Advertisement

Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries

  • Paul D. Hutchins
  • Jason D. Russell
  • Joshua J. CoonEmail author
Research Article
First Online:

Abstract

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow.

Graphical Abstract

ᅟ

Keywords

Lipidomics Mass spectrometry Spectral libraries In silico fragmentation modeling Lipid identifications 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

We gratefully acknowledge support from the National Institutes of Health Grant P41 GM108538 (awarded to J.J.C.) and the Morgridge Institute for Research Metabolism Theme. We also acknowledge support from the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Award Numbers DE-SC0018409 and DE-FC02-07ER64494. Additionally, we thank the Pagliarini Lab for generating the HAP1 cell lipid extracts.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no competing interests.

Supplementary material

13361_2018_2125_MOESM1_ESM.pdf (2.9 mb)
ESM 1 (PDF 2936 kb)

References

  1. 1.
    Domingo-Almenara, X., Montenegro-Burke, J.R., Benton, H.P., Siuzdak, G.: Annotation: a computational solution for streamlining metabolomics analysis. Anal. Chem. 90, 480–489 (2018)CrossRefGoogle Scholar
  2. 2.
    Wolf, S., Schmidt, S., Müller-Hannemann, M., Neumann, S.: In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics. 11, (2010).  https://doi.org/10.1186/1471-2105-11-148
  3. 3.
    Blaženović, I., Kind, T., Ji, J., Fiehn, O.: Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Meta. 8, 31 (2018)Google Scholar
  4. 4.
    Eng, J.K., Mccormack, A.L., Yates, J.R.: An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994)CrossRefGoogle Scholar
  5. 5.
    Hebert, A.S., Richards, A.L., Bailey, D.J., Ulbrich, A., Coughlin, E.E., Westphall, M.S., Coon, J.J.: The one hour yeast proteome. Mol. Cell. Proteomics. 13, 339–347 (2014)CrossRefGoogle Scholar
  6. 6.
    Bekker-Jensen, D.B., Kelstrup, C.D., Batth, T.S., Larsen, S.C., Haldrup, C., Bramsen, J.B., Sørensen, K.D., Høyer, S., Ørntoft, T.F., Andersen, C.L., Nielsen, M.L., Olsen, J.V.: An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599.e4 (2017)CrossRefGoogle Scholar
  7. 7.
    Kind, T., Tsugawa, H., Cajka, T., Ma, Y., Lai, Z., Mehta, S.S., Wohlgemuth, G., Barupal, D.K., Showalter, M.R., Arita, M., Fiehn, O.: Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev. 1–20 (2017).  https://doi.org/10.1002/mas.21535
  8. 8.
    Wishart, D.S., Knox, C., Guo, A.C., Eisner, R., Young, N., Gautam, B., Hau, D.D., Psychogios, N., Dong, E., Bouatra, S., Mandal, R., Sinelnikov, I., Xia, J., Jia, L., Cruz, J.A., Lim, E., Sobsey, C.A., Shrivastava, S., Huang, P., Liu, P., Fang, L., Peng, J., Fradette, R., Cheng, D., Tzur, D., Clements, M., Lewis, A., de Souza, A., Zuniga, A., Dawe, M., Xiong, Y., Clive, D., Greiner, R., Nazyrova, A., Shaykhutdinov, R., Li, L., Vogel, H.J., Forsythei, I.: HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, 603–610 (2009)CrossRefGoogle Scholar
  9. 9.
    Kind, T., Liu, K.-H., Lee, D.Y., DeFelice, B., Meissen, J.K., Fiehn, O.: LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods. 10, 755–758 (2013)CrossRefGoogle Scholar
  10. 10.
    Koelmel, J.P., Kroeger, N.M., Ulmer, C.Z., Bowden, J.A., Patterson, R.E., Cochran, J.A., Beecher, C.W.W., Garrett, T.J., Yost, R.A.: LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics. 18, 331 (2017)CrossRefGoogle Scholar
  11. 11.
    Hartler, J., Triebl, A., Ziegl, A., Trötzmüller, M., Rechberger, G.N., Zeleznik, O.A., Zierler, K.A., Torta, F., Cazenave-Gassiot, A., Wenk, M.R., Fauland, A., Wheelock, C.E., Armando, A.M., Quehenberger, O., Zhang, Q., Wakelam, M.J.O., Haemmerle, G., Spener, F., Köfeler, H.C., Thallinger, G.G.: Deciphering lipid structures based on platform-independent decision rules. Nat. Methods. 14, 1171–1174 (2017)CrossRefGoogle Scholar
