Nontargeted Brain Lipidomic Profiling Performed by UPLC-ESI-qToF-MS/MS

  • Alba Naudí
  • Rosanna Cabré
  • Mariona Jové
  • Reinald Pamplona
Protocol
Part of the Neuromethods book series (NM, volume 127)

Abstract

Lipidomics is a newly emerged discipline that has made a significant impact in neurobiological research, and it is defined as “the full characterization of lipid molecular species and of their biological roles with respect to expression of proteins involved in lipid metabolism and function, including gene regulation.” Lipids play diverse roles in brain cellular function which is reflected by an enormous variation in the structures of lipid molecules. The study of brain lipidomics can help to unravel the diversity and to disclose the specificity of these lipid traits and its alterations at physiologic and pathologic level and confer novel insights pertaining to the related pathogenesis and unveil potential markers to facilitate early disease diagnosis. In this chapter we detail a nontargeted approach to determine the global lipidomic profile of brain samples using ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC-ESI-qToF-MS/MS).

Key words

Mass spectrometry Lipidomics Brain Chromatography 

References

  1. 1.
    Pawson T, Nash P (2003) Assembly of cell regulatory systems through protein interaction domains. Science 300:445–452. doi:10.1126/science.1083653 CrossRefPubMedGoogle Scholar
  2. 2.
    Piomelli D, Astarita G, Rapaka R (2007) A neuroscientist’s guide to lipidomics. Nat Rev Neurosci 8:743–754. doi:10.1038/nrn2233 CrossRefPubMedGoogle Scholar
  3. 3.
    Farooqui AA (2009) Lipid mediators in the neural cell nucleus: their metabolism, signaling, and association with neurological disorders. Neuroscientist 15:392–407. doi:10.1177/1073858409337035 CrossRefPubMedGoogle Scholar
  4. 4.
    Gross RW, Han X (2011) Lipidomics at the interface of structure and function in systems biology. Chem Biol 18:284–291. doi:10.1016/j.chembiol.2011.01.014 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Fahy E, Cotter D, Sud M, Subramaniam S (2011) Lipid classification, structures and tools. Biochim Biophys Acta Mol Cell Biol Lipids 1811:637–647. doi:10.1016/j.bbalip.2011.06.009 CrossRefGoogle Scholar
  6. 6.
    Lipid M (2016) Lipid MAPS Lipidomics Gateway. http://www.lipidmaps.org/. Accessed 13 Dec 2016
  7. 7.
    Han X (2007) Neurolipidomics: challenges and developments. Front Biosci 12:2601–2615. doi:10.2741/2258 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Naudí A, Cabré R, Jové M, Ayala V, Gonzalo H, Portero-Otín M, Ferrer I, Pamplona R (2015) Lipidomics of human brain aging and Alzheimer’s disease pathology. Int Rev Neurobiol 122:133–189. doi:10.1016/bs.irn.2015.05.008 CrossRefPubMedGoogle Scholar
  9. 9.
    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 44:1071–1079. doi:10.1194/jlr.R300004-JLR200 CrossRefPubMedGoogle Scholar
  10. 10.
    Trushina E, Mielke MM (2014) Recent advances in the application of metabolomics to Alzheimer’s disease. Biochim Biophys Acta 1842:1232–1239. doi:10.1016/j.bbadis.2013.06.014 CrossRefPubMedGoogle Scholar
  11. 11.
    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. doi:10.1002/mas.20023 CrossRefPubMedGoogle Scholar
  12. 12.
    Wang M, Han X (2016) Advanced shotgun lipidomics for characterization of altered lipid patterns in neurodegenerative diseases and brain injury. Methods Mol Biol 1303:405–422. doi:10.1007/978-1-4939-2627-5 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Amoscato AA, Sparvero LJ, He RR, Watkins S, Bayir H, Kagan VE (2014) Imaging mass spectrometry of diversified cardiolipin molecular species in the brain. Anal Chem 86:6587–6595CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Chen Y, Allegood J, Liu Y, Wang E, Cachón-González B, Cox TM, Merrill AH, Sullards MC (2008) Imaging MALDI mass spectrometry using an oscillating capillary nebulizer matrix coating system and its application to analysis of lipids in brain from a mouse model of Tay-Sachs/Sandhoff disease. Anal Chem 80:2780–2788. doi:10.1021/ac702350g CrossRefPubMedGoogle Scholar
  15. 15.
    Yang K, Han X (2016) Lipidomics: techniques, applications, and outcomes related to biomedical sciences. Trends Biochem Sci 41:954–969. doi:10.1016/j.tibs.2016.08.010 CrossRefPubMedGoogle Scholar
  16. 16.
    Seppänen-Laakso T, Orešič M (2009) How to study lipidomes. J Mol Endocrinol 42:185–190. doi:10.1677/JME-08-0150 CrossRefPubMedGoogle Scholar
  17. 17.
    Watson AD (2006) Lipidomics: a global approach to lipid analysis in biological systems. J Lipid Res 47:2101–2111. doi:10.1194/jlr.R600022-JLR200 CrossRefPubMedGoogle Scholar
