Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses.
Shipei Xing1,2, Vincent Charron-Lamoureux1,2, Måns Ekelöf3
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
Biorxiv : the Preprint Server for Biology
|October 28, 2024
Summary
A new Python package, ms1-id, enables structural annotation of untargeted metabolomics data. This advances the reuse of valuable MS1-only mass spectrometry data for deeper biological insights.
Area of Science:
- Metabolomics
- Mass Spectrometry
- Bioinformatics
Background:
- Public mass spectrometry data repositories hold millions of files, with over 40% being MS1-only data.
- Limited annotation capabilities for MS1-only data hinder data reuse and potential biological discoveries.
Purpose of the Study:
- To present ms1-id, an open-source Python package for structural annotation of full-scan mass spectrometry data.
- To enable the reuse of MS1-only data for enhanced metabolomics research.
Main Methods:
- Leverages in-source fragments to generate pseudo MS/MS spectra via correlation analysis.
- Utilizes precursor-tolerant reverse spectral matching and peak intensity scaling.
- Applicable to both liquid chromatography-mass spectrometry (LC-MS) and MS imaging analyses.
Main Results:
- Successfully applied to inflammatory bowel disease cohorts and diverse MS imaging samples.
- Uncovers metabolites previously overlooked in traditional analyses.
- Enables level 2/3 structural annotation of MS1-only data.
Conclusions:
- The ms1-id package provides a unified solution for structural annotation of full-scan MS data.
- This strategy significantly enhances the data reuse potential of existing metabolomics repositories.
- Facilitates new biological insights from previously underutilized MS1-only mass spectrometry data.
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