The vertebrate embryo contains hundreds of functionally diverse cell types, however we lack a complete understanding of the regulatory code that specifies each cell type and how disruptions in this code affect relationships between lineages. Here, we propose two aims to dissect the accessible regions of the chromatin, the regulatory motifs and the putative transcription factors that mediate the specification of multiple cell types during embryogenesis. First, (Aim 1), we will utilize single cell ATAC-seq and scRNA-seq to uncover the dynamics of chromatin accessibility and the transcriptional profiles that define discrete cell types during early development. We will define the regulatory motifs in accessible regions of the chromatin that are preferentially engaged in different cell types.
Second (Aim 2), we will use manifold learning approaches to define co- regulated gene modules and apply a regulatory network inference framework for scRNA-seq and scATAC-seq data, and test the regulatory interactions by mutating key pioneer factors and investigate how they cooperate with other TFs to regulate chromatin accessibility and cell differentiation during development. This project will generate extensive, high-quality datasets as well as novel computational methods to enable quantitative and predictive models of embryonic development with the goal to decipher the gene regulatory network specifying cellular identity during development. Given that early development is conserved across vertebrate species, our findings have the potential to inform human development, and lay the foundation to investigate the etiologies of human developmental disorders and to engineer novel cell types and lineages in vitro for regenerative medicine.
This proposal aims to understand how different cell types in the developing embryo are generated, the transcription factors involved and the sequences in the genome activated in different cells. Our findings will inform human development and lay the foundation to investigate human developmental disorders.
