Automated single cell expression analysis in C. elegans

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DESCRIPTION (provided by applicant): The information stored in an organism's genome directs its development and behavior, but the regulatory networks that control the expression of that information are only beginning to be understood in metazoans. Determining precisely when and where genes, particularly transcription factor genes, are expressed is central to gaining a comprehensive understanding of these networks. The nematode C. elegans offers unique advantages for determining gene expression patterns and in turn the regulatory networks that control them. These advantages include a fixed cell lineage, a total somatic cell number of less than 1,000, a compact, fully sequenced genome and a transparent body throughout the life cycle. Exploiting these advantages in the past grant periods, we have developed 4D imaging technology that automatically determines expression patterns of individual genes in each cell with high temporal resolution over the first half of embryogenesis. We have applied this technology to determine the embryonic expression patterns of some 200 transcription factor genes, revealing a wide variety of expression patterns that suggest roles for these genes in specifying cell identity. More recently we have devised methods to collect timed series data for FACS sorted embryonic cells to provide coarser spatiotemporal estimates of embryonic expression of all genes in the genome. We propose in the coming grant period to extend and complete the catalog of expression patterns for transcription factors using the 4D technology. We will build a recently developed diSPIM microscope and use that to assay expression through embryogenesis. We will complete the construction of strains with GFP tagged transcription factors and use these to determine the detailed expression patterns for the bulk of transcription factors in the genome. To complement this we will perform RNA-seq. on FACS sorted tissues and cell populations from roughly synchronized embryos to measure the dynamics of gene expression for all genes in the genome. We will also explore technology to provide expression data on individual cells of the embryo. These data sets, combined with ChIP-seq. and other data sets will be used to build regulatory networks active in each cell throughout embryogenesis. All the data and strains will be made available to the community through our website, Worm Base and the Caenorhaditis Genetics Center. Knowledge of the regulatory networks of this simple metazoan will have direct implications for understanding regulatory networks in humans both in health and in disease.