Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

Abstract

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.