negatively charged parent ions (middle panel shows mass spectra triggered at 24.4 min of four coeluting diacyl species). Structure assignments are obtained by product ion analysis after collision-induced dissociation (four lower panels: see text).

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molecular species comprising this class are eluted between 19 and 28 min from a HPLC separation on a polar silica-diol column using a solvent mixture consisting of hexane/isopropanol/NH4 acetate 15 mM (60/30/2 vol/vol). More than 30 different molecular species are commonly authenticated in human RBC membranes with the high molecular weight and saturated species eluting before the short acyl chain species. In Fig. 4a, b the chromatographic separation of the subclass ether phosphatidylethanolamines (1 alkyl- and 1 alkenyl-PE, i.e. plasmanyl- and plasmenyl-PE) from the diester species is specifically improved at the expense of a sensitive detection favored by other solvent systems. Indeed the ether phosphatidylethanolamines are eluted clearly ahead (RT » 20 min) of the diester subclass (RT » 24 min.) with the ternary solvent mixture used for elution. Ether phosphatidylethanolamines are identified in Fig. 4a in the Enhanced Resolution mode for the parent ions (middle panel, ER) and structures are elucidated from the product ions spectra. For instance, the most prominent negative parent ions in human RBC membranes are eluted at RT » 20.6 min with m/z 722.6, 748.5, and 750.5. The parent ions are shown as negative ions (M − H)− in the middle panel (-ER) of Fig. 4a. The ER mode is triggered from the positive mode of ionization which serves for monitoring the neutral loss +141 (upper panel, TIC) to the nega­ tive mode of ionization for -ER (middle panel) in less than 1 s. Then the recording (»100 ms) in the enhanced resolution mode of the molecular species is triggered to produce -ER spectrum of the most abundant parent molecular species m/z 722.6, 748.5, and 750.5. The corresponding product ion mass spectrum (EPI) are also subsequently triggered (time duration »30 ms). The elu­ tion has to be sufficiently slow from the HPLC coupled to QTrap to take into account the time for subsequent recordings triggered in the negative mode before returning to the monitoring neutral loss of 141 from the positive ions. Four mass spectra of product ions obtained after CID of the most prominent peak mass detected in the middle panel are pre­ sented in the lower panel of Fig. 4a. The Enhanced Product Ions (EPI) spectrum which is triggered by the mass m/z 722.6 shows the mass peaks 303 and 259 corresponding to fatty acid arachido­ nate (20:4) and (20:4 – CO2), respectively. The ether bond of the fatty aldehyde at the position sn-1 is not cleaved by the low energy of CID (<50 eV). However the molecular species of ether PE corresponding to m/z 722.6 with 20:4 as the acyl group can be unambiguously assigned to alkenyl PE O-16:1 (1Z)-20:4. Similarly ether-PE of m/z 748.5 (Fig. 4a, lower right panel) shows two distinct fatty acid carboxylates in the EPI spectrum, 303 (20:4) and 329 (22:5). Taking account of the similar reten­ tion time, characteristically short for ether lipids, this suggests the assignment to two distinct alkenyl-PE with C38 and a total



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of six double bonds. The alkenyl PE O-18:2 (1Z, 9Z)-20:4 and alkenyl PE O-16:1 (1Z)-22:5 are the best candidate structures. The assignments are confirmed by mass peaks at m/z 436 and 462 corresponding to lysoderivatives M-H-22:5 and M-H-20:4 which are released from the candidate structures after cleavage of the sn-2 fatty acids. The EPI of mass peak m/z 750.8 also shows the cleavage of two product ions m/z 303 (20:4) and m/z 331 (22:4) consistent with the structure of two coeluting alkenyl Pes, O-18:1 (1Z)-20:4 and PE O-16:1 (1Z)-22:4. Finally, the EPI of m/z 723.7 is assigned to a structure similar to m/z 722.6 (alke­ nyl PE O-16:1 (1Z)-20:4) but enriched in a C13 carbon atom in the lyso derivative moiety (m/z 437). In this instance, the C13 carbon isotopomer is confirmatory of the structure previously described for m/z 722 (alkenyl PE O-16:1 (1Z)-20:4). Figure 4b shows the results obtained for a combination of the positive mode monitoring the neutral loss 141 specific for PE (M + H)+ (upper panel) and the analysis in the negative mode of enhanced resolution spectra around the retention time 24.4 min. At this time four species of the diester phosphatidylethanolamines are coeluted. The mass peaks are selected by the Information Dependent Acquisition software running on the data station and they are rescanned at an enhanced resolution in the ion trap. In the middle panel the mass m/z 716.6, 738.5, 764.6, and 766.6 are detected. Each of the peaks is associated with C13 isotopom­ ers (M + 1, M + 2, M + 3,…) of which an assignment and quan­ tification are queried after deisotopization of the spectrum. An example is peak m/z 766.6 the amplitude of which represents the sum of a C13 isotopomer of m/z 764.6 comprising 2 C13 atoms (M1+2) and another less unsaturated molecular species (five dou­ ble-bonds, M2 = 766.6). Deisotopisation software calculates the expected amplitude of the C13 isotopomer of 764.6 (PE 38:5) with two C13 atoms (9.6%) and it calculates after subtraction the remaining fraction of the mass peak m/z 766.5 due to PE38:4. The assignment of mass peaks in the diester subclass is usually straightforward because both of the two fatty acids substituting positions sn-1 and sn-2 of glycerol are detected in the EPI spec­ trum. For instance, m/z 716.6 (PE34:1) is cleaved into m/z 255 (palmitic acid, 16:0) and m/z 281 (oleic acid, m/z 281). m/z 738.6 corresponds to PE36:4. m/z 452 is the lyso derivative product ion corresponding to the cleavage of arachidonic acid. m/z 764.6 (PE38:5) is assigned to a mixture of two structures, one is PE16:0/22:5 (the EPI spectrum shows the peak for 16:0 (m/z 255) and 22:5 (m/z 329), and the other structure is 18:1 (m/z 281) and 20:4 (m/z 303 and 259 (303-CO2)). The resolution of such a mixture of isobars is a major advantage in phospholipid studies conducted with tandem MS2 coupled to HPLC. Hybrid tandem systems such as Q-Trap, which accumu­ lates parent ions during the HPLC elution for short periods, are

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useful for rapid solution of the isomeric composition of molecular species. It is explicit that no quantitative data can be determined without a careful calibration by internal standards. The amplitude of product ions (fatty acids) cannot be used for reliable quanti­ tation because of the widely different stability of the fatty acid carboxylate ions. Figure 5 shows the complete separation of ether- and diester subclasses of PEs achieved by a silica-diol HPLC. This chroma­ tographic separation has been optimized in the perspective of biomedical applications which are relevant to screening of per­ oxisomal biosynthetic defects in children. This investigation was previously developed for screening chondrodysplasia and neuro­ logical conditions in infants. However, recent developments are now scheduled in adult patients for dementia. PlsEtn levels were measured in five independent population collections comprising 400 clinically demented and 350 nondemented subjects. Circu­ lating PlsEtn levels were observed to be significantly decreased in serum from clinically and pathologically diagnosed Alzheimer dementia at all stages of dementia, and the severity of this decrease correlated with the severity of dementia (26). Figure 6 demonstrates a method for quantitation of RBC membrane phospholipids as a function of the fatty acid composition of the constituent molecular species. A potential application of this method is the detection of essential fatty acid deficit of the series n-3 (DHA, upper panel Fig. 6) and n-6 (AA and LA, middle and lower panel, respectively). The method is especially suited to detect early deficit of particular classes or molecular species of essential fatty acids that are subject to fast metabolic turnover. Potentially the method has a greater sensitivity than the total FA methyl ester profile. For example, EFA in PE-diester is expected to be altered earlier than in other phospholipid classes during the deficit and EFA resupplementation. The method should be appropriate for prediction of inflammatory and allergic pathologies where the bal­ ance of n-3/n-6 EFA series is assumed to play a modulatory role (27, 28). Because the collision energy required for the cleavage of the ester bonds is similar for most of the glyceroPLs classes (PI is a notable exception) a calibration with weighed standards allows the quantitative assay of EFA-enriched phospholipid species. An interesting application of lipidomics to RBC membranes is the investigation of oxidative damage to phospholipids. For example, the investigation is relevant to oxidative stress and selec­ tive mitochondrial cardiolipin damage (29), radiotherapy and selective cardiolipin and phosphatidylserine oxidation (30), and innate macrophage response (31). Presently, RBC membrane phosphatidylethanolamines serve as a simple biomarkers to monitor lipid oxidation in stored blood products. Figure 7a, b illustrate the consequences of RBC storage (ex vivo, 1 month at 4°C) on the phosphatidylethanolamine molecular species.

Fig. 5. Contour plot of phosphatidylethanolamine molecular species extracted from human erythrocyte membranes: HPLC separation of ether and diester subclasses on silica-diol (YMC Pack Diol 5 mm, 60A, 250 × 4 mm) is achieved by the quaternary mixture hexane/isopropanol/NH4 acetate 15 mM. Ether-PEs are eluted in the range 19.5–21 min. Ether-PEs are followed by the numerous species of diester-PEs in the range 21.7–28 min. The enhanced resolution mass spectra of the corresponding ranges are shown below the contour plot (left, ether-PEs, major mass peak is m/z 750 (p-PE 18:0/20:4; right, diester-PEs, major mass peaks are m/z 716 (PE 34:1), 738 (PE 36:4), 764 (PE 38:5), 766 (PE 38:4).

Lipidomics of the Red Cell in Diognosis of Human Disorders 153

Fig. 6. HPLC separation of red blood cell membrane phospholipids as a function of the fatty acid composition. (B) Upper panel shows the elution of phospholipids enriched in 22:6 (docosahexaenoic acid, DHA) monitored by the product ion m/z 327.2. The product ion is formed from the negative parent ions after collision-induced dissociation. The precursor PLs enriched in 22:6 are labeled in the chromatogram as ether PE (RT » 20.6 min) and diester-PE (RT » 23.1 min). PC enriched with DHA are eluted around 29.9 min from the silica-diol column. (b) Middle panel shows the elution of PLs enriched in 20:4 (arachidonic acid, AA) monitored by the product ion m/z 303.2. In addition to ether-PE and diester-PE the chromatogram shows the elution of AA containing species of phosphatidylinositol (PI, RT » 26.8 min.) and phosphatidylserine (PS, RT » 29.3 min). (c) Lower panel shows the elution around RT » 23.7 min of diester-PE species enriched with linoleic acid (18:2, LA) monitored by the product ion m/z 279.2.

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Fig. 7. (a) Separation of ether-, diacyl-, and oxidized RBC phosphatidylethanolamine molecular species is obtained on silica-diol (elution with hexane/isopropanol/ammonium acetate 15 mM: 70/20/10, vol/vol/vol). The elution of phosphatidylethanolamines is monitored in the negative mode of ionization by the class specific negative product ion m/z −196 (upper panel). The group “oxidized PE” accumulates during a long-term storage of the erythrocytes (only two groups were detected previously after a short-term storage as shown in Fig. 4). The most prominent mass peak (m/z 730) among the oxidized phosphatidylethanolamines (lower panel, right) is assigned by product analysis after CID to hydroxylated PE 34:2-OH.

Lipidomics of the Red Cell in Diognosis of Human Disorders 155

Fig. 7. (continued) (b) Structure assignment of oxidized phosphatidylethanolamines by product analysis after CID. Two prominent peaks eluting at RT »30 and 32 min are detected in the HPLC profile (upper panel). Peak at RT 30 min corresponds to the phosphatidylethanolamine m/z = 730 (middle panel, left) which is cleaved after CID into the product ion m/z 295 consistent with the mass of hydroxylated fatty acid 18:2-OH (middle panel, right). The mass peak at m/z 452 is also consistent with the lysoderivative formed from m/z 730 by the cleavage of the hydroxylated fatty acid m/z 295. The coelution of negative product ions m/z 730 and m/z 295 (lower panel) are indicative of the coexistence of two distinct positional isomers of the hydroxylated PE.

