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.
Highlights
Metabolomics is defined as the analysis of the whole metabolome of a biological system and aims to detect as many metabolites as possible in a biological sample [1,2]
Software evaluation in this study included the commercial software Compound Discoverer (CD) 3.1, which was developed for the used type of MS instrument; open source software workflows including a combination of XCMS Online and MetaboAnalyst 4.0; and a manually programmed tool using R
While XCMS-based software tools might be one of the best solutions for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS)/MS untargeted metabolomics, XCMS was used as a preprocessing tool in the case of the two open source workflows [15,19,20,21,22]
Summary
Metabolomics is defined as the analysis of the whole metabolome of a biological system and aims to detect as many metabolites as possible in a biological sample [1,2]. Metabolomic studies can be divided into two major strategies, untargeted and targeted approaches. Targeted metabolomics usually aims to detect and quantify specific metabolites of known structures. The untargeted or global approach usually aims to identify as many metabolites as possible without having any previous knowledge about them [1,3]. Due to its high selectivity and sensitivity, liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is currently the most commonly applied analytical technique in metabolomics [4,5,6]. To correctly interpret differences in specific metabolites and to gain a proper
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