Hybrid SWATH/MS and HR-SRM/MS acquisition for phospholipidomics using QUAL/QUANT data processing

  • Michel Raetz
  • Eva Duchoslav
  • Ron Bonner
  • Gérard HopfgartnerEmail author
Research Paper


A hybrid SWATH/MS and HR-SRM/MS acquisition approach using multiple unit mass windows and 100 u precursor selection windows has been developed to interface with a chromatographic lipid class separation. The method allows for the simultaneous monitoring of sum compositions in MS1 and up to 48 lipids in MS2 per lipid class. A total of 240 lipid sum compositions from five phospholipid classes could be monitored in MS2 (HR-SRM/MS) while there was no limitation in the number of analytes in MS1 (HR-SIM/MS). On average, 92 lipid sum compositions and 75 lipid species could be quantified in human plasma samples. The robustness and precision of the workflow has been assessed using technical triplicates of the subject samples. Lipid identification was improved using a combined qualitative and quantitative data processing based on prediction instead of library search. Lipid class specific extracted ion currents of precursors and the corresponding molecular species fragments were extracted based on the information obtained from lipid building blocks and a combinatorial strategy. The SWATH/MS approach with the post-acquisition processing is not limited to the analyzed phospholipid classes and can be applied to other analytes and samples of interest.

Graphical abstract


Glycerophospholipids Plasma HILIC SWATH QUAL/QUANT Data processing 



The authors are grateful to Yves J.C. LeBlanc (Sciex) for valuable discussion on the setup of the SWATH experiments. GH would like to thank SystemsX and the Swiss National Sciences Foundation for the financial support: Projects 51RTP0_151032 and 206021_170779.

Compliance with ethical standards

Anonymized plasma samples were provided by the Centre de Transfusion Sanguine, University Hospital Geneva, Geneva, Switzerland. The Human Research Act (HRA) does not apply for the anonymized plasma samples analyzed in the present work (Art. 2 para. 2 let. b and c).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2019_1946_MOESM1_ESM.pdf (242 kb)
ESM 1 (PDF 241 kb)


