MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. This approach still has many limitations, however; notably, the difficulty of estimating accurately the number of unique metabolites being 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 MS-FINDER can be ranked with respect to database chosen by the user, which improves identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions improved class separation resulting from multivariate data analysis and lead 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 euteich...Continue Reading