Given the extensive information from genetic studies about the transcriptional regulators that direct the early morphogenetic events of embryogenesis, it is now possible to use whole-genome comparative analysis to look for changes in gene expression in response to altering the levels of particular transcription factors. To identify direct targets of these transcription factors, chromatin immunoprecipitation (ChIP) followed by microarray hybridization on whole-genome tiling arrays can be used to determine in vivo
protein-DNA interactions. Work from the Berkeley Drosophila
Transcription Network Project (BDTNP) has characterized the in vivo
DNA binding sites of 21 transcription factors in the Drosophila
blastoderm embryo [11
]. These studies find that each transcription factor is bound to more than 1,000 different sites, suggesting a complex transcriptional program downstream of these regulators. Combining this approach with microarray studies has been successful at identifying direct targets of transcription factors required for embryonic segmentation [12
While microarray analysis and ChIP:chip data can provide insight into the temporal expression and transcriptional regulation of genes during development, these approaches do not offer clues as to the spatial distribution of gene expression. Combining in situ
expression patterns with genomic data on expression levels is one way to identify sets of genes involved in related developmental processes. In Drosophila
, two groups have determined the spatial and temporal expression pattern of around 25% of the genes expressed in the embryo [2
]. Lecuyer et al.
], used fluorescent in situ
hybridization (FISH) to generate high-resolution images for around 2,500 mRNAs throughout early Drosophila
embryogenesis. This approach, which allows for single-cell resolution, led to the surprising discovery that over 70% of detected mRNAs are subcellularly localized, suggesting that uncharacterized transcripts can be classified on the basis of their localization as well as their expression. Both groups have generated web-based databases that offer a range of search options, including stage, tissue, gene name and predicted function. Analysis of these rich datasets may identify genes involved in morphogenesis that have failed to be identified in genetic screens.
The ultimate goal of developmental biology is to define how each gene contributes to cell fate, cell shape and cell behavior during morphogenesis. However, functional studies are often labor-intensive and are not readily adapted to high-throughput analysis, creating a bottleneck in going from expression to function. RNAi screening is emerging as a powerful technique for functional analysis in vivo
]. Expression profiling has been coupled with double-stranded RNA injections in early embryos to identify genes required for cellularization and embryonic viability [3
]. One limitation of traditional genetic screens is the inability to identify genes with subtle or redundant phenotypes, as well as components involved in multiple processes throughout development. The genome sequence creates the potential to identify gene families, which can be tested for redundant functions using combinatorial RNAi [14