Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Extracting Biological Insight from Untargeted Lipidomics Data

  • Protocol
  • First Online:
Computational Methods and Data Analysis for Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2104))

Abstract

Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community’s ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gross RW, Han X (2011) Lipidomics at the interface of structure and function in systems biology. Chem Biol 18(3):284–291. https://doi.org/10.1016/j.chembiol.2011.01.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rustam YH, Reid GE (2018) Analytical challenges and recent advances in mass spectrometry based lipidomics. Anal Chem 90(1):374–397. https://doi.org/10.1021/acs.analchem.7b04836

    Article  CAS  PubMed  Google Scholar 

  3. Agmon E, Stockwell BR (2017) Lipid homeostasis and regulated cell death. Curr Opin Chem Biol 39:83–89. https://doi.org/10.1016/j.cbpa.2017.06.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Holthuis JC, Menon AK (2014) Lipid landscapes and pipelines in membrane homeostasis. Nature 510(7503):48–57. https://doi.org/10.1038/nature13474

    Article  CAS  PubMed  Google Scholar 

  5. Hilvo M, Denkert C, Lehtinen L, Muller B, Brockmoller S, Seppanen-Laakso T, Budczies J, Bucher E, Yetukuri L, Castillo S, Berg E, Nygren H, Sysi-Aho M, Griffin JL, Fiehn O, Loibl S, Richter-Ehrenstein C, Radke C, Hyotylainen T, Kallioniemi O, Iljin K, Oresic M (2011) Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res 71(9):3236–3245. https://doi.org/10.1158/0008-5472.can-10-3894

    Article  CAS  PubMed  Google Scholar 

  6. Lydic TA, Goo YH (2018) Lipidomics unveils the complexity of the lipidome in metabolic. diseases 7(1):4. https://doi.org/10.1186/s40169-018-0182-9

    Article  Google Scholar 

  7. Zhao YY, Miao H, Cheng XL, Wei F (2015) Lipidomics: novel insight into the biochemical mechanism of lipid metabolism and dysregulation-associated disease. Chem Biol Interact 240:220–238. https://doi.org/10.1016/j.cbi.2015.09.005

    Article  CAS  PubMed  Google Scholar 

  8. Lamari F, Mochel F, Saudubray JM (2015) An overview of inborn errors of complex lipid biosynthesis and remodelling. J Inherit Metab Dis 38(1):3–18. https://doi.org/10.1007/s10545-014-9764-x

    Article  CAS  PubMed  Google Scholar 

  9. Dautel SE, Kyle JE, Clair G, Sontag RL, Weitz KK, Shukla AK, Nguyen SN, Kim YM, Zink EM, Luders T, Frevert CW, Gharib SA, Laskin J, Carson JP, Metz TO, Corley RA, Ansong C (2017) Lipidomics reveals dramatic lipid compositional changes in the maturing postnatal lung. Sci Rep 7:40555. https://doi.org/10.1038/srep40555

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, Bandyopadhyay S, Jones KN, Kelly S, Shaner RL, Sullards CM, Wang E, Murphy RC, Barkley RM, Leiker TJ, Raetz CR, Guan Z, Laird GM, Six DA, Russell DW, McDonald JG, Subramaniam S, Fahy E, Dennis EA (2010) Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 51(11):3299–3305. https://doi.org/10.1194/jlr.M009449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van Meer G, de Kroon AI (2011) Lipid map of the mammalian cell. J Cell Sci 124(Pt 1):5–8. https://doi.org/10.1242/jcs.071233

    Article  CAS  PubMed  Google Scholar 

  12. Hu T, Zhang JL (2018) Mass-spectrometry-based lipidomics. J Sep Sci 41(1):351–372. https://doi.org/10.1002/jssc.201700709

    Article  CAS  PubMed  Google Scholar 

  13. Hyotylainen T, Oresic M (2015) Optimizing the lipidomics workflow for clinical studies—practical considerations. Anal Bioanal Chem 407(17):4973–4993. https://doi.org/10.1007/s00216-015-8633-2

    Article  CAS  PubMed  Google Scholar 

  14. Hyotylainen T, Oresic M (2016) Bioanalytical techniques in nontargeted clinical lipidomics. Bioanalysis 8(4):351–364. https://doi.org/10.4155/bio.15.244

    Article  CAS  PubMed  Google Scholar 

  15. Kyle JE, Crowell KL, Casey CP, Fujimoto GM, Kim S, Dautel SE, Smith RD, Payne SH, Metz TO (2017) LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data. Bioinformatics 33(11):1744–1746. https://doi.org/10.1093/bioinformatics/btx046

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Misra BB, Mohapatra S (2019) Tools and resources for metabolomics research community: a 2017-2018 update. Electrophoresis 40(2):227–246. https://doi.org/10.1002/elps.201800428