  18. 18.
    Jové M, Naudí A, Gambini J, Borras C, Cabré R, Portero-Otín M, Viña J, Pamplona R (2017) A stress-resistant lipidomic signature confers extreme longevity to humans. J Gerontol A Biol Sci Med Sci 72:30–37. doi:10.1093/gerona/glw048 CrossRefPubMedGoogle Scholar
  19. 19.
    Pizarro C, Arenzana-Rámila I, Pérez-Del-Notario N, Pérez-Matute P, González-Sáiz JM (2013) Plasma lipidomic profiling method based on ultrasound extraction and liquid chromatography mass spectrometry. Anal Chem 85:12085–12092. doi:10.1021/ac403181c CrossRefPubMedGoogle Scholar
  20. 20.
    Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D (2008) Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res 49:1137–1146. doi:10.1194/jlr.D700041-JLR200 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK (2013) Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 8:17–32. doi:10.1038/nprot.2012.135 CrossRefPubMedGoogle Scholar
  22. 22.
    Dane AD, Hendriks MMWB, Reijmers TH, Harms AC, Troost J, Vreeken RJ, Boomsma DI, Van Duijn CM, Slagboom EP, Hankemeier T (2014) Integrating metabolomics profiling measurements across multiple biobanks. Anal Chem 86:4110–4114. doi:10.1021/ac404191a CrossRefPubMedGoogle Scholar
  23. 23.
    Castro-Perez JM, Kamphorst J, Degroot J, Lafeber F, Goshawk J, Yu K, Shockcor JP, Vreeken RJ, Hankemeier T (2010) Comprehensive LC-MSE lipidomic analysis using a shotgun approach and its application to biomarker detection and identification in osteoarthritis patients. J Proteome Res 9:2377–2389. doi:10.1021/pr901094j CrossRefPubMedGoogle Scholar
  24. 24.
    Sana TR, Roark JC, Li X, Waddell K, Fischer SM (2008) Molecular formula and METLIN personal metabolite database matching applied to the identification of compounds generated by LC/TOF-MS. J Biomol Tech 19:258–266PubMedPubMedCentralGoogle Scholar
  25. 25.
    Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 43:W251–W257. doi:10.1093/nar/gkv380 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37:652–660. doi:10.1093/nar/gkp356 CrossRefGoogle Scholar
  27. 27.
    Embade N, Mariño Z, Diercks T, Cano A, Lens S, Cabrera D, Navasa M, Falcón-Pérez JM, Caballería J, Castro A, Bosch J, Mato JM, Millet O (2016) Metabolic characterization of advanced liver fibrosis in HCV patients as studied by serum 1H-NMR spectroscopy. PLoS One 11:1–19. doi:10.1371/journal.pone.0155094 CrossRefGoogle Scholar
  28. 28.
    Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychol Methods 14:323–348. doi:10.1037/a0016973.An CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Kind T, Liu K, Lee DY, DeFelice B, Meissen JK, Fiehn O (2013) LipidBlast-in silico tandem mass spectrometry database for lipid identification. Nat Methods 10:755–758. doi:10.4315/0362-028X.JFP-13-395.Knowledge CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    MEDLINE®/PubMed® Resources Guide U.S. National Library of Medicine. https://www.nlm.nih.gov/bsd/pmresources.html#. Accessed 15 Dec 2016
  31. 31.
    Ferrer I (2015) Selection of controls in the study of human neurodegenerative diseases in old age. J Neural Transm 122(7):941. doi:10.1007/s00702-014-1287-y CrossRefPubMedGoogle Scholar
  32. 32.
    Cabré R, Jové M, Naudí A, Ayala V, Piñol-Ripoll G, Gil-Villar MP, Dominguez-Gonzalez M, Obis È, Berdun R, Mota-Martorell N, Portero-Otin M, Ferrer I, Pamplona R (2016) Specific metabolomics adaptations define a differential regional vulnerability in the adult human cerebral cortex. Front Mol Neurosci 9:138. doi:10.3389/fnmol.2016.00138 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Folch J, Lees M, Sloane GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226(1):497–509PubMedGoogle Scholar
  34. 34.
    Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917. doi:10.1139/o59-099 CrossRefPubMedGoogle Scholar
  35. 35.
    Jové M, Portero-Otín M, Naudí A, Ferrer I, Pamplona R (2014) Metabolomics of human brain aging and age-related neurodegenerative diseases. J Neuropathol Exp Neurol 73:640–657. doi:10.1097/NEN.0000000000000091 CrossRefPubMedGoogle Scholar
  36. 36.
    Jové M, Ayala V, Ramírez-Núñez O, Naudí A, Cabré R, Spickett CM, Portero-Otín M, Pamplona R (2013) Specific lipidome signatures in central nervous system from methionine-restricted mice. J Proteome Res 12:2679–2689. doi:10.1021/pr400064a CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Alba Naudí
    • 1
  • Rosanna Cabré
    • 1
  • Mariona Jové
    • 1
  • Reinald Pamplona
    • 1
  1. 1.Department of Experimental MedicineUniversity of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida)LleidaSpain

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