Imperial College London
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DrElenaChekmeneva

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Research Associate - Structural Elucidation
 
 
 
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Contact

 

e.chekmeneva

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sands:2021:10.1021/acs.analchem.0c03848,
author = {Sands, CJ and Gómez-Romero, M and Correia, G and Chekmeneva, E and Camuzeaux, S and Izzi-Engbeaya, C and Dhillo, WS and Takats, Z and Lewis, MR},
doi = {10.1021/acs.analchem.0c03848},
journal = {Analytical Chemistry},
pages = {1924--1933},
title = {Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling.},
url = {http://dx.doi.org/10.1021/acs.analchem.0c03848},
volume = {93},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.
AU  - Sands,CJ
AU  - Gómez-Romero,M
AU  - Correia,G
AU  - Chekmeneva,E
AU  - Camuzeaux,S
AU  - Izzi-Engbeaya,C
AU  - Dhillo,WS
AU  - Takats,Z
AU  - Lewis,MR
DO  - 10.1021/acs.analchem.0c03848
EP  - 1933
PY  - 2021///
SN  - 0003-2700
SP  - 1924
TI  - Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling.
T2  - Analytical Chemistry
UR  - http://dx.doi.org/10.1021/acs.analchem.0c03848
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33448796
UR  - https://pubs.acs.org/doi/10.1021/acs.analchem.0c03848
UR  - http://hdl.handle.net/10044/1/86490
VL  - 93
ER  -
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