    Article  CAS  PubMed  Google Scholar 

  17. Fahy E, Subramaniam S, Murphy RC, Nishijima M, Raetz CR, Shimizu T, Spener F, van Meer G, Wakelam MJ, Dennis EA (2009) Update of the LIPID MAPS comprehensive classification system for lipids. J Lipid Res 50(Suppl):S9–S14. https://doi.org/10.1194/jlr.R800095-JLR200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, VanNieuwenhze MS, White SH, Witztum JL, Dennis EA (2005) A comprehensive classification system for lipids. J Lipid Res 46(5):839–861. https://doi.org/10.1194/jlr.E400004-JLR200

    Article  CAS  PubMed  Google Scholar 

  19. 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(1):497–509

    CAS  PubMed  Google Scholar 

  20. Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37(8):911–917. https://doi.org/10.1139/o59-099

    Article  CAS  PubMed  Google Scholar 

  21. 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(5):1137–1146. https://doi.org/10.1194/jlr.D700041-JLR200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liebisch G, Vizcaino JA, Kofeler H, Trotzmuller M, Griffiths WJ, Schmitz G, Spener F, Wakelam MJ (2013) Shorthand notation for lipid structures derived from mass spectrometry. J Lipid Res 54(6):1523–1530. https://doi.org/10.1194/jlr.M033506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Han X (2016) Lipidomics for studying metabolism. Nat Rev Endocrinol 12(11):668–679. https://doi.org/10.1038/nrendo.2016.98

    Article  CAS  PubMed  Google Scholar 

  24. Grosch S, Schiffmann S, Geisslinger G (2012) Chain length-specific properties of ceramides. Prog Lipid Res 51(1):50–62. https://doi.org/10.1016/j.plipres.2011.11.001

    Article  CAS  PubMed  Google Scholar 

  25. Veldhuizen R, Nag K, Orgeig S, Possmayer F (1998) The role of lipids in pulmonary surfactant. Biochim Biophys Acta 1408(2–3):90–108

    Article  CAS  PubMed  Google Scholar 

  26. Clair G, Reehl S Stratton KG, Monroe ME, Tfaily MM, Ansong C, Kyle JE (2019) Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data. Bioinformatics 35(2):4507–4508. https://doi.org/10.1093/bioinformatics/btz250

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kyle JE, Clair G, Bandyopadhyay G, Misra RS, Zink EM, Bloodsworth KJ, Shukla AK, Du Y, Lillis J, Myers JR (2018) Cell type-resolved human lung lipidome reveals cellular cooperation in lung function. Sci Rep 8(1):13455. https://doi.org/10.1038/s41598-018-31640-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kyle JE, Burnum-Johnson KE (2019) Plasma lipidome reveals critical illness and recovery from human Ebola virus disease. Proc Natl Acad Sci U S A 116(9):3919–3928. https://doi.org/10.1073/pnas.1815356116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Harayama T, Riezman H (2018) Understanding the diversity of membrane lipid composition. Nat Rev Mol Cell Biol 19(5):281–296. https://doi.org/10.1038/nrm.2017.138

    Article  CAS  PubMed  Google Scholar 

  30. van Meer G, Voelker DR, Feigenson GW (2008) Membrane lipids: where they are and how they behave. Nat Rev Mol Cell Biol 9(2):112–124. https://doi.org/10.1038/nrm2330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ghosh A, Nishtala K (2017) Biofluid lipidome: a source for potential diagnostic biomarkers. Clin Transl Med 6(1):22. https://doi.org/10.1186/s40169-017-0152-7

    Article  PubMed  PubMed Central  Google Scholar 

  32. Borghini I, Barja F, Pometta D, James RW (1995) Characterization of subpopulations of lipoprotein particles isolated from human cerebrospinal fluid. Biochim Biophys Acta 1255(2):192–200

    Article  PubMed  Google Scholar 

  33. Koch S, Donarski N, Goetze K, Kreckel M, Stuerenburg HJ, Buhmann C, Beisiegel U (2001) Characterization of four lipoprotein classes in human cerebrospinal fluid. J Lipid Res 42(7):1143–1151

    CAS  PubMed  Google Scholar 

  34. Mahley RW (2016) Central nervous system lipoproteins: ApoE and regulation of cholesterol metabolism. Arterioscler Thromb Vasc Biol 36(7):1305–1315. https://doi.org/10.1161/atvbaha.116.307023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Harrington MG, Fonteh AN, Oborina E, Liao P, Cowan RP, McComb G, Chavez JN, Rush J, Biringer RG, Huhmer AF (2009) The morphology and biochemistry of nanostructures provide evidence for synthesis and signaling functions in human cerebrospinal fluid. Cerebrospinal Fluid Res 6(10). https://doi.org/10.1186/1743-8454-6-10

  36. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, Dame ZT, Poelzer J, Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, Roehring C, Wishart DS (2013) The human urine metabolome. PLoS One 8(9):e73076. https://doi.org/10.1371/journal.pone.0073076

