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    Using MetaboAnalyst 6.0 for exposomics data analysis—from LC–MS2 spectra processing to dose–response modeling and causal inference

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    Using MetaboAnalyst 6.0 for exposomics data analysis—from LC–MS2 spectra processing to dose–response modeling and causal inference

    Abstract

    Exposomics is an emerging field of research that aims to comprehensively investigate individuals’ environmental exposures and how these exposures relate to health outcomes. Liquid chromatography–tandem mass spectrometry is widely used in exposomics studies. MetaboAnalyst (https://www.metaboanalyst.ca/) is a widely used platform for statistical and functional analysis of metabolomics data. The current MetaboAnalyst 6.0 release incorporates updates to meet the needs of exposomics studies, including improved support for tandem mass spectrometry compound identification, exposome annotation, dose–response analysis and linking to genetics and functions. Here we extend our 2022 Nature Protocol by providing step-by-step instructions on how to use MetaboAnalyst 6.0 for exposomics data analysis, including: liquid chromatography–tandem mass spectrometry spectra processing and compound identification (Stage 1), exposomics data processing and exploratory analysis (Stage 2), dose–response modeling to study metabolic responses to exposure levels (Stage 3) and leveraging known genetic associations for causal inference (Stage 4). We demonstrate Stages 1–3 using data from a recent blood exposomics study concerning electronic waste exposure. Stage 4 is illustrated through an investigation of the potential causal link between ʟ-isoleucine and type 2 diabetes. Stage 1 may take ~2 h to complete depending on server load, and the remaining stages may be executed in a total of ~90 min.

    Key points

    • This protocol provides instructions for four key tasks in liquid chromatography–tandem mass spectrometry-based exposomics studies: (1) raw spectra processing—enables sensitive peak detection and compound annotation; (2) exploratory data analysis—provides quality control, data cleaning, statistical analysis and visualization; (3) dose–response analysis—quantifies the relationship between exposure levels and biological or phenotypic effects; and (4) causal inference—estimates causal relationships between exposures and health outcomes using genetic variants as instrumental variables.

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    Subjects

    • Computational platforms and environments
    • Medical research
    • Software

    Data availability

    All data supporting this Protocol are available within the Protocol and its Supplementary Information. The original dataset and metadata table used as the e-waste example in this Protocol are available from our previously published paper13.

    Code availability

    MetaboAnalyst 6.0 is freely available at https://metaboanalyst.ca/. The underlying R package is available via GitHub at https://github.com/xia-lab/MetaboAnalystR. The current protocol is based on MetaboAnalystR v4.2.0 available via GitHub at https://github.com/xia-lab/MetaboAnalystR/releases/tag/v4.2.0. It is available via Zenodo at https://doi.org/10.5281/zenodo.17393838 (ref. 67). MetaboAnalyst-Pro Enterprise Solution is available for local installation at https://www.xialab.ai/protools.xhtml.

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    Acknowledgements

    This work is supported by the Canadian Foundation for Innovation (CFI), Genome Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), the Canada Research Chairs (CRC) program, the IVADO Postdoctoral Research Funding Program and the Kyoto-McGill International Collaborative Program. We are grateful to S. Barnes (University of Alabama at Birmingham) and his team for evaluating the protocol with their real world exposomics data and for their valuable feedback, which helped improve the MetaboAnalyst workflow and interface.

    Authors and Affiliations

    Contributions

    Z.P., G.Z., Y.L. and J.X. developed and updated MetaboAnalyst and MetaboAnalystR tools. Z.P., G.Z., Y.L. and J.X. designed the protocols and performed the data analysis. Z.P., G.Z., Y.L., H.O., C.V. and J.X. tested the entire workflow. Z.P., G.Z., Y.L. and J.X. wrote the manuscript. H.O., C.V. and F.M. provided critical comments for the manuscript. N.B. helped with the preparation of example data. J.X. supervised the study. All authors read and approved the final manuscript.

    Ethics declarations

    Competing interests

    J.X. is the founder of XiaLab Analytics, a startup created to support the long-term maintenance and sustainability of MetaboAnalyst and related omics tools. The other authors declare no competing interests.

    Peer review

    Peer review information

    Nature Protocols thanks Xiaotao Shen, who co-reviewed with Yijiang Liu; Stephen Barnes; Ethan Stancliffe; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

    Additional information

    Key references

    Pang, Z. et al. Nucleic Acids Res. 52, W398–W406 (2024): https://doi.org/10.1093/nar/gkae253

    Pang, Z. et al. Nat. Commun. 15, 3675 (2024): https://doi.org/10.1038/s41467-024-48009-6

    Chang, L. et al. Exposome. 4, osae005 (2024): https://doi.org/10.1093/exposome/osae005

    Pang, Z. et al. Metabolites14, 671 (2024): https://doi.org/10.3390/metabo14120671

    This Protocol is an extension to: Nat. Protoc. 17, 1735–1761 (2022): https://www.nature.com/articles/s41596-022-00710-w.

    Extended data

    Extended Data Fig. 1 Workflow of raw spectral processing.

    It includes MS1 peak profiling and MS2 compound annotation.

    Extended Data Fig. 2 Metabolome class details.

    The metabolome dialog includes complete chemical classification information of all compounds in the specific metabolome class.

    Extended Data Fig. 3 Exposome class details.

    The exposome dialog contains detailed information on the exposome class, including Category name, number of compounds in the category, sum of peak intensity, biological groups, compound names, as well as a compound list for downloading.

    Extended Data Fig. 4 PCA overview coupled with PERMANOVA results.

    Numbers in upper right panels are PERMANOVA p-values for group separation along each component pair.

    Extended Data Fig. 5 Dose–response analysis.

    A screenshot showing the available statistical models for curve fitting.

    Extended Data Fig. 6 SNP selection and harmonization for Mendelian randomization.

    The table displays candidate genetic instruments for ʟ-isoleucine, including SNP IDs, nearest genes and p-values for both exposure and outcome. Metadata regarding biofluid, population andcheckboxes indicate the final subset of SNPs selected after harmonization and QC filtering

    Extended Data Fig. 7 Supporting evidence for causal inference.

    A) Literature-based triangulation from the MelodiPresto knowledge base identifies curated mechanistic pathways linking ʟ-isoleucine to type 2 diabetes through intermediate biological entities such as serine, IRS1 and defensins. Each path is supported by published studies with links to PubMed identifiers for manual verification. B) Variant functional evidence from querying AlphaGenome reveals the predicted regulatory consequences of each instrumental SNP at single-base resolution. A larger quantile score indicates stronger evidence for the functional impact of the SNP.

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    Cite this article

    Pang, Z., Lu, Y., Zhou, G. et al. Using MetaboAnalyst 6.0 for exposomics data analysis—from LC–MS2 spectra processing to dose–response modeling and causal inference.
    Nat Protoc (2026). https://doi.org/10.1038/s41596-026-01415-0

    • Version of record:17 August 2026

    • DOI
      :https://doi.org/10.1038/s41596-026-01415-0

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