MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics.

Area of Science:

• Metabolomics
• Plant Science
• Bioinformatics

Background:

• Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is crucial for profiling biological samples.
• Current LC-MS methods face challenges in accurately quantifying unique metabolites and managing numerous MS ion signals.
• Feature degeneracy and suboptimal annotation rates limit the comprehensive analysis of metabolomic data.

Purpose of the Study:

• To introduce MS-CleanR, a novel workflow designed to address limitations in untargeted metabolomics.
• To improve metabolite feature detection, reduce signal redundancy, and enhance annotation accuracy.
• To facilitate the identification of bioactive compounds in plants, such as those involved in pathogen resistance.

Main Methods:

• Development of MS-CleanR, integrated with the MS-DIAL/MS-FINDER software suite.
• Implementation of a data processing workflow to reduce signal complexity and improve metabolite annotation.
• Application of MS-CleanR to analyze metabolomic data from Arabidopsis thaliana and Medicago truncatula.

Main Results:

• MS-CleanR significantly reduces the number of signals (nearly 80%) while preserving metabolite features (95%).
• The workflow enhances metabolite annotation accuracy through user-selectable database ranking.
• Analysis of Medicago truncatula identified glycosylated triterpenoids as potential contributors to pathogen resistance.

Conclusions:

• MS-CleanR offers an efficient solution for data processing in untargeted metabolomics.
• The workflow improves the reliability and accuracy of metabolite identification and discovery.
• MS-CleanR facilitates the identification of novel plant metabolites and their biological functions, including disease resistance mechanisms.