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Plant Physiology 138:80-91 (2005) © 2005 American Society of Plant Biologists Organ-Specific Expression of Arabidopsis Genome during Development1,[w]Peking-Yale Joint Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, People's Republic of China (L.M., X.W.D.); Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520–8104 (L.M., Y.J., X.W.D.); Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520 (N.S., H.Z.); and Laboratory of Molecular Cell Biology, Hebei Normal University, Shijiazhuang, Hebei 050016, People's Republic of China (X.L.)
The development of complex eukaryotic organisms can be viewed as the selective expression of distinct fractions of the genome in different organs or tissue types in response to developmental and environmental cues. Here, we generated a genome expression atlas of 18 organ or tissue types representing the life cycle of Arabidopsis (Arabidopsis thaliana). We showed that each organ or tissue type had a defining genome expression pattern and that the degree to which organs share expression profiles is highly correlated with the biological relationship of organ types. Further, distinct fractions of the genome exhibited expression changes in response to environmental light among the three seedling organs, despite the fact that they share the same photoperception and transduction systems. A significant fraction of the genes in the Arabidopsis genome is organized into chromatin domains exhibiting coregulated expression patterns in response to developmental or environmental signals. The knowledge of organ-specific expression patterns and their response to the changing environment provides a foundation for dissecting the molecular processes underlying development.
All complex eukaryotic organisms, including mammals and higher plants, consist of multiple organ and tissue types. The organ and tissue types for a given organism are generated during its life cycle through a temporally and spatially regulated process of selective expression of specific fractions of the same genome in different cells (Meyerowitz, 2002  ). Therefore, a long-sought objective of developmental biology has been to define the subset of genes expressed and their relative abundance for each organ or tissue type. Higher plants possess a relatively simple developmental process, with only three nonreproductive organ systems and fewer than 25 major tissue and cell types (Eeau, 1977), thereby providing a good model for defining the organ- and tissue-specific genome expression patterns during development. Early studies using an RNA-excess/single-copy DNA hybridization strategy have revealed much about the mRNA complexity in six selected organ types of a tetraploid tobacco (Nicotiana tabacum) strain (Goldberg, 1988; Goldberg and Barker, 1989). These studies suggested that about 24,000 to 27,000 average-sized mRNA species were present in leaf, root, stem, petal, anther, and ovary (Goldberg et al., 1978; Kamalay and Goldberg, 1980). However, it provided little information toward the identities and relative abundance of the individual mRNA species expressed in each organ type. Other approaches to determining the gene expression pattern include RNA blot and in situ hybridization. These later approaches can give detailed information regarding when and where the detected gene is expressed. However, due to the labor intensive nature of these approaches, they have been applied to only a small number of genes (Barker et al., 1988; Yanofsky et al., 1990).
DNA microarrays can measure the individual transcript level of tens of thousands of genes simultaneously, thus providing a high throughput means to analyze gene expression levels on a larger scale (Schena et al., 1995 In this study, a 70-mer oligo microarray that covers 25,676 unique known and predicted genes of Arabidopsis (Fig. 1A) was used to profile their expression level from 18 representative organ or tissue types throughout the life cycle of Arabidopsis. In addition, we also examined the light-responsive expression of the Arabidopsis genome among the three organs of seedlings. Several interesting insights on the regulation of genome expression during Arabidopsis development and its response to light were observed. In the process, it was observed that a significant fraction of adjacent genes are organized into chromatin domains with similar expression patterns.
Analysis of Arabidopsis Representative Organ Transcriptomes Supports Expression of Most Known and Predicted Genes During the life cycle of Arabidopsis, vegetative (root, stem, and leaf) and reproductive (petal, sepal, stamen, pistil, silique, and seed) organs are formed, and individual organs are specialized to carry out specific biological functions. We selected samples from 17 representative organs throughout the life cycle of Arabidopsis (Fig. 1B). We also included Arabidopsis suspension cultured cells as a common control, which allowed us to estimate the relative expression abundance for each transcript in different organs. We refer to this relative abundance of individual genes in each organ as the gene's expression level. We first estimated the number of known and predicted genes for which expression can be detected experimentally. Among the known and predicted genes covered by the array, the expression for 24,733 (96%) out of 25,676 can be detected in at least one of the 17 organs or cultured cells under our experimental conditions (Fig. 1A). To assess our results, we examined the expression of those genes that have an available expressed sequence tag (EST) match. There are 16,998 unique annotated genes represented in this microarray that have available EST matches, and 16,824 (99%) of them showed detectable expression in at least one of 17 organs or cultured cells (Fig. 1A). This result indicates that our detection of gene expression is adequately sensitive and that the vast majority of known and predicted genes are expressed during Arabidopsis development. Our results further showed that the majority (7,909 genes, or 89%) of the computer-predicted genes (a total of 8,852 genes represented by this microarray) that lacked prior confirmation are expressed during Arabidopsis development, which validates the notion that they correspond to real genes.
