Enhancing tandem mass spectrometry-based metabolite annotation with online chemical labeling

Metabolite identification in non-targeted mass spectrometry-based metabolomics remains a major challenge due to limited spectral library coverage and difficulties in predicting metabolite fragmentation patterns. Here, we introduce Multiplexed Chemical Metabolomics (MCheM), which employs orthogonal post-column derivatization reactions integrated into a unified mass spectrometry data framework. MCheM generates orthogonal structural information that substantially improves metabolite annotation through in silico spectrum matching and open-modification searches, offering a powerful new toolbox for the structure elucidation of unknown metabolites at scale.

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Publication Details

Journal
Nature Communications
Published
2025-07-26
DOI
https://doi.org/10.1038/s41467-025-61240-z
Citations
3
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
2.42

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article

Enhancing tandem mass spectrometry-based metabolite annotation with online chemical labeling

Daniel Petras, Mohammad Reza Zare Shahneh, Shu-Ning Xia, Yvonne Mast et al.
3 citations
Nature Communications
Metabolomics and Mass Spectrometry Studies
2.42
article

Enhancing tandem mass spectrometry-based metabolite annotation with online chemical labeling

Daniel Petras, Mohammad Reza Zare Shahneh, Shu-Ning Xia, Yvonne Mast, Chambers C. Hughes, Sebastian Böcker, Heike Brötz‐Oesterhelt, Mingxun Wang, Robin Schmid, Kai Dührkop, Corinna Brungs, Giovanni Andrea Vitale
article en
3 citations

Abstract

Metabolite identification in non-targeted mass spectrometry-based metabolomics remains a major challenge due to limited spectral library coverage and difficulties in predicting metabolite fragmentation patterns. Here, we introduce Multiplexed Chemical Metabolomics (MCheM), which employs orthogonal post-column derivatization reactions integrated into a unified mass spectrometry data framework. MCheM generates orthogonal structural information that substantially improves metabolite annotation through in silico spectrum matching and open-modification searches, offering a powerful new toolbox for the structure elucidation of unknown metabolites at scale.

Nature CommunicationsVol. 16(1)
University of California, Riverside (US), University of Vienna (AT), Institute for Applied Systems Technology Bremen (DE), German Center for Infection Research (DE), Jena Bioscience (Germany) (DE), Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry (CZ), Friedrich Schiller University Jena (DE), Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures (DE), University of Tübingen (DE)
U.S. Department of Energy, Deutsches Zentrum für Infektionsforschung, Joint Genome Institute, European Commission, Science Foundation Ireland, Deutsche Forschungsgemeinschaft, Akademie Věd České Republiky, National Institutes of Health, Office of Science
Openalex Percentile: Top 11%
Metabolomics and Mass Spectrometry Studies
2.42
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