  12. 12.
    Kind, T., Okazaki, Y., Saito, K., Fiehn, O.: LipidBlast templates as flexible tools for creating new in-silico tandem mass spectral libraries. Anal. Chem. 86, 11024–11027 (2014)CrossRefGoogle Scholar
  13. 13.
    Ma, Y., Kind, T., Vaniya, A., Gennity, I., Fahrmann, J.F., Fiehn, O.: An in silico MS/MS library for automatic annotation of novel FAHFA lipids. J. Cheminform. 7, 53 (2015)CrossRefGoogle Scholar
  14. 14.
    Tsugawa, H., Ikeda, K., Tanaka, W., Senoo, Y., Arita, M., Arita, M.: Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library. J. Cheminform. 9, 1–12 (2017)CrossRefGoogle Scholar
  15. 15.
    Hsu, F.F., Turk, J.: Electrospray ionization with low-energy collisionally activated dissociation tandem mass spectrometry of glycerophospholipids: mechanisms of fragmentation and structural characterization. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 877, 2673–2695 (2009)CrossRefGoogle Scholar
  16. 16.
    Pauling, J.K., Hermansson, M., Hartler, J., Christiansen, K., Gallego, S.F., Peng, B., Ahrends, R., Ejsing, C.S.: Proposal for a common nomenclature for fragment ions in mass spectra of lipids. PLoS One. 12, (2017).  https://doi.org/10.1371/journal.pone.0188394
  17. 17.
    Stein, S.E., Scott, D.R.: Optimization and testing of mass-spectral library search algorithms for compound identification. J. Am. Soc. Mass Spectrom. 5, 859–866 (1994)CrossRefGoogle Scholar
  18. 18.
    Kochen, M.A., Chambers, M.C., Holman, J.D., Nesvizhskii, A.I., Weintraub, S.T., Belisle, J.T., Islam, M.N., Griss, J., Tabb, D.L.: Greazy: open-source software for automated phospholipid tandem mass spectrometry identification. Anal. Chem. 88, 5733–5741 (2016)CrossRefGoogle Scholar
  19. 19.
    Hutchins, P.D., Russell, J.D., Coon, J.J.: LipiDex: an integrated software package for high-confidence lipid identification. Cell Syst. 6, 621–625.e5 (2018)CrossRefGoogle Scholar
  20. 20.
    Chambers, M.C., MacLean, B., Burke, R., Amodei, D., Ruderman, D.L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., Hoff, K., Kessner, D., Tasman, N., Shulman, N., Frewen, B., Baker, T.A., Brusniak, M.Y., Paulse, C., Creasy, D., Flashner, L., Kani, K., Moulding, C., Seymour, S.L., Nuwaysir, L.M., Lefebvre, B., Kuhlmann, F., Roark, J., Rainer, P., Detlev, S., Hemenway, T., Huhmer, A., Langridge, J., Connolly, B., Chadick, T., Holly, K., Eckels, J., Deutsch, E.W., Moritz, R.L., Katz, J.E., Agus, D.B., MacCoss, M., Tabb, D.L., Mallick, P.: A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012)CrossRefGoogle Scholar
  21. 21.
    Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., Arita, M.: MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods. 12, 523–526 (2015)CrossRefGoogle Scholar
  22. 22.
    Lam, H., Deutsch, E.W., Eddes, J.S., Eng, J.K., King, N., Stein, S.E., Aebersold, R.: Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics. 7, 655–667 (2007)CrossRefGoogle Scholar
  23. 23.
    Lam, H., Deutsch, E.W., Eddes, J.S., Eng, J.K., Stein, S.E., Aebersold, R.: Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods. 5, 873–875 (2008)CrossRefGoogle Scholar
  24. 24.
    Han, X.: Fragmentation patterns of glycerolipids. In: Lipidomics: comprehensive mass spectrometry of lipids, pp. 217–228. John Wiley & Sons, Ltd, Hoboken (2016)CrossRefGoogle Scholar
  25. 25.
    Ryan, E., Nguyen, C.Q.N., Shiea, C., Reid, G.E.: Detailed structural characterization of sphingolipids via 193 nm ultraviolet photodissociation and ultra high resolution tandem mass spectrometry. J. Am. Soc. Mass Spectrom. 28, 1406–1419 (2017)CrossRefGoogle Scholar
  26. 26.
    Klein, D.R., Brodbelt, J.S.: Structural characterization of phosphatidylcholines using 193 nm ultraviolet photodissociation mass spectrometry. Anal. Chem. 89, 1516–1522 (2017)CrossRefGoogle Scholar
