4.4 Article

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Journal

METABOLOMICS
Volume 4, Issue 3, Pages 202-215

Publisher

SPRINGER
DOI: 10.1007/s11306-008-0112-8

Keywords

metabolomics; liquid chromatography-mass spectrometry; nuclear magnetic resonance; correlations; principal component analysis

Funding

  1. EU [FOOD-CT-2006-036220]
  2. Improving Human Potential Program [HPRI-CT-1999-00085, HPRI-CT-1999-50018]
  3. Centre of BioSystems Genomics (CBSG)
  4. The Netherlands Genomics Initiative/Netherlands Organization
  5. Wageningen NMR Centre

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Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both H-1 NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR-NMR, LCMS-LCMS) and inter-method (NMR-LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites.

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