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

Details

Serval ID
serval:BIB_E8044FA17AB4
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data.
Journal
Metabolites
Author(s)
Ivanisevic J., Want E.J.
ISSN
2218-1989 (Print)
ISSN-L
2218-1989
Publication state
Published
Issued date
17/12/2019
Peer-reviewed
Oui
Volume
9
Number
12
Language
english
Notes
Publication types: Journal Article ; Review
Publication Status: epublish
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.
Keywords
data processing, experimental design, liquid chromatography–mass spectrometry (LC-MS), metabolic pathway and network analysis, metabolism, metabolite identification, sample preparation, univariate and multivariate statistics, untargeted metabolomics
Pubmed
Web of science
Open Access
Yes
Create date
03/01/2020 17:09
Last modification date
28/01/2020 7:20
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