Annotation of genes using gene ontology (GO) functional categories assigns functions to genes with a dynamic and controlled vocabulary (Gene Ontology Consortium, 2000
Examination of the fractions of the genome expressed in each organ type revealed that the percentage of expressed genes varies from organ to organ. Over 70% of the total genes examined were expressed in stamen, petal, rosette leaf, and sepal, while about 40% of the total genes were expressed in root, hypocotyl, germinating seed, and late-stage silique (Fig. 2). These numbers are consistent with previous results from a tetraploid tobacco (Nicotiana tabacum), in which leaf and petal had the highest expressed mRNA species among the six organs examined (Goldberg et al., 1978
Relatedness of Genome Expression Correlates with Developmental Relationship of Organs in Arabidopsis
We next used the overall genome expression profiles of individual organs relative to cultured cells to examine the relatedness of the genome expression changes across the selected organs based on the average linkage clustering with correlation distance (Eisen et al., 1998
Organ-Enriched Expression of the Arabidopsis Genome
We designated genes as organ enriched if they fulfilled the two criteria: (1) showing differential expression (P < 0.05) based on the F test in a group of samples; and (2) their expression levels were at least 2-fold higher than that in any other nonhomologous organs. With these criteria, the expression of 699 (2.7%), 747 (2.9%), 762 (3.0%), 805 (3.2%), 827 (3.2%), 143 (0.5%), 89 (0.4%), 317 (1.2%), 36 (0.1%), and 187 (0.7%) genes were found to be enriched in germinating seed, rosette leaf, root, stamen, petal, sepal, silique, pistil, hypocotyl, and stem, respectively (Fig. 4A; Supplemental Table I). Most of the specific functional groups of genes followed similar trends, as shown by the transcription factor genes (Fig. 4A). We further analyzed the organ-enriched genes within several organ groups. For example, 731 (2.9%), 669 (2.7%), 261 (1.0%), and 143 (0.6%) genes showed specifically high expression in pistil 1 d before pollination, 1, 3, and 8 d after pollination, respectively (Fig. 4B; Supplemental Table II). There were 628 (2.5%), 155 (0.6%), and 778 (3.1%) genes whose expressions were enriched in rosette leaf, cauline leaf, and light-grown cotyledon, respectively (Fig. 4C; Supplemental Table III). Among the three floral organs, the expressions of 558 (2.1%), 1,710 (6.6%), and 1,282 (5.0%) genes were enriched in sepal, petal, and stamen, respectively (Fig. 4D; Supplemental Table IV). These organ-specific expression data are consistent with previously documented expression patterns of known genes. For example, four well-characterized floral pattern determination genes (AP1, AP3, PI, and AG) exhibited expression patterns among floral organs (Fig. 5) as previously reported (Yanofsky et al., 1990
Distinct Portions of the Genome Respond to Light Regulation in the Three Arabidopsis Seedling Organs
Arabidopsis seedling development is dramatically regulated by light. The three seedling organs (root, hypocotyl, and cotyledon) exhibit distinct developmental responses to light (Fig. 6A), even though the same photoperception and signaling systems seem to operate in all three organs (Cashmore et al., 1999
Organ-Specific Light Regulation of Metabolic Pathways in Arabidopsis Seedlings
In a previous study using whole seedlings, more than 26 metabolic and regulatory pathways were found to be regulated by light in Arabidopsis (Ma et al., 2001
Many pathways are subjected to light regulation in only one of the three organs. For example, the starch and Suc biosynthetic pathways were up-regulated by light only in cotyledon (Fig. 7E). The genes encoding light repressible receptor-like protein kinase and several other receptor-like kinases were repressed only in root (Fig. 7F).
