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).

采用液相色谱-质谱联用法（LC-MS）的非靶向代谢组学，目前是解析生物样品中全部化学多样性的金标准技术。然而该方法仍存在诸多局限，其中尤为突出的是：在色谱图产生的数千个质谱离子信号中，精准估算所表征的独特代谢物数量极具挑战性。本研究介绍一种基于MS-DIAL/MS-FINDER套件的全新分析流程MS-CleanR，可解决特征峰简并问题并提升注释率。研究表明，MS-CleanR的应用可将信号数量降低近80%，同时保留95%的独特代谢物特征。此外，MS-FINDER生成的注释结果可根据用户选定的数据库进行排序，从而提升鉴定准确性。将MS-CleanR应用于三种不同培养条件下的拟南芥（Arabidopsis thaliana）分析，经多变量数据分析后实现了样本组分离，且最终特征峰的注释率达到75%。本研究将完整流程应用于三种不同菌株的豆科植物蒺藜苜蓿（Medicago truncatula）的代谢组谱分析，这些菌株对卵菌病原菌嗜根丝囊霉（Aphanomyces euteiches）的敏感性存在差异。研究在抗病株系中鉴定到一组富集的糖基化三萜类化合物，可作为赋予病原菌抗性的候选化合物。MS-CleanR通过Shiny界面实现，便于终端用户直观操作，其开源地址为https://github.com/eMetaboHUB/MS-CleanR。