Comparing cell types across mutants and species at single cell resolution

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ABSTRACT Single cell gene expression atlases are now routinely generated for human tissues and entire model organism embryos and have shed light on the diversity of cell types and regulation of gene expression. While these wild-type single cell atlases can predict candidate regulatory genes across development for focused studies, further work is needed to determine the regulatory mechanisms and functional importance of the observed expression patterns at scale. A key problem is how to identify homologous cells between datasets in which their expression may be altered, for example data from the same tissue across evolution, or from animals that have experienced a genetic or pharmacological perturbation. This project will use the widely used model organism Caenorhabditis elegans to develop and test methods to compare cells across such conditions. Our focus is on two biological problems. In Aim 1, we will compare expression in single cells between C. elegans and four other related nematode embryos. These nematode species have nearly identical embryonic lineages to C. elegans despite substantial sequence divergence (>1 substitution per neutral site), making them an ideal test case for alignment of single cell datasets across evolution. We will generate large single cell RNA-sequencing datasets for embryos of each species (C. remanei, C. brenneri, C. briggsae and C. nigoni). We will compare both automated homology transfer and de novo lineage inference methods to identify cell types in each species. We will use quantitative imaging approaches (smFISH and live imaging of GFP knock ins) to validate the results of the single cell experiments. The resulting data will allow us to classify genes and cell types by the conservation of their gene expression, providing insight into the evolution of cell types. In Aim 2, we will test the role of conserved regulators in the specification and diversification of the mesodermal “MS” lineage (which produces pharynx, body wall muscle, and some specialized mesodermal cell types). We will measure gene expression by scRNA-seq after conditional loss of these mesodermal regulators using an auxin degron approach. As in Aim 1, we will test and validate automated alignment methods for these datasets to identify cells. The resulting data will allow us to distinguish homeotic fate transformations from the formation of novel cell states, to distinguish likely direct or context specific targets from indirect targets of each regulator, and to generate a genome-wide mesodermal regulatory network of a developing animal embryo.