Analytical and Bioanalytical Chemistry

, Volume 409, Issue 24, pp 5767–5778 | Cite as

Optimized experimental workflow for tandem mass spectrometry molecular networking in metabolomics

Research Paper

Abstract

New omics sciences generate massive amounts of data, requiring to be sorted, curated, and statistically analyzed by dedicated software. Data-dependent acquisition mode including inclusion and exclusion rules for tandem mass spectrometry is routinely used to perform such analyses. While acquisition parameters are well described for proteomics, no general rule is currently available to generate reliable metabolomic data for molecular networking analysis on the Global Natural Product Social Molecular Networking platform (GNPS). Following on from an exploration of key parameters influencing the quality of molecular networks, universal optimal acquisition conditions for metabolomic studies are suggested in the present paper. The benefit of data pre-clustering before initiating large datasets for GNPS analyses is also demonstrated. Moreover, an efficient workflow dedicated to Agilent Technologies instruments is described, making the dereplication process easier by unambiguously distinguishing isobaric isomers eluted at different retention times, annotating the molecular networks with chemical formulas, and giving access to semi-quantitative data. This specific workflow foreshadows future developments of the GNPS platform.

Keywords

Tandem mass spectrometry Metabolomics Molecular networking Natural products Annotation 

Notes

Acknowledgments

The authors are very grateful to E. Boyer, J. Smadja, S. Técher-Giraud, E. Girard-Valenciennes, I. Grondin, D. Strasberg (UMR C_53 PVBMT), and V. Dumontet for their contribution to plant material identification and species collection. We also would like to acknowledge G. Grelier for its helpful assistance for Python script redaction. This work has benefited from an “Investissement d’Avenir” grant managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

216_2017_523_MOESM1_ESM.pdf (5 mb)
ESM 1(PDF 5071 kb)

References

  1. 1.
    Wu CC, MacCoss MJ. Shotgun proteomics: tools for the analysis of complex biological systems. Curr Opin Mol Ther. 2002;4(3):242–50.Google Scholar
  2. 2.
    Bateman NW, Goulding SP, Shulman NJ, Gadok AK, Szumlinski KK, MacCoss MJ, et al. Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA). Mol Cell Proteomics. 2014;13(1):329–38.CrossRefGoogle Scholar
  3. 3.
    Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotech. 2016;34(8):828–37.CrossRefGoogle Scholar
  4. 4.
    Hufsky F, Scheubert K, Bocker S. New kids on the block: novel informatics methods for natural product discovery. Nat Prod Rep. 2014;31(6):807–17.CrossRefGoogle Scholar
  5. 5.
    Heyman HM, Dubery IA. The potential of mass spectrometry imaging in plant metabolomics: a review. Phytochem Rev. 2016;15(2):297–316.CrossRefGoogle Scholar
  6. 6.
    Bouslimani A, Sanchez LM, Garg N, Dorrestein PC. Mass spectrometry of natural products: current, emerging and future technologies. Nat Prod Rep. 2014;31(6):718–29.CrossRefGoogle Scholar
  7. 7.
    Nielsen KF, Larsen TO. The importance of mass spectrometric dereplication in fungal secondary metabolite analysis. Front Microbiol. 2015;6(71).Google Scholar
  8. 8.
    Wolf S, Schmidt S, Müller-Hannemann M, Neumann S. In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinform. 2010;11(1):148.CrossRefGoogle Scholar
  9. 9.
    Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84(11):5035–9.CrossRefGoogle Scholar
  10. 10.
    Kuhl C, Tautenhahn R, Böttcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012;84(1):283–9.CrossRefGoogle Scholar
  11. 11.
    Kind T, Fiehn O. Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinform. 2007;8:105.CrossRefGoogle Scholar
  12. 12.
    Dührkop K, Shen H, Meusel M, Rousu J, Böcker S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci. 2015;112(41):12580–5.CrossRefGoogle Scholar
  13. 13.
    Klitgaard A, Nielsen JB, Frandsen RJN, Andersen MR, Nielsen KF. Combining stable isotope labeling and molecular networking for biosynthetic pathway characterization. Anal Chem. 2015;87(13):6520–6.CrossRefGoogle Scholar
  14. 14.
    Watrous J, Roach P, Alexandrov T, Heath BS, Yang JY, Kersten RD, et al. Mass spectral molecular networking of living microbial colonies. Proc Natl Acad Sci. 2012;109(26):E1743–E52.CrossRefGoogle Scholar
  15. 15.
    Crone WJK, Vior NM, Santos-Aberturas J, Schmitz LG, Leeper FJ, Truman AW. Dissecting bottromycin biosynthesis using comparative untargeted metabolomics. Angew Chem Int Ed. 2016;55(33):9639–43.CrossRefGoogle Scholar
  16. 16.
    Yang JY, Sanchez LM, Rath CM, Liu X, Boudreau PD, Bruns N, et al. Molecular networking as a dereplication strategy. J Nat Prod. 2013;76(9):1686–99.CrossRefGoogle Scholar
  17. 17.
    Kessner D, Chambers M, Burke R, Agus D, Mallick P. Proteo Wizard: open source software for rapid proteomics tools development. Bioinformatics. 2008;24(21):2534–6.CrossRefGoogle Scholar
  18. 18.
    Frank AM, Bandeira N, Shen Z, Tanner S, Briggs SP, Smith RD, et al. Clustering millions of tandem mass spectra. J Proteome Res. 2008;7(1):113–22.CrossRefGoogle Scholar
  19. 19.
    Olivon F, Palenzuela H, Girard-Valenciennes E, Neyts J, Pannecouque C, Roussi F, et al. Antiviral activity of flexibilane and tigliane diterpenoids from Stillingia lineata. J Nat Prod. 2015;78(5):1119–28.CrossRefGoogle Scholar
  20. 20.
    Assenov Y, Ramírez F, Schelhorn S-E, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24(2):282–4.CrossRefGoogle Scholar
  21. 21.
    Olivon F, Grelier G, Roussi F, Litaudon M, Touboul D. MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability. Anal Chem. 2017; asap, doi:10.1021/acs.analchem.7b01563
  22. 22.
    Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinf. 2010;11(1):395–406Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institut de Chimie des Substances Naturelles, CNRS UPR 2301Université Paris-Sud, Université Paris-SaclayGif-sur-YvetteFrance

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