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.
Tandem mass spectrometry Metabolomics Molecular networking Natural products Annotation
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).
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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
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
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
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
Heyman HM, Dubery IA. The potential of mass spectrometry imaging in plant metabolomics: a review. Phytochem Rev. 2016;15(2):297–316.CrossRefGoogle Scholar
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
Nielsen KF, Larsen TO. The importance of mass spectrometric dereplication in fungal secondary metabolite analysis. Front Microbiol. 2015;6(71).Google Scholar
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
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
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
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
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
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
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