University of Cambridge > > Microsoft Research Computational Science Seminars > Quantitative comparison of gene expression at cellular resolution in Drosophilids

Quantitative comparison of gene expression at cellular resolution in Drosophilids

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Abstract: Understanding how gene regulatory networks evolve requires us to measure the functional consequences of even small changes in sequence. We have applied high-resolution microscopy and image processing methods to blastoderm embryos of D. melanogaster and D. pseudoobscura to determine the expression patterns of key transcriptional regulators and a subset of their targets in their native context at cellular resolution in 3D over the hour prior to gastrulation. These two species shared their last common ancestor nearly 30 million years ago, and comparative sequence analysis reveals a wide variety of changes in cis-regulatory elements. Our imaging techniques allow multiple types of statistically rigorous inter-species comparisons to be made, both between individual embryos and between composite multi-gene models, revealing widespread quantitative changes in expression patterns. We measure multiple types of gene-specific variation, including changes in spatial position, number of cells comprising a pattern, and the dynamics of expression. Our comparative analyses aim to put these differences in the context of complete developing embryos. Which changes are due to differences in the geometry of the embryos and which are due to genetic differences in the transcriptional networks? Furthermore, which are changes initiated by variation in the trans-network, and which are due to changes in how cis-regulatory sequences interpret that network? Differentiating these types of variation will allow us to interpret which specific sequence changes have functional consequences for gene expression, and provide insights into the functional constraints under which cis-regulatory elements evolve.

Biography: Angela DePace received her PhD in Biochemistry from the University of California San Francisco in 2002, where she worked with Jonathan Weissman on the molecular mechanism of yeast prion propagation. By developing an imaging based approach to measure the growth of individual prion fibers, she was able to show that a single protein can misfold into multiple different self-propagating conformations with different growth kinetics. This was powerful evidence for the protein-only hypothesis, which had long been troubled by the existence of different prion strains, which would require that a prion protein be able to form not only one pathogenic conformation, but multiple pathogenic conformations, each giving rise to different phenotypes. She continued her work on quantitative imaging based approaches in a new context when she began her postdoctoral studies in Michael Eisen’s lab at UC Berkeley. There she worked with the Berkeley Drosophila Transcription Network Project to apply 2 photon confocal imaging and image processing to characterize the transcriptional network directing embryonic development of multiple species of Drosophila. She started her own lab in the Department of Systems Biology at Harvard Medical School in April 2008.

This talk is part of the Microsoft Research Computational Science Seminars series.

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