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Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging

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Abstract

Background

The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

Aim of review

The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.

Key scientific concepts of review

We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

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References

  • Alseekh, S., Aharoni, A., Brotman, Y., Contrepois, K., D’Auria, J., Ewald, J., Fraser, P. D., Giavalisco, P., Hall, R. D., Heinemann, M., Link, H., Luo, J., Neumann, S., Nielsen, J., Perez de Souza, L., Saito, K., Sauer, U., Schroeder, F. C., Schuster, S., … Fernie, A. R. (2021). Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Natue Methods, 18, 747–756. https://doi.org/10.1038/s41592-021-01197-1

    Article  CAS  Google Scholar 

  • Beger, R. D., Dunn, W. B., Bandukwala, A., Bethan, B., Broadhurst, D., Clish, C. B., Dasari, S., Derr, L., Evans, A., Fischer, S., Flynn, T., Hartung, T., Herrington, D., Higashi, R., Hsu, P. C., Jones, C., Kachman, M., Karuso, H., Kruppa, G., … Zanetti, K. A. (2019). Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics, 15, 1–5. https://doi.org/10.1007/s11306-018-1453-6

    Article  CAS  Google Scholar 

  • Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., Cortes, A., Welsh, S., Young, A., Effingham, M., McVean, G., Leslie, S., Allen, N., Donnelly, P., & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562, 203–209. https://doi.org/10.1038/s41586-018-0579-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Claeys, T., Van Den Bossche, T., Perez-Riverol, Y., Gevaert, K., Vizcaíno, J. A., & Martens, L. (2023). lesSDRF is more: Maximizing the value of proteomics data through streamlined metadata annotation. Nature Communications, 14, 6743. https://doi.org/10.1038/s41467-023-42543-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Creek, D. J., Dunn, W. B., Fiehn, O., Griffin, J. L., Hall, R. D., Lei, Z., Mistrik, R., Neumann, S., Schymanski, E. L., Sumner, L. W., Trengove, R., & Wolfender, J.-L. (2014). Metabolite identification: Are you sure? And how do your peers gauge your confidence? Metabolomics, 10, 350–353. https://doi.org/10.1007/s11306-014-0656-8

    Article  CAS  Google Scholar 

  • Dashti, H., Westler, W. M., Markley, J. L., & Eghbalnia, H. R. (2017). Unique identifiers for small molecules enable rigorous labeling of their atoms. Scientific Data, 4, 170073. https://doi.org/10.1038/sdata.2017.73

    Article  PubMed  PubMed Central  Google Scholar 

  • Deutsch, E. W., Vizcaíno, J. A., Jones, A. R., Binz, P.-A., Lam, H., Klein, J., Bittremieux, W., Perez-Riverol, Y., Tabb, D. L., Walzer, M., Ricard-Blum, S., Hermjakob, H., Neumann, S., Mak, T. D., Kawano, S., Mendoza, L., Van Den Bossche, T., Gabriels, R., Bandeira, N., … Orchard, S. E. (2023). Proteomics standards initiative at twenty years: Current activities and future work. Journal of Proteome Research, 22, 287–301. https://doi.org/10.1021/acs.jproteome.2c00637

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fahy, E., & Subramaniam, S. (2020). RefMet: A reference nomenclature for metabolomics. Nature Methods, 17, 1173–1174. https://doi.org/10.1038/s41592-020-01009-y

    Article  CAS  PubMed  Google Scholar 

  • Gilroy, S. P., & Kaplan, B. A. (2019). Furthering open science in behavior analysis: an introduction and tutorial for using GitHub in research. Perspectives on Behavior Science, 42, 565–581.

