Global analysis of patterns of gene expression during Drosophila embryogenesis

Read Also

A defining feature of multi-cellular organisms is their ability to differentially utilize the information contained in their genomes to generate morphologically and functionally specialized cell types during development. Regulation of gene expression in time and space is a major driving force of this process and is achieved primarily by the regulation of transcription initiation.

A gene’s expression pattern can be defined as a series of differential accumulations of its products in subsets of cells as development progresses. Patterns of gene expression are studied by two principal methods – microarray analysis (Brown and Botstein 1999) and in situ hybridization (Jones and Robertson 1970). Microarray analysis provides the ability to quantitatively measure gene expression levels for all genes in parallel, providing an overview of the temporal dynamics of gene expression regulation (Arbeitman, Furlong et al. 2002). A major limitation of microarray analysis is that obtaining spatial information depends on the dissection or cell-sorting of specific tissues or cell types (Furlong, Andersen et al. 2001; Klebes, Biehs et al. 2002). RNA in situ hybridization has the potential to reveal both spatial and temporal aspects of gene expression during development. However, RNA in situ hybridization is not highly quantitative (Wilcox 1993).  For these reasons, we have used both methods in parallel and integrated the analysis of the resultant data sets.

There are several reasons for choosing Drosophila melanogaster as an organism for the global study of gene expression during embryonic development. Genetic and molecular analyses have led to a deep understanding of many embryonic processes in this animal (St Johnston and Nusslein-Volhard 1992). Classical embryology has provided a solid framework of anatomical description of all embryonic stages (Hartenstein 1997) and robust high-throughput methods for assaying gene expression by whole mount in situ hybridization have been developed (Kopczynski, Noordermeer et al. 1998; Simin, Scuderi et al. 2002; Tomancak, Beaton et al. 2002). In many cases, the wild-type gene expression pattern has informed the interpretation of the phenotype produced by its mutation (Hafen, Kuroiwa et al. 1984). Such studies have provided unprecedented insights into animal development; the process that governs the early embryonic patterning of the Drosophila body plan is now the best understood example of a complex cascade of transcriptional regulation during development (Carroll 1990; Rivera-Pomar and Jackle 1996).

We have assembled a comprehensive atlas of gene expression patterns during Drosophila embryogenesis. Taking advantage of non-redundant gene collections (Stapleton, Carlson et al. 2002; Stapleton, Liao et al. 2002), we performed an unbiased survey of gene expression patterns by using RNA in situ hybridization of gene specific probes to fixed Drosophila embryos (Tomancak, Beaton et al. 2002). We documented the patterns of each gene by set of digital photographs representing all stages of embryonic development. The dataset was systematically validated with independently derived Affymetrix microarray time-course measurements. We describe the tissue specificity of gene expression at each stage using a selected terms from controlled vocabulary (CV) for embryo anatomy (Grumbling and Strelets 2006). The CV integrates the spatial and temporal dimensions of the gene expression patterns by linking together intermediate tissues that develop from one another. It also integrates morphological and molecular description of development by allowing for structures that are morphologically indistinguishable and can be defined only on the basis of gene expression. We present here the collection and analysis of a highly curated and annotated dataset that describes the spatial and temporal gene expression of 44% of the genes in the Drosophila genome. We show that the gene sample interrogated in our study is largely representative of the genome as a whole, allowing the global analysis of gene expression during the embryonic development of a multicellular organism. We organized the complex gene expression space by a hybrid fuzzy-clustering approach that uses microarray profiles to supplement the CV annotation of in situ patterns. We divided the resulting clusters into two categories, broad and restricted. Broad patterns are characterized by quantitative enrichment in tissues that are related by specific cellular states. Restricted patterns are highly diverse and provide a basis for defining gene sets expressed in related tissues and with related predicted functions.