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First published online April 20, 2004; 10.1105/tpc.021741 © 2004 American Society of Plant Biologists Genome-Wide Analysis of Spatial Gene Expression in Arabidopsis FlowersCalifornia Institute of Technology, Division of Biology 156-29, Pasadena, California 91125 2 To whom correspondence should be addressed. E-mail meyerow{at}caltech.edu; fax 626-449-0756.
We have compared the gene expression profiles of inflorescences of the floral homeotic mutants apetala1, apetala2, apetala3, pistillata, and agamous with that of wild-type plants using a flower-specific cDNA microarray and a whole genome oligonucleotide array. By combining the data sets from the individual mutant/wild type comparisons, we were able to identify a large number of genes that are, within flowers, predicted to be specifically or at least predominantly expressed in one type of floral organ. We have analyzed the expression patterns of several of these genes by in situ hybridization and found that they match the predictions that were made based on the microarray experiments. Moreover, genes with known floral organ–specific expression patterns were correctly assigned by our analysis. The vast majority of the identified transcripts are found in stamens or carpels, whereas few genes are predicted to be expressed specifically or predominantly in sepals or petals. These findings indicate that spatially limited expression of a large number of genes is part of flower development and that its extent differs significantly between the reproductive organs and the organs of the perianth.
Much progress has been made in recent years in understanding the initiation of organ formation, in plants as well as in animals, by identifying many of the master regulatory genes that trigger the developmental programs required for organogenesis. However, in most cases, the molecular mechanisms by which the activation of these genes results in organ function are not well understood. One example that illustrates our limited understanding of organogenesis is the development of floral organs. In Arabidopsis thaliana, the floral mutants apetala1 (ap1), ap2, ap3, pistillata (pi), and agamous (ag) show homeotic phenotypes, that is, the replacement of one type of floral organ (namely, sepals, petals, stamens, and carpels) by another (Figure 1). In ap1, sepals are transformed into bract-like organs, and petals are often completely absent. Furthermore, secondary flowers arise from the axils of the outermost whorl organs. In ap2, petals are missing or are transformed into stamens, and sepals are transformed into carpel-like organs. In addition, the number of stamens is reduced. In the mutants ap3 and pi, petals are replaced by sepals, and stamens are replaced by carpels. In ag, stamens are replaced by petals, and carpels are replaced by extra whorls of sepals and petals. Based on the phenotypes of these mutants, the ABC model of floral organ identity determination was proposed (Bowman et al., 1991  ; Coen and Meyerowitz, 1991). According to this model, the activities of the genes affected in the mutants can be assigned to three different functions, namely A (AP1 and AP2), B (AP3 and PI), and C (AG), with each function required for organ specification in different floral regions. The functions act combinatorially in the specification of floral organ identity: A function alone leads to the formation of sepals, whereas the combination of A and B functions determines petal identity. The combination of B and C functions results in stamens, and C function alone determines carpel formation. The floral organ identity genes were cloned and found to encode transcription factors (Yanofsky et al., 1990; Jack et al., 1992; Mandel et al., 1992; Goto and Meyerowitz, 1994; Jofuku et al., 1994). Their expression patterns and (in some cases) molecular interactions (Honma and Goto, 2001) suggest that the combinatorial nature of the ABC model, as indicated by genetics, is reflected at the molecular level.
However, in spite of their well-characterized roles in floral organ specification, it is unclear to what extent these transcription factors also participate in the continuing formation of floral tissues throughout organogenesis. Furthermore, it is for the most part unknown whether they control the expression of other transcriptional regulators or instead directly affect the transcript levels of genes that encode structural proteins required for cellular differentiation. Thus, for a better understanding of floral organ formation, it is necessary to decipher the regulatory networks that control gene expression during the various stages of flower development. A possible strategy for this would be to identify the components of these networks first and then to determine how they are linked to each other. So far, the identification of regulatory genes involved in flower development has been primarily accomplished by the characterization of mutants that show severe phenotypic alterations. By comparison, relatively few mutants have been described in which, for example, only a certain floral cell type is affected. One explanation for this is that the inactivation of these genes results in phenotypic alterations that are subtle and are easily missed among thousands of plants in a screening population. It is also possible that functional redundancy prevents the identification of many regulatory genes by conventional mutagenesis.
