Lipidomics: Technologies and Applications

Focusing on the practical applications, this user–oriented guide presents current technologies and strategies for systems–level lipid analysis, going beyond basic research to concentrate on commercial uses of lipidomics in biomarker and diagnostic development, as well as within pharmaceutical drug discovery and development. The editor and authors have experience of the most recent analytical instruments and techniques, allowing them to provide here first–hand practical experience for newcomers to the field. The first half of the book covers current methodologies, ranging from global to targeted lipidomics and shotgun approaches, while the second part discusses the role of lipidomics in biomedical and pharmaceutical research, covering such diverse fields as inflammation, metabolic syndrome, cardiovascular and neurological disease. Both small and large–scale, high–throughput approaches are discussed, resulting in an invaluable source for academic and industrial research and development.

Preface XIII List of Contributors XV 1 Lipidomics Perspective: From Molecular Lipidomics to Validated Clinical Diagnostics 1 Kim Ekroos 1.1 Introduction 1 1.2 Hierarchical Categorization of the Analytical Lipid Outputs 2 1.2.1 Lipid Class 3 1.2.2 Sum Compositions 4 1.2.3 Molecular Lipids 5 1.2.4 Structurally Defined Molecular Lipids 6 1.3 The Type of Lipid Information Delivers Different Biological Knowledge 7 1.4 Untying New Biological Evidences through Molecular Lipidomic Applications 9 1.5 Molecular Lipidomics Approaches Clinical Diagnostics 11 1.6 Current Roadblocks in Lipidomics 14 1.7 Conclusions 16 References 16 2 Lipids in Cells 21 Kai Simons, Christian Klose, and Michal Surma 2.1 Introduction 21 2.2 Basis of Cellular Lipid Distribution 22 2.3 Lipid Distribution by Nonvesicular Routes 23 2.4 Lipids in Different Cell Types 24 2.5 Functional Implications of Membrane Lipid Composition 26 2.6 Outlook: Collectives and Phase Separation 29 References 30 3 High–Throughput Molecular Lipidomics 35 Marcus Stahlman, Jan Boren, and Kim Ekroos 3.1 Introduction 35 3.2 Lipid Diversity 35 3.3 Function of Molecular Lipids 38 3.4 Automated Sample Preparation 39 3.5 Different Approaches to Molecular Lipidomics 41 3.5.1 Untargeted versus Targeted Approaches 41 3.5.2 Shotgun Lipidomics 42 3.5.3 Analytical Validation of the Shotgun Approach 44 3.5.4 Targeted LC–MS Lipidomics 45 3.6 Data Processing and Evaluation 46 3.7 Lipidomic Workflows 47 3.8 Conclusions and Future Perspectives 48 References 49 4 Multidimensional Mass Spectrometry–Based Shotgun Lipidomics 53 Hui Jiang, Michael A. Kiebish, Daniel A. Kirschner, and Xianlin Han 4.1 Introduction 53 4.2 Multidimensional Mass Spectrometry–Based Shotgun Lipidomics 53 4.2.1 Intrasource Separation 54 4.2.2 The Principle of Multidimensional Mass Spectrometry 55 4.2.3 Variables in Multidimensional Mass Spectrometry 57 4.2.3.1 Variables in Fragment Monitoring by Tandem MS Scans 57 4.2.3.2 Variables Related to the Infusion Conditions 57 4.2.3.3 Variables under Ionization Conditions 57 4.2.3.4 Variables under Collision Conditions 58 4.2.3.5 Variables Related to the Sample Preparations 58 4.3 Application of Multidimensional Mass Spectrometry–Based Shotgun Lipidomics for Lipidomic Analysis 59 4.3.1 Identification of Lipid Molecular