Large portions of genes in the Arabidopsis genome are classified into gene families based on their sequence homologies (Arabidopsis Genome Initiative, 2000
We further examined the gene expression patterns for different gene families among all 17 organs examined. We found that the organ-specific expression pattern for gene members in the same family is not identical in all 17 organs. Again, we used the above-mentioned three gene families to do cluster analyses. Because we compared each organ with cultured cells to obtain the genome expression profile for each organ, we used the expression ratio of organ and cultured cells to show the gene expression patterns among all 17 organs. As shown in Figure 9, different members in the gene family had distinct expression patterns in the same organ. Further, organ-specific regulation of a gene family was also distinct among organs, while similar organ types, for example, rosette and cauline leaf, pistil and silique, tended to show similar expression patterns (Fig. 9, A–C). Such diversified expression patterns for members of the same gene family would be consistent with the notion that different members in a gene family might have evolved distinct functions.
Coregulation of Gene Expression Patterns in the Arabidopsis Genome
Recent studies from several species suggest that a significant fraction of those organisms' genomes may be organized into chromatin domains that contain a number of adjacent genes whose expressions are coordinately regulated (Cohen et al., 2000
In this study, we provide an organ-specific genome expression atlas during Arabidopsis development by analyzing the genome expression profile of individual representative organs using a 70-mer oligomer microarray. This analysis provides experimental evidence for a large fraction of those predicted genes in the Arabidopsis genome (Fig. 1). In addition, we observed that different organs expressed distinct sets of genes from the Arabidopsis genome (Figs. 2 and 4), and only about 16% of the total genes are expressed in all examined organs (Fig. 1). Based on those organ-specific genome expression profiles, we further demonstrated that the relatedness of the organ types based on development is reflected by their whole-genome expression patterns as well (Fig. 3). These results support the conclusion that the genome expression patterns are defining characteristics of Arabidopsis organs during development. Therefore, defining the exact genome expression pattern (the abundance of each individual expressed gene) of each organ will provide us with much-needed information in understanding the developmental characteristics of each organ type. Further, the comprehensive genome expression information made available by this and other related works, together with genome-wide insertional mutagenesis (Alonso et al., 2003 ), should provide a foundation toward description of the molecular mechanism leading to the observed genome expression pattern changes during Arabidopsis development.
Development in plants is often reprogrammed by environmental signals. For example, light is one of most important environmental signals for controlling plant growth and development (Kendrick and Kronenberg, 1994 An interesting feature is that only a small set of light-regulated genes are shared among the three seedling organ types. Some of the genes that differ among the three organ types even show opposite light regulation patterns (e.g. light induces a gene expression in cotyledon, while the same gene is repressed by light in root or hypocotyl; Fig. 6, C–E). These results suggest that the light signal triggers expression changes in distinct target genes in different organs or in some instances distinct responses of the same target genes. Therefore, although light signals are perceived by the same photoreceptors and may be transduced by the same transduction pathways, distinct changes in genome expression occur in different seedling organ types. The mechanism responsible for light regulation in different organ types is not yet clear.
Recent results from human (Caron et al., 2001
Plant Materials
The wild-type Arabidopsis (Arabidopsis thaliana) used in this study was the Columbia ecotype. Surface sterilization, cold treatment of the seed, and seedling growth were performed as described previously (Ma et al., 2001
The 70-mer oligo set for the Arabidopsis genome was designed and synthesized by Qiagen/Operon (http://oligos.qiagen.com/arrays/omad.php) based on the Arabidopsis genome information available on February 20, 2002. The oligos were purchased from Qiagen (Valenica, CA) and printed onto polylysine coated microscope slides in the DNA microarray laboratory at Yale University (http://info.med.yale.edu/wmkeck/dna_arrays.htm). There were 26,090 unique oligos, and 12 distinct negative control oligos. Each negative control oligo was printed 16 times at well-spaced locations on each slide. Thus, each slide included a total of 26,090 oligo spots and 192 negative control spots. The negative controls were positioned all over the slide to avoid potential errors caused by spatial effects. These negative controls do not have a match in the genome sequence.
