A Conserved Developmental Patterning Network Produces Quantitatively Different Output in Multiple Species of Drosophila

Differences in the level, timing, or location of gene expression can contribute to alternative phenotypes at the molecular and organismal level. Understanding the origins of expression differences is complicated by the fact that organismal morphology and gene regulatory networks could potentially vary even between closely related species. To assess the scope of such changes, we used high-resolution imaging methods to measure mRNA expression in blastoderm embryos of Drosophila yakuba and Drosophila pseudoobscura and assembled these data into cellular resolution atlases, where expression levels for 13 genes in the segmentation network are averaged into species-specific, cellular resolution morphological frameworks. We demonstrate that the blastoderm embryos of these species differ in their morphology in terms of size, shape, and number of nuclei. We present an approach to compare cellular gene expression patterns between species, while accounting for varying embryo morphology, and apply it to our data and an equivalent dataset for Drosophila melanogaster. Our analysis reveals that all individual genes differ quantitatively in their spatio-temporal expression patterns between these species, primarily in terms of their relative position and dynamics. Despite many small quantitative differences, cellular gene expression profiles for the whole set of genes examined are largely similar. This suggests that cell types at this stage of development are conserved, though they can differ in their relative position by up to 3–4 cell widths and in their relative proportion between species by as much as 5-fold. Quantitative differences in the dynamics and relative level of a subset of genes between corresponding cell types may reflect altered regulatory functions between species. Our results emphasize that transcriptional networks can diverge over short evolutionary timescales and that even small changes can lead to distinct output in terms of the placement and number of equivalent cells

Spatial gene expression quantification: a tool for analysis of <it>in situ </it>hybridizations in sea anemone <it>Nematostella vectensis</it>

<p>Abstract</p> <p>Background</p> <p>Spatial gene expression quantification is required for modeling gene regulation in developing organisms. The fruit fly <it>Drosophila melanogaster</it> is the model system most widely applied for spatial gene expression analysis due to its unique embryonic properties: the shape does not change significantly during its early cleavage cycles and most genes are differentially expressed along a straight axis. This system of development is quite exceptional in the animal kingdom.</p> <p>In the sea anemone <it>Nematostella vectensis</it> the embryo changes its shape during early development; there are cell divisions and cell movement, like in most other metazoans. <it>Nematostella</it> is an attractive case study for spatial gene expression since its transparent body wall makes it accessible to various imaging techniques.</p> <p>Findings</p> <p>Our new quantification method produces standardized gene expression profiles from raw or annotated <it>Nematostella in situ</it> hybridizations by measuring the expression intensity along its cell layer. The procedure is based on digital morphologies derived from high-resolution fluorescence pictures. Additionally, complete descriptions of nonsymmetric expression patterns have been constructed by transforming the gene expression images into a three-dimensional representation.</p> <p>Conclusions</p> <p>We created a standard format for gene expression data, which enables quantitative analysis of <it>in situ</it> hybridizations from embryos with various shapes in different developmental stages. The obtained expression profiles are suitable as input for optimization of gene regulatory network models, and for correlation analysis of genes from dissimilar <it>Nematostella</it> morphologies. This approach is potentially applicable to many other metazoan model organisms and may also be suitable for processing data from three-dimensional imaging techniques.</p

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

Summary. Three-dimensional gene expression PointCloud data generated by th