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Plant Physiology 139:1125-1137 (2005) © 2005 American Society of Plant Biologists A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles1,[w]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.
Functional genomics technologies designed to assess gene activity (transcriptomics) and protein accumulation (proteomics) are now well established in the quest to link gene to function (Holtorf et al., 2002  ). Subsequently, metabolomics approaches have been forwarded as a means to link the functional biochemical phenotype to other functional genomics data (Weckwerth and Fiehn, 2002; Sumner et al., 2003; Bino et al., 2004; Hall et al., 2005). Like transcriptomics and proteomics, metabolomics involves two main components: instrumental analysis (analytical) and data analysis (bioinformatics). Both topics need to be as comprehensive as possible for true, broad, metabolic profiling and comparative analysis of the biochemical status of living organisms. Several analytical methods for metabolomics have already been reported using model plants in genomic studies (Fiehn et al., 2000a, 2000b; Roessner et al., 2000, 2001; Huhman and Sumner, 2002; Tolstikov and Fiehn, 2002; Roessner-Tunali et al., 2003; Kopka et al., 2004; Desbrosses et al., 2005). A significant number of these studies have, however, been dedicated to metabolic profiling specifically of the nonvolatile compounds involved in primary plant metabolism using gas chromatography (GC) coupled to mass spectrometry (MS). Another significant part of the plant metabolome, comprising the volatile metabolites, is of a particular interest, since they play an important role in fundamental processes such as signaling mechanisms and interorganism interactions (Shulaev et al., 1997; Seskar et al., 1998; Ozawa et al., 2000; Arimura et al., 2002; Liechti and Farmer, 2002; Dicke et al., 2003; Dudareva et al., 2004; Engelberth et al., 2004; Ryu et al., 2004). In addition, these components are also of great agronomic importance as volatile metabolites are major determinants of food and flower quality in terms of flavor and fragrance (Buttery and Ling, 1993; Baldwin et al., 2000, 2004; Yilmaz et al., 2001; Tandon et al., 2003; Krumbein et al., 2004; Simkin et al., 2004; Ruiz et al., 2005).
Solid phase microextraction (SPME-GC-MS) is an analytical approach that is suitable for metabolomics studies of volatiles since it is renowned for its high sensitivity, reproducibility, and robustness (Yang and Peppard, 1994
Hundreds of different metabolites can be detected in crude plant extracts using GC-MS. This is, however, just a small fraction of the more than 10,000 metabolites that have been described in plants (Fiehn et al., 2000b ). However, even this limited amount of biochemical information cannot be fully subjected to a comparative metabolomic analysis when conventional strategies are used. Such strategies, in general, consist of three consecutive steps (Fig. 1B). First, metabolites must be recognized and (or) identified from the tens of thousands of molecular fragments that constitute a typical GC-MS profile (Fig. 1B-a). Second, quantitative values (often relative) of the identified metabolites are aligned throughout all the metabolic profiles of the genotypes (Fig. 1B-b) in order to perform the third step, comparative analyses of their metabolic phenotypes using multivariate exploratory techniques dedicated for metabolomics (e.g. hierarchical cluster analysis [HCAi], principal component analysis [PCA], self-organizing maps, etc.; Fig. 1B-c). The metabolic data can then also be linked to other data derived using other functional genomics technologies. The comprehensiveness of this strategy therefore depends on the number of metabolites that can be identified in the samples to be compared. However, the extreme complexity of the plant metabolome already generates a bottleneck at the first step in this algorithm: Despite using chromatographic separation, metabolites still coelute prior to being subjected to MS. Consequently, this coelution results in overlapping of the unique fragmentation patterns. In addition to the problem of coelution, a high variability in metabolite quantity within large numbers of biological samples further complicates metabolite identification and thus limits the entire analysis to a metabolite subset that includes only those compounds that can be reliably identified throughout all genotypes. Many other possible metabolic differences may then be overlooked. To overcome these limitations and make the metabolomic data analysis truly comprehensive and unbiased, we offer a novel strategy for data analysis (Fig. 1C). This strategy is based on a fully automated alignment of metabolic profiles at the level of individual molecular fragments without prior assignment to the chemical structures of the metabolites they represent. Subsequently, a multivariate comparative analysis of individual metabolic profiles is performed, which is based on all chemical information derived by an analytical approach. Although this strategy initially removes the need for prior metabolite identification, this is eventually still required in order to put a biological meaning to the differences found. To relate the thousands of molecular fragments normally constituting a chromatogram to their parental metabolites, a novel approach, multivariate mass spectra reconstruction (MMSR), has been developed. Using MMSR, clusters of related metabolite fragments can be recognized and the corresponding metabolites subsequently identified.