    Article  PubMed  PubMed Central  Google Scholar 

  • Heller, S. R., McNaught, A., Pletnev, I., Stein, S., & Tchekhovskoi, D. (2015). InChI, the IUPAC international chemical identifier. Journal of Cheminformatics, 7, 23. https://doi.org/10.1186/s13321-015-0068-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jeppesen, M. J., & Powers, R. (2023). Multiplatform untargeted metabolomics. Magnetic Resonance in Chemistry, 1, 1–26. https://doi.org/10.1002/mrc.5350

    Article  CAS  Google Scholar 

  • Kale, N. S., Haug, K., Conesa, P., Jayseelan, K., Moreno, P., Rocca-Serra, P., Nainala, V. C., Spicer, R. A., Williams, M., Li, X., Salek, R. M., Griffin, J. L., & Steinbeck, C. (2016). MetaboLights: an open-access database repository for metabolomics data. Current Protocols in Bioinformatics. https://doi.org/10.1002/0471250953.bi1413s53

  • Kirwan, J. A., Gika, H., Beger, R. D., Bearden, D., Dunn, W. B., Goodacre, R., Theodoridis, G., Witting, M., Yu, L. R., & Wilson, I. D. (2022). Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics, 18, 70. https://doi.org/10.1007/s11306-022-01926-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Maciejewski, M. W., Gryk, M. R., Moraru, I. I., Romero, P. R., Ulrich, E. L., Eghbalnia, H. R., Livny, M., Delaglio, F., & Hoch, J. C. (2017). NMRbox: a resource for biomolecular NMR computation. Biophysical Journal, 112, 1529–1534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Navarro, S. L., Nagana Gowda, G. A., Bettcher, L. F., Pepin, R., Nguyen, N., Ellenberger, M., Zheng, C., Tinker, L. F., Prentice, R. L., Huang, Y., Yang, T., Tabung, F. K., Chan, Q., Loo, R. L., Liu, S., Wactawski-Wende, J., Lampe, J. W., Neuhouser, M. L., & Raftery, D. (2023). Demographic, health and lifestyle factors associated with the metabolome in older women. Metabolites, 13, 514.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Perez-Riverol, Y., Bai, M., da Veiga Leprevost, F., Squizzato, S., Park, Y. M., Haug, K., Carroll, A. J., Spalding, D., Paschall, J., Wang, M., Del-Toro, N., Ternent, T., Zhang, P., Buso, N., Bandeira, N., Deutsch, E. W., Campbell, D. S., Beavis, R. C., Salek, R. M., … Hermjakob, H. (2017). Discovering and linking public omics data sets using the Omics discovery index. Nature Biotechnology, 35, 406–409. https://doi.org/10.1038/nbt.3790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peter, K. T., Phillips, A. L., Knolhoff, A. M., Gardinali, P. R., Manzano, C. A., Miller, K. E., Pristner, M., Sabourin, L., Sumarah, M. W., Warth, B., & Sobus, J. R. (2021). Nontargeted analysis study reporting tool: a framework to improve research transparency and reproducibility. Analytical Chemistry, 93, 13870–13879. https://doi.org/10.1021/acs.analchem.1c02621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Phinney, K. W., Ballihaut, G., Bedner, M., Benford, B. S., Camara, J. E., Christopher, S. J., Davis, W. C., Dodder, N. G., Eppe, G., Lang, B. E., Long, S. E., Lowenthal, M. S., McGaw, E. A., Murphy, K. E., Nelson, B. C., Prendergast, J. L., Reiner, J. L., Rimmer, C. A., Sander, L. C., … Castle, A. L. (2013). Development of a Standard reference material for metabolomics research. Analytical Chemistry, 85, 11732–11738. https://doi.org/10.1021/ac402689t

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Powers, R., Andersson, E. R., Bayless, A. L., Brua, R. B., Chang, M. C., Cheng, L. L., Clendinen, C. S., Cochran, D., Copié, V., Cort, J. R., Crook, A. A., Eghbalnia, H. R., Giacalone, A., Gouveia, G. J., Hoch, J. C., Jeppesen, M. J., Maroli, A. S., Merritt, M. E., Pathmasiri, W., … Wishart, D. S. (2024). Best practices in NMR metabolomics: current state. TrAC Trends in Analytical Chemistry, 171, 117478. https://doi.org/10.1016/j.trac.2023.117478

