Imperial College London

ProfessorPaulElliott

Faculty of MedicineSchool of Public Health

Chair in Epidemiology and Public Health Medicine
 
 
 
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Contact

 

+44 (0)20 7594 3328p.elliott Website

 
 
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Assistant

 

Miss Jennifer Wells +44 (0)20 7594 3328

 
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Location

 

154Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Garcia:2020:10.1038/s41596-020-0343-3,
author = {Garcia, Perez I and Posma, JM and Serrano, Contreras JI and Boulange, C and Chan, Q and Frost, G and Stamler, J and Elliott, P and Lindon, J and Holmes, E and Nicholson, J},
doi = {10.1038/s41596-020-0343-3},
journal = {Nature Protocols},
title = {Identifying unknown metabolites using NMR-based metabolic profiling techniques},
url = {http://dx.doi.org/10.1038/s41596-020-0343-3},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopy based metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments.
AU - Garcia,Perez I
AU - Posma,JM
AU - Serrano,Contreras JI
AU - Boulange,C
AU - Chan,Q
AU - Frost,G
AU - Stamler,J
AU - Elliott,P
AU - Lindon,J
AU - Holmes,E
AU - Nicholson,J
DO - 10.1038/s41596-020-0343-3
PY - 2020///
SN - 1750-2799
TI - Identifying unknown metabolites using NMR-based metabolic profiling techniques
T2 - Nature Protocols
UR - http://dx.doi.org/10.1038/s41596-020-0343-3
UR - http://hdl.handle.net/10044/1/80046
ER -