Early work on tobacco (Nicotiana tabacum) using RNA-excess single-copy DNA hybridization reactions had shown for different plant organs that the composition of their nuclear RNA sequence population is developmentally regulated and contains a set of transcripts that is specific to each organ (Kamalay and Goldberg, 1984 In this study, we have analyzed spatial gene expression in Arabidopsis flowers by comparing the gene expression profiles of inflorescences of the floral homeotic mutants with that of wild-type inflorescences. To this end, we have used a cDNA microarray whose composition is strongly enriched for elements representing flower-specific transcripts as well as a whole genome oligonucleotide array. The fact that different organ types are missing in the mutants allowed us to identify transcripts that are, within flowers, specifically expressed, or are at least strongly enriched, in one type of floral organ by combining the data sets from the individual experiments. We have determined the expression patterns of several previously uncharacterized genes by in situ hybridization and found that the predictions based on the microarray data are in agreement with the actual expression patterns. In addition, genes that had previously been shown to have floral organ–specific expression patterns were correctly assigned to the different organ groups by the microarray experiments. Our results indicate that spatially limited expression of several genes is part of Arabidopsis flower development. Furthermore, they suggest that the extent to which spatially limited gene expression is required for floral organ formation differs significantly between the organs of the perianth and of the reproductive organs. Our data also provide a rich source of target genes for reverse genetics approaches and candidates for floral organ–specific or cell type–specific markers.
Analysis of Gene Expression Profiles of Floral Homeotic Mutants In recent years, gene expression analysis with DNA microarrays has become a powerful tool for the analysis of developmental processes in animals and plants (Reinke and White, 2002 ). To allow the use of microarray technology for the analysis of flower development, we have constructed a cDNA microarray whose elements are strongly enriched for flower-specific transcripts. The array is comprised of 10,816 elements representing  5000 to 6000 genes (see supplemental data online for a detailed description of the strategy used for the construction of the array). We used this array and, in later stages of this study, whole genome microarrays comprised of 26,090 oligonucleotides to analyze the gene expression profiles of inflorescences of the floral homeotic mutants ap1, ap2, ap3, pi, and ag. To this end, tissue samples were collected from the different mutants that contained the inflorescence meristem and floral buds corresponding to developmental stages 1 to 13 (Smyth et al., 1990). Total RNA was isolated from these samples, and the RNA was used to synthesize dye-labeled cDNA (for the cDNA array) or antisense RNA (for the oligonucleotide array) that was cohybridized to the microarrays with control samples derived from inflorescences of wild-type plants (see Methods for further details). In all cases, at least three independent biological samples were used in separate hybridizations. To avoid dye-related artifacts, the dyes used for the labeling of the cohybridized samples were switched in the replicate experiments.
After quantitation of the signal intensities, the data were normalized to compensate for nonlinearity of intensity distributions and differences in probe labeling (Figure 2 and see Methods and supplemental data online for further details). We next identified elements showing significant differences in expression in the replicate experiments using either significance analysis of microarrays (SAM; Tusher et al., 2001
For ap3, pi, and ag, we observed a large number of elements that were determined significantly changed in the experiments. Most of these elements corresponded to downregulation in the mutants compared with the wild type. In the A function mutants ap2 and especially ap1, a smaller number of elements reporting significant expression changes was detected, and the difference between the number of elements indicating upregulation or downregulation was less pronounced.