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Using product analysis after CID the most abundant oxidized species of phosphatidylethanolamines in the RBC membrane found after long-term storage at 4°C are m/z 730 (PE 16:0/18:2 OH, characteristical hydroxylated product ion m/z 295), m/z 728 (PE 16:0/18:3 OH, characteristical hydroxylated product ion m/z 293), and m/z 754 (PE 16:0/20:4 OH, characteristical hydroxylated product ion m/z 319). A more detailed consideration of this topic can be found in the chapter by Kagan’s group in (32, 33).

4. Notes Analysis of the human red cell membrane provides a reliable method for diagnosis of a variety of human disorders of lipid metabolism and nutrition (Subheading 3.1). The utility of the method depends on convenient methods of lipid extraction and analysis (Subheading 3.2). The high sensitivity and resolution which are required for the measurement of minor lipid com­ pounds (metabolomics) is not a priority for the study of struc­ tural erythrocyte membrane lipids but practical considerations regarding manpower and throughput are of concern to develop the clinical applications. Mass spectrometry is the basis of the current analytic methods coupled with a direct infusion into the ESI source (Subheading 3.3.1) or coupled with a HPLC separation methods to deliver analytes. QTrap tandem MS (Subheading 3.3.2) provides the complete analysis of molecular species using the capability of ion-trap throughout the elution of phospholipids. TQ equipment is preferentially used for quantitative measurements of preset phospholipid molecular species. It is expected that the determination of fatty acid profiles which is currently offered by diagnosis center will be substituted favorably in the next future by MS-based methods resolving the whole lipid structures.

Acknowledgments This work was aided by a grant from the Human Frontier Science Programme (RGP0016/2005C).

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13. Shen, J. X., Motyka, R. J., Roach, J. P., and Hayes, R. N. (2005) Minimization of ion sup­ pression in LC-MS/MS analysis through the application of strong cation exchange solidphase extraction (SCX-SPE) J Pharm Biomed Anal 37, 359–67. 14. Tsui, Z. C., Chen, Q. R., Thomas, M. J., Samuel, M., and Cui, Z. (2005) A method for profiling gangliosides in animal tissues using electrospray ionization-tandem mass spec­ trometry Anal Biochem 341, 251–8. 15. Koivusalo, M., Haimi, P., Heikinheimo, L., Kostiainen, R., and Somerharju, P. (2001) Quantitative determination of phospholi­ pid compositions by ESI-MS: effects of acyl chain length, unsaturation, and lipid concen­ tration on instrument response J Lipid Res 42, 663–72. 16. Christie, W. W. (2003) Lipid analysis: isolation, separation, identification and structural analysis of lipids, Oily Press,3rd ed. Bridgwater. 17. Folch, J., Lees, M., and Stanley, G. H. S. (1957) A simple method for the isolation and purification of total lipids from animal tissues J Biol Chem 226, 497–509. 18. Bligh, E. G., and Dyer, W. J. (1959) A rapid method of total lipid extraction and puri­ fication Canadian J Biochem Physiol 37, 911–17. 19. Ruiz-Gutierrez, V., and Perez-Camino, M. C. (2000) Update on solid-phase extraction for the analysis of lipid classes and related com­ pounds J Chromatogr A 885, 321–41. 20. Carboni, E., Wieland, S., Lan, N. C., and Gee, K. W. (1996) Anxiolytic properties of endogenously occurring pregnanediols in two rodent models of anxiety Psychopharmacol­ ogy (Berl) 126, 173–8. 21. Persson, E., Lofgren, L., Hansson, G., Abra­ hamsson, B., Lennernas, H., and Nilsson, R. (2007) Simultaneous assessment of lipid classes and bile acids in human intestinal fluid by solid-phase extraction and HPLC methods J Lipid Res 48, 242–51. 22. Matyash, V., Liebisch, G., Kurzchalia, T. V., Shevchenko, A., and Schwudke, D. (2008) Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics J Lipid Res 49, 1137–46. 23. Sobolik, K. D., Gremillion, K. J., Whitten, P. L., and Watson, P. J. (1996) Sex determina­ tion of prehistoric human paleofeces Am J Phys Anthropol 101, 283–90. 24. Rose, H. G., and Oklander, M. (1965) Improved procedure for the extraction of lip­ ids from human erythrocytes J Lipid Res 6, 428–31.



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25. Rutters, H., Mohring, T., Rullkotter, J., Griep-Raming, J., and Metzger, J. O. (2000) The persistent memory effect of triethylamine in the analysis of phospholipids by liquid chro­ matography/mass spectrometry Rapid Com­ mun Mass Spectrom 14, 122–3. 26. Goodenowe, D. B., Cook, L. L., Liu, J., Lu, Y., Jayasinghe, D. A., Ahiahonu, P. W., Heath, D., Yamazaki, Y., Flax, J., Krenitsky, K. F., Sparks, D. L., Lerner, A., Friedland, R. P., Kudo, T., Kamino, K., Morihara, T., Takeda, M., and Wood, P. L. (2007) Peripheral ethanolamine plasmalogen deficiency: a logical causative fac­ tor in Alzheimer’s disease and dementia J Lipid Res 48, 2485–98. 27. Chilton, F. H., Rudel, L. L., Parks, J. S., Arm, J. P., and Seeds, M. C. (2008) Mechanisms by which botanical lipids affect inflammatory disorders Am J Clin Nutr 87, 498S–503S. 28. Surette, M. E., Stull, D., and Lindemann, J. (2008) The impact of a medical food con­ taining gammalinolenic and eicosapentaenoic acids on asthma management and the quality

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Chapter 8 HPLC/MS/MS-Based Approaches for Detection and Quantification of Eicosanoids Susanna L. Lundström, Fabio L. D'Alexandri, Kasem Nithipatikom, Jesper Z. Haeggström, Åsa M. Wheelock, and Craig E. Wheelock Summary Eicosanoids are oxygenated, endogenous, unsaturated fatty acids derived from arachidonic acid. Detection and quantification of these compounds are of great interest because they play important roles in a number of significant diseases, including asthma, chronic obstructive pulmonary disease (COPD), cardiovascular disease, and cancer. Because the endogenous levels of eicosanoids are quite low, sensitive and specific analytical methods are required to reliably quantify these compounds. High-performance liquid chromatography mass spectrometry (HPLC/MS) has emerged as one of the main techniques used in eicosanoid profiling. Herein, we describe the main LC/MS techniques and principles as well as their application in eicosanoid analysis. In addition, a protocol is given for extracting eicosanoids from biological samples, using bronchoalveolar lavage fluid (BALF) as an example. The method and instrument optimization procedures are presented, followed by the analysis of eicosanoid standards using reverse phase HPLC interfaced with an ion trap mass spectrometer (LC/MS/MS). This protocol is intended to provide a broad description of the field for readers looking for an introduction to the methodologies involved in eicosanoid quantification. Key words:  Eicosanoid, Oxylipin, Arachidonic acid, Mass spectrometry, Electrospray, Ion trap, LC/MS/MS, Bronchoalveolar lavage fluid, BALF

1. Introduction The biological relevance of arachidonic acid-derived mediators (eicosanoids) has been demonstrated in numerous studies (1–5). These metabolic products comprise several different classes including leukotrienes (LTs), prostaglandins (PGs), thromboxanes (TXs), hydroxyeicosatetraenoic acids (HETEs), epoxyeicosatrienoic acids (EETs), and oxo-fatty acids. Eicosanoid biosynthesis is accomplished via several distinct enzymatic pathways including Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, doi 10.1007/978-1-60761-322-0_8, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP). These three different pathways together are responsible for the biosynthesis of the entire suite of compounds collectively known as the arachidonic acid cascade, forming a complex interactive network that is present in almost all tissues (1, 2). The most well-known eicosanoids are the PGs, TXs, HETEs, and LTs, but more recently, the lipoxins (LXs) and EETs have attracted considerable attention. The distribution and synthesis of eicosanoids are key targets for studies involving a range of inflammatory pathologies including asthma and chronic obstructive pulmonary disease (COPD), (6, 7) nephritis, (8, 9) cardiovascular diseases (9, 10), and cancer (11, 12). Historically the detection and quantification of eicosanoids have mainly been performed by high-performance liquid chromatography (HPLC) or gas chromatography coupled to mass spectrometry (GC/MS), as well as the more sensitive radio- and enzyme-immunoassay techniques (RIA and EIA) (13, 14). RIA and EIA approaches have two limitations (1) generally, only a single product can be detected at a time and (2) cross reactivity can cause variability in sample quantification. HPLC and GC/ MS methods are generally less sensitive than RIA and EIA, but offer increased selectivity for the detection of multiple eicosanoids simultaneously (15–17). However, compounds must be both volatile and thermally stable in order to perform GC/MSbased analyses, which necessitates the use of chemical derivatization for the analysis of most eicosanoids. An initial limitation in the development of a combined LC/ MS method was the interface between the HPLC and the mass spectrometer. However, with the development of MS ionization techniques such as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI), eicosanoids can now be analyzed directly from aqueous samples. A major advantage of this approach is the simplification of sample preparation (18). As a result, liquid chromatography tandem mass spectrometry (LC/ MS/MS or LC/MSn) has become one of the most powerful techniques to quantify large numbers of eicosanoids in a variety of biological matrices (3, 7, 19–21). The HPLC separates the compounds based upon physical properties, followed by unambiguous identification based upon the characteristic product ions in the MS. This is generally achieved by scanning in selective reaction monitoring (SRM) mode. The acquisition rate, sensitivity, and selectivity provided by SRM enables the acquisition of high quality data for more compounds at faster acquisition rates. The advent of ultra performance liquid chromatography (UPLC) methodologies will further increase this process based on increased chromatographic capacity (22). HPLC/MS/MS has consequently become the only platform capable of performing concurrent quantification of the large numbers of analytes



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required for studying the biological role of eicosanoids. The choice of chromatography type and mass spectrometer depend on the characteristics of the eicosanoids in the sample and the purpose of the study. Studies have been performed in which a few selected class-specific eicosanoids are analyzed as well as large metabolic-profiling studies of a range of eicosanoids. In this protocol we present some of the main LC/MS/MS techniques and methods used for the detection and quantification of eicosanoids in biological samples. A brief description of MS instrumentation, ionization methodologies, and mass analyzers is discussed followed by the principles and use of SRM in eicosanoid quantification. A method is also described for extracting eicosanoids from biological samples using bronchoalveolar lavage fluid (BALF) as an example. The steps for preparing eicosanoid standards, gradient solvents, and optimization of MS parameters for developing an LC/MS/MS-based quantification method for eicosanoid analysis with an ion trap mass spectrometer are then presented. Finally, the instrumentation parameters and preliminary results for method development are shown. It is expected that readers will gain an understanding of the range in technologies available for LC/MS/MS-based analyses of eicosanoids. 1.1. Analysis of Eicosanoids Using Reversed Phase HighPerformance Liquid Chromatography

Endogenous eicosanoids are present in low levels in biological tissues (~pmol/mg to fmol/mg range) (3, 23). Accordingly, the majority of analytical methods begin with a clean-up and/ or separation step to enrich the sample for the compounds of interest. While technically possible (24), direct injection of complex samples into the MS is challenging. Subsequently, the use of an HPLC interfaced with the MS results in improved detection limits and overall quality of the MS data (18). Separating the analytes reduces background noise and problems associated with ion suppression from coeluting compounds as well as minimizes isobaric interferences. The choice of column, particle size, and mobile phase composition depends on the quantity and chemical properties of the target compounds; however there is a limited variety of mobile phases compatible with MS interfaces. Because most eicosanoids are medium to nonpolar, reverse phase (RP) chromatography is most commonly used (3, 7, 20, 21, 25–31). Analytes elute in order of increasing hydrophobicity with a hydrophobic stationary phase (e.g., C18) and a gradient changing gradually from aqueous solvents (with the addition of volatile salts, acids, or bases that improve ionization and retention on the HPLC column) to organic solvents (usually methanol and acetonitrile). The gradient is most commonly run under acidic conditions using a small percentage (~0.01–0.1% v/v) of acetic or formic acid. However there are examples of gradients run under both neutral and basic conditions (32, 33).

