Plant Physiology 139:1125-1137 (2005)
© 2005 American Society of Plant Biologists
BREAKTHROUGH TECHNOLOGIES
A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles1,[w]
Yury Tikunov,
Arjen Lommen,
C.H. Ric de Vos,
Harrie A. Verhoeven,
Raoul J. Bino,
Robert D. Hall and
Arnaud G. Bovy*
Centre for BioSystems Genomics, 6700 AB Wageningen, The Netherlands (Y.T., A.L., C.H.R.d.V., H.A.V., R.J.B., R.D.H., A.G.B.); Plant Research International, 6700 AA Wageningen, The Netherlands (Y.T., C.H.R.d.V., H.A.V., R.J.B., R.D.H., A.G.B.); RIKILT, Institute for Food Safety, 6700 AE Wageningen, The Netherlands (A.L.); and Laboratory for Plant Physiology, Wageningen University, 6703 BD Wageningen, The Netherlands (R.J.B.)
To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.
1 This work was supported by the research program of the Centre of BioSystems Genomics, which is part of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Arnaud G. Bovy (arnaud.bovy{at}wur.nl).
[w] The online version of this article contains Web-only data.
www.plantphysiol.org/cgi/doi/10.1104/pp.105.068130.
* Corresponding author; e-mail arnaud.bovy{at}wur.nl; fax 31–317–418094.
Received July 6, 2005;
returned for revision September 13, 2005;
accepted September 13, 2005.
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