The entire strategy of data analysis is universal for many kinds of mass spectral data and exceeds approaches of unbiased metabolomic data analysis in terms of resolution and comprehensiveness (Nielsen et al., 1998
The strategy was used for a comparative multivariate analysis of a set of 94 contrasting tomato (Lycopersicon esculentum Mill.) genotypes covering the variation in the germplasm of commercial tomato varieties. The analysis was based on the profiles of all volatiles that could be detected by the analytical method used (SPME-GC-MS) and revealed a total of 322 different compounds in the entire genotype set. This covers approximately 80% of the more than 400 tomato volatile compounds, which have been detected in tomato fruit using different analytical methods (for review, see Petro-Turza, 1987
Automated Sequential Headspace SPME-GC-MS: Method Development
In order to produce and release volatiles, tomato material (e.g. juice or pulp) is usually incubated for a fixed period, during which essential enzymes such as lipoxygenase and hydroperoxide lyases are allowed to remain active. This is followed by the addition of concentrated CaCl2 to stop enzyme activity and to drive the volatiles into the headspace (Bezman et al., 2003
In total, 94 tomato fruit samples, in duplicate, were profiled for volatile metabolites. Consequently, including the daily external reference samples, 198 GC-MS datasets were obtained in this tomato volatile study.
Step 1 (Fig. 1C-a) is as follows. The entire 198-sample GC-MS dataset was analyzed using the dedicated MetAlign software package. After automated baseline correction, intensities of approximately 20,000 molecular fragments with corresponding retention times were aligned throughout 198 GC-MS profiles by MetAlign. Step 2 (Fig. 1C-b) is as follows. A common problem in SPME-GC-MS analyses is the production of molecular fragments originating from contaminants coming from the SPME fiber material. These molecular fragments had a typical pattern of occurrence throughout the samples, which was very different from the plant-derived molecular fragments. These could therefore be efficiently recognized by means of, for example, HCA (Fig. 2). The cluster of molecular fragments, which was clearly separate from the other clusters (Fig. 2), had mass and retention time characteristics that were identical to those of nonplant compounds identified in blank injections. Therefore, this entire group of nonplant molecular fragments, which were highly correlated to the contaminant-specific fragments (such as m/z 207, 267, 355, etc.) related to a number of polysiloxanes, could readily be excluded from the dataset before further analysis. This is an essential prerequisite before effective comparison of the plant-specific data can be made.
Step 3 (Fig. 1C-c) is as follows. The data matrix cleaned of the fiber contaminants was subjected to a multivariate comparative analysis. First, HCA of the 94 tomato genotypes was performed using the Pearson correlation between means of genotype analytical replicates. The HCA revealed a high correlation between the reference samples, which were analyzed daily during the entire experiment in order to monitor the stability of the analytical system (Fig. 3A). The cherry genotypes formed a distinct cluster, clearly separated from the round and beef varieties. The latter two tomato types could not be separated into distinct groups. One cherry genotype could be regarded as intermediate by its volatile composition, due to its location at the very edge of the round-beef cluster.