    Article  CAS  Google Scholar 

  • Romero, P. R., Kobayashi, N., Wedell, J. R., Baskaran, K., Iwata, T., Yokochi, M., Maziuk, D., Yao, H., Fujiwara, T., Kurusu, G., Ulrich, E. L., Hoch, J. C., & Markley, J. L. (2020). BioMagResBank (BMRB) as a resource for structural biology. Methods in Molecular Biology, 2112, 187–218. https://doi.org/10.1007/978-1-0716-0270-6_14

    Article  CAS  PubMed  Google Scholar 

  • Salek, R. M., Neumann, S., Schober, D., Hummel, J., Billiau, K., Kopka, J., Correa, E., Reijmers, T., Rosato, A., Tenori, L., Turano, P., Marin, S., Deborde, C., Jacob, D., Rolin, D., Dartigues, B., Conesa, P., Haug, K., Rocca-Serra, P., … Steinbeck, C. (2015). Coordination of standards in metabolomics (COSMOS): facilitating integrated metabolomics data Access. Metabolomics, 11, 1587–1597. https://doi.org/10.1007/s11306-015-0810-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sansone, S. A., Fan, T., Goodacre, R., Griffin, J. L., Hardy, N. W., Kaddurah-Daouk, R., Kristal, B. S., Lindon, J., Mendes, P., Morrison, N., Nikolau, B., Robertson, D., Sumner, L. W., Taylor, C., van der Werf, M., van Ommen, B., & Fiehn, O. (2007a). The metabolomics standards initiative. Nature Biotechnology, 25, 846–848. https://doi.org/10.1038/nbt0807-846b

    Article  CAS  PubMed  Google Scholar 

  • Sansone, S. A., Schober, D., Atherton, H. J., Fiehn, O., Jenkins, H., Rocca-Serra, P., Rubtsov, D. V., Spasic, I., Soldatova, L., Taylor, C., Tseng, A., Viant, M. R., & Members, O. W. G. (2007b). Metabolomics standards initiative: Ontology working group work in progress. Metabolomics, 3, 249–256. https://doi.org/10.1007/s11306-007-0069-z

    Article  CAS  Google Scholar 

  • Smirnov, A., Liao, Y., Fahy, E., Subramaniam, S., & Du, X. (2021). ADAP-KDB: a spectral knowledgebase for tracking and prioritizing unknown GC-MS spectra in the NIH’s metabolomics data repository. Analytical Chemistry, 93, 12213–12220. https://doi.org/10.1021/acs.analchem.1c00355

    Article  CAS  PubMed  Google Scholar 

  • Steinbeck, C., Conesa, P., Haug, K., Mahendraker, T., Williams, M., Maguire, E., Rocca-Serra, P., Sansone, S. A., Salek, R. M., & Griffin, J. L. (2012). MetaboLights: towards a new COSMOS of metabolomics data management. Metabolomics, 8, 757–760. https://doi.org/10.1007/s11306-012-0462-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., Edison, A., Fiehn, O., Higashi, R., Nair, K. S., Sumner, S., & Subramaniam, S. (2016). Metabolomics workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Research, 44, D463–D470. https://doi.org/10.1093/nar/gkv1042

    Article  CAS  PubMed  Google Scholar 

  • Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W., Fiehn, O., Goodacre, R., Griffin, J. L., Hankemeier, T., Hardy, N., Harnly, J., Higashi, R., Kopka, J., Lane, A. N., Lindon, J. C., Marriott, P., Nicholls, A. W., … Viant, M. R. (2007). Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics, 3, 211–221. https://doi.org/10.1007/s11306-007-0082-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Szymańska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 8, 3–16. https://doi.org/10.1007/s11306-011-0330-3

