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Systematic determination of patterns of gene expression during Drosophila embryogenesis

Pavel Tomancak1, Amy Beaton1, Richard Weiszmann2, Elaine Kwan1, ShengQiang Shu2, Suzanna E Lewis2, Stephen Richards23, Michael Ashburner4, Volker Hartenstein5, Susan E Celniker23 and Gerald M Rubin12*

Author Affiliations

1 Howard Hughes Medical Institute, Department of Molecular and Cell Biology, University of California at Berkeley, 539 Life Sciences Addition, Berkeley, CA 94720-3200, USA

2 Berkeley Drosophila Genome Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

3 Genome Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

4 Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK

5 Department of Molecular Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095, USA

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Genome Biology 2002, 3:research0088-0088.14  doi:10.1186/gb-2002-3-12-research0088

This article is part of a series of refereed research articles from Berkeley Drosophila Genome Project, FlyBase and colleagues, describing Release 3 of the Drosophila genome, which are freely available at

Published: 23 December 2002



Cell-fate specification and tissue differentiation during development are largely achieved by the regulation of gene transcription.


As a first step to creating a comprehensive atlas of gene-expression patterns during Drosophila embryogenesis, we examined 2,179 genes by in situ hybridization to fixed Drosophila embryos. Of the genes assayed, 63.7% displayed dynamic expression patterns that were documented with 25,690 digital photomicrographs of individual embryos. The photomicrographs were annotated using controlled vocabularies for anatomical structures that are organized into a developmental hierarchy. We also generated a detailed time course of gene expression during embryogenesis using microarrays to provide an independent corroboration of the in situ hybridization results. All image, annotation and microarray data are stored in publicly available database. We found that the RNA transcripts of about 1% of genes show clear subcellular localization. Nearly all the annotated expression patterns are distinct. We present an approach for organizing the data by hierarchical clustering of annotation terms that allows us to group tissues that express similar sets of genes as well as genes displaying similar expression patterns.


Analyzing gene-expression patterns by in situ hybridization to whole-mount embryos provides an extremely rich dataset that can be used to identify genes involved in developmental processes that have been missed by traditional genetic analysis. Systematic analysis of rigorously annotated patterns of gene expression will complement and extend the types of analyses carried out using expression microarrays.