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bioRxiv · 10.1101/2024.10.14.618269

Annotating full-scan MS data using tandem MS libraries

Xing, S.·Charron-Lamoureux, V.·El Abiead, Y.·Dorrestein, P. C.
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Abstract

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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Xing, S., Charron-Lamoureux, V., El Abiead, Y., Dorrestein, P. C.. 2024-10-17. Annotating full-scan MS data using tandem MS libraries. https://doi.org/10.1101/2024.10.14.618269

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