Defining causal roles of genomic variants on gene regulatory networks with spatiotemporally-resolved single-cell multiomics

A fundamental question in biology is to understand how genetic variation affects genome function to influence phenotypes. The majority of genetic variants associated with human diseases are located within non-coding genomic regions and may affect genome functions and phenotypes through modulating the activity of cis-regulatory elements and cell-type specific gene regulatory networks (GRNs). However, our knowledge about the impact of genomic variants (alone or as combinations) on gene expression, GRN activity and ultimately cellular phenotypes are rather limited. Further, because transcription factors (TFs) and related cis-regulatory elements are known to have distinct functions based on cell-type and state, how genomic variants influence cell-type/state-specific activity of functional elements and phenotypes remains to be characterized in much greater details. This proposal aims to leverage a panel of well-characterized human induced pluripotent stem cell (hiPSC) lines as well as recent advances in single-cell time-resolved or multi-omics technologies, predictive modeling of regulatory networks by machine learning and high throughput perturbation methods to study the functional impact of genomic variations on regulatory network and cellular phenotypes. First, we will establish a robust experimental framework of deploying advanced time-resolved and multi-omic single-cell technologies for detecting functional genetic variants at single-cell level. Next, we will develop novel computational methods for integration of single-cell data across different modalities and for accurate reconstruction and predictive modeling of GRNs driving cellular identify, developmental dynamics (cardiac and neural lineage cell fate transition). Finally, we will apply high-throughput combinatorial genetic or epigenetic perturbation approaches to modulate activity of key genes or putative cis- regulatory elements at single-cell levels to improve our understanding of network level relationships among genomic variants and phenotypes.