Systematic Assessment of Combinatorial Transcription Factor Activity

项目总结 尽管有大量的转录本，但我们对转录状态的理解主要是描述性的。 在这项建议中，我们试图通过解决 知识的根本鸿沟：驱动细胞转录的基因和调控网络是什么 州政府？这些知识对于理解细胞状态的分子基础和操纵细胞状态是至关重要的 用于生物医学应用。转录因子协同驱动基因调控网络(GRN) 建立转录状态。值得注意的是，强迫诱导的转录因子可以通过超编程来改变基因的表达状态。 种植现有的GRN。因此，TF和GRN是对细胞的预测性理解的基础 转录状态。一个关键的挑战是，一般而言，信托基金和赠款之间的关系尚不清楚。 而且很难准确预测。这一挑战源于当前的几个问题：缺乏事实依据 GRN源自实验性的Tf扰动，依赖静态转录数据库来推断GRN 对于TF鸡尾酒预测，以及预测非线性TF行为的难度。直到我们能够理解 TFS协同影响GRN，我们预测细胞状态的Tf驱动因素的能力将仍然有限。我们的长- 学期目标是了解转录状态的分子基础，以便在细胞工程中应用。 为了实现这一目标，本提案的目的是产生一种独特的资源，以便直接衡量GRN 简单的TF组合，并使用这个功能知识库来预测和基准复杂的TF 用于转录重新编程的鸡尾酒。我们假设，实验衍生的GRN将改善 用于转录重编程的预测的TF鸡尾酒的性能。我们的理由是，这些研究将 1)提供了一种新的急需的转铁蛋白功能活性资源和实验衍生的GRN 社区，2)提供对转录状态的分子驱动因素的洞察，以及3)将转录因子与 为生物医学研究设计基因表达状态的可能性。我们提出了以下具体目标： (目标1)测量转录因子的组合活性；(目标2)改进计算框架 预测转录重编程的Tf鸡尾酒；(目标3)在初始细胞中推广Tf驱动的GRN 背景和基准预测。这项提议是创新的，因为它将使用我们的单细胞平台Re- Program-Seq 2.0用于高通量转录重编程，以生成独特的功能资源 信托基金和GRN的活动。我们希望这一资源能够推动新的研究领域。这项建议具有重要意义- 因为它将通过量化组合转铁蛋白的活性来扩展我们对基因组功能的理解，例如 周边衍生GRN，并提供新的工具来设计基因表达状态。

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.