From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data
Author(s)
Ivanisevic, Julijana
Want, Elizabeth J
Type
Journal Article
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.
Date Issued
2019-12-17
Date Acceptance
2019-12-12
Citation
Metabolites, 2019, 9 (12), pp.1-30
ISSN
2218-1989
Publisher
MDPI AG
Start Page
1
End Page
30
Journal / Book Title
Metabolites
Volume
9
Issue
12
Copyright Statement
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000506676500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
untargeted metabolomics
liquid chromatography-mass spectrometry (LC-MS)
metabolism
experimental design
sample preparation
data processing
metabolite identification
univariate and multivariate statistics
metabolic pathway and network analysis
DATA-DEPENDENT ACQUISITION
MASS-SPECTROMETRY DATA
UNTARGETED METABOLOMICS
EXPERIMENTAL-DESIGN
QUALITY-ASSURANCE
POLAR METABOLITES
HIGH-THROUGHPUT
CHROMATOGRAPHY
COVERAGE
MS
Publication Status
Published
Article Number
ARTN 308
Date Publish Online
2019-12-17