PCA revealed two major types of metabolic differences within the 94 tomato genotypes (Fig. 3B). First, in accordance with HCA, PCA showed a clear between-fruit-type variation, separating the cherry tomatoes, on the one hand, from the round and beef tomatoes, on the other hand (vector 1). In addition, PCA revealed a clear within-type variation in metabolite content, separating the 94 tomato cultivars into two groups independent of fruit type (vector 2). The daily replicated reference samples are located in the middle of both vectors of genotype differentiation. This is logical, since the reference sample was created by pooling of fruit material of several genotypes of each fruit type. The molecular fragments determining both the between- and within-type variations could be found by projection of the genotype differentiation vectors onto the PCA plot showing the distribution of the molecular fragments (Fig. 3C). Step 4 (Fig. 1C-d) is as follows. A novel MMSR strategy was developed to reconstruct chemical structures of metabolites from the molecular fragment information of GC-MS profiles and subsequently to discover a biochemical meaning of the metabolic differences found.
The approach is based on two points. First, since fragmentation of a metabolite by the mass spectrometer occurs after chromatographic separation, molecular fragments derived from the same metabolite will appear within a peak of a certain width at a certain retention time in a chromatogram. Second, the relative ratio between intensities of molecular fragments derived from the same metabolite is constant. Therefore, the expression patterns of these molecular fragments must be identical throughout a set of variable metabolic profiles and hence must be highly correlated to each other. Based on these points, a metabolite may be defined as a group of highly correlated molecular fragments situated within a certain retention time window. Proceeding from this definition, all of the 20,000 molecular fragments were subjected to HCA by calculating the Pearson correlation between their intensity patterns throughout the GC-MS profiles of all the tomato genotypes analyzed. HCA resulted in clustering of molecular fragments showing identical or highly similar patterns of intensities throughout all GC-MS datasets (Fig. 4A). Those molecular fragments, which clustered together with a Pearson correlation coefficient equal to or higher than 0.8 and were situated within a maximal deviation in retention time of
Phe-Derived Volatiles Mostly Explain the Difference in the Composition of the Tomato Fruit Volatile Metabolome The MMSR and NIST library matching results revealed that the molecular fragments (Fig. 3C), which were most discriminative between the tomato genotypes (Fig. 3B), belonged to two groups of volatile metabolites, derived from the phenolic (depicted in pink) and phenylpropanoid (depicted in blue) pathways (Fig. 3D). Interestingly, both groups originate from the amino acid Phe. Cherry tomatoes could be distinguished from round and beef by a relatively high accumulation of phenolic-derived volatiles (Fig. 3, D and F, vector 1). Two phenolic alcohols, phenylethanol and benzyl alcohol, showed the highest contribution to the cherry versus round/beef contrast. Volatiles derived from the phenylpropanoid pathway, including methyl and ethyl salicylate, guaiacol, eugenol, and salicylaldehyde, were responsible for the division of the genotypes into two groups independent of the tomato fruit type (Fig. 3D, vector 2). Both types of Phe-derived volatiles revealed the largest relative variation across the 94 genotypes (Table I). Besides phenolic volatiles, cherry tomatoes also contained relatively high levels of lipid derivatives (Fig. 3F) and low levels of terpenoids, open-chain carotenoid derivatives, and Leu/Ile-derived products (Fig. 3E).
The 322 compounds were subjected to HCA using the Pearson correlation coefficient. This revealed the presence of a few major compound clusters, as shown in a correlation matrix (Fig. 5). Compounds situated in the clusters were subjected to a putative identification by matching their mass spectra to the NIST library. Reliable matching results were obtained for 100 of them, of which 70 metabolites had previously been described as tomato fruit volatiles (Petro-Turza, 1987
The identification results revealed that each of the compound clusters contained compounds that have a common biochemical precursor or belong to the same metabolic pathway (Fig. 5): Phe derivatives (phenolic and phenylpropanoid volatiles), Leu and Ile derivatives, lipid derivatives, and isoprenoid derivatives, consisting of open-chain and cyclic carotenoid breakdown products and terpenes (Buttery and Ling, 1993 ; Baldwin et al., 2000).
