Defining gene regulatory networks controlling cell fate

项目摘要 细胞类型特异性转录网络是基因调控网络，其动态地重新配置以驱动基因的转录。 精确的基因时空表达模式。这些网络是细胞类型特异性的核心， 在许多疾病中经常被破坏。这些网络的结构由一个反式分量定义， 哪些调节蛋白控制基因的表达，以及一种顺式组分， 可以调控基因表达的基因。识别这些监管网络 由于基因的潜在调节因子的数量， 以及精确定义这些网络所需的大量测定。单细胞组学研究进展 技术，如单细胞RNA-seq（scRNA-seq）和单细胞ATAC-seq（scATAC-seq），提供了新的 定义细胞类型特异性调控网络的机会，因为它们能够全面分析 转录组和数千个单个细胞的可访问性。然而，计算方法 整合这些数据以定义细胞谱系结构和细胞类型特异性调节网络是有限的。 大多数方法只使用一种类型的分析，集中在顺式或反式组分上， 多样本数据集的建模时间或层次相关性。最后，计算性能 与实验检测的网络相比，网络推理方法仍然很低。解决 面对这些挑战，我们将开发新的计算方法和强大的资源来定位基因， 调控网络动力学驱动细胞类型特异性。我们的目标是（a）开发一个计算工具包， 整合scRNA-seq和scATAC-seq数据集以推断细胞类型谱系（Aim 1）和细胞类型特异性 来自scRNA-seq和ATAC-seq数据的转录调控网络（Aim 2），（B）鉴定重新连接的网络 在动态过程中，如细胞重编程（目标2），和（c）开发一个积极的 基于学习的方法来推断因果调节网络，并应用这个框架来完善监管 细胞重编程网络（目标3）。我们将把我们的工具应用于公共和新收集的数据集， 这个项目的一部分。我们的分析将揭示与细胞命运相关的顺式和反式调控网络组件 在动态过程（如重新编程或开发）期间，规范。我们的主动学习方法 将使用Perturb-Seq执行调节器扰动，以验证预测的网络，并 建立一个对转化和基础研究具有重要意义的系统的改进的黄金标准。的 该项目生成的工具和数据集将公开提供，并将作为强大的资源， 了解细胞命运规范中的调控网络动态。我们的工具应该广泛适用于 定义不同生物过程的调控网络动态。

PROJECT SUMMARY Cell type-specific transcriptional networks are gene regulatory networks that dynamically reconfigure to drive precise spatio-temporal expression patterns of genes. These networks are central to cell type specificity and are often disrupted in many diseases. The structure of these networks is defined by a trans component that specifies which regulatory proteins control a gene’s expression and a cis component that species the regulatory regions that can regulate a gene’s expression both proximally and distally. Identifying these regulatory networks has been a significant challenge for mammalian cell types because of the number of potential regulators of a gene and the large number of assays needed to define these networks accurately. Advances in single cell omics technologies, such as single cell RNA-seq (scRNA-seq) and single cell ATAC-seq (scATAC-seq), offer new opportunities to define cell type-specific regulatory networks because of their ability to comprehensively profile the transcriptome and accessibility for thousands of individual cells. However, computational methods for integrating these data to define both cell lineage structure and cell-type specific regulatory networks are limited. Most methods have used only one type of assay focusing either on the cis or trans components and have not modeled temporal or hierarchical relatedness of multi-sample datasets. Finally, performance of computational network inference methods has remained low when compared to experimentally detected networks. To address these challenges, we will develop novel computational methods and powerful resources for mapping gene regulatory network dynamics driving cell type specificity. Our aims are to (a) develop a computational toolkit to integrate scRNA-seq and scATAC-seq datasets to infer both cell type lineage (Aim 1) and cell type-specific transcriptional regulatory networks from scRNA-seq and ATAC-seq data (Aim 2), (b) identify the rewired network components during a dynamic progress such as cellular reprogramming (Aim 2), and (c) develop an active learning based approach to infer causal regulatory networks and apply this framework to refine the regulatory networks for cellular reprogramming (Aim 3). We will apply our tools to public and newly collected datasets as part of this project. Our analysis will reveal cis and trans regulatory network components associated with cell fate specification during a dynamic process such as reprogramming or development. Our active learning approach will use Perturb-Seq to perform regulator perturbations to both validate the predicted networks as well as to establish improved gold standards for a system with high significance for translational and basic research. The tools and datasets generated by this project will be publicly available and will serve as a powerful resource to understand regulatory network dynamics in cell fate specification. Our tools should be broadly applicable to define regulatory network dynamics for diverse biological processes.