Root genomics: towards digital in situ hybridization
© BioMed Central Ltd 2004
Published: 27 May 2004
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© BioMed Central Ltd 2004
Published: 27 May 2004
Separation of cell types and developmental stages in the Arabidopsis root and subsequent expression profiling have yielded a valuable dataset that can be used to select candidate genes for detailed study and to start probing the complexities of gene regulation in plant development.
The availability of genome-wide expression analysis tools allows one to investigate the details of transcriptional regulation during development. Clustering methods can be used to group genes whose expression varies in a similar way in response to developmental changes. Such clustering methods can reveal two major trends. First, they can reveal groups of genes that are co-regulated, and therefore suggest which genes function together during a given developmental process. Second, clustering methods can reveal which conditions resemble each other, pointing out similarities - or dissimilarities - in developmental states that might not be obvious otherwise. Two major developmental parameters for analysis by gene-expression profiling are progression in time ('developmental stage') and tissue, region or cell-type specificity. Previous studies of gene expression during the development of multicellular organisms have mostly emphasized either the developmental stage or the cell-type aspect. For example, clusters of genes co-expressed during the entire life cycle have been defined in Caenorhabditis elegans , and changes at the transition from cell proliferation to cell differentiation have been described for the Drosophila eye . Another C. elegans study emphasized cell-type-specific gene-expression programs . Both temporal and spatial aspects of gene expression have been analyzed by transcript profiling of the slime mold Dictyostelium discoideum, an organism in which cell aggregation leads to a multicellular structure with two different mature cell types . Recently, Birnbaum et al.  have conducted a global gene-expression analysis of a more complex mix of cell types at three developmental stages in the small weed Arabidopsis, and have generated a digital reconstruction of gene expression in the root - a 'digital in situ hybridization'.
Several approaches have been designed for obtaining RNA from specific stages or cell types. Stage-specific promoters can be fused to the green fluorescent protein (GFP), and cell populations can be purified by fluorescence-activated cell sorting (FACS) of trypsin-dissociated cells . Alternatively, cell-type-specific expression of epitope-tagged RNA-binding proteins can be used to enrich mRNA . Laser-assisted microdissection of specific cells is also possible [8, 9]. RNA from specific developmental stages or tissue regions obtained in these ways can be analyzed by microarray technology or serial analysis of gene expression (SAGE). The recent study from the Benfey group  used oligonucleotide chips to analyze gene expression in Arabidopsis roots; they first dissected out the major tissues by enzymatically dissociating cells (protoplasting) and doing FACS analysis of transgenic lines expressing GFP under region- or cell-type-specific promoters (Figure 1b). It may perhaps seem tricky to enzymatically digest cell walls and then sort protoplasts, asking them to maintain cell-fate- or region-specific expression patterns for 1.5 hours. After all, plant biologists are used to the flexibility of cell-fate determination in the plant kingdom, with the - somewhat overstated - textbook dogma that plant cells are totipotent and maintain their identity only in the context of the organism. Yet, amazingly, this approach proved successful. Only a minor set of genes appeared to be induced by protoplasting and sorting, and these were removed from the analysis.
Hence, Birnbaum et al.  were able to isolate RNA from GFP-expressing, sorted vascular, ground-tissue and epidermal cells (see Figure 1b,1c) and hybridize it to the Affymetrix ATH1 GeneChip, which has probes for approximately 22,000 Arabidopsis genes, covering about 90% of the genome. In a separate experiment, manual dissection of three developmental zones allowed the authors to determine the relative level of expression of each gene in zones roughly representing three different stages: cell proliferation, cell expansion and cell differentiation (Figure 1c). For every gene, this percentage was then superimposed on the expression values per tissue or cell type. Validation experiments using both previously documented and new genes confirmed that this method gives reliable expression data for the majority of genes.
While the starting dataset is already impressive, the method used lends itself to future improvements that will further enhance the resolution. First, by means of bootstrapping, the promoters of candidate cell- or region-specific genes that emerge from the first analysis can now be used to refine the set of GFP lines that are used for cell sorting. In the future it is likely to be possible to sort all the different root cell types separately. Second, the stage-specific and the tissue-specific gene-profiling data are currently combined by calculation, which works best if genes have sharp expression transitions and the same distribution over the three developmental stages in every cell type, which will not be the case for all genes. For example, a gene with a graded transcript distribution, or a gene whose stage-dependent transcription differs from one tissue to another, will not be recognized as such in the current dataset (Figure 1d–g). In the future this limitation can be overcome by sorting cell types from separately dissected stages. Another option is to combine stage- and cell-type-specific markers, and to sort cells that possess both.