High-Throughput Screening of Volatiles
Volatile tomato fruit metabolites have been profiled using headspace SPME-GC-MS, which is a procedure that has been used in the past for many plant matrices including tomato fruits (Song et al., 1998
For a high-throughput analysis of a large number of biological samples, an automated sequential manipulation of the samples is required. To obtain reliable data in this way, the metabolic composition has to be stable during the entire period of experimentation. This is especially important when analyzing complex native plant materials such as fruit tissue. To develop an automated high-throughput SPME-GC-MS method to screen and profile fruit volatiles of 94 tomato cultivars, the initial focus was placed on 15 volatile metabolites that are of particular importance in determining tomato fruit flavor (Buttery and Ling, 1993
Metabolomics aims to generate a comprehensive overview of the identity and quantity of metabolites in biological materials. The general principle currently used is that all compounds are identified prior to their, often relative, quantification and subsequent comparison throughout the biological samples. When using GC-MS, each chemical compound is classified both on its relative retention time and its mass spectrum. This mass spectrum gives a unique fingerprint of the chemical resulting from its fragmentation on entering the mass spectrometer. However, when using complex plant extracts, despite effective prior chromatographic separation, mass spectra of many compounds inevitably often coelute, thus complicating their discrimination. Consequently, the compound discrimination step (not always unbiased) limits the comprehensiveness of the metabolomic analyses. As an alternative strategy for comparative metabolomic analysis, we propose here a protocol that is based upon an unbiased empirical quantification and search for metabolic differences at the level of molecular fragments (ions) prior to compound identification. This approach avoids the time-consuming need for any prior assignment of chemical information to the molecular structure for hundreds of datasets and thus makes it possible to gain a faster, more unbiased and nontargeted metabolomic overview. Furthermore, this approach facilitates our desire to home in specifically on those mass peaks that are discriminatory between samples. This approach, however, depends on the initial ability to align the spectral patterns of the tens of thousands of molecular fragments present throughout all the GC-MS datasets to be compared. For this, we used the MetAlign software package to eliminate noise, compensate retention time shifts, and align the mass spectral information. This resulted in a data matrix of about 4,000,000 data points (198 datasetsx20,000 mass peaks detected). Each row of this data matrix displays the intensities of a unique molecular fragment throughout the 198 GC-MS datasets. However, a number of contaminants resulting from the fiber material can usually be found when using SPME. A multivariate analysis (HCA) of the molecular fragment patterns throughout the GC-MS profiles obtained allowed us to extract the fragments related to the fiber and to remove them from the dataset automatically. The complete mass spectral alignment of metabolic profiles has thus allowed us to perform a reliable, multivariate comparative analysis of the 94 genotypes studied. This analysis revealed both between-fruit-type metabolic differences, discriminating cherry tomatoes from round and beef, as well as within-fruit-type metabolic differences, which were independent of fruit type, and allowed the discrimination of the molecular fragments determining the variation between genotypes. However, to get subsequently biologically relevant information, we have to be able to relate these discriminative molecular fragments to their parent compounds in order to perform a putative identification. To overcome the limitations of metabolite recognition and identification that are due to high metabolome complexity and variability, we developed an approach that allows an automated reconstruction of the mass spectra of individual compounds (MMSR). This approach is based on the fixed ratio of molecular fragment intensities resulting from the fragmentation of a particular molecule. Logically, even if the abundance of a compound varies between samples, the ratios of its molecular fragment intensities derived from the parent molecule should remain the same throughout all the samples. Consequently, when molecular fragments cluster together after being subjected to a multivariate analysis such as HCA and their relative retention time does not exceed a predefined window, it can be concluded that they relate to the same chemical compound. Using MMSR, we were able to discriminate the full array of chemical compounds present in all datasets using one automated procedure, even in cases of complex overlapping mass spectra (Fig. 4). In comparison, when using AMDIS alone—a software package dedicated to resolve compound overlap cases by means of automated mass spectra deconvolution—for chemical compound discrimination, we were unable to get an equally reliable prediction of the number and chemical identity of overlapping compounds. This is due to their variable mass intensities in the wide range of the different samples (data not shown).