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1.2. Analysis of Oxylipin Stereoisomers by Liquid Chromatography Mass Spectrometry

The use of chiral columns for HPLC-based analysis is a valuable tool for the separation of lipid enantiomers. Chiral liquid chromatography has been extended to both LC/MS and LC/MS/MS for the determination of oxylipin enantiomers. In nature, several oxylipins including hydroperoxyeicosatetraenoic acids (HPETEs), hydroperoxyoctadecadienoic acids (HPODEs), hydroxyoctadecadienoic acids (HODEs), HETEs, and EETs are formed as racemic mixtures via nonenzymatic reactions (34, 35). On the other hand, when these lipids are oxidized enzymatically, enantiopure products are predominantly produced (36). Thus, chiral phase LC/MS is a valuable tool for diagnosing the mechanism and source of enantiomer formation. To improve the limit of detection (LOD), the carboxyl groups of these oxylipins can be derivatized to pentafluorobezyl esters (PFB) (37) and further analyzed by chiral phase liquid chromatography/electron capture/atmospheric pressure chemical ionization/mass spectrometry (chiral LC/EC/APCI/MS). A number of enantiomers of regioisomeric HODEs and HETEs have been analyzed in biological samples by chiral LC/EC/ APCI/MS (38) and the results have been reviewed elsewhere (39, 40). Although PFB-derivatization and chiral LC/EC/APCI/MS have improved the LOD of chiral oxylipins, the method has some disadvantages including extra derivatization steps and sample clean up as well as low yield for conversion to the corresponding PFB-esters. Attempts have been made to analyze oxylipins in their free acid forms. For example, chiral LC/MS/MS was used to analyze underivatized enantiomeric oxylipins and subsequently identified 12(S)-HETE as the major enantiomer in the urine of patients with diabetes mellitus (DM) (41). The effects of phenobarbital on the synthesis of enantiomers of cytochrome P450 metabolites of arachidonic acid and EETs in rat liver were determined by chiral LC/APCI/MS (42). At present, the main limitation of chiral LC/MS for the analysis of oxylipin enantiomers is the availability of chiral columns. For example, chiral columns are produced predominantly as normal phase, which is not compatible with ESI, but can be used with APCI under well-defined experimental conditions. However, even these limitations are being addressed as demonstrated by the recent development of a capillary tandem column chiral phase LC/MS/MS method for the analysis of EET enantiomers (43). This method can simultaneously quantify all eight EET enantiomers as the free acids with limits of quantification in the low pg range.

1.3. Mass Spectrometry Techniques Used for Eicosanoid HPLC/ MS/MS Analysis

The first step in mass spectrometry analysis is to convert analyte molecules into gas phase ions. Following ion production, the ions are separated by a mass analyzer that measures the mass to charge ratio (m/z). There are a number of different mass analyzers, which



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vary mainly by their mass range limits (the upper limit of the mass of the ion that can be measured); acquisition rate (the rate at which the mass analyzer measures scans over a particular mass range); transmission range (the ratio of the number of ions reaching the detector to the number of ions leaving the source); mass accuracy (accuracy of the ion mass measurement provided by the mass analyzer); and resolution (ability of a mass analyzer to yield 50% valley separation between distinct signals of two ions). This value is typically defined as the ratio of detected m/z to the full width at half-height maximum (FWHM) of the peak. Essentially all mass analyzers can be used for lipidomics or eicosanoid analysis; however, the field has demonstrated a preference for certain mass analyzers that are particularly suitable for quantifying lipids in complex samples. In this section we will discuss the main ionization techniques and mass analyzers currently used in eicosanoid HPLC/MS/MS analysis. 1.3.1. Ionization Techniques 1.3.1.1. Electrospray Ionization

Electrospray Ionization (ESI) was first proposed as a source of gas phase ions by Dole et al. (44) in 1968. However, the success of this technique began when Fenn et al. (45) used ESI in mass spectrometry and demonstrated that multiply charged ions were obtained from proteins. This enabled the determination of the molecular mass of a compound using instruments with limited mass range. In ESI the ionization process occurs by applying a strong electric field (kV), under atmospheric pressure, to a liquid passing through a capillary tube. This field induces a charge accumulation at the liquid surface located at the end of the capillary forming a Taylor cone (46, 47). The charge accumulation causes droplets that contain an excess positive or negative charge to detach from the capillary tip and move toward the mass analyzer. An uncharged carrier gas (e.g., nitrogen) is often used to help nebulize the liquid and evaporate the solvent in the droplets. As the solvent evaporates, the molecules are forced closer together which increases the Coulombic electrostatic repulsion (i.e., resulting in high surface charge densities on the droplets). This process continues until the Debye length is reached, after which the repulsion overcomes the surface tension of the droplets, which then break up forming ions in a process that is still not well understood. The main advantage of ESI/MS over other MS techniques is that ESI/MS overcomes the propensity of many biomolecules to fragment following ionization and enables the formation of multiply charged ions. Thus, ESI/MS is critical for the detailed structural analysis of large biomolecules, but it is also useful for the analysis of small polar molecules. Another feature of this technique is its relation with the concentration of the target compound, and not to the total quantity of sample injected in the source. This fact has led to the development of nanoflow rate ESI (nano-ESI). Consequently, ESI has been widely applied in the analysis of all

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classes of eicosanoids (20, 23, 29, 48, 49). Moreover it is not necessary to chemically modify eicosanoids to enhance ionization efficiently when using this technique. Even when chemical modifications (such as the use of methoxime (MO) derivatization) are used to improve the chromatography it does not affect the ionization efficiency of the eicosanoids. The disadvantages of ESI are its low robustness to matrix interferences and changes of mobile phase and flow rates that can produce ion suppression (caused by ionization competition between compounds) and poor ionization of some compounds. 1.3.1.2. Atmospheric Pressure Chemical Ionization Mass Spectrometry

Atmospheric Pressure Chemical Ionization Mass Spectrometry (APCI/MS) has proven to be valuable for the analysis of lipids from a variety of classes (50, 51). APCI is a soft ionization technique, but unlike ESI, APCI usually produces some degree of fragmentation that can be useful for structural characterization. In contrast to ESI, solvent evaporation and analyte ionization are two separate processes. The latter being a chemical ionization at atmospheric pressure with the mobile phase acting as the reactant gas. As the evaporation of the mobile phase is performed at high temperatures (up to 500°C), APCI is less mild than ESI, thus the thermal stability of compounds is important. APCI normally produces singly charged ions through the addition or abstraction of a proton. This technique is frequently used in the LC/MS/MS analysis of eicosanoids (42, 52, 53). Compared to ESI methodologies, LC/MS analysis using an APCI source performs best using normal phase (NP) chromatography methods (54, 55). Under these conditions the ion detection capability of APCI methodology is greater due to the low interference of the mobile phase on compound ionization; however, the necessary safety precautions need to be taken.

1.3.1.3. Electron Capture/APCI

Electron Capture (EC)/APCI is a variation of APCI that can be used to improve ion production and subsequently the LOD. This technique requires sample derivatization with an electron capturing group that will enhance formation of negative ions (see Subheading 1.2). EC/APCI gives essentially equivalent results with either NP- or RP-based systems. The technique has not been used extensively for eicosanoid analysis (38, 39). However, it has great potential for use in the identification of eicosanoid enantiomers and regioisomers, one of the biggest challenges in eicosanoid analysis (34, 38). The main disadvantages are that it requires derivatization and the PFB derivatives give relatively weak signals under conventional positive APCI conditions when NP solvents are used.

1.3.1.4. Atmospheric Pressure Photo Ionization

Atmospheric Pressure Photo Ionization (APPI) is an ionization technique that extends the range of ionizable compounds (56, 57). APPI can ionize compounds that ionize poorly by ESI or APCI,



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especially nonpolar compounds. The APPI source is a modified APCI source that uses a discharge lamp that emits photons rather than a corona discharge needle that emits electrons. APPI uses photons to ionize gas phase molecules after the sample is vaporized by a heated nebulizer similar to the one used in APCI. The analytes interact with photons, which induces a series of gas phase reactions that lead to the ionization of the sample molecules. APPI gives cleaner spectra and is less susceptible to ion suppression, because it is not based on charge affinity. Cai and Sayge (58) used this technique to analyze the methyl esters of eicosapentaenoic acid (EPA) as well as other fatty acids and described in detail the potential mobile phase alterations (59). One drawback with the technique is that it highly depends on the chromatographic system applied (e.g., RP vs. NP) because the solvents can interfere with ion formation. 1.3.2. Mass Analyzers 1.3.2.1. Quadrupole

1.3.2.2. on Trap

The quadrupole analyzer consists of four parallel metal rods that use the stability of the trajectories in oscillating electric fields to separate ions according to their m/z ratios (60, 61). This mass analyzer can be used in either a single or triple quadrupole configuration, with the triple quadrupole used to perform MS/ MS experiments. In the triple quadrupole configuration, the first and the last quadrupole serve as mass filters and the center quadrupole (or hexapole) is a collision cell consisting of an RF quadrupole (or hexapole) with optimum ion collimating behavior. This geometry enables different types of MS/MS experiments to be conducted such as product, precursor, and constant neutral loss (CNL) scanning as well as SRM. Using a triple quadrupole LC/ MS system, Kingsley et al. (48) were able to quantify prostaglandin glyceryl esters from cells with a 25 fmol limit of quantification (LOQ) and Masoodi et al. (29) analyzed 20 mono and polyhydroxy-fatty acid derivatives of linoleic acid, AA, EPA, and docosahexaenoic acid (DHA) with a limit of quantitation with a LOQ of 60–177 fmol. Kita et al. (62) used a triple quadrupole system to develop a high-throughput method for the detection of 18 different eicosanoids from biological samples. Using a single quadrupole equipment, Blewett et al. were able to identify 23 eicosanoids in a single HPLC–MS run (20). However, despite these qualities, quadrupoles are fundamentally low resolution instruments and high mass accuracy can only be achieved at the expense of sensitivity. In addition, MSn experiments can only be performed by multiquadrupole instruments. Paul and Steinwedel first described the ion trap in 1960 (61, 63), followed by Stafford et al. (64) who modified it for use in a mass spectrometer. An ion trap uses an oscillating electric field to trap ions in either two or three dimensions. Thus, ion traps exist in two types: 2D ion traps (also referred to as linear ion traps) and