Species by 2D Mass Spectrometry 59 4.3.1.1 Identification of Anionic Lipids 59 4.3.1.2 Identification of Weakly Anionic Lipids 59 4.3.1.3 Identification of Charge Neutral but Polar Lipids 59 4.3.1.4 Identification of Sphingolipids 59 4.3.1.5 The Concerns of the MDMS–Based Shotgun Lipidomics for Identification of Lipid Species 61 4.3.2 Quantification of Lipid Molecular Species by MDMS–Based Shotgun Lipidomics 61 4.3.2.1 The Principle of Quantification of Individual Lipid Species by MS 62 4.3.2.2 Quantification by Using a Two–Step Procedure in MDMS–Based Shotgun Lipidomics 62 4.3.2.3 Quantitative Analysis of PEX7 Mouse Brain Lipidome by MDMS–Based Shotgun Lipidomics 63 4.4 Conclusions 66 References 68 5 Targeted Lipidomics: Sphingolipidomics 73 Ying Liu, Yanfeng Chen, and M. Cameron Sullards 5.1 Introduction 73 5.2 Sphingolipids Description and Nomenclature 75 5.3 Sphingolipids Analysis via Targeted LC–MS/MS 76 5.3.1 Sphingolipid Internal Standards 77 5.3.2 Biological Sample Preparation and Storage 78 5.3.3 Sphingolipid Extraction Protocol 79 5.3.4 Liquid Chromatography 81 5.3.4.1 LCBs and Cer1P 83 5.3.4.2 Cer, HexCer, LacCer, SM, ST, and Cer1P 84 5.3.4.3 Separation of GlcCer and GalCer 85 5.3.5 Mass Spectrometry 85 5.3.5.1 Electrospray Ionization 85 5.3.5.2 Tandem Mass Spectrometry 86 5.3.5.3 Multiple Reaction Monitoring 88 5.3.6 Generation of Standard Curves 89 5.3.7 Data Analysis 90 5.3.8 Quality Control 90 5.4 Applications of Sphingolipidomics in Biology and Disease 91 5.4.1 LC–MS/MS 91 5.4.2 Transcriptomic Guided Tissue Imaging Mass Spectrometry 92 5.5 Conclusions 94 References 94 6 Structural Lipidomics 99 Todd W. Mitchell, Simon H.J. Brown, and Stephen J. Blanksby 6.1 Introduction 99 6.2 Lipid Structure 100 6.3 Structural Analysis of Lipids by Mass Spectrometry 100 6.4 sn Position 105 6.5 Double Bond Position 107 6.5.1 Untargeted Fragmentation 108 6.5.2 Targeted Fragmentation 115 6.6 Double Bond Stereochemistry 122 6.7 Conclusions 123 References 124 7 Imaging Lipids in Tissues by Matrix–Assisted Laser Desorption/Ionization Mass Spectrometry 129 Robert M. Barkley, Joseph A. Hankin, Karin A. Zemski Berry, and Robert C. Murphy 7.1 Introduction 129 7.2 Sample Preparation 130 7.3 Matrix 131 7.3.1 Techniques for Matrix Application 131 7.3.2 Matrix Compounds 133 7.4 Instrumentation 134 7.4.1 Lasers and Rastering 134 7.4.2 Ion Formation 136 7.4.3 Mass Analyzers and Ion Detection 137 7.5 Data Processing 139 7.6 Conclusions 141 References 142 8 Lipid Informatics: From a Mass Spectrum to Interactomics 147 Christer S. Ejsing, Peter Husen, and Kirill Tarasov 8.1 Introduction 147 8.2 Lipid Nomenclature 148 8.3 Basic Properties of Lipid Mass Spectrometric Data 151 8.3.1 Mass Spectrum 152 8.3.2 Mass Accuracy and Reproducibility 154 8.3.3 Isotopes, Deisotoping, and Isotope Correction 154 8.4 Data Processing 158 8.4.1 De Novo Lipid Identification 159 8.4.2 Targeted Export of Lipidomic Data 161 8.4.3 Normalization of Lipidomic Data 162 8.5 Lipidomic Data Mining and Visualization 165 8.5.1 Comparative Lipidomics 165 8.5.2 Multivariate Data Analysis 166 8.5.3 Lipidomics in Biomarker Research 166 8.6 Lipidomic Data Integration 168 8.7 Conclusions and Future Perspectives 169 References 170 9 Lipids in Human Diseases 175 M. Mobin Siddique and Scott A. Summers 9.1 Introduction 175 9.2 Obesity 