Lipidomics: Volume 1: Methods and Protocols (Methods in Molecular Biology)

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)

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

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

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.

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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.

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

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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.



Global Analysis of Retina Lipids

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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.

58

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

59

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-



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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.

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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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spectrometry after one-step derivatization. Anal Chem 2007;79(4):1569–74. 36. Cai SS, Syage JA. Comparison of atmospheric pressure photoionization, atmospheric pressure chemical ionization, and electrospray ionization mass spectrometry for analysis of lipids. Anal Chem 2006;78(4):1191–9.

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

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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

83

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.

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

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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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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.

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

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3. Micropipetts (see Note 3).

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4. Glass (Hamilton) syringes of different sizes.

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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).

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8. Standard laboratory centrifuge.

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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).

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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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.

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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.

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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.

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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

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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

605

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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

Acknowledgments

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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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References 1. Christie, W. W. (2003) Lipid Analysis, Oily Press, Bridgwater. 2. Wymann, M. P. and Schneiter, R. (2008) Lipid signalling in disease. Nat. Rev. Mol. Cell. Biol. 9, 162–176. 3. Matsumoto, T., Kobayashi, T., and Kamata, K. (2007) Role of lysophosphatidylcholine (LPC)

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in atherosclerosis. Curr. Med. Chem. 14, 3209– 3220. 4. Fuchs, B., Schiller, J., Wagner, U., Häntzschel, H. and Arnold, K. (2005) The phosphatidylcholine/lysophosphatidylcholine ratio in human plasma is an indicator of the severity of rheumatoid arthritis: investigations by 31P

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