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the 3D ion traps (also referred to as Paul ion traps). The 2D ion trap is based upon a four-rod quadrupole that ends in lenses that repel the ions inside the rods. The 3D ion trap is composed of a ring electrode and two cap electrodes in order to form a closed area where the ions are trapped. 2D and 3D ion traps work similarly; however, 2D ion traps have greater than tenfold higher ion trapping capacity compared with 3D traps. The 2D ion trap cannot perform all the scan functions present in 3D traps due to problems with ion ejection from the trap. The main disadvantage of the ion trap is that precursor ion and neutral loss scans cannot be conducted. Ion traps also have a poor dynamic range and quantification capacity, characteristics that limit their use in the quantification of complex samples. Ion trap mass analyzers exhibit high sensitivity and are most strongly characterized by the ability to perform multiple stages of mass spectrometry (MSn). Up to 12 stages of tandem mass spectrometry (MS12) have been performed using an ion trap (65), which greatly increases the amount of structural information obtainable for a given molecule. Ion traps are normally coupled to ESI or APCI ionization sources for the structural characterization of eicosanoids. Liminga and Oliw (66), performed quantitative analysis of LOX products in bovine tissues. They demonstrated that 5-, 12-, and 15-HPETE can be identified by scanning the carboxylated ion of the hydroperoxide and the anion of its dehydration product, [M − H − H2O]−. Using MS2 and MS3 scanning mode they elucidated the structures of these compounds, demonstrated the specificity of transition ions and showed how to use transition ions for quantification. Kiss et  al. (67) used capillary LC, photodiode array (PDA), and ESI/MS2 in negative mode in order to simultaneously identify and quantitate relevant CYP, sEH, LOX, and COX-derived eicosanoids (EETs, 20-HETE, LTs, LXs, PGs, TXs, HETEs, dihydro-xeicosatrienoic acids [DHETs], dihydroxyeicosatetraenoic acids [DHETEs], 12-hydroxyheptadecatrienoic acid [12-HHT]) and free isoprostanes (iPTs). This method was capable of identifying 44 eicosanoids in a 50 min run. Kiss et al. (67) also reported a detailed list of transition ions that can be used for SRM analysis of eicosanoids as well as several MS2 spectra of eicosanoid standards. The cysteinyl-containing leukotrienes (LTC4, LTD4, and LTE4) and the 5-oxo-7-glutathionyl-8,11,14-eicosatrienoic acid (FOG7) were analyzed by Hevko and Murphy (68) using an ESI/MS with a 3D ion trap and a triple quadrupole mass spectrometer. Both singly charged negative and positive ions could be observed. However, in contrast to the triple quadrupole, the collisional activation of the negative ions in the ion trap required high collision energy, and even then few product ions were produced. It is therefore advisable to analyze these compounds in positive ion mode if an ion trap is used. MS2 analyses



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in this mode showed a common series of fragment ions at m/z 319, 301, 283, 265, and 189, which can be observed for all cysteinyl leukotrienes (CysLTs). The ion at m/z 319 was formed by cleavage of the carbon–sulfur bond to afford a protonated epoxide cation and loss of the neutral cysteine. The other three ions result from the sequential losses of water, and the ion at m/z 189 represents the cleavage of the C6–C7 bond of the lipid backbone. For FOG7 the MS2 spectrum is strikingly simple with only one major fragment ion for cleavage of the g-glutamate amino acid (m/z 497). Accordingly, MS3 analysis was necessary to provide unambiguous structural characterization. Hong et al. (69) used electrospray low energy collision-induced dissociation (CID) tandem mass spectrometry analysis and reported the correlations between spectra and structure and the fragmentation mechanisms of resolvin D1, protectin D1, monohydroxy-DHA, and related hydroperoxy-DHA. 1.3.2.3. Time of Flight

This mass analyzer measures the time it takes ions of different masses to move from the ion source to the detector. Although the principle of Time of Flight (TOF) has been known since Thomson carried out his experiments on ionized particles in 1897 (70), its use in a mass spectrometer was not proposed until 1946 (71). In principle, a TOF has no upper mass range limit, which makes it especially suitable for soft ionization techniques. Other advantages of these instruments include their high transmission efficiency that leads to increased sensitivity and easy mass calibration. The disadvantages include the limited precursor ion selectivity for most MS/MS experiments and the inability to work in MSn mode. TOF is one of the most commonly used mass analyzers for protein analysis, but it can also be used for lipids and eicosanoids (18, 72). Dickinson and Murphy (73) explored the high resolution of a quadrupole time of flight (QTOF) instrument to elucidate the elemental composition of the ions generated by the collisional activation of the carboxylated anion of PGI2. Harks et al. (74) used a QTOF equipped with an ESI probe to identify the production of PGF2a (identified as the sodium adduct ion [M + Na]+) in normal rat kidney fibroblasts.

1.3.2.4. Fourier Transform Ion Cyclotron Resonance

Because biological activity is often regioisomer-specific, it is important to have techniques with the ability to identify complex isomeric oxylipins. Fourier transform ion cyclotron resonance (FTICR/MS) has emerged as a powerful tool for this application. FTICR/MS provides high mass accuracy and high mass resolution to obtain accurate molecular masses and elemental compositions (75, 76). MSn analysis of FTICR/MS can be achieved by sustained off resonance irradiation (SORI)/CID, infrared multiphoton dissociation (IRMPD), and electron capture dissociation (ECD) (77–81). Among these dissociation techniques, ECD provides a softer ionization and gives unique fragments for proteins and

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peptides. SORI/CID and IRMPD yield similar fragmentation patterns, but each method gives specific spectral characteristics that can be useful for compound identification (49). The application of FTICR/MS has been demonstrated for the determination of regioisomers of trihydroxyeicosatrienoic acids (THETAs) (82–84) in biological samples (49). With three hydroxyl groups, liquid chromatographic separation of 11,12,15-, 11,14,15- and 13,14,15-THETA is more difficult and their MS/MS spectra are more complex than HETEs and DHETs. In general, the SORI/CID spectra of these THETAs are similar to IRMPD spectra, suggesting similar major fragmentation pathways for these compounds. The most abundant ions for 11,12,15-, 11,14,15-, and 13,14,15-THETA are m/z 197, 167, and 193, respectively. The secondary unique characteristic ions for these THETAs are m/z 157, 85, and 59, respectively. The different positions of the third hydroxyl group and the third double bond in the structures such as in 11,12,15-THETA and 11,14,15-THETA have a remarkable effect on their major fragmentation pathways (49). The intensities of the m/z 205 in the IRMPD spectrum for 11,14,15-THETA increased twofold while the abundance of m/z 85 decreased as compared with SORI/ CID. For 13,14,15-THETA, the abundance of the m/z 59 and 173 decreased tenfold in the IRMPD spectrum as compared with SORI/CID. LC/FTICR using SORI/CID and IRMPD (without a complete LC separation) identified 11,12,15-THETA and 11,14,15-THETA as arachidonic acid metabolites that were synthesized in rabbit aorta (49). MS/MS analysis of relatively simple HETEs and DHETs has been successfully achieved by ion trap and triple quadrupole mass spectrometers (18, 23, 85–88). In general FTICR/MS generates similar MS/MS spectra for these compounds. However, the high mass accuracy and resolution of FTICR/MS suggest some different molecular fragments for the oxylipins. Characteristic ions indicating the loss of the CO2 group is unique for FTICR/MS spectra. For example, the m/z 149 and 257 ions of 11-HETE are abundant when a FTICR/MS instrument is used. In contrast they have not previously been detected using a triple quadrupole mass spectrometer (18, 49, 86). One example of the powerful ability of FTICR/MS in identifying the fragments using mass accuracy and resolution is the identification of m/z 163 for 12-HETE, 11,12-DHET, and 14,15-DHET. This fragment was previously assigned as the same ion formed by the same fragmentation mechanistic pathway for all compounds (18). From the FTICR/MS spectra, the m/z 163 for 12-HETE and 11,12DHET has masses of 163.11239 and 163.11235, respectively. This ion has the elemental composition of [C11H15O]−. However, the m/z 163 for 14,15-DHET has the mass of 163.14947 with the elemental composition of [C12H19]−. These accurate masses



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indicate that the fragmentation mechanism of the m/z 163 for 11-HETE/11,12-DHET is different from 14,15-DHET and they are in fact different fragments (49). 1.3.2.5. Hybrid Analyzers

A recent trend in mass analyzer development is to combine different analyzers in sequence to form hybrid instruments. This increases the versatility and enables multiple experiments to be performed. Quadrupole TOF instruments, ion trap FT ion cyclotron resonance instruments, and quadrupole ion trap mass spectrometers, combine the strengths of each analyzer while often avoiding their weaknesses. Accordingly, improved performance can be obtained with a hybrid instrument relative to individual analyzers. Nowadays, the most popular hybrid MS used for eicosanoid analysis is the quadrupole ion trap mass spectrometer (Quad/trap), which provides excellent quantification, thus it is ideal for complex sample analysis. Using a hybrid quadrupole linear ion trap mass spectrometer, Harkewicz et al. (89) were able to detect 70 diverse eicosanoids produced from macrophage-like cells. Following metabolic labeling of the cells with deuterated arachidonic acid (AA-d8) and stimulation with endotoxin, they were able to identify a “doublet” pattern of eicosanoid products. The method enabled sensitive, comprehensive, and quantitative analysis of eicosanoid biology without any previous knowledge or assumptions of the molecular species involved. Using the same method, they were able to identify a series of dihomoprostaglandins produced by endotoxin-stimulated RAW cells (dihomo-(PGE2, PGD2, PGF2a, 11(R)-HETE, PGJ2, and 15-deoxy-∆12,14-PGD2)), further demonstrating the utility of this technique. See Note 1 for more information of hybrid instruments and applications.

1.4. Selective Reaction Monitoring of Eicosanoids

SRM is used in conjunction with LC/ESI/MS to improve the specificity and detection limit for quantification. SRM is a tandem mass spectrometry technique that delivers unique fragment ions that can be monitored and quantified in the midst of a complicated matrix. SRM spectra are highly specific and usually contain only a single peak. This characteristic makes the SRM signal ideal for quantification. In order to use this technique, it is necessary to know the fragmentation patterns of the compounds of interest. A fragment ion is selected from the known mass product spectrum and used for quantification. It is important not to choose a fragment peak ion close to the parent peak, such as dehydrated ions. These type of ions do not have structural significance and can easily be produced equally for isomers (e.g., the range of HETEs), or for unknown contaminants. It is also important to choose a stable peak that consistently appears scan after scan with a constant response.

1.5. Quantitation of Eicosanoids Using HPLC/MS/MS

One of the most important components in developing methods for the quantification of eicosanoids involves the preparation of standards. While beyond the scope of this chapter, we will

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provide a brief summary of some of the more important points. Interested readers are directed to the literature (19, 90–92). 1. Internal standards (IS) are added prior to sample preparation in order to account for variable losses during the preparation steps. The choices of IS are critical. The ideal standard possesses characteristics similar to those of the eicosanoid during the entire experiment (i.e., extraction, chromatography, and mass spectrometry), but does not interfere with the target analyte(s). Many methods employ isotopically labeled versions of the analytes or odd chain fatty acids. However, since there are cost concerns with the use of multiple labeled standards, usually a single IS is employed for a group of eicosanoids with similar physical properties or structure. For example, the deuterated eicosanoid 5-HETE-d8 is an ideal IS for 5-HETE; however, it can also be used for other HETEs (19). Other examples include 10(11)-epoxyheptadecanoic acid (10(11)-EpHep), which is an odd chain length IS for EETs and 10,11-dihydroxydecanoic acid (10,11-DHN), which is appropriate for DHETEs (3, 7, 27). It is notable that the IS concentration does not need to be accurate, but that it is crucial that the exact same volume is used for both the samples and the primary standards (19). An important precaution is that sometimes deuterated standards can contain a small percent of nondeuterated analyte. Consequently, it is important to also run all IS separately, in order to obtain information on purity (19). To account for changes in volume, instrument variability, and matrix interferences, an additional technical standard (for example N-cyclohexyl-N'dodecanoic acid urea [CUDA]) can be added prior to analysis (7, 27). The technical standard should be a stable compound that does not interfere with other standards and analytes. 2. Primary standards (PS) are prepared for quantification purposes in order to create calibration curves (or standard curves). Consequently the concentration of PS must be known with high accuracy. Calibration curves are generated by the addition of defined amounts of analyte (unlabeled) followed by spiking with an aliquot of the IS (3, 19, 21, 90). The calibration curves are based on the ratio of standard peak area to the IS peak area in the primary standards plotted vs. the amount of primary standard. The concentrations of the standards are generally selected such that the resultant curve follows a linear regression in the desired detection range. The slope of the curve is a measurement of the sensitivity of the quantification method. The detection range is restricted on the low end by the detection limit of the MS and the upper limit is restricted by ion self-suppression and detection saturation issues. Standards should be run before and after each set of samples in order to monitor for changes in instrument performance during the run.