Because the B function mutants ap3 and pi have phenotypes that are almost indistinguishable from each other, we calculated the correlation coefficient of the data sets obtained from the microarray experiments and found a high degree of correlation (for the cDNA array, r = 0.98; for the oligonucleotide array, r = 0.91), suggesting that the similar phenotypes coincide with similar gene expression profiles. This result is in agreement with the idea that PI and AP3 are obligate partners in a regulatory complex and that the disruption of one of the genes results in a loss of function of the complex (Riechmann et al., 1996
Prediction of Organ-Specific Transcripts
Petals are absent in ap2, ap3, and pi and are strongly reduced in number in ap1, whereas extra petals are found in flowers of ag. In accordance with the criteria used for the prediction of sepal-specific transcripts, we initially assumed that elements representing petal-specific transcripts show statistically significant downregulation in all four A and B function mutants and are either unchanged or upregulated in ag. However, it is possible that elements representing petal-specific transcripts might be missed with these stringent screening criteria. For example, an element representing a petal-specific transcript would be missed if it were not considered significantly downregulated in one of the four experiments perhaps because of variability in the replicate experiments. We therefore slightly loosened the screening criteria by assuming that the downregulation of petal-specific transcripts should be statistically significant in at least three of the four A and B function mutants. This led to the identification of 18 genes with predicted petal-specific expression. For the prediction of stamen-specific transcripts, we assumed significant downregulation in ap3, pi, and ag because stamens are completely missing in these B and C function mutants. These criteria led to the identification of 1162 genes. Because stamens are reduced in number in ap2, the identified genes should be downregulated, at least to some extent, in this mutant as well. We calculated the log2-transformed median expression ratios for the predicted stamen-specific transcripts and obtained values of –0.7 and –1.1 (for data from the cDNA array and from the oligonucleotide array, respectively), indicating that the corresponding transcripts are indeed generally downregulated in ap2. The only mutant used in this study that lacks carpels is ag. In the other mutants, the carpel number is either unchanged (ap1), or extra carpelloid tissues are present (ap2, ap3, and pi). We identified several hundred genes that were downregulated only in ag but were upregulated or unchanged in the other mutants. Among these genes, we found several with known carpel-specific expression patterns (see below). However, in contrast with all other types of floral organs, these predictions were based on the significant downregulation of an element in a single mutant; thus, the predictions are potentially less reliable than those for the other organs. To improve the prediction of carpel-specific transcripts, we therefore introduced a second criterion that was based on the assumption that the difference between the expression ratios of ag and the mutants containing extra carpel tissue should be relatively large. For the selection of appropriate cutoff values, we calculated the mean expression ratios and standard deviations for known carpel-specific transcripts (Table 2) for ap2, ap3, and pi. The mean log2-transformed expression ratios minus two standard deviations were used as cutoff values. This led to the prediction of 260 carpel-specific transcripts.
Of the 1453 genes with predicted organ-specific expression (see supplemental data online for a list of all genes), 1380 were identified in experiments with the oligonucleotide array and 247 with the cDNA array (Table 1). A total of 174 genes were detected with both types of arrays. Of the 73 genes that were identified with the cDNA array but not with the oligonucleotide array, 18 are not represented on the oligonucleotide array. Thus, we found a 76% overlap (174 of 229 possible genes) between the data sets derived from the cDNA and oligonucleotide arrays.
Verification of Predicted Organ-Specific Gene Expression As mentioned above, several known carpel-specific transcripts were identified in the carpel group (Table 2). The corresponding genes all encode transcription factors that are predominantly expressed in carpel walls (e.g., SHATTERPROOF1, SHATTERPROOF2, and FRUITFULL) or in ovules (SEEDSTICK, INNER NO OUTER, and SUPERMAN) during late stages of flower development. The correct assignment of genes such as INNER NO OUTER, which is expressed only in a small domain of ovules (Villanueva et al., 1999 ), suggests that expression changes of relatively rare transcripts were reliably detected by the microarray analysis. In the stamen group, several genes with known expression in anthers, especially in the tapetal cell layer or in pollen, were found (Table 3). Among the few transcripts assigned to the petal group, one (At5g45950) previously had been predicted to be petal specific (Zik and Irish, 2003). The sepal group contained the organ identity gene AP1. Although AP1 is indeed strongly expressed in sepals, it is also expressed in petals (Mandel et al., 1992), suggesting that the organ groups might contain some genes whose expression is not strictly organ specific.