Deconvolution procedures are generally reliable and frequently used to handle individual GC-MS datasets. However, when analyzing hundreds of samples, a limited number of datasets that are assumed to fully represent the compound diversity of the entire sample set analyzed have to be selected for deconvolution visually. The compounds that can be discriminated in these representative datasets are subsequently used for a comparative analysis of the entire sample set. Such procedures, based on a prior mass spectral deconvolution of GC-MS profiles, have been used successfully, and this has allowed the discrimination and identification of many compounds in plant extracts (Taylor et al., 2002
In our tomato study, a total of 322 tomato volatile compounds could be discriminated in 198 datasets. This is approximately 80% of all the volatile metabolites (>400 different volatiles) that have so far been reported in tomato fruit (Petro-Turza, 1987
All isoprenoid volatiles can be roughly separated into three subclusters representing terpenoids, open-chain, and cyclic carotenoid derivatives (Fig. 5B, groups e, f, and g, respectively). Interestingly, the terpenoids
Mathematical analyses of metabolic pathway databases of many organisms have led to the concept of hierarchical modularity in the organization of metabolic networks. This concept implies that cellular functionality is organized in a set of functional modules, which consequently are organized in a few large modules, which in turn can be grouped into even larger modules (Jeong et al., 2000
The high-resolution, comprehensive, and unbiased strategy for metabolomic data analysis presented here is novel and opens new directions of discovery in the field of metabolomics. Full mass spectral alignment of GC-MS metabolic profiles followed by a universal strategy for chemical compound discrimination has allowed us to perform a high-resolution, unbiased, and fast multivariate comparative analysis of volatile biochemical composition of 94 tomato genotypes (198 complex plant extracts) based on metabolic information derived by the analytical method. The large-scale picture of the volatile part of the tomato fruit metabolome reflects the hierarchical modularity of metabolism organization that is assumed to be common for different levels of a biological system. Further projecting the data into data from other "omics" technologies will pave the way for a true systems biology approach to investigating cell networks and more directed gene discovery. The main goal of this study was to describe this novel efficient approach for unbiased analysis of complex biochemical datasets. A detailed biological interpretation of the data obtained is beyond the scope of this article, but it is anticipated that this will provide much new information on the heterogeneity in biochemical composition within tomato varieties, and this will be the subject of our future investigations.
Plant Material Ninety-four tomato (Lycopersicon esculentum Mill.) genotypes were obtained from six different tomato seed companies, each with its own breeding program. As such, the cultivars should represent a considerable collection of genetic and therefore phenotypic variation, not just between tomato types (cherry, round, and beef), but also within the individuals of each type. This study was deliberately performed blind. We only received information from the tomato breeders of the companies supplying the material concerning the tomato fruit types and not their genetic background. For classification, breeders generally use a combination of (1) fruit diameter and (2) number of locules in the fruit (fl). For the latter, the criteria were as follows: cherry-type fl = 2; round fl = 3; beef fl = 4 or more. All cultivars were grown in the summer of 2003 under greenhouse conditions at a single location in Wageningen, The Netherlands. Nine plants, randomly distributed over three adjacent greenhouse compartments, were grown for each cultivar. Pink-staged tomato fruits of all plants were picked on two consecutive days. To mimic the conditions from the farm to the fork, fruits were stored for 1 week at 15°C and turned to 20°C at 24 h prior to freezing. During this period, the fruits continued to ripen slowly and, at the moment of sampling, the fruits were fully red ripe, resembling the conditions at the time of consumption. For each cultivar, a selection of red ripe fruits (12 for round and beef tomatoes and 18 for cherry tomatoes) was pooled to make a representative fruit sample. The fruit material was immediately frozen in liquid nitrogen, ground in an analytical electric mill, and stored at –80°C before analyses.
