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Meta-analysis of untargeted metabolomic data from multiple profiling experiments

Nature Protocols volume 7, pages 508–516 (2012)Cite this article

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Abstract

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry–based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

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Figure 1: Introduction to pairwise and second-order comparison.
Figure 2: Data reduction by meta-analysis.
Figure 3: Visualization of theoretical meta-analysis applied to identify biomarkers of disease severity.
Figure 4: Overview of the computational workflow.
Figure 5: MSConvertGUI.exe, the graphical user interface of the ProteoWizard file converter.
Figure 6: Retention-time correction curves generated by XCMS.
Figure 7: Graphical user interface of metaXCMS.
Figure 8: Graphical user interface of metaXCMS.
Figure 9: Graphical user interface of metaXCMS.
Figure 10: Graphical user interface of metaXCMS.

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Acknowledgements

This work was supported by the California Institute of Regenerative Medicine (grant TR1-01219), the US National Institutes of Health (grants R24 EY017540-04, P30 MH062261-10 and P01 DA026146-02) and a US National Institutes of Health/National Institute on Aging grant (L30 AG0 038036; to G.J.P.). Financial support was also received from the US Department of Energy (grants FG02-07ER64325 and DE-AC0205CH11231).

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Author notes

  1. Gary J Patti and Ralf Tautenhahn: These authors contributed equally to this work.

Authors and Affiliations

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

    Gary J Patti

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

    Gary J Patti

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

    Gary J Patti

  4. Department of Chemistry and Molecular Biology, Center for Metabolomics, Scripps Research Institute, La Jolla, California, USA

    Ralf Tautenhahn & Gary Siuzdak

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G.J.P., R.T. and G.S. contributed to the development of the protocol and the writing of the manuscript.

Corresponding author

Correspondence to Gary Siuzdak.

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Patti, G., Tautenhahn, R. & Siuzdak, G. Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat Protoc 7, 508–516 (2012). https://doi.org/10.1038/nprot.2011.454

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