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The ratio of the unknown analyte peak area to IS peak area in each sample is then compared to the respective standard curve in order to calculate the amount of analyte. 3. In addition to using standards for quantification, compound recoveries have to be estimated during sample preparation and instrumental analysis. This can be achieved by preparing a set of samples where a known amount of the analytes is added to the matrix (e.g., phosphate buffered saline (PBS) solution in the protocol below). The samples are then analyzed by LC/ MS/MS and the eicosanoid peak intensities are compared to those standards that have not been through the sample preparation procedure. One can also compare the standards with samples in which only the matrix alone has gone through the sample preparation steps. The aliquot of the standard mixture is then added in the last step prior analysis. Recovery values in the range of 75–120% are preferred (19). 4. Even though the procedures described above limit the effect of systematic errors during quantification, random errors will still occur. Thus, there will always be a difference between the reported and “true” value, and since the possibility of error increases close to the detection limit a formal determination of the LOD is recommended. A common preconception in analytical chemistry is that the signal (S) has to be three times larger than the background noise (N), resulting in the LOD being equivalent to a S/N ratio of 3. However, the formal definition of the LOD is the concentration of analyte required to give a signal equal to the background noise (blank) plus three times the standard deviation (STD) of the blank (3). Similarly the LOQ is defined as the concentration of analyte required to give a signal equal to that of the blank plus ten times the standard deviation of the blank (3, 93). Importantly, sample preparations prior to analyses always involve extra factors of error and sample loss. Therefore the overall method detection limit (MDL) can be estimated by determining the STD across seven or more technical replicates of the sample for a concentration within the range or 1–5 times the expected LOD. Subsequently, the MDL can be calculated by multiplying the STD with the Student’s t value for the appropriate degree of freedom and the 99% confidence limit (2.143 for n = 7). A selection of t values can be found at http://www. itl.nist.gov/div898/handbook/eda/section3/eda3672.htm (92). The detection limits depend upon the instrument and configuration. Furthermore it depends on the number of eicosanoids and classes of eicosanoids that are being quantified. 5 and 20 fmol and LOQ values between 5 and 50 fmol have been reported for the quantification of prostaglandin glyceryl esters (48). Recently a ethod was presented where

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over 70 diverse eicosanoids were screened with LODs in the range of 2–25,000 fmol (19, 89).

2. Materials 2.1. Equipment

1. Surveyor HPLC system (Thermo Finnigan, San Jose, CA, USA) equipped with a Surveyor Autosampler Plus and a Surveyor MS Pump Plus (four solvent channels). 2. LCQ Deca ion trap (Thermo Finnigan) working with an electrospray ionization source equipped with a stainless steel needle kit (Thermo Fisher Scientific, Waltan, MA, USA). 3. Optiplex 755 computer (Dell, Round Rock, TX, USA) equipped with a Windows XP operating system. The system software Xcalibur™ 2.0 SR2 (Thermo Finnigan) was used for data processing.

2.2. Reagents

1. LC/MS grade acetonitrile and methanol and HPLC grade glacial acetic acid were obtained from Fischer Scientific (Loughborough, LE, UK). All other solvents were acquired from Rathburn Chemicals Ltd. (Walkerburn, Scotland). 2. 20-Hydroxy-5Z,8Z,11Z,14Z-eicosatetraenoic acid (20-HETE) and N-cyclohexyl-N¢-dodecanoic acid urea (CUDA) were obtained from Cayman Chemical (Ann Arbor, MI, USA). All other standards were obtained from Biomol (Plymouth, PA, USA).

2.3. Supplies

1. Luna C18 column (2.0 × 150 mm, 5 mm) (Phenomenex, Allerod, Denmark). 2. C18 security guard cartridges (4 × 2 mm) (Phenomenex). 3. 2 mL amber glass autosampler vials with plastic screw caps (8 mm) and Teflon-faced seals (8 mm) (Chromacol, Thermo Fisher Scientific). 4. 150 mL polyspring insert for 2 mL autosampler vials (Thermo Fisher scientific). 5. 2 mL amber glass vials with plastic crew caps (8 mm) and Teflonfaced seals (8 mm) (Chromacol, Thermo Fisher Scientific). 6. Glass mL syringes (10, 100, and 500 mL) with PTFE-tipped plungers (Hamilton Company, Bonaduz, Switzerland). 7. 2 mL amber volumetric flasks (Volac) (Poulten & Graf Ltda, Barking, UK).



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8. Fluoropore™ PTFE membrane filters (47 mm, 0.2 mm) (Millipore, Billerica, MA, USA). 9. Nylon membrane filters (47 mm, 0.2 mm) (Millipore).

3. Methods 3.1. Protocol

Eicosanoids are frequently measured in a variety of biological matrices including urine, cell culture, plasma, and BALF(3, 7, 27, 32). Several different methods for sample preparation are available (3, 7, 21, 27, 32, 94–96). Generally sample preparation procedures are straight forward, with the exception of biological tissues, which require prior extraction steps (21, 23, 96, 97). A protocol optimized for the preparation of BALF samples for LC/ MS/MS analysis is given below, however the protocol can be readily adapted to other body fluids with slight modifications (7, 27). The extraction method is based upon methods developed by Hammock and coworkers (3, 5, 7, 27, 98) and is described in detail in a recent publication by Yang et al. (99). Subsequently, a set of oxylipin standards was analyzed under various conditions in order to provide an example of the experimental optimizations that need to be performed prior to analysis of biological samples (see Subheading 3.2). For limitations of this method see Note 2.

3.1.1. Offline Solid Phase Extraction

1. Clean Oasis HLB offline solid phase extraction (SPE) 60 mg columns with one column volume of ethyl acetate followed by two column volumes of methanol. Precondition columns with two column volumes of H2O/MeOH (95:5) in 0.1% acetic acid (wash solution). Elute the solvents by gravity and avoid letting the sorbents go dry. 2. Thaw BALF aliquots (4 mL) before extraction. Add 200 mL wash solution to columns, 10 mL of IS (400 nM/standard), and 10 mL antioxidant and enzyme inhibitor solution (0.2 mg/ mL of butylhydroxytoluene (BHT), ethylenediaminetetra acetic acid (EDTA), thiamine pyrophosphate (TPP), and indomethacin). Apply samples to the columns. In addition to samples, prepare matrix blanks (~2 blanks/30 samples) by using 0.1 M PBS instead of BALF. 3. Wash by gravity with two column volumes of wash solution, vacuum dry for 10 min at 5 psi. 4. Elute eicosanoids with 0.5 mL methanol followed by 1.5 mL ethyl acetate. Collect in 2 mL eppendorf tubes with 6 mL 30% glycerol in methanol. 5. Use speed vacuum or nitrogen stream to remove solvent until only the glycerol plug remains.

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6. Cap and freeze samples for storage at −80°C or continue to step 7. 7. Prior to LC/MS analysis, reconstitute residues to 50 mL with 200 nM CUDA, centrifuge and transfer supernatant to glass autosampler vials. 3.1.2. Standards, Solvents, and MS Optimization Preparations

1. For method development purposes ionization optimization standard solutions were prepared for all analytes as follows: 1–10 mL of the standard stock solution (0.05–0.5 mg/mL) were transferred to 2 mL amber glass vials with screw caps and Teflon-faced seals and then diluted in methanol to a final volume of 1 mL (0.5 ng/mL). Similarly a chromatography optimization solution containing all standards was prepared by adding 2–20 mL/standard of the stock solutions to a 2 mL amber glass volumetric flask. The standard mixture was then diluted with methanol to a final concentration of 0.5 ng/mL. See Note 3 for further comments on sample treatments. 2. LC/MS grade acetonitrile and methanol were vacuum filtered (0.2-mm PTFE filter) and prepared in a ratio of 88:12. For preparation of the aqueous phase, MilliQ water was vacuum filtered (0.2-mm Nylon filter). In addition to the neutral aqueous (1) and organic (2) phases, an acidic phase (3) containing (1) and (2) in the ratio of 85:15 was prepared with an additive of 0.1% glacial HPLC graded acidic acid. See Note 4 for more comments on solvent treatments. 3. In order to optimize the MS parameters for optimal peak detection the ionization optimization standard solutions (0.5 ng/mL) were introduced into the MS source directly using a syringe pump. The flow rate was set to 10 mL/min and T-coupled to the HPLC in order to achieve a flow rate of 350 mL/min. The LC gradient was set to a similar ratio of solvent (1) and (2) as when the respective compound was known to elute from the column. By recording and comparing intensity changes in the molecular ion to changed parameters of the MS (e.g., heated capillary temperature, capillary voltages, sheath gas flow, etc.) optimal conditions could be obtained and saved (as tune files). Compounds were also fragmented in order to determine which fragment ions to select for SRM analysis (based on abundance and specificity). An instrument method was developed based on the compounds’ retention times, tune files, and selected fragment ions. The chromatographic run was divided into different time segments in order to reduce the number of scan events over time, subsequently improving the detection limit (see Note 5). The chromatography optimization solution was repeatedly run over time in sequences with up to 15 injections in order to determine the reproducibility of the method. It is vital that the system is



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reproducible, with little fluctuation in retention times and peak areas (see Note 6). 3.1.3. Instrument Operation Parameters

HPLC: Autosampler temperature: 10°C Column temperature: 40°C Mobile Phase: (1) MilliQ water 18 MW (2) Acetonitrile:Methanol (88:12) (3) Water:Acetonitrile:Methanol (85:13:2), 0.1% Acetic acid Gradient: Parameters are described in Table 1 for a set of 24 compounds. Flow rate: 350 mL/min Column temperature: 40°C LCQ Ion Trap: Capillary temperature: 300°C Collision energy (resonance energy, percent of 5 V): 40–50% Spray voltage: 4 kV Sheath gas flow rate: 70 arbitrary U Auxiliary gas flow rate: 20 arbitrary U Scan mode: Negative SRM: parameters are described in Table 2.

Table 1  Gradient parameters for Fig. 2 Time (min)

1 (%)a

2 (%)b

3 (%)c

0.0

85

15

0

0.5

85

15

0

2.0

70

30

0

8.0

45

55

0

28.0

25

75

0

28.5

0

100

0

34.0

0

100

0

37.0

0

0

100

45.0

0

0

100

1: Water. 2: Acetonitrile:Methanol (88:12). c 3: Water:Acetonitrile:Methanol (85:13:2), 0.1% Acetic acid. Solvent 3 is used to precondition the column prior to the next run. a

b

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Table 2  Method parameters for analyzed eicosanoids Transition (m/z)

Collision energy (%)

LOD (pg/mL)

7.0

369 > 315

40

0.5

20-hydroxy-LTB4

7.4

351 > 195

40

0.5

20-carboxy-LTB4

7.4

365 > 347

40

2.5

TBX2

8.1

369 > 195

40

0.5

PGF2a

8.6

353 > 309

40

0.5

11-dehydro-TBX2

9.3

367 > 305

40

0.5

LXA4

10.4

351 > 115

40

5.0

LTB5

11.5

333 > 195

40

0.5

LTD4

11.7

495 > 177

40

0.5

LTE4

12.0

438 > 333

50

250

15-dPGJ2

16.9

315 > 271

40

25

20-HETE

17.0

319 > 275

40

5.0

13-HODE

18.6

295 > 195

40

0.5

9-HODE

18.9

295 > 171

40

0.5

15-HETE

19.4

319 > 219

40

2.5

15-KETE

20.6

317 > 113

40

0.5

12-HETE

21.0

319 > 179

40

0.5

5-HETE

22.6

319 > 115

40

0.5

14,15-EET

23.3

319 > 219

40

2.5

11,12-EET

25.0

319 > 167

40

25

5-KETE

25.1

317 > 273

40

25

8,9-EET

25.1

319 > 155

40

2.5

DHA

30.9

327 > 283

50

5.0

AA

31.3

303 > 259

40

5.0

Segment

Analyte

1

6-keto-PGF1a

2

3

4

3.2. Results

Rt (min)

1. We compared differences in separation and detection of eicosanoids under acidic (pH 4), neutral, and basic (pH 8) conditions (Fig. 1a–c, Table 3). Results clearly showed that analytes demonstrated improved performance (i.e., narrower peak shapes, reduced tailing, and improved baseline resolution) under weak acidic conditions. However, a basic mobile phase can potentially be useful for certain eicosanoid targets.