We next tested the expression of previously uncharacterized genes. For this, we first confirmed the results of the microarray experiments by real-time PCR for several genes (data not shown). Next, we generated probes for these genes and performed in situ hybridizations. Expression of a NAC-family transcription factor (At1g61110; assigned to the stamen group) was found in stamens in the tapetum at stage 11 of flower development (Figures 4A and 4B). Two of the studied genes were expressed during late stages of pollen development (Figures 4C and 4I): At2g07040, encoding a putative receptor-like kinase, and At2g43230, coding for a putative protein kinase. Both genes were correctly assigned to the stamen group by the microarray analysis. Another gene in the stamen group, At3g26860 (encoding a putative self-incompatibility protein), is expressed during early pollen development in tetrads at floral stage 9 (Figure 4L). For At3g61160, encoding the SHAGGY-like kinase ASKß, predominant expression in pollen had been shown previously by RNA gel blot analysis and reverse transcriptase PCR (Tichtinsky et al., 1998 ). In addition to its expression in pollen, we also observed expression in ovules, probably in egg cells (Figures 4J and 4K).
An example of a gene predicted to be expressed in carpels is At4g12960, encoding a protein with unknown function. This gene is expressed starting at floral stage 12 inside of ovules and likely marks the inner integuments (Figures 4G and 4H).
The homeodomain transcription factor WUSCHEL (WUS) is expressed during floral organ formation in the nucellus of developing ovules (Gross-Hardt et al., 2002 The criteria applied for the analysis of the microarray data were designed to minimize the number of falsely identified organ-expressed transcripts. However, a reduction of false positives coincides with an increase in the number of genes that are organ expressed but were missed in the analysis. Based on the expression ratios, we were able to identity several elements that likely represent false negatives (data not shown). In situ hybridization for one of the corresponding genes, At1g75030, showed that this gene, coding for a thaumatin-like protein, is expressed in the tapetum of anthers and thus was missed by the microarray analysis (Figure 4F).
The results of a recent study allowed us to identify genes of the stamen group expressed most likely specifically in pollen. In this study, the transcriptome of pollen was analyzed by comparing RNA isolated from mature pollen to RNA isolated from whole plants at various stages of development using an Affymetrix 8k Arabidopsis GeneChip array (Honys and Twell, 2003
Functional Assessment of the Identified Genes We also analyzed the distribution of gene families among the organ-expressed genes and identified several underrepresented or overrepresented families (see supplemental data online for results). For example, members of the class III peroxidase family or the glycoside hydrolase family 28 were enriched, whereas members of the relatively large families of core cell cycle genes and of putative sugar transporters were completely absent in the data set.
We next searched in the data set for pairs of closely related genes that might indicate possible functional redundancy. To this end, we first determined the closest related protein sequence in the Arabidopsis proteome for every organ-expressed gene product using BLAST (Altschul et al., 1990
Among the 1453 organ-expressed genes, 80 or 5.5% encode putative transcription factors (genes are listed in the supplemental data online). This number is close to the percentage of transcription factors in the Arabidopsis proteome (
Because most of the organ identity genes on which this study was based encode MADS box transcription factors and we detected an enrichment of MADS box factors among the organ-expressed genes, we analyzed the distribution of CArG boxes (consensus 5'-CC(A/T)6GG-3'), the putative binding sites of these factors (Pollock and Treisman, 1990 The identification of organ-expressed genes allowed the search for overrepresented motifs in their putative promoters that might mediate organ-specific expression. Five different prediction programs were used to analyze the regions 1000 bp upstream of the translation start site (see supplemental data online for details) of genes assigned to the sepal, petal, and carpel groups as well as the genes with predicted pollen-specific expression (see above). The latter group was chosen as an example for genes with potentially cell type–specific expression. Although some overrepresented motifs were identified by the promoter analysis (data not shown), these are present in only a small portion of the genes in an analysis group, suggesting that spatially limited gene expression in flowers might not be mediated by a small number of conserved cis-regulatory elements.