The current dataset of gene expression in the root  provides a rich resource for those interested in plant development. Cell-type-specific expression of each researcher's favorite gene in the root suggests a starting point for searching for mutant phenotypes of interest, and the ease with which cellular details of phenotypes can be visualized in the root can facilitate detailed analysis of genes that may first be identified from studies in other organs. For those interested in root development itself, functional redundancy can now be overcome more easily by selecting homologs of genes that have overlapping expression profiles. Potential targets for known transcription factors can be pre-selected or validated because they should be co-expressed in at least a subset of the cell types that express the transcription factor of interest. The mRNA enrichment obtained by sorting can be exploited to enhance the sensitivity of detecting transcriptional differences in mutants, after gene induction experiments or after drug treatments. Map-based cloning of genes can be accelerated because expression patterns matching with region-specific root phenotypes can be selected when mapping intervals are still large. The excellent Arabidopsis resources for the recovery of insertion mutants , and mutants induced by ethylmethane sulfonate (EMS) through the TILLING procedure  provide useful and rapid follow-up resources for such a candidate-gene approach. In all these, and probably more, applications, the dataset is used as a starting point for further analysis.
A major question that remains to be answered is the extent to which complex gene-expression maps reveal underlying regulatory features. Many computational tools can be used to cluster gene-expression data into meaningful groups, and the tool chosen largely determines what information is highlighted from the dataset . In the Drosophila eye, hierarchical clustering using expression data and gene function as input revealed a cluster with cell-cycle and cell-growth regulators enriched in proliferating cells, a signaling and adhesion cluster in early-stage differentiating cells, and a cluster enriched in transcription factors in the mixed population of photoreceptor and cone cells . In the slime mold, aggregation of single-celled amoebae leads to a dramatic morphological change, giving rise to a multicellular organism with two mature cell types. In this case, a striking amount of gene regulation could be observed by fitting all differentially expressed genes to a hypothetical gene-induction curve; and the similarities between expression profiles for all genes in each developmental stage revealed that the transition from unicellular to multicellular stages was accompanied by a dramatic change in gene-expression programs involving changes in around 25% of all transcripts. Purification of cell types and their precursors, subsequent microarray analysis and fitting the data to functions that represent particular kinds of cell-type enrichment, revealed the existence of clear cell-type-specific clusters .
The major clusters found by Birnbaum et al.  reveal some other trends in root development that raise interesting questions. For example, consistent with the presence of mature layers of lateral root cap surrounding the meristem at close proximity to the tip, it is not surprising that genes enriched in the lateral root cap appear in the proliferation stage. Interestingly however, vascular and ground-tissue cells appear to achieve their tissue-specific expression patterns at a larger distance from the apex than the epidermal cells do. It is not clear why genes enriched in epidermal cells would be switched on at closer proximity to the stem cells than genes enriched in vascular cells, while overt differentiation characteristics in both tissues appear at roughly similar distances from the apex. A simple explanation may be that early cell-type-specific genes in the vasculature may be diluted beyond detection because, in contrast to the epidermis and endodermis, the vascular tissue is a mixture of cell types.
A rich resource like the root expression map opens up numerous possibilities for data analysis. For example, 'similarity' calculations like those used in Dictyostelium  reveal expression profiles of vascular and ground-tissue cells to be much more similar to each other than to the outer epidermal and lateral root cap cells (Figure 2b,c). Selection and sorting for cell-type-specific expression, on the other hand, provides an estimate for the critical differences between cell types (Figure 2d). By viewing the data in these and other ways, different aspects of the dataset are highlighted, each providing useful new insights.
With the first version of the root digital in situ hybridization map at hand, more regularities within the datasets can be explored. Candidate tissue- or stage-specific transcription factors can be analyzed for direct or indirect roles in the expression of their co-regulated genes, which might explain at least part of the data as resulting from the activity of a transcription-factor network. How easy this is will depend on how many layers of regulation at the post-transcriptional level are responsible for the ultimate distribution of mRNAs in the root, and how many of the transcriptional differences are pre-established by factors no longer expressed at the post-embryonic stage.
It is to be expected that, as new tissue- or stage-specific datasets are provided from other regions of Arabidopsis (see, for example, [13–15]), the root data can be inspected using many additional filters. For example, truly root-specific genes can be separated from those that are expressed in other organs, creating interesting new groups such as root proliferation-stage genes that are also expressed in the shoot apical meristem. While much work remains to be done to refine the root expression map and to integrate it with other expression data, the initial work presented by Birnbaum et al.  opens the doors to these possibilities and others yet to be foreseen.