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Fig. 1. Chromatographic separation of eight eicosanoids ((1) 6-keto PFG1a, (2) LTB4, (3) 15-HETE, (4) 5-KETE, (5) 12-HETE, (6) 5-HETE, (7) 14,15-EET, and (8) 11,12-EET). Gradient parameters are described in Table 3. (a) Gradient run with an additive of 0.1% Acetic acid. (b) Gradient run with an additive of 0.01% NH4+solution (25%). *This peak contains all eight eicosanoids. (c) Gradient run without an additive (neutral conditions). (d) Gradient run without additive, but preconditioned under the same acidic conditions as in a.

Optimal resolution was achieved after preconditioning of the column at acidic pH followed by a neutral gradient (Fig. 1d). These conditions were therefore used for further development of the method. 2. Twenty-four arachidonic and linoleic acid metabolites were analyzed by LC/MS/MS using a 45 min pH gradient (Table 1) and scanning in SRM mode (Table 2). LOD values from 0.5 to 250 pg/mL were observed (Table 2). Figure 2 shows a total ion chromatogram of the analyzed compounds and ion chromatograms obtained for eicosanoids detected 6–10 min into the run. Compounds with similar properties (i.e., PGs, LTs, HETEs, HODEs, KETEs, and EETs) have close retention times but can readily be distinguished by differences in fragmentation pattern. An example of this is shown in Fig. 3

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Table 3  Gradient parameters for Fig. 1a Time (min)

1 (%)b

2 (%)c

3 (%)d

0.0

85

15

0

0.5

85

15

0

1.0

70

30

0

5.0

45

55

0

10.0

25

75

0

10.5

0

100

0

15.0

0

100

0

16.0

85

e

15

100f

21.0

85e

15e

100f

e

a  Solvent (1) and (2) were run under neutral conditions or with an additive of either 0.1% Acetic acid or 0.01% NH4+(25% aqueous solution) depending on desired pH with a flow rate of 350 mL/min. b  1: Water. c  2: Acetonitrile:Methanol (88:12). d  3: Water:Acetonitrile:Methanol (85:13:2), 0.1% Acetic acid. e  Used parameters for chromatograms a–c in Fig. 1. f  Used parameters for chromatogram d in Fig. 1.

Fig. 2. ESI/MS/MS chromatogram obtained for the eicosanoids in Table 2. Details for all SRM parameters are provided in Table 2. (a) Total ion chromatogram. (b–g) Compounds detected in Segment 1: (b) 6-keto PGF1a, (c) 20-hydroxy-LTB4, (d) 20-carboxy-LTB4, (e) TXB2, (f) PGF2a, and (g) 11-dehydro-TXB2.



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Fig. 3. MS2 analysis of 5-HETE (m/z 319) (a) and of 12-HETE (m/z 319) (b). The fragment ions at m/z 115 and 179 are specific for 5-HETE and 12-HETE, respectively, and can be used in SRM analysis for compound identification and subsequent quantification.

in which the MS2 spectra of 5-HETE (a) and 12-HETE (b) are shown. The dehydrated ion at m/z 301 is present in both spectra, as well as the ion at m/z 257. However, scanning in SRM mode for the diagnostic fragment peaks at m/z 115 for 5-HETE and at m/z 179 for 12-HETE enabled the routine detection and quantification of these compounds.

4. Notes 1. The use of new instrumentation such as a quadrupole ion trap time of flight (QITTOF) or FTICR/MS will result in lower LODs in the range of attomol or even zeptomol

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levels of constituent quantities. The use of these techniques for eicosanoid quantification can greatly increase our ability to detect and quantify a variety of eicosanoids in biological samples that have not been detected previously. This ability is important for clinical applications where the amount of sample available is often a limiting factor. Furthermore, the development of multi-isotope imaging mass spectrometry (MIMS) opens a new field of opportunities, enabling studies of the interaction, detection, and quantification of eicosanoids within a single cell. 2. There are specific challenges with the quantification of CysLTs, which are best analyzed directly by online SPE coupled to the HPLC/MS/MS run in positive mode (7, 95). This procedure is recommended because CysLTs are sensitive to oxidation and offline SPE extraction can result in decreased recoveries. 3. Eicosanoids are sensitive to oxidation and consequently standards should always be stored at low temperatures (e.g., £ −80°C) under an inert atmosphere (e.g., argon) in the absence of light, moisture, and active surfaces. Samples should be kept in a temperature-controlled environment both before as well as during the run (a common feature of many autosamplers). 4. It is important to use HPLC and MS grade solvents for the preparation of chromatographic mobile phases to reduce background noise and keep the instrument in a good working condition. The aqueous phase should be frequently replaced (once per week) in order to reduce the possibility of microorganism growth. In addition, the system should be regularly flushed with organic solvent (e.g., methanol). 5. The limit for the number of compounds that can be identified in a single run depends upon the HPLC separation as well as the ion dwell times (based on the number of analytes in each SRM, the interscan delays, and the peak width of the analytes). Accordingly, this number will vary greatly with different instruments. 6. System stability is particularly important when operated with large sample sets where the MS conducts multiple scans within a single run.

Acknowledgments We gratefully acknowledge the assistance of Malin Nording, Katrin Georgi, Jun Yang, and Pavel Aronov for helpful discussions and critical reading of the manuscript. This research was



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supported by The Åke Wibergs Stiftelse, the Fredrik and Ingrid Thurings Stiftelse, The Royal Swedish Academy of Sciences, The Swedish Council for Strategic Research, and The Swedish Research Council and the European Commission. C.E.W. was supported by a fellowship from the Centre for Allergy Research (Cfa). S. L. L. was supported by the Osher Initiative for severe Asthma. References 1. Funk CD. (2001) Prostaglandins and leukotrienes: advances in eicosanoid biology. Science 294, 1871–1875. 2. Conrad DJ. (1999) The arachidonate 12/15 lipoxygenases. A review of tissue expression and biologic function. Clin Rev Allergy Immunol 17, 71–89. 3. Newman JW, Watanabe T, Hammock BD. (2002) The simultaneous quantification of cytochrome P450 dependent linoleate and arachidonate metabolites in urine by HPLCMS/MS. J Lipid Res 43, 1563–1578. 4. Natarajan R, Reddy MA. (2003) HETEs/ EETs in renal glomerular and epithelial cell functions. Curr Opin Pharmacol 3, 198–203. 5. Schmelzer KR, Inceoglu B, Kubala L, et  al. (2006) Enhancement of antinociception by coadministration of nonsteroidal anti-inflammatory drugs and soluble epoxide hydrolase inhibitors. Proc Natl Acad Sci U S A 103, 13646–13651. 6. Samuelsson B, Dahlen SE, Lindgren JA, Rouzer CA, Serhan CN. (1987) Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science 237, 1171–1176. 7. Schmelzer KR, Wheelock AM, Dettmer K, Morin D, Hammock BD. (2006) The role of inflammatory mediators in the synergistic toxicity of ozone and 1-nitronaphthalene in rat airways. Environ Health Perspect 114, 1354–1360. 8. Hoagland KM, Maier KG, Moreno C, Yu M, Roman RJ. (2001) Cytochrome P450 metabolites of arachidonic acid: novel regulators of renal function. Nephrol Dial Transplant 16, 2283–2285. 9. Roman RJ. (2002) P-450 metabolites of arachidonic acid in the control of cardiovascular function. Physiol Rev 82, 131–185. 10. Natarajan R, Nadler JL. (2004) Lipid inflammatory mediators in diabetic vascular disease. Arterioscler Thromb Vasc Biol 24, 1542–1548. 11. Hussey HJ, Tisdale MJ. (1996) Inhibition of tumour growth by lipoxygenase inhibitors. Br J Cancer 74, 683–687.

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fragmentation and structure. Anal Chem 67, 2498–2509. 78. Flora JW, Hannis JC, Muddiman DC. (2001) High-mass accuracy of product ions produced by SORI-CID using a dual electrospray ionization source coupled with FTICR mass spectrometry. Anal Chem 73, 1247–1251. 79. Hofstadler SA, Sannes-Lowery KA, Griffey RH. (1999) Infrared multiphoton dissociation in an external ion reservoir. Anal Chem 71, 2067–2070. 80. Stensballe A, Jensen ON, Olsen JV, Haselmann KF, Zubarev RA. (2000) Electron capture dissociation of singly and multiply phosphorylated peptides. Rapid Commun Mass Spectrom 14, 1793–1800. 81. Flora JW, Muddiman DC. (2004) Determination of the relative energies of activation for the dissociation of aromatic versus aliphatic phosphopeptides by ESI-FTICR-MS and IRMPD. J Am Soc Mass Spectrom 15, 121–127. 82. Pfister SL, Spitzbarth N, Nithipatikom K, Edgemond WS, Falck JR, Campbell WB. (1998) Identification of the 11,14,15- and 11,12, 15-trihydroxyeicosatrienoic acids as endothelium-derived relaxing factors of rabbit aorta. J Biol Chem 273, 30879–30887. 83. Pfister SL, Spitzbarth N, Zeldin DC, Lafite P, Mansuy D, Campbell WB. (2003) Rabbit aorta converts 15-HPETE to trihydroxyeicosatrienoic acids: potential role of cytochrome P450. Arch Biochem Biophys 420, 142–152. 84. Falck JR, Barma DK, Mohapatra S, et  al. (2004) Asymmetric synthesis of the stereoisomers of 11,12,15(S)-trihydroxyeicosa5(Z),8(Z),13(E)-trienoic acid, a potent endothelium-derived vasodilator. Bioorg Med Chem Lett 14, 4987–4990. 85. Kerwin JL, Torvik JJ. (1996) Identification of monohydroxy fatty acids by electrospray mass spectrometry and tandem mass spectrometry. Anal Biochem 237, 56–64. 86. Nakamura T, Bratton DL, Murphy RC. (1997) Analysis of epoxyeicosatrienoic and monohydroxyeicosatetraenoic acids esterified to phospholipids in human red blood cells by electrospray tandem mass spectrometry. J Mass Spectrom 32, 888–896. 87. Nakamura T, Henson PM, Murphy RC. (1998) Occurrence of oxidized metabolites of arachidonic acid esterified to phospholipids in murine lung tissue. Anal Biochem 262, 23–32. 88. Wheelan P, Zirrolli JA, Murphy RC. (1993) Low-energy fast atom bombardment tandem mass spectrometry of monohydroxy substituted unsaturated fatty acids. Biol Mass Spectrom 22, 465–473.



