Imperial College London
Alternate

Dr Elizabeth Want

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Senior Lecturer
 
 
 
//

Contact

 

+44 (0)20 7594 3023e.want

 
 
//

Location

 

E315CBurlington DanesHammersmith Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Ivanisevic:2019:10.3390/metabo9120308,
author = {Ivanisevic, J and Want, EJ},
doi = {10.3390/metabo9120308},
journal = {Metabolites},
pages = {1--30},
title = {From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data},
url = {http://dx.doi.org/10.3390/metabo9120308},
volume = {9},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Ivanisevic,J
AU  - Want,EJ
DO  - 10.3390/metabo9120308
EP  - 30
PY  - 2019///
SN  - 2218-1989
SP  - 1
TI  - From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data
T2  - Metabolites
UR  - http://dx.doi.org/10.3390/metabo9120308
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000506676500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.mdpi.com/2218-1989/9/12/308
UR  - http://hdl.handle.net/10044/1/90401
VL  - 9
ER  -
Download