Fifteen analytical grade chemicals (all obtained from Sigma) were used as authentic standards to optimize the SPME-GC-MS method for automated sequential analysis of hundreds of samples. These were cis-3-hexenal,
Frozen fruit powder (1 g fresh weight) was weighed in a 5-mL screw-cap vial, closed, and incubated at 30°C for 10 min. An EDTA-NaOH water solution was prepared by adjusting of 100 mM EDTA to a pH of 7.5 with NaOH. Then, 1 mL of the EDTA-NaOH solution was added to the sample to a final EDTA concentration of 50 mM. Solid CaCl2 was then immediately added to give a final concentration of 5 M. The closed vials were then sonicated for 5 min. A 1-mL aliquot of the pulp was transferred into a 10-mL crimp cap vial (Waters), capped, and used for SPME-GC-MS analysis. Each of the 94 tomato fruit samples was analyzed using two replicated aliquots. In total, 22 freshly prepared samples were measured per day (two series of 11 samples). In addition, reference tomato samples were made by mixing fruit powders from several genotypes of the round, beef, and cherry fruit phenotypes. This mixture was routinely analyzed every day of experimentation as an external control in order to monitor the stability of the analytical system. The samples were automatically extracted and injected into the GC-MS via a Combi PAL autosampler (CTC Analytics AG). Headspace volatiles were extracted by exposing a 65-µm polydimethylsiloxane-divinylbenzene SPME fiber (Supelco) to the vial headspace for 20 min under continuous agitation and heating at 50°C. The fiber was inserted into a GC 8000 (Fisons Instruments) injection port and volatiles were desorbed for 1 min at 250°C. Chromatography was performed on an HP-5 (50 mx0.32 mmx1.05 µm) column with helium as carrier gas (37 kPa). The GC interface and MS source temperatures were 260°C and 250°C, respectively. The GC temperature program began at 45°C (2 min), was then raised to 250°C at a rate of 5°C/min, and finally held at 250°C for 5 min. The total run time, including oven cooling, was 60 min. Mass spectra in the 35 to 400 m/z range were recorded by an MD800 electron impact MS (Fisons Instruments) at a scanning speed of 2.8 scans/s and an ionization energy of 70 eV. The chromatography and spectral data were evaluated using Xcalibur software (http://www.thermo.com).
1. For automated baseline correction, mass spectra extraction, and subsequent spectral data alignment, in total 198 GC-MS datasets were processed simultaneously using the dedicated MetAlign metabolomics software package (http://www.metalign.nl; Fig. 2A). 2. The metabolic profiles aligned were subjected to multivariate analyses: HCA (Pearson correlation coefficient was used) and PCA to search for metabolic differences between the tomato genotypes at the level of molecular fragments (Fig. 2, A–C). The multivariate analyses were performed using the GeneMaths software package (http://www.applied-maths.com). A log2 transformation was applied to the data prior to the multivariate analyses. 3. MMSR was used to assign the molecular fragments to compounds. For this, the patterns of all molecular fragments were subjected to HCA. Those molecular fragments that revealed a Pearson correlation equal to or more then 0.8 and were situated within a 6-s retention time window (which corresponds to an average peak width at one-half height in the chromatograms we obtained) were considered as belonging to the spectrum of one compound.
4. For compound identification, the following steps were used: (1) for each compound selected for putative identification, the most optimal chromatogram is selected with respect to relative abundance and overlap with other compounds at the specific position; (2) for each selected compound, specific molecular fragments (ions, m/z) were selected from the corresponding fragment cluster derived by MMSR; (3) the selected fragments were used as a basis for deconvolution of the chromatographic peak at the corresponding retention time using AMDIS (Stein, 1999
The authors are grateful to Syngenta Seeds, Seminis, Enza Zaden, Rijk Zwaan, Nickerson-Zwaan, and De Ruiter Seeds for providing seeds of the 94 tomato cultivars. We would like to thank Mrs. Fien Meijer-Dekens, Mrs. Petra van den Berg, Dr. A.W. van Heusden, and Dr. Pim Lindhout for excellent greenhouse management and plant cultivation, and Dr. Harro Bouwmeester and Mr. Francel Verstappen for helpful discussions and technical support. Received July 6, 2005; returned for revision September 13, 2005; accepted September 13, 2005.
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.
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ABSTRACT
RESULTS
6 s (corresponding to an average peak width at one-half height in the chromatograms we obtained), were considered to belong to the mass spectrum of one and the same metabolite. In total, 322 molecular fragment clusters were obtained. The mass spectra of the 15 key flavor-related tomato volatiles (Baldwin et al., 2000
- and 
-citral appeared in the group of open-chain carotenoid derivatives. This is in line with previous observations that the citral isomers may be derived as a degradation product of lycopene (Cole and Kapur, 1957