In this study, we have analyzed spatial gene expression in flowers by comparing the gene expression profiles of floral homeotic mutants. We have identified a large number of genes that are, within flowers, specifically or predominantly expressed in one type of floral organ and are probable components of the gene networks involved in floral organ development. Several of these genes encode proteins with presumed regulatory function, most of which have not yet been characterized in detail.
In Arabidopsis, the targeted inactivation of genes has become a very powerful approach for functional analysis. RNA interference can be used to induce loss-of-function phenotypes (Chuang and Meyerowitz, 2000
In large microarray data sets, such as the one presented in this study, not every observation can be confirmed by independent methods. Therefore, we chose selection criteria that aimed to minimize the number of transcripts that are falsely predicted to be floral organ specific. For the analysis of the individual mutant/wild type comparisons, we have used parameters that led to a false discovery rate for significantly changed elements of Among the genes in the different organ groups, we found several with known floral organ–specific expression that were correctly assigned (Tables 2 and 3). This result indicates that the selection criteria applied here were suitable for the identification of organ-expressed transcripts. The analysis of expression patterns of previously uncharacterized genes further supported this conclusion (Figure 4). For all genes tested, the actual expression patterns matched the predictions that were based on the microarray data. However, the results of in situ hybridizations also showed that some genes that were assigned to one of the organ groups are also expressed in other floral organs either at low levels or in relatively small domains. Examples of such genes are WUS and ASKß. Here, the strong expression of the genes in anthers or in pollen, respectively, masks their expression in small domains of ovules.
We observed a large overlap between the data sets from the two different types of microarrays used in this study. Seventy-six percent of the genes identified with the flower-specific cDNA array were also identified with the oligonucleotide array. The differences between the data sets are likely because of differences in the properties of the two array platforms that affect critical parameters such as sensitivity, probe specificity, or reproducibility (Kothapalli et al., 2002
A limitation of our analysis is that the cell type in which a gene is expressed in a floral organ cannot be predicted based on the microarray data. To further characterize the localization of the organ-expressed transcripts, several approaches could be taken. First, genes of interest could be studied by in situ hybridizations as demonstrated in this study. A second possibility would be to analyze the expression profiles of mutants or transgenic lines whose phenotypic defects are limited to certain floral tissues or cell types. Examples for such mutants are extra sporogenous cells/excess microsporocytes1 that do not form the tapetal cell layer in anthers (Canales et al., 2002 The results of in situ hybridizations as well as the expression patterns of the previously characterized organ-specific transcripts suggest that the majority of transcripts are likely expressed at late stages of flower development. The earliest expression found by in situ hybridization for an organ-expressed transcript was detected for At3g26860 in tetrads during pollen development at floral stage 9 (Figure 4L). This observation can be explained by the fact that the RNA preparations used for the experiments were, because of the differences in size of young and old floral buds, strongly enriched for RNA from older buds. Therefore, genes that are expressed in one of the floral organs during early stages of flower development might have been too dilute in the RNA samples to be detected in these experiments. The majority of the genes that were predicted to be organ expressed were assigned to the stamen and carpel groups, whereas only very few were assigned to the organs of the perianth. This difference in number is likely because of key developmental events, such as the formation of pollen and ovules that occur during late stages of flower development in the reproductive organs. In addition, the reproductive organs contain many different tissues and cell types, whereas the anatomy of sepals and petals appears to be less complex.
In a previous study, the expression profiles of inflorescences of different strains, including ap3 and pi, were compared, leading to the identification of 47 genes that were predicted to be expressed in petals and/or stamens (Zik and Irish, 2003
The elements of the cDNA microarray used in the previous study were derived from ESTs from all stages of the Arabidopsis life cycle (Zik and Irish, 2003
Underrepresentation of low abundance transcripts on cDNA arrays could also explain why only few transcription factors were found downstream of AP3/PI (Zik and Irish, 2003
Strains and Plant Growth The mutant strains used in this study were ap1-1, ap2-2, ap3-3, pi-1, and ag-3 (Bowman et al., 1989 , 1991, 1993; Jack et al., 1992). Plants of the accession Landsberg erecta were used as wild-type control. Plants were grown on a soil:vermiculite:perlite mixture under constant illumination at 21°C.
