Systematic Assessment of Combinatorial Transcription Factor Activity

项目摘要 尽管有大量的转录组目录，我们对转录状态的理解仍然主要是描述性的。 在这个提议中，我们试图通过解决细胞状态的预测性理解来推进该领域。 知识的根本差距：驱动细胞转录的基因和调控网络是什么？ 州？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（TF）协同驱动基因调控网络（GRNs）， 建立转录状态。值得注意的是，TF的强制诱导可以通过超表达来重编程基因表达状态。 种植现有的GRNs因此，TF和GRN是预测理解细胞的功能的基石。 转录状态一个关键的挑战是，一般来说，TF和GRN之间的关系是未知的 并且难以准确预测。这一挑战源于当前的几个问题：缺乏基本事实 GRNs来自实验TF扰动，依赖于静态转录组数据库来推断GRNs 的TF鸡尾酒预测，以及预测非线性TF行为的困难。直到我们弄清楚 TF协同影响GRNs，我们预测TF驱动细胞状态的能力仍然有限。我们长久以来- 本学期的目标是了解转录状态的分子基础，以应用于细胞工程。 为了实现这一目标，本提案的目的是产生一种独特的资源，直接衡量 TF的简单组合，并使用此功能知识库来预测和基准复杂TF 转录重编程的鸡尾酒我们假设，实验衍生的GRNs将改善 预测的TF鸡尾酒用于转录重编程的性能。我们的理由是，这些研究将 1)提供了一种新的和迫切需要的TF功能活性和实验衍生的GRNs资源， 社区，2）提供对转录状态的分子驱动因素的见解，以及3）识别具有 潜在的工程基因表达状态的生物医学研究。我们提出以下具体目标： (Aim 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;（目的3）在初始细胞中推广TF驱动的GRNs 上下文和基准预测。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，以产生功能性的独特资源。 为TF和GRN开展活动。我们希望这种资源能够推动新的研究视野。这一建议意义重大-- 不能，因为它将通过量化组合TF活性，例如， 实验性推导GRNs，并提供新的工具，工程基因表达状态。

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.