A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

Abstract

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.

Figure 1.

GC-MS-based metabolomics. A, Analytical approach used. B, Conventional approach. C, Alternative, unbiased approach to GC-MS data analysis.

Figure 2.

HCA of >20,000 molecular fragments based on their expression patterns throughout 198 GC-MS profiles. To simplify the view, only the highest branches of the dendrogram are displayed, showing the main groups of compounds as triangles. This procedure produced a dendrogram revealing a distinct cluster of nonplant components, comprising molecular fragments derived from constituents of the SPME fiber material that could then be readily removed from the dataset prior to further analysis.

Figure 3.

Multivariate analyses of 94 tomato genotypes. A, Hierarchical tree of the 94 tomato genotypes based on intensity patterns of >20,000 individual molecular fragments. B, PCA plot showing two major types of differences between the tomato genotypes: between-type variation, discriminating the cherry tomatoes from round and beef tomatoes along vector 1, and within-type variation, independent of fruit type, along vector 2. C, PCA plot showing the distribution of >20,000 molecular fragments: Those molecular fragments (a) distributed along vector 1 determine the between-type variation, and molecular fragments (b) distributed along vector 2 determine the within-type variation. D, PCA plot showing the distribution of the identified volatile metabolites determining the main differences between the tomato genotypes. E and F, Two enlarged parts of the PCA plot shown in D: Compounds are shown as colored shapes and the numbers refer to the compounds presented in Table II. The smaller black dots represent unknown compounds.

Figure 4.

MMSR-driven discrimination of mass spectra. A, Dendrogram showing a clustering of intensity patterns of ions situated in the retention time window 20.8 to 21.07 min into several molecular fragment clusters. B, MMSR indicated the presence of five individual compounds within a visually single total ion count (TIC) peak within the chosen time window. C-1, An experimental mass spectrum, obtained by plotting of the original intensities of the molecular fragments of compound b could be matched to the mass spectrum of the chemical standard analog of 2-isobutylthiazole (C-2), which also has a retention time falling within the chosen window.

Figure 5.

Metabolite-metabolite correlation matrix of the 322 plant-derived compounds. A, The main compound clusters are situated along the diagonal line (groups a–g). Correlations between metabolites are shown in grayscale: the darker the color gray, the higher the percentage of similarity between metabolite expression patterns. B, Detailed dendrogram of each compound cluster with putative compound identity as described in Table II. Compound cluster: a, phenylpropanoid volatiles; b, other phenolic volatiles; c, Leu and Ile derivatives (c1 and c2, respectively); d, lipid derivatives. Isoprenoids: e, terpenoids; f, open-chain carotenoid derivatives; g, cyclic carotenoid derivatives.