  1. 1.
    Chandler PD, Song Y, Lin J, Zhang S, Sesso HD, Mora S, et al. Lipid biomarkers and long-term risk of cancer in the Women’s Health Study. Am J Clin Nutr. 2016;103(6):1397–407. Scholar
  2. 2.
    Sigruener A, Kleber ME, Heimerl S, Liebisch G, Schmitz G, Maerz W. Glycerophospholipid and sphingolipid species and mortality: the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. PLoS One. 2014;9(1):e85724. Scholar
  3. 3.
    Pena-Bautista C, Vigor C, Galano JM, Oger C, Durand T, Ferrer I, et al. Plasma lipid peroxidation biomarkers for early and non-invasive Alzheimer disease detection. Free Radic Biol Med. 2018;124:388–94. Scholar
  4. 4.
    Cai T, Yang F. Phospholipid and phospholipidomics in health and diseases. In: Wang X, Wu D, Shen H, editors. Lipidomics in health & disease: methods & application. Singapore: Springer Singapore; 2018. p. 177–202.CrossRefGoogle Scholar
  5. 5.
    Liebisch G, Ekroos K, Hermansson M, Ejsing CS. Reporting of lipidomics data should be standardized. Biochim Biophys Acta Mol Cell Biol Lipids. 2017;1862(8):747–51. Scholar
  6. 6.
    Burla B, Arita M, Arita M, Bendt AK, Cazenave-Gassiot A, Dennis EA, et al. MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines. J Lipid Res. 2018;59(10):2001–17. Scholar
  7. 7.
    Fahy E, Sud M, Cotter D, Subramaniam S. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 2007;35(Web Server issue):W606–12. Scholar
  8. 8.
    Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res. 2010;51(11):3299–305. Scholar
  9. 9.
    Takeda H, Izumi Y, Takahashi M, Paxton T, Tamura S, Koike T, et al. Widely-targeted quantitative lipidomics method by supercritical fluid chromatography triple quadrupole mass spectrometry. J Lipid Res. 2018;59(7):1283–93. Scholar
  10. 10.
    Lisa M, Holcapek M. High-throughput and comprehensive lipidomic analysis using ultrahigh-performance supercritical fluid chromatography-mass spectrometry. Anal Chem. 2015;87(14):7187–95. Scholar
  11. 11.
    Cifkova E, Holcapek M, Lisa M, Ovcacikova M, Lycka A, Lynen F, et al. Nontargeted quantitation of lipid classes using hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry with single internal standard and response factor approach. Anal Chem. 2012;84(22):10064–70. Scholar
  12. 12.
    Almeida R, Pauling JK, Sokol E, Hannibal-Bach HK, Ejsing CS. Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer. J Am Soc Mass Spectrom. 2015;26(1):133–48. Scholar
  13. 13.
    Donot F, Cazals G, Gunata Z, Egron D, Malinge J, Strub C, et al. Analysis of neutral lipids from microalgae by HPLC-ELSD and APCI-MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2013;942-943:98–106. Scholar
  14. 14.
    Hines KM, Herron J, Xu L. Assessment of altered lipid homeostasis by HILIC-ion mobility-mass spectrometry-based lipidomics. J Lipid Res. 2017;58(4):809–19. Scholar
  15. 15.
    Schwalbe-Herrmann M, Willmann J, Leibfritz D. Separation of phospholipid classes by hydrophilic interaction chromatography detected by electrospray ionization mass spectrometry. J Chromatogr A. 2010;1217(32):5179–83. Scholar
  16. 16.
    Lintonen TPI, Baker PRS, Suoniemi M, Ubhi BK, Koistinen KM, Duchoslav E, et al. Differential mobility spectrometry-driven shotgun lipidomics. Anal Chem. 2014;86(19):9662–9. Scholar
  17. 17.
    Shvartsburg AA, Isaac G, Leveque N, Smith RD, Metz TO. Separation and classification of lipids using differential ion mobility spectrometry. J Am Soc Mass Spectrom. 2011;22(7):1146–55. Scholar
  18. 18.
    Ghaste M, Mistrik R, Shulaev V. Applications of Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap based high resolution mass spectrometry in metabolomics and lipidomics. Int J Mol Sci. 2016;17(6).
  19. 19.
    Schuhmann K, Almeida R, Baumert M, Herzog R, Bornstein SR, Shevchenko A. Shotgun lipidomics on a LTQ Orbitrap mass spectrometer by successive switching between acquisition polarity modes. J Mass Spectrom. 2012;47(1):96–104. Scholar
  20. 20.
    Triebl A, Trotzmuller M, Hartler J, Stojakovic T, Kofeler HC. Lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry and its application to complex biological samples. J Chromatogr B Analyt Technol Biomed Life Sci. 2017;1053:72–80. Scholar
  21. 21.
    Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012;11(6):O111 016717. Scholar
  22. 22.
    Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio-De-Castillia C, Lisacek F, et al. The use of variable Q1 isolation windows improves selectivity in LC–SWATH–MS acquisition. J Proteome Res. 2015;14(10):4359–71. Scholar
  23. 23.
    Simons B, Kauhanen D, Sylvanne T, Tarasov K, Duchoslav E, Ekroos K. Shotgun lipidomics by sequential precursor ion fragmentation on a hybrid quadrupole time-of-flight mass spectrometer. Metabolites. 2012;2(1):195–213. Scholar
  24. 24.
    Zhou J, Liu C, Si D, Jia B, Zhong L, Yin Y. Workflow development for targeted lipidomic quantification using parallel reaction monitoring on a quadrupole-time of flight mass spectrometry. Anal Chim Acta. 2017;972:62–72. Scholar
  25. 25.
    Rampler E, Criscuolo A, Zeller M, El Abiead Y, Schoeny H, Hermann G, et al. A novel lipidomics workflow for improved human plasma identification and quantification using RPLC-MSn methods and isotope dilution strategies. Anal Chem. 2018;90(11):6494–501. Scholar
  26. 26.
    Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10(8):755–8. Scholar
  27. 27.
    Hutchins PD, Russell JD, Coon JJ. LipiDex: an integrated software package for high-confidence lipid identification. Cell Syst. 2018;6(5):621–5 e5. Scholar
  28. 28.
    Husen P, Tarasov K, Katafiasz M, Sokol E, Vogt J, Baumgart J, et al. Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One. 2013;8(11):e79736. Scholar
  29. 29.
    Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12(6):523–6. Scholar
  30. 30.
    Folch J, Lees M, Sloane Stanley GH. A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 1957;226(1):497–509.Google Scholar
  31. 31.
    Raetz M, Duchoslav E, Bonner R, Hopfgartner G (2018) High resolution selected reaction monitoring based quantification of phospholipids using unit mass SWATH acquisition and targeted data processing. Proceedings of the 66th/nd ASMS conference on mass spectrometry and allied topics 2018.Google Scholar
  32. 32.
    Bonner R, Hopfgartner G. SWATH acquisition mode for drug metabolism and metabolomics investigations. Bioanalysis. 2016;8(16):1735–50. Scholar
  33. 33.
    Bowden JA, Heckert A, Ulmer CZ, Jones CM, Koelmel JP, Abdullah L, et al. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-metabolites in frozen human plasma. J Lipid Res. 2017;58(12):2275–88. Scholar
  34. 34.
    Ejsing CS, Duchoslav E, Sampaio J, Simons K, Bonner R, Thiele C, et al. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Anal Chem. 2006;78(17):6202–14. Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Life Sciences Mass Spectrometry, Department of Inorganic and Analytical ChemistryUniversity of GenevaGeneva 4Switzerland
  2. 2.SCIEXConcordCanada
  3. 3.Ron Bonner ConsultingNewmarketCanada

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