MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics
- 21 July 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Analytical Chemistry
- Vol. 92 (14), 9971-9981
- https://doi.org/10.1021/acs.analchem.0c01594
Abstract
Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the goldstandard technique to determine the full chemical diversity in biological samples. However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MSFINDER can be ranked according to the database chosen by the user, which enhance identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches. A group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at https://github.com/eMetaboHUB/MS-CleanR).Funding Information
- Secr?tariat g?n?ral pour l'investissement
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