Tredwell, Gregory D, Behrends, Volker, Geier, Florian M, Liebeke, Manuel and Bundy, Jacob G (2011) Between-person comparison of metabolite fitting for NMR-based quantitative metabolomics. Analytical chemistry, 83 (22). pp. 8683-8687. ISSN 0003-2700

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Abstract

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform
for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative
simplicity and rapid acquisition times. The spectral data can then be analysed either with a chemometric
workflow, or by an initial deconvolution, or fitting, step to generate a list of identified metabolites and
associated sample concentrations. Various software tools exist to simplify the fitting process but at least
for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we
know how much person-to-person variability affects the results, in order to be able to judge between
different studies. Here we tested a commercially-available software package (Chenomx’ NMR Suite)
for fitting metabolites to a set of NMR spectra of yeast extracts, and compared the output of five
different people for both metabolite identification and quantitation. An initial comparison showed good
agreement for a restricted set of common metabolites with characteristic well-resolved resonances, but
wide divergence in the overall identities and number of compounds fitted; re-fitting according to an
agreed set of metabolites and spectral processing approach increased the total number of metabolites
fitted, but did not dramatically increase the quality of the metabolites that could be fitted without prior
knowledge about peak identity. Hence, robust peak assignments are required in advance of manual
deconvolution, when the widest range of metabolites is desired. However, very low concentration
metabolites still had high coefficients of variation even with shared information on peak assignment.
Overall, the effect of person was less than experimental group (in this case, sampling method) for
almost all metabolites.

Item Type:	Article
Identifier:	10.1021/ac202123k
Keywords:	Mathematical methods, Metabolism, Metabolomics, Nuclear magnetic resonance spectroscopy, Software
Subjects:	Natural sciences > Cell and molecular biology Natural sciences
Depositing User:	Volker Behrends
Date Deposited:	20 May 2024 12:09
Last Modified:	20 May 2024 12:09
URI:	https://repository.uwl.ac.uk/id/eprint/11450

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