MS-CleanR: A Feature-Filtering Workflow for 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. 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 MS-FINDER 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).