Microarray Setup The oligonucleotide array was based on the Arabidopsis Genome Oligo Set version 1.0 (Qiagen Operon, Alameda, CA). This set consists of 26,090 oligonucleotides that correspond to 23,542 annotated genes in release 4.0 of the Institute for Genomic Research Arabidopsis thaliana Genome Annotation Database.
Tissue Collection and RNA Isolation for Microarray Experiments
Probe Labeling and Microarray Hybridization Protocols
Data Acquisition, Normalization, and Statistical Analysis For experiments with the cDNA array, data were analyzed as follows. Before normalization, the data points were removed if a spot was flagged during data acquisition (e.g., as a bad spot, absent spot, etc.) or if a spot intensity was calculated based on >5% of saturated pixels. In addition, low intensity elements were removed if the sum of the median signal intensities of a spot was below the sum of the median background intensities plus two standard deviations of background. For the remaining elements, the median background intensities were subtracted from the median spot intensities. Subsequently, the data sets for the replicate experiments were combined. Elements for which less than three data points were available were excluded from further analysis. In addition, all the elements whose amplification during the generation of the array did not result in high-quality PCR products were removed from the data sets.
After quantitation of the signal intensities, the data were normalized to compensate for nonlinearity of intensity distributions and differences in probe labeling. Commonly used normalization methods include global normalization or linear regression. These methods are based on the assumption that the percentage of the elements reporting significant expression changes is small and that the number of upregulated and downregulated genes is similar (Causton et al., 2003 After normalization, ratios of signal intensities were calculated for each experiment followed by the coefficient of variation of the ratios for the replicate experiments. To assess the overall reproducibility of the experiments, the median coefficient of variation was calculated for each mutant/wild type comparison. The following values were obtained: 16% (ap1), 19% (ap2), 15% (ap3), 16% (pi), and 16% (ag).
Next, we performed SAM (Tusher et al., 2001
The log2-transformed mean ratios of the elements that were judged significant in SAM in at least one of the experiments were loaded into the program CLUSTER (Eisen et al., 1998
For experiments with the oligonucleotide array, data were analyzed using the Rosetta Resolver gene expression data analysis system (Rosetta Biosoftware, Kirkland, WA). In Resolver, spots that are flagged during data acquisition by the Genepix software are not included in the analysis. In addition, if for a given spot intensities in both channels are below zero after background subtraction, the data are excluded from further analysis. Additional data processing in this system consists of error correction and calculation of a P value of differential expression using the intensity-error estimation from the .gpr file. Error correction consists of a simplified version of the algorithm described by Schadt et al. (2001)
In Situ Hybridizations Microarray data from this article have been deposited with the NCBI Gene Expression Omnibus data repository (http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE1265 through GSE1275.
We are very grateful to Vijaya Rao and Ali Mortazavi for help with microarray data analysis, to Arnavaz Garda for technical assistance, and to members of the Meyerowitz laboratory for discussion and for comments on the manuscript. We thank S. Tabata (Kazusa DNA Research Institute, Japan) for the generous gift of the flower-derived EST collection. This work was supported by National Institutes of Health Grant GM45697 to E.M.M. and by the Millard and Muriel Jacobs Genetics and Genomics Laboratory at California Institute of Technology. F.W. was supported by an Emmy Noether fellowship of the Deutsche Forschungsgemeinschaft. M.A.-F. is indebted to the Instituut voor Plantenbiotechnologie voor Ontwikkelingslanden and Marc Van Montagu for a fellowship sponsored by Aventis Crop Sciences.
1 Current address: Department of Genetics, Federal University of Rio de Janeiro, Av. Pau Brasil 211 A2-76, 21941-590, Rio de Janeiro, Brazil. 
Online version contains Web-only data. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the Instructions for Authors (www.plantcell.org) is: Elliot M. Meyerowitz (meyerow{at}caltech.edu). Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.021741. Received February 11, 2004; accepted February 12, 2004.
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), upregulated; (
), downregulated; (?), questionable.
