Metabolite fingerprinting and profiling in plants using NMR

Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multi...

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Veröffentlicht in:Journal of experimental botany 2005-01, Vol.56 (410), p.255-265
Hauptverfasser: Krishnan, P, Kruger, N. J, Ratcliffe, R. G
Format: Artikel
Sprache:eng
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Zusammenfassung:Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned ¹H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the ¹H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of ¹H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.
ISSN:0022-0957
1460-2431
1460-2431
DOI:10.1093/jxb/eri010