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Deciphering lipid structures based on platform-independent decision rules

Nature Methods volume 14, pages 1171–1174 (2017)Cite this article

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Abstract

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

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Figure 1: Tandem mass spectra of lipid molecular species depend on platform and collision energy.

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Acknowledgements

Support by the Austrian Science Fund (FWF Project Grant P26148 to G.G.T.) and the Austrian Ministry for Science, Research and Economy (HSRSM Grant Omics Center Graz, BioTechMed-Graz to G.G.T.) is gratefully acknowledged. M.J.O.W. and Q.Z. were funded by the BBSRC (UK; Grant BBS/E/B/000C0415). We thank R. Salek for his extensive help in MetaboLights upload. Furthermore, we thank AB Sciex, Agilent Technologies, Bruker Daltonics, and Thermo Fisher Scientific for providing permission to distribute the WiffReader SDK, the MassHunter DAC, the CompassXtract, and the MSFileReader libraries in the software.

Author information

Author notes

  1. Jürgen Hartler and Alexander Triebl: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute of Computational Biotechnology, Graz University of Technology, Graz, Austria

    Jürgen Hartler, Andreas Ziegl & Gerhard G Thallinger

  2. Center for Medical Research, Medical University of Graz, Graz, Austria

    Jürgen Hartler, Alexander Triebl, Martin Trötzmüller & Harald C Köfeler

  3. Omics Center Graz, BioTechMed-Graz, Graz, Austria

    Jürgen Hartler, Martin Trötzmüller, Gerald N Rechberger, Harald C Köfeler & Gerhard G Thallinger

  4. Department of Molecular Biosciences, University of Graz, Graz, Austria

    Gerald N Rechberger, Kathrin A Zierler, Guenter Haemmerle & Friedrich Spener

  5. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Oana A Zeleznik

  6. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA

    Oana A Zeleznik

  7. Singapore Lipidomics Incubator, National University of Singapore, Singapore

    Federico Torta, Amaury Cazenave-Gassiot & Markus R Wenk

  8. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Alexander Fauland & Craig E Wheelock

  9. School of Medicine, University of California San Diego, La Jolla, California, USA

    Aaron M Armando & Oswald Quehenberger

  10. The Babraham Institute, Babraham Research Campus, Cambridge, UK

    Qifeng Zhang & Michael J O Wakelam

  11. Department of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria

    Friedrich Spener

Authors
  1. Jürgen Hartler
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  2. Alexander Triebl
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  3. Andreas Ziegl
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  4. Martin Trötzmüller
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  5. Gerald N Rechberger
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  6. Oana A Zeleznik
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  7. Kathrin A Zierler
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  8. Federico Torta
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  9. Amaury Cazenave-Gassiot
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  10. Markus R Wenk
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  11. Alexander Fauland
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  12. Craig E Wheelock
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  13. Aaron M Armando
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  14. Oswald Quehenberger
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  15. Qifeng Zhang
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  16. Michael J O Wakelam
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  17. Guenter Haemmerle
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  18. Friedrich Spener
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  19. Harald C Köfeler
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  20. Gerhard G Thallinger
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Contributions

J.H., A.T., M.T., F.S., G.H., H.C.K., and G.G.T. designed the study. J.H., A.T., M.T., G.N.R., and H.C.K. designed the experiments. K.A.Z. and G.H. provided the biological samples. A.T., M.T., G.N.R., F.T., A.C.-G., M.R.W., A.F., C.E.W., A.M.A., O.Q., Q.Z., and M.J.O.W. designed and performed the mass spectrometric experiments. J.H. and A.Z. implemented the algorithm and the software. J.H., A.T., O.A.Z., and H.C.K. developed the decision rule sets. J.H. and A.T. benchmarked the algorithm in comparison to LipidBlast and prepared the spectral evidence for the novel species. J.H. and H.C.K. prepared and uploaded the data in MetaboLights. J.H., A.T., F.S., and G.G.T. wrote the manuscript in cooperation with all contributing authors.

Corresponding authors

Correspondence to Harald C Köfeler or Gerhard G Thallinger.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14 and Supplementary Notes 1–5 (PDF 7548 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Supplementary Table 1

Overview of experiments performed and mass spectrometric platforms used. (XLSX 9 kb)

Supplementary Table 2

Lipid standards for control experiment 1. (XLSX 271 kb)

Supplementary Table 3

Isomeric pairs of lipid standards for different lipid subclasses applied in control experiment 2. (XLSX 29 kb)

Supplementary Table 4

Data evaluation of isomeric subclasses/adducts (control experiment 2). (XLSX 8 kb)

Supplementary Table 5

Pairs of structurally isomeric lipid standards for control experiment 3. (XLSX 48 kb)

Supplementary Table 6

Data evaluation of structurally isomeric pairs (described in Supplementary Table 5) measured in positive ion mode (control experiment 3). (XLSX 13 kb)

Supplementary Table 7

Data evaluation of structurally isomeric pairs (described in Supplementary Table 5) measured in negative ion mode (control experiment 3). (XLSX 12 kb)

Supplementary Table 8

Novel lipid molecular species identified in murine liver. (XLSX 14 kb)

Supplementary Table 9

Lipid species and lipid molecular species from murine liver detected by the seven MS/MS platforms and correctly identified by LDA. (XLSX 570 kb)

Supplementary Table 10

Overview on cross-platform species detection of seven MS/MS platforms based on murine liver samples. (XLSX 211 kb)

Supplementary Table 11

Sensitivity and positive predictive value (PPV) of LDA and LipidBlast (LB) in negative ion mode based on data acquired on Orbitrap Velos Pro in CID mode. (XLSX 9 kb)

Supplementary Table 12

Sensitivity and positive predictive value (PPV) of LDA and LipidBlast (LB) in positive ion mode based on data acquired on 4000 QTRAP. (XLSX 9 kb)

Supplementary Table 13

Sensitivity and positive predictive value (PPV) of LDA and LipidBlast (LB) in negative ion mode based on data acquired on 4000 QTRAP. (XLSX 9 kb)

Supplementary Table 14

Data evaluation of regio-isomeric pairs of chromatographically separated sn-1,2- and sn-1,3-diacylglycerols (DG). (XLSX 10 kb)

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Hartler, J., Triebl, A., Ziegl, A. et al. Deciphering lipid structures based on platform-independent decision rules. Nat Methods 14, 1171–1174 (2017). https://doi.org/10.1038/nmeth.4470

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