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Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Nature Protocols volume 15, pages 1954–1991 (2020)Cite this article

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Abstract

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule–focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user’s underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking—one of the main analysis tools used within the GNPS platform—creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

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Fig. 1: Schematic representation of the process for creating a molecular network from tandem mass spectra acquired for metabolites in complex sample mixtures.
Fig. 2: Flowchart of the protocol, delineating the workflow through Step 32 (Steps 33–53 address optional analyses, visualizations and sharing of data and molecular networks).
Fig. 3: Mouse duodenum global molecular network created from MassIVE dataset and visualized in Cytoscape.
Fig. 4
Fig. 5
Fig. 6: Propagation of molecular networking to discover relationships between molecules.
Fig. 7: Networking of the stenothricin natural product molecular family (MSV000083381) detected in Streptomyces sp. DSM5940 (purple nodes), S. roseosporus NRRL 15998 (green nodes) or both strains (yellow nodes).
Fig. 8: Molecular family (a subnetwork) of quinolones detected in lung tissue extracts and cultured Pseudomonas isolates created from MassIVE dataset MSV000083359.

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Data availability

All LC–MS data used in this paper are publicly available at the GNPS-MassIVE repository under the following accession numbers.

MSV000083437 (GF and SPF mice, data not shown)

MSV000083359 (3D cartography of diseased human lung47)

MSV000083381 (stenothricin-GNPS analogs11)

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Acknowledgements

We acknowledge funding from the following: National Research System (SNI) of SENACYT Panama (C.A.B.P., M.H.C., J.L.-B., and M.G.); the Gordon and Betty Moore Foundation (P.C.D., N.B., and K.L.M.); the National Institutes of Health (GM122016-01; K.L.M.); the National Science Foundation (DEB1354944; R.M.T.) and (IOS-1656481; A.M.C.R and P.C.D). A.K.J. acknowledges an American Society for Mass Spectrometry 2018 Postdoctoral Career Development Award. D.P. was supported through the Deutsche Forschungsgemeinschaft (DFG; PE 2600/1). F.T. and N.N. acknowledge Shimadzu South Africa (Pty) Ltd for the support and training. We are grateful for grant R03 CA211211 (P.C.D.) on reuse of metabolomics data and grant P41 GM103484 (P.C.D., N.B.) to the Center for Computational Mass Spectrometry, as well as instrument support though NIH S10RR029121 (P.C.D.). A.I.C. and Y.Z. were supported through an Auburn University Presidential Award for Interdisciplinary Research (PAIR).

Author information

Author notes

  1. These authors contributed equally: Allegra T. Aron, Emily C. Gentry, Kerry L. McPhail.

Authors and Affiliations

  1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Allegra T. Aron, Emily C. Gentry, Louis-Félix Nothias, Mélissa Nothias-Esposito, Amina Bouslimani, Daniel Petras, Julia M. Gauglitz, Nicole Sikora, Fernando Vargas, Madeleine Ernst, Christine M. Aceves, Andrés Mauricio Caraballo-Rodríguez, Irina Koester, Kelly C. Weldon, Kunyang Sun, Alexander A. Aksenov, Alan K. Jarmusch, Mingxun Wang & Pieter C. Dorrestein

  2. Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, USA

    Kerry L. McPhail & Richard M. Tehan

  3. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Daniel Petras & Irina Koester

  4. Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA

    Fernando Vargas

  5. Bioinformatics Group, Wageningen University, Wageningen, the Netherlands

    Justin J. J. van der Hooft

  6. College of Pharmacy, Sookmyung Women’s University, Seoul, Korea

    Kyo Bin Kang & Catherine Roullier

  7. Center of Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Kelly C. Weldon

  8. Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, Nantes, France

    Samuel Bertrand

  9. ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, Nantes, France

    Samuel Bertrand & Catherine Roullier

  10. Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama

    Cristopher A. Boya P., Martin H. Christian, Marcelino Gutiérrez, Randy Mojica-Flores, Johant Lakey-Beitia & Nicole Tayler

