Integrated frameworks for single-cell epigenomics based transcriptional regulatory networks

PROJECT SUMMARY Transcription factors (TFs) compose a subset of proteins that regulate the expression of a wide range of genes in cells. Instructing many tissue- and cell-type specific gene expression programs in the body, transcriptional regulation is one of the major mechanisms of cell differentiation and polarization induced by TFs. Understanding the dynamics of transcriptional regulation is crucial since it is a critical component of cell, tissue, organ and system development and its dysregulation can lead to many complex diseases. Transcriptional regulation is best modeled via directed networks in which the edges originating from transcription factors to their downstream targets represent regulatory relationships. However, building, optimizing and analyzing transcriptional regulatory networks (TRNs) is highly challenging due to inherent complexity of such networks. Moreover, the dynamic wiring in these networks evolves over time during cell and tissue differentiation and presents a continuous trajectory, instead of discrete states. Current approaches for understanding this dynamic system are mainly based on gene expression and are underpowered to accurately model such networks because alterations in gene regulation often take place via changes in chromatin architecture. Further, existing methods either disregard or oversimplify the heterogeneous nature of network states in cell populations, thereby leading to a loss of resolution. In this proposal, we hypothesize that continuous cell differentiation trajectories are driven by evolutions in the transcriptional network wiring, which are induced by alterations in the chromatin architecture. Our overarching goal in this research program is to elucidate the continuous evolution of regulatory wirings associated with developmental stages or disease conditions using cell-specific TRNs that are constructed from single-cell epigenomic data. To reach this goal, we will build TRNs using motif analysis coupled with multiple single-cell epigenomic sequencing data, including chromatin accessibility (ATAC-Seq), DNA methylation (BS-Seq), histone modification (ChIP-Seq) and three-dimensional chromatin interaction (Hi-C) at single-cell resolution. We will use these networks to uncover the regulatory changes associated with cell differentiation and discover the key transcriptional regulators that drive the cells along developmental trajectories or across the disease states. We will also detect transcriptional regulatory modules within these networks to discover pathways associated with cell differentiation. Finally, we will apply our approach to multiple domains and test our hypothesis using biological models. Altogether these studies will establish a system of dynamic network models for unraveling epigenetic regulation at a high resolution. This integrated set of models will not only facilitate an accurate understanding of epigenetic regulation in development but will also be a powerful asset for discovering targets for therapeutic interventions for a wide range of complex diseases associated with transcriptional dysregulation.