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dashti M, Kulik W, Hoek F, Veerman EC, Peppelenbosch MP, Rezaee F (2011) A phospholipidomic analysis of all defined human plasma lipoproteins. Sci Rep 1:139. https://doi.org/10.1038/srep00139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kim SH, Yang JS, Lee JC, Lee JY, Lee JY, Kim E, Moon MH (2018) Lipidomic alterations in lipoproteins of patients with mild cognitive impairment and Alzheimer’s disease by asymmetrical flow field-flow fractionation and nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. J Chromatogr A 1568:91–100. https://doi.org/10.1016/j.chroma.2018.07.018

    Article  CAS  PubMed  Google Scholar 

  39. Kontush A, Lhomme M, Chapman MJ (2013) Unraveling the complexities of the HDL lipidome. J Lipid Res 54(11):2950–2963. https://doi.org/10.1194/jlr.R036095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Serna J, Garcia-Seisdedos D, Alcazar A, Lasuncion MA, Busto R, Pastor O (2015) Quantitative lipidomic analysis of plasma and plasma lipoproteins using MALDI-TOF mass spectrometry. Chem Phys Lipids 189:7–18. https://doi.org/10.1016/j.chemphyslip.2015.05.005

    Article  CAS  PubMed  Google Scholar 

  41. Wiesner P, Leidl K, Boettcher A, Schmitz G, Liebisch G (2009) Lipid profiling of FPLC-separated lipoprotein fractions by electrospray ionization tandem mass spectrometry. J Lipid Res 50(3):574–585. https://doi.org/10.1194/jlr.D800028-JLR200

    Article  CAS  PubMed  Google Scholar 

  42. Hodson L, Skeaff CM, Fielding BA (2008) Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Prog Lipid Res 47(5):348–380. https://doi.org/10.1016/j.plipres.2008.03.003

    Article  CAS  PubMed  Google Scholar 

  43. Sales S, Graessler J, Ciucci S, Al-Atrib R, Vihervaara T, Schuhmann K, Kauhanen D, Sysi-Aho M, Bornstein SR, Bickle M, Cannistraci CV, Ekroos K, Shevchenko A (2016) Gender, contraceptives and individual metabolic predisposition shape a healthy plasma lipidome. Sci Rep 6:27710. https://doi.org/10.1038/srep27710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chua EC, Shui G, Lee IT, Lau P, Tan LC, Yeo SC, Lam BD, Bulchand S, Summers SA, Puvanendran K, Rozen SG, Wenk MR, Gooley JJ (2013) Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans. Proc Natl Acad Sci U S A 110(35):14468–14473. https://doi.org/10.1073/pnas.1222647110

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yuana Y, Sturk A, Nieuwland R (2013) Extracellular vesicles in physiological and pathological conditions. Blood Rev 27(1):31–39. https://doi.org/10.1016/j.blre.2012.12.002

    Article  CAS  PubMed  Google Scholar 

  46. Huang d W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  47. Huang da W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. https://doi.org/10.1093/nar/gkn923

    Article  CAS  PubMed  Google Scholar 

  48. Luo W, Brouwer C (2013) Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14):1830–1831. https://doi.org/10.1093/bioinformatics/btt285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Luo W, Pant G, Bhavnasi YK, Blanchard SG Jr, Brouwer C (2017) Pathview Web: user friendly pathway visualization and data integration. Nucleic Acids Res 45(W1):W501–w508. https://doi.org/10.1093/nar/gkx372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pedersen HK, Forslund SK, Gudmundsdottir V, Petersen AO, Hildebrand F, Hyotylainen T, Nielsen T (2018) A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat Protoc 13(12):2781–2800. https://doi.org/10.1038/s41596-018-0064-z

    Article  CAS  PubMed  Google Scholar 

  51. Eisfeld AJ, Halfmann PJ, Wendler JP, Kyle JE, Burnum-Johnson KE, Peralta Z, Maemura T, Walters KB, Watanabe T, Fukuyama S, Yamashita M, Jacobs JM, Kim YM, Casey CP, Stratton KG, Webb-Robertson BM, Gritsenko MA, Monroe ME, Weitz KK, Shukla AK, Tian M, Neumann G, Reed JL, van Bakel H, Metz TO, Smith RD, Waters KM, N’Jai A, Sahr F, Kawaoka Y (2017) Multi-platform ‘Omics analysis of human Ebola virus disease pathogenesis. Cell Host Microbe 22(6):817–829.e818. https://doi.org/10.1016/j.chom.2017.10.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

I would like thank Geremy Clair and Ernesto S. Nakayasu for their comments and careful review of the manuscript. This work was supported by an administrative supplement to grant U19AI106772, provided by the National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Health (NIH) (USA). Research conducted on the lung samples were supported by grant HL122703 from the National Heart Lung Blood Institute of NIH.

Author information

Authors and Affiliations

  1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Jennifer E. Kyle

Authors
  1. Jennifer E. Kyle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer E. Kyle .

Editor information

Editors and Affiliations

  1. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Shuzhao Li

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kyle, J.E. (2020). Extracting Biological Insight from Untargeted Lipidomics Data. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0239-3_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0238-6

  • Online ISBN: 978-1-0716-0239-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation