Systematic Assessment of Combinatorial Transcription Factor Activity

点击翻译按钮获取中文摘要

PROJECT SUMMARY Despite vast catalogs of transcriptomes, our understanding of transcriptional state remains mostly descriptive. In this proposal, we seek to advance the field towards a predictive understanding of cell state by addressing the fundamental gap in knowledge: what are the genes and regulatory networks that drive a cell’s transcriptional state? This knowledge will be critical to understand the molecular basis of cell state and to manipulate cell state for biomedical applications. Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Notably, forced induction of TFs can reprogram gene expression states by sup- planting existing GRNs. Thus, TFs and GRNs are the building blocks to a predictive understanding of a cell’s transcriptional state. One key challenge is that, in general, the relationship between TFs and GRNs is not known and is difficult to accurately predict. This challenge arises from several current problems: a lack of ground truth GRNs derived from experimental TF perturbation, a reliance on static transcriptomic databases to infer GRNs for TF cocktail prediction, and the difficulty of predicting non-linear TF behaviors. Until we can understand how TFs cooperatively influence GRNs, our ability to predict the TF drivers of cell state will remain limited. Our long- term goal is to understand the molecular basis of transcriptional state for applications in cellular engineering. Towards this goal, the objective of this proposal is to generate a unique resource to directly measure GRNs for simple combinations of TFs, and to use this functional knowledgebase to predict and benchmark complex TF cocktails for transcriptional reprogramming. We hypothesize that experimentally-derived GRNs will improve the performance of predicted TF cocktails for transcriptional reprogramming. Our rationale is that these studies will 1) provide a novel and urgently needed resource of TF functional activity and experimentally-derived GRNs for the community, 2) provide insights into the molecular drivers of transcriptional state, and 3) identify TFs with potential to engineer gene expression states for biomedical research. We propose the following specific aims: (Aim 1) Measure the combinatorial activities of transcription factors; (Aim 2) Improve computational frameworks to predict TF cocktails for transcriptional reprogramming; (Aim 3) Generalize TF-driven GRNs across initial cell contexts and benchmark predictions. This proposal is innovative because it will use our single-cell platform Re- program-Seq 2.0 for high-throughput transcriptional reprogramming to generate a unique resource of functional activities for TFs and GRNs. We expect this resource to propel new research horizons. This proposal is signifi- cant because it will expand our understanding of genome function by quantifying combinatorial TF activity, ex- perimentally deriving GRNs, and providing new tools to engineer gene expression states.