176 9.3 Dyslipidemia 177 9.4 Diabetes 177 9.5 Cardiovascular Disorders 179 9.6 Hereditary Sensory Neuropathy 181 9.7 Neurodegeneration 182 9.8 Cancer 184 9.9 Lysosomal Storage Disorders 186 9.10 Cystic Fibrosis 187 9.11 Anti–Inflammatory Lipid Mediators 188 9.12 Conclusions 188 References 189 10 Lipidomics in Lipoprotein Biology 197 Marie C. Lhomme, Laurent Camont, M. John Chapman, and Anatol Kontush 10.1 Introduction 197 10.2 Metabolism of Lipoproteins 198 10.3 Lipoproteinomics in Normolipidemic Subjects 200 10.3.1 Phospholipids 202 10.3.1.1 Phosphatidylcholine 202 10.3.1.2 Lysophosphatidylcholine 202 10.3.1.3 Phosphatidylethanolamine 203 10.3.1.4 Phosphatidylethanolamine Plasmalogens 203 10.3.1.5 Phosphatidylinositol, Phosphatidylserine, Phosphatidylglycerol, and Phosphatidic Acid 203 10.3.1.6 Cardiolipin 203 10.3.1.7 Isoprostane–Containing PC 203 10.3.2 Sphingolipids 203 10.3.2.1 Sphingomyelin 204 10.3.2.2 Lysosphingolipids 204 10.3.2.3 Ceramide 204 10.3.2.4 Minor Sphingolipids 204 10.3.3 Sterols 205 10.3.4 Cholesteryl Esters 205 10.3.5 Triacylglycerides 205 10.3.6 Minor Lipids 205 10.4 Altered Lipoproteinomics in Dyslipidemia 206 10.4.1 Phospholipids 206 10.4.1.1 Phosphatidylcholine 206 10.4.1.2 Lysophosphatidylcholine 207 10.4.1.3 Phosphatidylethanolamine 208 10.4.1.4 Phosphatidylethanolamine Plasmalogens 208 10.4.1.5 Phosphatidylinositol 208 10.4.1.6 Isoprostane–Containing PC 208 10.4.2 Sphingolipids 209 10.4.2.1 Sphingomyelin 209 10.4.2.2 Lysosphingolipids: S1P and Dihydro S1P 209 10.4.2.3 Ceramide 210 10.4.3 Free Cholesterol 210 10.4.4 Cholesteryl Esters 210 10.4.5 Triacylglycerides 210 10.4.6 Minor Lipids 211 10.4.6.1 Nonesterified Fatty Acids 211 10.4.6.2 Ganglioside GM1 211 10.4.6.3 Oxidized Lipids 211 10.5 Conclusions 211 References 212 11 Mediator Lipidomics in Inflammation Research 219 Makoto Arita, Ryo Iwamoto, and Yosuke Isobe 11.1 Introduction 219 11.2 PUFA–Derived Lipid Mediators: Formation and Action 219 11.3 LC–ESI–MS/MS–Based Lipidomics 222 11.3.1 Sample Preparation 222 11.3.2 LC–ESI–MS/MS Analysis 223 11.4 Mediator Lipidomics in Inflammation and Resolution 226 11.5 Conclusion and Future Perspective 230 References 230 12 Lipidomics for Elucidation of Metabolic Syndrome and Related Lipid Metabolic Disorder 233 Ryo Taguchi, Kazutaka Ikeda, and Hiroki Nakanishi 12.1 Introduction 233 12.2 Basic Strategy of Lipidomics for Elucidating Metabolic Changes of Lipids at the Level of their Molecular Species in Metabolic Syndrome and Related Diseases 234 12.3 Analytical Systems by Mass Spectrometry in Lipidomics 235 12.3.1 LC–MS and LC–MS/MS Analyses for Global Detection of Phospholipids and Triglycerides 235 12.3.2 Infusion Analysis with Precursor Ion and Neutral Loss Scanning 236 12.3.3 Targeted Analysis by Multiple Reaction Monitoring for Oxidized Lipids and Lipid Mediators by LC–MS/MS on Triple–Stage Quadrupole Mass Spectrometers 236 12.4 Lipidomic Data Processing 236 12.4.1 Strategy of Lipid Search 236 12.4.2 Application and Identification Results of “Lipid Search” 237 12.5 Analysis of Lipids as Markers of Metabolic Syndrome 239 12.5.1 Oxidized Phospholipids 239 12.5.1.1 Application for Myocardial Ischemia–Reperfusion Model 239 12.5.2 Bioactive Acidic Phospholipids 240 12.5.2.1 Lysophosphatidic Acid 240 12.5.2.2 Phosphoinositides 241 12.5.3 Oxidative Triglycerides 