UC Irvine

Cellular process is meticulously regulated by means of transcription and additional epigenetic mechanisms. This regulation is further complicated by the communication between cells, which coordinate gene expression across multiple cells and tissues. Epigenetic modifications such as DNA methylation, histone modification can induce heritable changes in gene expression without alterations to the DNA. Dysregulation of epigenome or transcriptome often leads to various diseases, such as cancer, developmental disorders, and neurodegenerative diseases. Recent advances in single-cell RNA sequencing (scRNA-seq) and spatially resolved sequencing has provided us unprecedented insights into cellular heterogeneity, developmental trajectories, spatial organization, and cell-cell communications. However, how the cells are spatially and temporally regulated through transcriptome, epigenome, and intercellular communication, is still not clearly understood. In this dissertation, we studied this issue from three distinct perspectives. First, we investigated the maintenance of DNA methylation throughout the cell cycle. Specifically, by analyzing experimental data, we found the post-replication methylation maintenance rates are correlated between nearby CpGs in a region-specific manner. Through stochastic modeling, we derived evidences for genome-wide methyltransferase processivity in cells, and developed an approach to infer lengthscales of linear diffusion of DNA-binding proteins using the rate correlation. In the second project, we developed a deep learning method to identify the spatiotemporal organization of cells. We further proposed the pseudo-Spatiotemporal Map (pSM) as a spatial counterpart of the pseudotime in scRNA-seq. We validated the accuracy of the pSM using public datasets and demonstrated its usefulness in revealing developmental sequences and providing insights into cancer progression. In the last project, we developed a method for summarizing the major cell-cell communication (CCC) patterns in spatial transcriptome data, which can greatly reduce the analysis burden for researchers who have to analyze thousands of spatial CCC patterns from ligand-receptors. This method also offers new biological insights into CCC through the interpretation for these patterns. For example, we provided the pattern associated ligand-receptors (LRs) which helps to explore potential interacting ligand-receptor network in a tissue specific manner. Overall, our studies provide an approach to reveal the DNA methylation dynamics in post-replication; a method to unravel the dynamics of spatiotemporal organization of cells; and a tool to understand how cells communicate to perform specific functions in tissue.