HPLC/MS/MS-Based Approaches for Detection and Quantification

89. Harkewicz R, Fahy E, Andreyev A, Dennis EA. (2007) Arachidonate-derived dihomoprostaglandin production observed in endotoxin-stimulated macrophage-like cells. J Biol Chem 282, 2899–2910. 90. Hall LM, Murphy RC. (1998) Electrospray mass spectrometric analysis of 5-hydroperoxy and 5-hydroxyeicosatetraenoic acids generated by lipid peroxidation of red blood cell ghost phospholipids. J Am Soc Mass Spectrom 9, 527–532. 91. Currie LA. (1995) Nomenclature in evaluation of analytical methods including detection and quantification capabilities. Pure and Appl Chem 67, 1699–1723. 92. Revised Assessment of Detection and Quantitation Approaches; 2004. Report No.: EPA821-B-04–005. 93. Assigning Values to Nondetected/Nonquantified Pesticide Residues in Human Health Dietary Exposure Assessments; 2000. Report No.: OPP-00570A 94. Song WL, Lawson JA, Wang M, Zou H, FitzGerald GA. (2007) Noninvasive assessment of the role of cyclooxygenases in cardiovascular health: a detailed HPLC/MS/MS method. Methods Enzymol 433, 51–72.

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Chapter 9 Brain Phosphoinositide Extraction, Fractionation, and Analysis by MALDI-TOF MS

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Roy A. Johanson and Gerard T. Berry

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Summary 

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Matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) can provide rapid, sensitive determinations of lipids from small tissue samples in both single determinations and automated high-throughput assays. MALDI-TOF MS is a sensitive, high-throughput technique for the determination of lipids such as the phosphoinositides, PtdIns (phosphatidylinositol), PIP (phosphatidylinositol-4-phosphate, and PIP2 (phosphatidylinositol-4,5-bisphosphate), but in crude extracts the signals are weak or not observed due in large part to ion suppression by phosphatidylcholine and other cationic lipids. A rapid separation step using a small column of a strong cation exchange (SCX) gel can be utilized easily and effectively to adsorb or capture cationic lipids from chloroform/methanol lipid extracts and provide substantially improved signals for the phosphoinositides. In this review, we describe the use of fractionation of a crude lipid extracts using cation exchange columns in conjunction with MALDI-TOF MS and appropriate internal standards to quantify the levels of phosphoinositides in small mammalian brain samples.

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Key words:  MALDI-TOF MS, Phosphoinositides

1. Introduction

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The utilization of mass spectrometry (MS) in studies of lipids has been expanding in recent years as instrumentation has improved and new methods of lipid sample preparation and handling have been developed. MS has been used increasingly to characterize changes in lipid composition related to lipid metabolism and lipid-mediated cellular signaling and structural changes (1–3). MALDI-TOF MS can provide rapid, sensitive determinations of lipids from small tissue samples in both single determinations and

Donald Armstrong (ed.), Lipidomics, Methods in Molecular Biology, vol. 579, doi 10.1007/978-1-60761-322-0_9, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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automated high-throughput assays, and is relatively insensitive to impurities such as buffer salts compared with other MS methods (3–7). Both qualitative and quantitative determinations of lipids can be performed using MALDI-TOF MS (3). When MALDI-TOF MS is used to study crude lipid extracts from biological samples, signals for many of the lipids are weak or not observed. This is due in large part to ion suppression by phosphatidylcholine and other cationic lipids such as lysophosphatidylcholine which has been shown to markedly decrease the sensitivity and reproducibility of signals obtained for the noncationic lipids in a lipid mixture (6, 8). Fractionation of the compounds in crude extracts prior to MALDI-TOF MS analysis (8), such as by thin-layer chromatography (9), is necessary in order to overcome the effect of ion suppression with detection of lipids such as the phosphoinositides in crude lipid extracts from biological samples. The phosphoinositides, phosphatidylinositol (PtdIns), and its phosphorylated derivatives such as phosphatidylinositol-4,5-bisphosphate (PIP2), are important biologically because the polyphosphoinositides play key roles in intracellular signaling and in the localization of proteins to cellular membranes. Advances in analytical methods for phosphoinositides will improve the ability to study the metabolic disruptions and test hypotheses in appropriate biological models. The lipid extraction procedure described here was developed and optimized with the primary goal of determining the levels of the phosphoinositides, PtdIns, PIP, and PIP2 in small samples (10–250 mg) of brain tissue. Consequently, strong acid conditions (1 M HCl) are used in the extraction in order to recover the PIP2 in the organic extract phase (10). We have reported that a rapid separation step using a small column of a strong cation exchange (SCX) gel (RediSep® SCX silica gel) can be utilized easily and effectively to adsorb or capture cationic lipids including phosphatidylcholine species from chloroform/ methanol lipid extracts from mammalian brain (4). In this review we describe the extraction of lipids from tissue followed by fractionation of the crude lipid extracts using cation exchange columns and then MALDI-TOF MS with internal standards for the determination of the levels of PIP2 and other phosphoinositides in brain samples.

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2. Materials

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2.1. Equipment

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1. MALDI-TOF MS instrument. 2. Glass Teflon (Potter-Elvehjem) homogenizers.



2.2. Reagents

2.3. Supplies

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1. MALDI–MS grade 2,5-dihydroxybenzoic acid (DHB) from Fluka (Switzerland).

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2. Soybean PtdIns from Avanti Polar Lipids (Alabaster, AL).

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3. Bovine liver PtdIns from Avanti Polar Lipids (Alabaster, AL).

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4. Synthetic 1-stearoyl-2-arachidonoyl-phosphatidylinositol-4,5bisphosphate (PIP2) from Avanti Polar Lipids (Alabaster, AL).

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5. Synthetic dipalmitoyl-PIP2 was obtained from both Sigma (St.Louis, MO) and Cayman Chemical (Ann Arbor, MI).

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6. RediSep® SCX silica gel was from Teledyne Isco (Lincoln, NE).

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C57BL/6 mice were obtained from Charles River (Wilmington, MA).

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3. Methods 3.1. Tissue Harvest

3.2. Lipid Extraction 3.2.1. Solvent Preparation

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The murine brains are harvested as quickly as possible, immediately frozen in liquid N2, and stored and maintained at −80°C until the homogenization is started in order to minimize the appearance of degradation products in the mass spectra (11–15). Adult murine brains were removed and clamp frozen in liquid N2 within 40–45 s after the mouse was sacrificed. The frozen brains were stored at −80°C. Embryonic day 18.5 (E18.5) brains are obtained from fetuses in pregnancies timed by the appearance of vaginal plugs following mating.

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The extraction procedure is based on methods described by Bligh and Dyer (16) and Schacht (10) and is carried out using an extraction solvent containing a ratio of CHCl3:CH3OH:H2O:conc.HCl such that the final ratio of CHCl3:CH3OH:H2O:conc.HCl, including the H2O in the sample, was 10:20:7:1. Adult brains are estimated to be 80% H2O by weight and fetal brains are estimated to be 85% H2O in calculating how much solvent to use. 1. Extraction solvent CHCl3:CH3OH:HCl:H2O, 10:20:1:3

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2. Wash solvent CH3OH:H2O:HCl, 20:19:1 3. SCX column solvent CHCl3:CH3OH:H2O, 20:9:1 4. SCX column salt elution CHCl3:CH3OH:500 mM NaCl in H2O, 20:9:1

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3.2.2. Preparation of SCX Column

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1. A column containing Teledyne ISCO RediSep® SCX silica gel is packed in a glass–wool plugged 5¾ in. long Pasteur pipette. The column bed is packed to 5 cm total height, including taper (at full bore, 1 ml = approximately 3.7 cm). This column volume is satisfactory for extracts from up to 200 mg wet wt brain tissue.

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2. Prior to use, the column is washed/regenerated with three column volumes freshly mixed acetonitrile/1 M HCl (100 ml HCl/ml acetonitrile) as recommended by the manufacturer.

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3. The column is equilibrated using five column volumes of the column solvent, CHCl3:CH3OH:H2O, 20:9:1 (17–19).

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4 After use, the column is washed with CH3OH prior to storage.

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3.2.3. Extraction of Lipids

1. Aliquots of the internal standards, soybean PtdIns and/or dipalmitoyl PIP2, that are dissolved in CHCl3 and/or CH3OH, are first pipetted into glass–teflon homogenizers (see Note 1) and the solvent evaporated. To prevent degradation of phospholipids (11–15), the homogenizers are then chilled on dry ice. Frozen brain samples are weighed into the chilled homogenizers (see Note 2). Extraction solvent, 6.8 ml CHCl3:CH3OH:H2O:conc HCl (10:20:3:1)/mg wet wt adult brain (7.22 ml/mg wet wt for fetal brain), is added to the samples. The still-frozen murine brain samples are rapidly homogenized as the homogenizer and tissue are warmed by mechanical friction and hand warmth to above freezing temperature. Defrosting and homogenizing the brain tissue works best when the homogenizer capacity is 4–8 times the total volume of the sample plus extraction solvent. Care should be taken to minimize evaporative losses by minimizing exposure to air and transferring to glass centrifuge tubes (see Note 3) and capping for comparison, immediately after homogenization. All subsequent steps are carried out at room temperature. CHCl3 and H2O are added to obtain the separation of phases. First 2.0 ml CHCl3/mg wet wt (2.12 ml/mg wet wt for fetal brain) is added followed by moderate vortex mixing. Then 2.0 ml H2O/ mg wet wt (2.12 ml/mg wet wt for fetal brain) is added followed by mixing. The phases are separated by centrifugation at 100 × g and the lower CHCl3 phase recovered. If the volume of fluffy emulsion that fills more than ~10% of the upper portion of the lower phase, it can generally be reduced by rocking gently to break up the emulsion and recentrifuging to separate the phases (see Note 4). 2. The upper phase and precipitate are reextracted with 2.0 ml CHCl3/mg wet wt (2.12 ml/mg for fetal brain), and the lower phase recovered and combined with the first lower phase (see Note 5). The combined lower phases are then washed 2 times by shaking with 1.5 volumes CH3OH:H2O:HCl, 20:19:1, separating the phases by centrifugation, and recovering the lower phase. If unfractionated extract is to be saved, it should be stored at −80°C.

3.2.4. Lipid Fractionation

3.3. Mass Spectrometry

3.4. Data Analysis

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1. Sufficient methanol and H2O is added to the washed chloroform phase lipid extract from the lipid extraction procedure to bring the CHCl3:CH3OH:H2O, ratio to 20:9:1. The sample (0.5–1.5 ml) is passed through a 1.3 ml column of RediSep ® SCX that is packed in a glass–wool plugged Pasteur pipette. Prior to use, the column should be washed/regenerated with freshly mixed acetonitrile/1 M HCl as recommended by the manufacturer, and equilibrated in CHCl3:CH3OH:H2O, 20:9:1 (17–19). The lipid containing flow through fraction is collected.

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2. The H2O/CH3OH phase is separated from the CHCl3 phase by adding 26% volume H2O:HCl, 10:1.

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3. The extracted sample should be dried thoroughly under a stream of N2 and stored under N2 at −80°C.

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For an overview of mass spectrometry, see the review by Baldwin (20), and the description on the American Society for Mass Spectrometry website, http://www.asms.org/whatisms/. MALDI-TOF mass spectrometry was carried out using Ettan MALDI-ToF/Pro (GE Healthcare, Piscataway, NJ). The appropriate aliquots of dissolved samples were prepared, dried using N2, and then dissolved at 2.5–15 mg lipid/ml in 0.5 M DHB in CHCl3:CH3OH, 1:1. Aliquots of 0.1 ml were spotted on the target slides. The MALDI-TOF MS measurements were carried out at 20 kV or −20 kV in the reflectron mode using selective accumulation.

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The S/N was calculated using the equation S/N = (I−(Nmax + Nmin)/2)/(Nmax−Nmin) where I = the maximum intensity of a peak, and Nmax and Nmin are the maximum and minimum of pure noise. Excel (Microsoft, Redmond, WA) and SigmaPlot (Systat Software, Inc, Point Richmond, CA) were used for graphing and linear regression analysis. Since the amounts of sample and internal standard mixed with DHB and loaded on the target spots are operationally maintained within a relatively narrow optimum range, the levels of 16:0–18:2 PtdIns and 18:0–20:4 PtdIns can be readily set up so that, in effect, the range of the samples being assayed will be within the range of the standards. A range of external standard levels was selected for calibration curves that spanned the range of 18:0–20:4 PtdIns levels found in the optimum range of sample levels with MALDITOF MS analysis. Since there is a peak in the mass spectra of the brain extract at m/z 834.5, it is best to set the 16:0–18:2 PtdIns in the soy PtdIns at the high end of the scale to minimize interference by the m/z 834.5 peak. When the ratio of I885/I833 is plotted vs. the increasing level of an external standard, it has been demonstrated that a linear plot is obtained (4).