  11. Department of Biotechnology, Acharya Nagarjuna University, Guntur, Nagarjuna Nagar, India

    Cristopher A. Boya P. & Nicole Tayler

  12. Biomedical Innovation Department, CICESE, Ensenada, Mexico

    Aldo Moreno Ulloa & Javier Andres Tejeda Mora

  13. Departamento de Química, Universidad Autónoma de Chiriquí (UNACHI), David, Chiriquí, Panama

    Randy Mojica-Flores

  14. Centro de Investigaciones en Productos Naturales (CIPRONA), Universidad de Costa Rica, San José, Costa Rica

    Victor Vásquez-Chaves

  15. Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA

    Yilue Zhang & Angela I. Calderón

  16. School of Chemical & Physical Sciences, Victoria University of Wellington, Wellington, New Zealand

    Robert A. Keyzers

  17. Centre for Plant Metabolomics Research, Department of Biochemistry, University of Johannesburg, Auckland Park, South Africa

    Fidele Tugizimana & Nombuso Ndlovu

  18. International R&D Division, Omnia Group (Pty) Ltd., Johannesburg, South Africa

    Fidele Tugizimana

  19. Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany

    Robin Schmid

  20. Department of Molecular Microbiology, John Innes Centre, Norwich, UK

    Andrew W. Truman

  21. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Nuno Bandeira

  22. Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, CA, USA

    Pieter C. Dorrestein

  23. Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA

    Pieter C. Dorrestein

  24. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Pieter C. Dorrestein

Authors
  1. Allegra T. Aron
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  2. Emily C. Gentry
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Contributions

Design and oversight of the project: P.C.D., M.W., N.B. Instrument acquisition parameters: A.T.A., E.C.G., R.A.K., K.L.M., R.M.T., K.B.K., S.B., C.R., A.W.T., F.T., N.N., A.K.J., A.M.U. Data conversion and upload: K.L.M., E.C.G., A.T.A., J.J.J.v.d.H., M.E. GNPS documentation: M.W., L.F.N., E.C.G., A.T.A., K.L.M., J.J.J.v.d.H., M.E., M.N.-E. Cytoscape documentation: M.N.-E., F.V., K.C.W., I.K., A.M.C.-R. Metadata curation: J.M.G., C.M.A., F.V., A.M.C.-R. Mass spectra annotations: D.P., R.S., M.E. Theoretical tools and advanced features, statistical analysis: L.F.N., A.A.A. Supplementary information: A.T.A., N.S., E.C.G., K.L.M., M.E. Testing the workflows described and improving the descriptions: Y.Z., A.I.C., A.B., K.S., N.T., A.M.U., J.A.T.M., M.H.C., C.A.B.P., M.G., V.V.-C., J.L.-B., R.M.-F., M.E.

Corresponding authors

Correspondence to Nuno Bandeira, Mingxun Wang or Pieter C. Dorrestein.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Vinayak Agarwal, Mehdi Beniddir, Alfonso Mangoni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key reference(s) using this protocol

Vermeeren, P., Sun, X. & Bickelhaupt, F.M. Sci. Rep. 8, 10729 (2018): https://doi.org/10.1038/s41598-018-28998-3

Sun, X., Soini, T. M., Poater, J., Hamlin, T. A. & Bickelhaupt, F. M. J. Comput. Chem. 40, 2227–2233 (2019): https://doi.org/10.1002/jcc.25871

Key data used in this protocol

Quinn, R. A. et al. Sci. Adv. 4, eaau1908 (2018): https://doi.org/10.1126/sciadv.aau1908

Wang, M. et al. Nat. Biotechnol. 34, 828–837 (2016): https://doi.org/10.1038/nbt.3597

Garg, N. et al. Cell Host Microbe 22, 705–716.e4 (2017): https://doi.org/10.1016/j.chom.2017.10.001

Preprint version of this protocol

Aron, A. T. et al. Preprint at https://doi.org/10.26434/chemrxiv.9333212.v1 (2019)

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Supplementary Methods and Supplementary Tables 1–4.

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Aron, A.T., Gentry, E.C., McPhail, K.L. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc 15, 1954–1991 (2020). https://doi.org/10.1038/s41596-020-0317-5

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