From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Abstract

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows

“…Recently, there has been an increasing awareness in the international metabolomics community about the need for implementing quality assurance (QA) and quality control (QC) processes to ensure data quality and reproducibility [ 1 , 2 , 3 , 4 , 5 , 6 ]. Challenges in untargeted metabolomics workflows are associated with pre-analytical, analytical, and post-analytical steps [ 1 , 2 , 3 , 4 , 5 , 7 , 8 , 9 , 10 , 11 ].…”

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC–MS Metabolomics

“…The putative identification of a metabolic feature for which the assignment of its structure is highly likely, but not validated through chemical-reference standards, is defined as 'annotation' [2]. As the accessibility and analysis of the complete set of potential metabolites is not always feasible, annotation based on MS and MS n information is widely used as a suboptimal alternative.…”

“…This strategy limits the reuse of data sets as it requires access to the samples and additional technical bias might also be introduced during sample re-analysis in a separate experiment. So, different DDA MS 2 experiments have been proposed to increase the coverage of metabolites for which MS 2 data is acquired. The identification of artefactual features from background contamination and isotopes has been used to generate a preferred ion list to guide precursor selection, thus increasing its efficiency and the MS 2 coverage [2,3].…”

“…So, different DDA MS 2 experiments have been proposed to increase the coverage of metabolites for which MS 2 data is acquired. The identification of artefactual features from background contamination and isotopes has been used to generate a preferred ion list to guide precursor selection, thus increasing its efficiency and the MS 2 coverage [2,3]. Furthermore, the use of time-staggered precursor ion lists for DDA has been proposed to improve the MS 2 coverage of metabolomes [4].…”

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease

“…Lipidomics, the comprehensive analysis of lipids in biological systems, remains challenging and must involve not only efficient analytical techniques but also appropriate sample processing and integrative computational approaches [8]. Electrospray ionization (ESI) mass spectrometry (MS), either through a shotgun approach or hyphenated to liquid chromatography (LC), has become the gold standard of lipidome study [8,9]. Indeed, lipidomic analysis using an infusion approach represents a useful strategy to easily access a large part of the lipidome [10,11].…”