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The first-order regression for the results can be expressed as I885/I833 = m(18:0−20:4 PtdIns)+b where I885 is the intensity at m/z 885 from variable amounts of 18:0–20:4 PtdIns in the external standard or sample, and I833,s is the intensity at m/z 833 for the amount of internal standard, 16:0–18:2 PtdIns, used in running the standard curve (s). For a different amount of internal standard (a) the relationship between the intensity at m/z 833 from a mg 16:0–18:2 PtdIns compared to the intensity at m/z 833 from s mg 16:0–18:2 PtdIns (I833,a) can be expressed as I833,a/ a = I833,s. By rearranging this equation to I833,s=(s/a)I833,a and then substituting into the regression equation and rearranging, Eq. 1 is obtained.

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18 : 0 − 20 : 4 PtdIns = a (I 885 / I 883,a ) / ms − b / m.

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3.5. Results

(1)

1. Mass spectra of the standards that were used are shown in Fig. 1. Peaks at several m/z were observed in the mass spectra

Fig. 1. Negative mode mass spectra of purified bovine liver PtdIns and soybean PtdIns. Separate samples (40 mg each) in CHCl3 were dried and dissolved in 10 ml of 0.5 M DHB in CHCl3:CH3OH, 1:1. Aliquots of 0.1 ml containing approximately 400 ng PtdIns (440 pmol bovine PtdIns or 470 pmol soybean PtdIns) were spotted on each spot of the target slides. The mass spectra for the bovine PtdIns and the soybean PtdIns are shown in Panel a and b, respectively.



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of both the purified bovine PtdIns (Panel A) and purified soybean PtdIns (Panel B). The mass spectra of the bovine PtdIns showed peaks at m/z 836.5, 857.5, 859.5, 861.5, 863.5, 883.5, 885.5, and 887.5 and the averages of the results for the relative intensities of the peaks in five mass spectra were 3, 2, 3, 10, 9, 9, 37, and 26%, respectively. All of these peaks could be accounted for by the analysis of the acyl species provided by the vendor, Avanti Polar Lipids. A similar analysis of soybean PtdIns showed peaks at m/z 831.5, 833.5, 855.5, 857.5, 859.5, 861.5, and 883.5. The 16:0–18:2 PtdIns peak intensity at m/z 833.5 was 46% of the summed intensities of the peaks for soybean PtdIns and the S/N ratio of the peak was 72. For our calibration curve analyses, the external and internal standard masses for the bovine 18:0–20:4 PtdIns and the soybean 16:0–18:2 PtdIns were calculated using the fractions 0.37 and 0.46, respectively, and the manufacturer’s specified concentration, 10 mg/ml. The results obtained based on the specified concentration were consistent, lot to lot, and consistent with published values (4).

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2. Mass spectra run in the positive ion mode showed that the cation exchange minicolumns effectively removed the cationic lipids including the phosphatidylcholine species from the lipid extracts. Mass spectra of brain extracts run in the negative ion mode are shown for extracts from adult murine brain (Fig. 2) and fetal brain (Fig. 3). Peaks for PIP and PIP2 were barely discernable in the extract from the adult brain prior to the removal of the cationic lipids, and after the cationic lipids were removed, clear peaks were obtained. A similar improvement in the mass spectra was obtained for the extracts from the fetal brain.

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3. When mass spectra of a dilution series of PtdIns was run using a fixed amount of an added standard of closely similar chemistry and mass, the mass spectra peaks increased linearly with increasing PtdIns (see Fig. 4). When the samples were spiked with a fixed amount of murine brain extract, the curve obtained was displaced upward by the PtdIns in the extract. The curve in the presence of the added brain extract was parallel to that obtained without added extract indicating that the presence of nonphosphoinositide compounds in the extract did not alter the comparative responses observed for the internal standard and the analytes in MALDI-TOF MS.

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Fig. 2. Mass spectra obtained in negative ion mode of lipid extract fractions from adult murine brains. The CHCl3:CH3OH:HCl lipid extracts were prepared and then fractionated using RediSep® SCX silica gel as described in Subheading 3. The internal standards (indicated by IS), 2.00 mg soybean PtdIns and 0.50 mg dipalmitoyl PIP2 per g wet wt brain, were added before the homogenization step for the extracts shown in panel a and b. Dried samples of the extracts were dissolved in 0.5 M DHB in CHCl3:CH3OH, 1:1 and 0.1 ml aliquots were spotted on the target slides. Panel a shows the mass spectra obtained in the negative ion mode of the unfractionated extract, and panel b shows the spectra obtained of the lipids remaining in the extract following passage through the SCX column.

Fig. 3. (continued) of unfractionated extract, and panel b shows the spectra obtained for an extract that had been treated with an SCX column. For the mass spectra shown in Panels c and d, soybean PtdIns containing 141 mg 16:0–18:4 PtdIns and 150 mg synthetic dipalmitoyl PIP2 were added as internal standards to a pool of 4 E18.5 brains with a total wet weight of 290 mg prior to the homogenization and extraction procedures. Panel c shows the mass spectra of a crude extract with internal standards and Panel d shows the SCX treated extract with internal standards. The inset in Panel d shows a portion of the spectra for an extract that was prepared using precautions to minimize contamination by sodium ion by using plastic ware and thoroughly rinsed glassware including all disposable glassware in the extraction procedure. For this extract, two internal standards, soybean PtdIns containing 38 mg 16:0–18:2 PtdIns and 40 mg synthetic dipalmitoyl PIP2, had been added to a 83 mg wet wt E18.5 brain before the homogenization and extraction procedures.



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Fig. 3. Negative ion mode mass spectra of lipid extract fractions from E18.5 murine brains. The CHCl3:CH3OH:HCl lipid extracts were prepared and then fractionated using RediSep® SCX silica gel as described in Subheading 3. The dried samples of the extracts were dissolved in 0.5 M DHB in CHCl3:CH3OH, 1:1 and 0.1 ml aliquots were spotted on the target slides. Panel a shows the mass spectra obtained in the negative ion mode

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Fig. 4. PtdIns calibration curves. Samples containing a constant amount (9.2 mg) of the internal standard, soybean 16:0–18:2 PtdIns, and varying amounts of an external standard, bovine 18:0–20:4 PtdIns, were prepared without and with added SCX treated murine brain extract. The mixed samples were dried, dissolved in 20 ml 0.5 M DHB in CHCl3:CH3OH, 1:1 and 0.1 ml aliquots were spotted on the target slides. The maximum intensities for the mass spectral peaks at m/z 833.5 for 9.2 mg 16:0–18:2 PtdIns and m/z 885.5 for the 18:0–20:4 PtdIns were determined. The plots show the ratios of the signal intensities of the peaks at m/z 885.5 for variable amounts of 18:0–20:4 PtdIns (I885) to the intensities at m/z 833.5 from 9.2 mg 16:0–18:2 PtdIns (I833,9.2 mg) in the soybean PtdIns. The data points shown are the mean ± SD from five determinations. In Panel a, mixtures of the indicated amounts of bovine liver 18.0–20.4 PtdIns with 9.2 mg 16:0–18:2 PtdIns are shown by the filled circles and the results when an aliquot of SCX treated extract from one adult murine brain proportional to that obtained from 7 mg wet wt tissue was added to identical mixtures of bovine PtdIns and soybean PtdIns is shown by the open circles. In Panel b, the filled squares are the results for mixtures of



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4. Notes

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1. Glass Teflon homogenizers are recommended for homogenization because they work well in homogenizing the small, 50–500 mg, frozen tissue samples as they are thawing and also because they produce a minimal amount of aeration with concomitant changes in the solvent composition from the proportionally more rapid evaporation of CHCl3.

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2. Since evidence of enzymatic degradation products of the lipids such as the appearance of lysophosphatidylcholine and increased diacylglycerols were evident in the mass spectra when the tissue was allowed to thaw even briefly before homogenization, the homogenizer should be maintained at dry ice temperatures until the homogenization is started in order to keep the time span to an absolute minimum between when the tissue becomes defrosted and when it is homogenized and solubilized in the acidic organic solvent.

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3. When mass spectra are to be measured in the positive ion mode, glassware should be used throughout the preparation of the lipid extracts because of known problems with strong organic solvents dissolving or leaching various compounds such as plasticizers from plastic labware that appear as artifact peaks in mass spectra run in the positive ion mode (3). When only negative ion mode mass spectra are to be determined, plastic ware such as polypropylene tubes and “Eppendorf” tips are suitable, as well as other plastic ware that is compatible with CHCl3 keeping in mind that unusual peaks from compounds which come from the plastic may appear randomly.

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4. When the phases are separated following the initial extraction, there is a tendency for a fluffy emulsion to form in the upper portion of the lower phase especially if the mixing is too vigorous. This fluffy emulsion can fill most of the lower phase. If the layers are gently rocked to disperse the emulsion and recentrifuged, the emulsion will break down and the fluffy layer will become smaller. This may take more than one repeat of the rocking and centrifuging to get this layer to a minimum width band in the top of the lower phase thus maximizing the recovery of the lower phase.

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5. The upper phase can be recovered for determinations of metabolites such as inositol which partition to this phase.

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Fig.4. (continued) bovine and soybean PtdIns identical to the samples shown by the filled circles in Panel a, and the open squares show the results when SCX treated extract from 12 mg wet wt E18.5 brain was added to the mixtures. The lines and equations are shown for the first order regressions where I885 is the signal intensity at m/z 885.5 from varying amounts of 18:0–20:4 PtdIns, I833,9.2 mg is the signal intensity at m/z 833.5 from 9.2 mg 16:0–18:2 PtdIns, and PtdIns is mg of 18:0–20:4 PtdIns.

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References

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Chapter 10 Lipidomic Analysis of Biological Samples by Liquid Chromatography Coupled to Mass Spectrometry

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Giuseppe Astarita, Faizy Ahmed, and Daniele Piomelli

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Summary 

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Lipidomics studies the large-scale changes in nonwater-soluble metabolites (lipids) accompanying perturbations of biological systems. Because lipids are involved in crucial biological mechanisms, there is a growing scientific interest in using lipidomic approaches to understand the regulation of the lipid metabolism in all eukaryotic and prokaryotic organisms. Lipidomics is a powerful tool in system biology that can be used together with genomics, transcriptomics, and proteomics to answer biological questions arising from various scientific areas such as environmental sciences, pharmacology, nutrition, biophysics, cell biology, physiology, pathology, and disease diagnostics. One of the main challenges for lipidomic analysis is the range of concentrations and chemical complexity of different lipid species. In this chapter, we present a lipidomic approach that combines sample preparation, chromatographic, and intrasource ionization separation coupled to mass spectrometry for analyzing a broad-range of lipid molecules in biological samples.

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Key words:  Lipidomics, Lipids, Liquid chromatography mass spectrometry, Fatty acids, Phospholipids, Cholesterol, Lipid profile, Lipid biomarkers, Large-scale analysis

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1. Introduction

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Lipids are natural molecules that are insoluble or partially soluble in water. These hydrophobic or amphipathic molecules can be either biosynthesized or absorbed from the environment and are vital for the life of all eukaryotic and prokaryotic organisms. Lipids play crucial biological roles through three general mechanisms (1) they affect the cellular membrane structures and protein–membrane interactions, (2) they provide a source of energy through processes of oxidation