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March 7th, 2025
Version: 2
University of California, San Diego
bioinformatics
biorxiv

Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses

Xing, S.Open in Google Scholar•Charron-Lamoureux, V.Open in Google Scholar•Ekelöf, M.Open in Google Scholar•El Abiead, Y.Open in Google Scholar•Yu, H.Open in Google Scholar•Fiehn, O.Open in Google Scholar•Alexandrov, T.Open in Google Scholar•Dorrestein, P. C.Open in Google Scholar

Public liquid chromatography-mass spectrometry (LC-MS) and MS imaging metabolomics data repositories contain millions of files, with over 40% of them consisting solely of MS1 (full-scan) information, creating a significant gap in data reuse potential due to limited annotation capabilities. Here, we present ms1-id, an open-source Python package providing a unified solution for structural annotation of full-scan MS data applicable to both LC-MS and MS imaging analyses. Our approach leverages in-source fragments to generate pseudo MS/MS spectra through correlation analysis in either chromatographic or spatial domains. We introduce precursor-tolerant reverse spectral matching that accommodates multiple ion forms simultaneously and peak intensity scaling that enables matching of low-energy in-source fragments against existing reference MS/MS libraries. Applied to inflammatory bowel disease cohorts and diverse MS imaging samples, our method uncovers metabolites previously overlooked in traditional analyses. This strategy effectively addresses a critical need in metabolomics data reuse by enabling level 2/3 structural annotation of MS1-only data, facilitating new biological insights from existing repository data that was previously only annotated at the molecular formula level.

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