241 12.5.3.1 Application for Mouse White Adipose Tissue 242 12.5.4 Sphingolipids 244 12.5.4.1 Application for Sphinogolipid Metabolism 244 12.6 Direct Detection of Lipid Molecular Species in Specific Tissue Domains by Disease–Specific Changes 245 12.7 Conclusions 245 References 246 13 Lipidomics in Atherosclerotic Vascular Disease 251 Minna T. J€anis and Reijo Laaksonen 13.1 Introduction 251 13.2 Lipids and Atherosclerotic Vascular Disease 253 13.2.1 Lipoproteins 254 13.2.2 Atherosclerotic Plaque 255 13.2.3 Molecular Lipids 256 13.2.3.1 Eicosanoids 256 13.2.3.2 Sphingolipids and Cholesterol 257 13.2.3.3 Phospholipids 258 13.2.4 Animal Models of Atherosclerotic Research 259 13.3 Diagnostics and Treatment 260 13.3.1 Diagnostic Biomarkers of Atherosclerosis 260 13.3.2 Lipidomics in Efficacy and Safety Measurements 261 13.4 Conclusions 262 References 263 14 Lipid Metabolism in Neurodegenerative Diseases 269 Lynette Lim, Guanghou Shui, and Markus R. Wenk 14.1 Introduction 269 14.1.1 Brain Lipids 270 14.1.2 Mass Spectrometry of Brain Lipids 272 14.2 Alzheimer’s Disease 275 14.2.1 Cholesterol and Cholesterol Esters 276 14.2.2 Sulfatides 277 14.2.3 Plasmalogen Ethanolamines 277 14.2.4 Phospholipases 278 14.2.4.1 Phospholipase A2 278 14.2.4.2 Phospholipase C and Phospholipase D 279 14.3 Parkinson’s Disease 281 14.3.1 Cerebrosides 283 14.3.2 Coenzyme Q 284 14.3.3 Endocannabinoids 285 14.4 Conclusions 287 References 288 15 The Tumor Mitochondrial Lipidome and Respiratory Bioenergetic Insufficiency 297 Thomas N. Seyfried, Jeffrey H. Chuang, Lu Zhang, Xianlin Han, and Michael A. Kiebish 15.1 Introduction 297 15.1.1 Lipidomic Abnormalities in Tumor Mitochondria 298 15.2 Cardiolipin and Electron Transport Chain Abnormalities in Mouse Brain Tumor Mitochondria 299 15.3 Complicating Influence of the in vitro Growth Environment on Cardiolipin Composition and Energy Metabolism 307 15.4 Bioinformatic Methods to Interpret Alterations in the Mitochondrial Lipidome 311 15.5 Conclusions 314 References 314 16 Lipidomics for Pharmaceutical Research 319 Yoshinori Satomi 16.1 Introduction 319 16.2 Biomarkers for Pharmaceutical Research 320 16.3 Strategy for Biomarker Discovery 321 16.4 Conclusions 326 References 326 Index 329

Kim Ekroos currently heads the bioanalytics division at Zora Biosciences in Espoo (Finland). He holds a Ph.D. from the Technical University of Dresden (Germany) and has conducted research in the group of Professor Kai Simons and Dr. Andrej Shevchenko at the Max–Planck Institute of Molecular Cell Biology and Genetics in Dresden. Dr. Ekroos has also worked at the European Molecular Biology Laboratory in Heidelberg (Germany). He has made major contributions to the advancement of basic research on lipids and their study with advanced mass spectroscopy methods and software tools. In addition, he has pharmaceutical industry experience from Astra Zeneca where he spent three years successfully developing and utilizing high–throughput molecular lipidomics methods. Today he is focusing on applied molecular lipidomics for unscrambling the mechanistic details by which alterations in tissue–specific lipid metabolism are directly linked to the etiology of lipid–mediated disorders for the benefit of basic science, drug target and lipid biomarker discovery, and development of clinical diagnostics.