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An accelerated workflow for untargeted metabolomics using the METLIN database

Nature Biotechnology volume 30, pages 826–828 (2012)Cite this article

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Metabolites, which are typically recognized as small molecules that are involved in cellular reactions, provide a functional signature of phenotype that is complementary to the upstream biochemical information obtained from genes, transcripts and proteins. The high correlation between metabolites and phenotype has created a surge of interest in the field that is reflected in the number of metabolomic publications growing from just a few articles in 1999 to over 5,000 in 2011. Although relatively new compared with its genomic and proteomic predecessors, research in the field of metabolomics has already led to the discovery of biomarkers for disease, fundamental insights into cellular biochemistry and clues related to disease pathogenesis1,2.

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Figure 1: Schematic representation of the traditional metabolomic workflow involving six steps and the new METLIN-based workflow with only two steps.
Figure 2: Estimate of the physiological relevance of metabolite coverage in metabolomic databases.

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Acknowledgements

This work was supported by the California Institute of Regenerative Medicine (TR1-01219), the US National Institutes of Health (R24 EY017540, P30 MH062261, RC1 HL101034 and P01 DA026146) and the US National Institutes of Health National Institute on Aging L30 AG0 038036 (G.J.P.). Financial support was also received from the US Department of Energy (grants FG02-07ER64325 and DE-AC0205CH11231).

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Authors and Affiliations

  1. Department of Chemistry, Center for Metabolomics, The Scripps Research Institute, La Jolla, California, USA

    Ralf Tautenhahn, Kevin Cho, Winnie Uritboonthai, Zhengjiang Zhu & Gary Siuzdak

  2. Department of Molecular Biology, The Scripps Research Institute, La Jolla, California, USA

    Ralf Tautenhahn, Kevin Cho, Winnie Uritboonthai, Zhengjiang Zhu & Gary Siuzdak

  3. Department of Chemistry, Washington University School of Medicine, St. Louis, Missouri, USA

    Gary J Patti

  4. Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA

    Gary J Patti

  5. Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA

    Gary J Patti

Authors
  1. Ralf Tautenhahn
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  2. Kevin Cho
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  3. Winnie Uritboonthai
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Correspondence to Gary J Patti or Gary Siuzdak.

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The authors declare no competing financial interests.

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Supplementary Materials and Methods, Supplementary Table 1, and Supplementary Figues 1–32 (PDF 1992 kb)

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Tautenhahn, R., Cho, K., Uritboonthai, W. et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30, 826–828 (2012). https://doi.org/10.1038/nbt.2348

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