  27. 27.
    Thomas, M.C., Mitchell, T.W., Harman, D.G., Deeley, J.M., Nealon, J.R., Blanksby, S.J.: Ozone-induced dissociation: elucidation of double bond position within mass-selected lipid ions. Anal. Chem. 80, 303–311 (2008)CrossRefGoogle Scholar
  28. 28.
    Han, X.: Lipidomics: comprehensive mass spectrometry of lipids. John Wiley & Sons, Hoboken (2016)Google Scholar
  29. 29.
    Han, X., Yang, K., Gross, R.W.: Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom. Rev. 31, 134–178 (2012)CrossRefGoogle Scholar
  30. 30.
    Herzog, R., Schuhmann, K., Schwudke, D., Sampaio, J.L., Bornstein, S.R., Schroeder, M., Shevchenko, A.: Lipidxplorer: a software for consensual cross-platform lipidomics. PLoS One. 7, 15–20 (2012)Google Scholar
  31. 31.
    Herzog, R., Schwudke, D., Schuhmann, K., Sampaio, J.L., Bornstein, S.R., Schroeder, M., Shevchenko, A.: A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol. 12, R8 (2011)CrossRefGoogle Scholar
  32. 32.
    Shao, W., Zhu, K., Lam, H.: Refining similarity scoring to enable decoy-free validation in spectral library searching. Proteomics. 13, 3273–3283 (2013)CrossRefGoogle Scholar
  33. 33.
    Keller, A., Nesvizhskii, A.I., Kolker, E., Aebersold, R.: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002)CrossRefGoogle Scholar
  34. 34.
    Bowden, J.A., Heckert, A., Ulmer, C.Z., Jones, C.M., Koelmel, J.P., Abdullah, L., Ahonen, L., Alnouti, Y., Armando, A.M., Asara, J.M., Bamba, T., Barr, J.R., Bergquist, J., Borchers, C.H., Brandsma, J., Breitkopf, S.B., Cajka, T., Cazenave-Gassiot, A., Checa, A., Cinel, M.A., Colas, R.A., Cremers, S., Dennis, E.A., Evans, J.E., Fauland, A., Fiehn, O., Gardner, M.S., Garrett, T.J., Gotlinger, K.H., Han, J., Huang, Y., Neo, A.H., Hyötyläinen, T., Izumi, Y., Jiang, H., Jiang, H., Jiang, J., Kachman, M., Kiyonami, R., Klavins, K., Klose, C., Köfeler, H.C., Kolmert, J., Koal, T., Koster, G., Kuklenyik, Z., Kurland, I.J., Leadley, M., Lin, K., Maddipati, K.R., McDougall, D., Meikle, P.J., Mellett, N.A., Monnin, C., Moseley, M.A., Nandakumar, R., Oresic, M., Patterson, R., Peake, D., Pierce, J.S., Post, M., Postle, A.D., Pugh, R., Qiu, Y., Quehenberger, O., Ramrup, P., Rees, J., Rembiesa, B., Reynaud, D., Roth, M.R., Sales, S., Schuhmann, K., Schwartzman, M.L., Serhan, C.N., Shevchenko, A., Somerville, S.E., St John Williams, L., Surma, M.A., Takeda, H., Thakare, R., Thompson, J.W., Torta, F., Triebl, A., Trötzmüller, M., Ubhayasekera, S.J.K., Vuckovic, D., Weir, J.M., Welti, R., Wenk, M.R., Wheelock, C.E., Yao, L., Yuan, M., Zhao, X.H., Zhou, S.: Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950—metabolites in frozen human plasma. J. Lipid Res. 58, 2275–2288 (2017)CrossRefGoogle Scholar
  35. 35.
    Elias, J.E., Gibbons, F.D., King, O.D., Roth, F.P., Gygi, S.P.: Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nat. Biotechnol. 22, 214–219 (2004)CrossRefGoogle Scholar
  36. 36.
    Scheubert, K., Hufsky, F., Petras, D., Wang, M., Nothias, L.F., Dührkop, K., Bandeira, N., Dorrestein, P.C., Böcker, S.: Significance estimation for large scale metabolomics annotations by spectral matching. Nat. Commun. 8, (2017).  https://doi.org/10.1038/s41467-017-01318-5
  37. 37.
    Palmer, A., Phapale, P., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., Kovalev, V., Fuchser, J., Nikolenko, S., Pineau, C., Becker, M., Alexandrov, T.: FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods. 14, 57–60 (2016)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of Wisconsin–MadisonMadisonUSA
  2. 2.Genome Center of WisconsinMadisonUSA
  3. 3.Morgridge Institute for ResearchMadisonUSA
  4. 4.Department of Biomolecular ChemistryUniversity of Wisconsin–MadisonMadisonUSA

Personalised recommendations

Cite article

Buy options