Defining gene regulatory networks controlling cell fate

项目总结 细胞类型特定的转录网络是基因调控网络，它动态地重新配置以驱动 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，并且 经常在许多疾病中中断。这些网络的结构由TRANS组件定义，该组件指定 哪些调控蛋白控制基因的表达和调控区域的顺式成分 它可以调节基因的近端和远端的表达。识别这些监管网络具有 对于哺乳动物细胞类型来说是一个巨大的挑战，因为一个基因的潜在调节器的数量 以及准确定义这些网络所需的大量分析。单细胞组学研究进展 单细胞RNA-seq(scRNA-seq)和单细胞atac-seq(scATAC-seq)等技术提供了新的 定义特定细胞类型的监管网络的机会，因为它们能够全面描述 数以千计的单个细胞的转录组和可及性。然而，计算方法 整合这些数据来定义细胞谱系结构和细胞类型特定的调控网络是有限的。 大多数方法只使用了一种检测顺式或反式成分的方法，并且没有 多样本数据集的建模时间或层次相关性。最后，计算的性能 与实验检测的网络相比，网络推理方法仍然很低。致信地址 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来 整合scRNA-seq和scATAC-seq数据集以推断细胞类型谱系(目标1)和特定细胞类型 来自scRNA-seq和atac-seq数据的转录调控网络(目标2)，(B)识别重新连接的网络 组件在动态过程中，例如细胞重新编程(目标2)，以及(C)发展主动的 一种基于学习的方法来推断因果监管网络并应用该框架来细化监管 蜂窝重编程网络(目标3)。我们将把我们的工具应用于公共数据集和新收集的数据集，如下所示 这是这个项目的一部分。我们的分析将揭示与细胞命运相关的顺式和反式调节网络组件 在动态过程中的规范，例如重新编程或开发。我们的主动学习方式 将使用扰动序列来执行调节器扰动，以验证预测的网络以及 为翻译和基础研究具有重要意义的系统建立完善的黄金标准。这个 此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 为不同的生物过程定义调控网络动力学。

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.