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

Julijana Ivanisevic,Elizabeth J Want

doi:10.3390/metabo9120308

Julijana Ivanisevic, Elizabeth J Want

Open Access

https://doi.org/10.3390/metabo9120308

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Journal: Metabolites	Publication Date: Dec 17, 2019
Citations: 69	License type: CC BY 4.0

Affiliation: University of Lausanne, Imperial College London

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.

Highlights

It is assumed that metabolite identification remains a major challenge in untargeted mass spectrometry (MS)-based metabolomics
We summarize different liquid chromatography–mass spectrometry (LC-MS) strategies in order to acquire high quality MS and MS/MS data (reversed phase (RP) LC and hydrophilic interaction liquid chromatography (HILIC) coupled to full scan high resolution (HR) MS data-dependent and data-independent acquisition (DDA and DIA)), while maximizing the metabolome and lipidome coverage, parameters to pay attention to for data pre-processing, and, feature annotation
Many metabolomics studies are complex in design and may incorporate several classes, e.g., control subjects versus those receiving low and high dose of a drug (Figure 1), healthy subjects versus those with a benign condition and cancer

Summary

Introduction

It is assumed that metabolite identification remains a major challenge in untargeted mass spectrometry (MS)-based metabolomics. Should there be greater effort to design experiments in a smarter, more streamlined way, and to know how to reduce noise and redundancy in untargeted metabolomics datasets? A meta-analysis comparative strategy can be used, where several pairwise comparisons are performed (with the same control group), followed by second-order or meta-analysis to prioritize the identification of the shared deregulated metabolites [1,2]. We provide tips on how to design metabolomics experiments in an optimal way, considering sample size, confounders, and bias. We discuss important factors in sample preparation and describe how preparation approaches should be tailored to each biofluid or tissue

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