Time-lapse epigenome profiling in support of next generation cellular reprogramming - Project Summary/Abstract Early human development generates thousands of precise cell fates from a single zygote with extreme fidelity. This is largely accomplished via the coordinated regulation of gene expression in cell type- specific patterns. Extensive observation of these patterns has nominated candidate genes that can be exogenously expressed in non-target originating cell types to generate desired cell types through a process called cellular reprogramming. For instance, the overexpression of defined transcription factors (TFs) in human fibroblasts can be convert them directly into a variety of therapeutically useful cell types, including neurons and cardiomyocytes. However, most reprogramming schemes produce heterogeneously differentiated cells that do not faithfully adopt the gene regulatory programs of the target cell types, and consequently are unsuited for therapeutic applications. It is increasingly appreciated that epigenetic factors, including the structure and composition of chromatin, might influence the efficiency of reprogramming in a given cell. For instance, nucleosomal wrapping of DNA and compaction in heterochromatin can silence natively active genes to promote cell type conversion. Therefore, epigenetic events represent unobserved variables that can confer differential competence for reprogramming. Here we propose to observe these variables directly using a novel time-lapse epigenome profiling method, in which multiple snapshots of heterochromatin protein enrichment and how they change over time are captured in the same single cells. We will use time-lapse information to identify key spatiotemporal changes in chromatin-mediatedgene regulation that are associated with successful reprogramming and use catalytically inactivated Cas9 fused to chromatin regulatory domains to functionally test whether these events can lead to more efficient iEP outcomes. We further will produce rich, standardized datasets from time-lapse profiling of common reprogramming trajectories for public use, with a focus on their utility for deep learning approaches to discover new modulators of reprogramming efficiency In the long term, we anticipate that these improved methods we introduce will improve the rational design of efficient cell type conversion schemes for tissue regeneration and other important therapeutic purposes.