    Article  CAS  PubMed  Google Scholar 

  • Tolstikov, V., Moser, A. J., Sarangarajan, R., Narain, N. R., & Kiebish, M. A. (2020). Current status of metabolomic biomarker discovery: impact of study design and demographic characteristics. Metabolites, 10, 224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Villalba, H., Llambrich, M., Gumà, J., Brezmes, J., & Cumeras, R. (2023). A metabolites merging strategy (MMS): harmonization to enable studies’ intercomparison. Metabolites, 13, 1167.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weininger, D. (1988). SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28, 31–36. https://doi.org/10.1021/ci00057a005

    Article  CAS  Google Scholar 

  • Wigh, D. S., Goodman, J. M., & Lapkin, A. A. (2022). A review of molecular representation in the age of machine learning. Wires Computational Molecular Science, 12, e1603. https://doi.org/10.1002/wcms.1603

    Article  Google Scholar 

  • Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

    Article  PubMed  PubMed Central  Google Scholar 

  • Worley, B., & Powers, R. (2013). Multivariate analysis in metabolomics. Current Metabolomics, 1, 92–107. https://doi.org/10.2174/2213235x11301010092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xi, B., Gu, H., Baniasadi, H., & Raftery, D. (2014). Statistical analysis and modeling of mass spectrometry-based metabolomics data. Methods in Molecular Biology, 1198, 333–353. https://doi.org/10.1007/978-1-4939-1258-2_22

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Metabolomics Association of North America (MANA), NMR Special Interest Group, Edmonton, Canada

    Goncalo Jorge Gouveia, Thomas Head, Leo L. Cheng, Chaevien S. Clendinen, John R. Cort, Xiuxia Du, Arthur S. Edison, Candace C. Fleischer, Jeffrey Hoch, Nathaniel Mercaldo, Wimal Pathmasiri, Daniel Raftery, Tracey B. Schock, Lloyd W. Sumner, Panteleimon G. Takis, Valérie Copié, Hamid R. Eghbalnia & Robert Powers

  2. Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, University of Maryland, Gudelsky Drive, Rockville, MD, 20850, USA

    Goncalo Jorge Gouveia

  3. University of British Columbia, Kelowna, BC, V1V 1V7, Canada

    Thomas Head

  4. Department of Pathology and Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Leo L. Cheng

  5. Earth and Biological Sciences Directorate, Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA

    Chaevien S. Clendinen

  6. Earth and Biological Sciences Directorate, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA

    John R. Cort

  7. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9291 University City Blvd, Charlotte, NC, 28223, USA

    Xiuxia Du

  8. Department of Biochemistry, University of Georgia, Athens, GA, USA

    Arthur S. Edison

  9. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30322, USA

    Candace C. Fleischer

  10. Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, 06030-3305, USA

    Jeffrey Hoch & Hamid R. Eghbalnia

  11. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Nathaniel Mercaldo

  12. Department of Nutrition, School of Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Wimal Pathmasiri

  13. Department of Anesthesia and Pain Medicine, University of Washington, Seattle, WA, 98109, USA

    Daniel Raftery

  14. Chemical Sciences Division, National Institute of Standards and Technology (NIST), Charleston, SC, 29412, USA

    Tracey B. Schock

  15. Department of Biochemistry, MU Metabolomics Center, Bond Life Sciences Center, Interdisciplinary Plant Group, University of Missouri, Columbia, MO, 65211, USA

    Lloyd W. Sumner

  16. Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, SW7 2AZ, UK

    Panteleimon G. Takis

  17. Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, W12 0NN, UK

    Panteleimon G. Takis

  18. Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717-3400, USA

    Valérie Copié

  19. Department of Chemistry, Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, 722 Hamilton Hall, Lincoln, NE, 68588-0304, USA

    Robert Powers

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  1. Goncalo Jorge Gouveia
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  2. Thomas Head
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Contributions

All authors contributed to the overall construct and composition of the review. VC, HRE, and RP wrote the original draft of the manuscript. All authors read, revised, and approved the manuscript.

Corresponding author

Correspondence to Robert Powers.

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Gouveia, G.J., Head, T., Cheng, L.L. et al. Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging. Metabolomics 20, 41 (2024). https://doi.org/10.1007/s11306-024-02090-6

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