Total RNA was extracted from the above-mentioned organs using the Qiagen RNeasy Plant Mini prep kit. RNA preparations from two to four independent biological samples for each test were made and used for probe synthesis. Thus, each experiment produced two to four biological replicate data sets. Total RNA (50 µg) was first labeled with aminoallyl-dUTP (aa-dUTP; Sigma, St. Louis) by direct incorporation of aa-dUTP during reverse transcription, as described previously (Ma et al., 2002
We first blasted the sequences of 26,090 oligos individually against Munich Information Center for Protein Sequences (MIPS) Arabidopsis gene annotation (March 20, 2003 version) to associate oligos with gene IDs (e.g. AT3G20980) if they matched to the annotated genes. We used the criterion that the matching identity between oligo sequence and genome sequence should be 70% (or at least matching 49 out of 70 nucleotides) or higher, to define the match between oligo and gene locus. In fact, the vast majority of oligos (97%) were mapped to the chromosomal genes with more than 90% identity. For oligos with no match in the MIPS annotated gene sequences, we blasted them against The Institute for Genomic Research (TIGR) annotation (July 31, 2002 version) downloaded from TAIR (ftp://ftp.arabidopsis.org/home/tair/Sequences/blast_datasets/). More corresponding gene loci were obtained, and their locus IDs were assigned to those oligos. In total, we are able to assign 25,822 oligos that represent 25,676 unique locus IDs (genes). Among them, the majority (94%) of the oligos had a single match to a unique gene locus. A small fraction of the oligos (6%, 1,553) fall into the following two categories: one oligo matches two or more unique genes, or one unique gene matches more than one oligo (with the 70% identity cutoff). In the former case, we assigned the oligo with multiple locus IDs to cover all possible genes that may contribute to the detected expression signal. While in the latter case, as multiple oligos assigned the same locus ID, the median intensity of those oligo spots was taken as its expression level. To define the number of genes covered by the oligo array that have EST hits, we joined together the information in ESTtoAT and mRNAtoAT at TAIR (ftp://ftp.arabidopsis.org/home/tair/Sequences/) and ESTmatchingtoAT at MIPS (http://mips.gsf.de/proj/thal/; downloaded on July 8, 2003) to obtain the unique locus numbers with at least one EST or mRNA hit from the above three resources. This analysis resulted in the number (16,998) of the genes from the above 25,676 unique locus IDs that have at least one EST or mRNA hit.
GO annotation for all gene models were downloaded from TAIR (Rhee et al., 2003
Spot intensities were quantified using Axon GenePix Pro 3.0 image analysis software. The net intensities for each channel and channel ratios were measured using the GenePix Pro 3.0 median of intensity or ratio method. Replicates were normalized first to remove artifacts due to experimental variations using custom-designed programs (http://bioinformatics.med.yale.edu/software.html). Then normalization based on median of intensities was performed among all the experiments. We followed a commonly used strategy (Rinn et al., 2003
To identify differentially expressed genes among organ groups (Fig. 4), we fitted normalized replicate intensities of all organs together with a cultured-cell control into an ANOVA model. For each data set, the gene intensities of both organ and reference cultured cell were used. The model is given by yijkl = µ + Ai + Dj + ADij + Gl + VGkl + DGjl + AGil + Similar statistical analyses were performed for the identification of light regulated genes. A student's t test was performed for each gene between light-growth condition and dark-growth condition. We consider genes with a P value <0.05 as light regulated. To reduce the occurrence of false positives, we applied an additional 2-fold expression changed for some subsequent analysis.
For other analyses in this work, normalized intensities were averaged among all replicates of the same sample to obtain a single statistic, which is considered a relative expression for a gene. Similarly, we also used a single statistic for ratios for each gene. We calculated the expression ratio for a sample pair only when at least one channel showed experimentally detectable expression as defined above.
We used the method reported by Spellman and Rubin (2002) All the microarray data described in this study were deposited into the NCBI GEO database (accession no. GSE 1599).
We thank Mr. Matthew Holford for assisting with the Java programming, Elizabeth Strickland, Jessica Habashi, and Lei Li for reading and commenting on this manuscript, and the Yale DNA microarray laboratory of the Keck Biological Resource Center for the production of the microarray used in this study (http://info.med.yale.edu/wmkeck/dna_arrays.htm). Received October 12, 2004; returned for revision February 14, 2005; accepted February 14, 2005.
1 This work was supported by the National Science Foundation of China (strategic international corporation project grant no. 30221120261), by the National Institutes of Health (grant nos. GM–47850 to X.W.D. and GM59507 to H.Z.), and by the National Science Foundation (grant no. DMS 0241160). L.M. is a long-term postdoctoral fellow of the Human Frontier Science Program. 
[w] The online version of this article contains Web-only data. www.plantphysiol.org/cgi/doi/10.1104/pp.104.054783. * Corresponding author; e-mail xingwang.deng{at}yale.edu; fax 203–432–3854.
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ijkl, where yijkl denotes the logarithm transformed signal for gene l on slide i labeled with dye j of sample k. The overall mean effect was represented by µ; A, D, and G represented main effects from array, dye, and gene. The interaction terms AD, VG, DG, and AG represented array by dye, sample (variation) by gene, dye by gene, and array by gene. The random error was denoted by 
