Defining gene regulatory networks controlling cell fate

定义控制细胞命运的基因调控网络

• 批准号: 10669280
• 负责人: Sushmita Roy
• 金额: $ 32.86万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669280
• 关键词: ATAC-seq; Active Learning; Address; Affect; Agreement; Automobile Driving; Basic Science; Bayesian Network; Biological Assay; Biological Process; CISH gene; Cell Fate Control; Cell Lineage; Cells; Cellular Assay; Chromatin; Chromosome Mapping; Clustered Regularly Interspaced Short Palindromic Repeats; Computing Methodologies; Data; Data Set; Development; Dimensions; Disease; Disease model; Distal; Enhancers; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic Transcription; Genomics; Goals; Gold; Graph; Individual; Joints; Learning; Mammalian Cell; Measurement; Measures; Methods; Modeling; Multiomic Data; Mus; Nucleic Acid Regulatory Sequences; Output; Patients; Performance; Play; Process; Publishing; Regulator Genes; Resolution; Resources; Role; Sampling; Specific qualifier value; Specificity; Structure; System; Techniques; Technology; Transgenes; Translational Research; Transposase; causal variant; cell fate specification; cell type; computerized tools; data integration; experimental study; gene interaction; gene regulatory network; genetic regulatory protein; improved; induced pluripotent stem cell; insight; molecular phenotype; multiple omics; multitask; network models; novel; promoter; single-cell RNA sequencing; spatiotemporal; tool; transcription regulatory network; transcriptome

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.

项目概要 细胞类型特异性转录网络是动态重新配置以驱动的基因调控网络 基因的精确时空表达模式。这些网络对于细胞类型特异性至关重要，并且 在许多疾病中经常被破坏。这些网络的结构由一个 trans 组件定义，该组件指定 哪些调节蛋白控制基因的表达以及对调节区域进行分类的顺式组件 它可以调节基因的近端和远端表达。识别这些监管网络已经 由于基因潜在调节因子的数量，对哺乳动物细胞类型来说是一个重大挑战 准确定义这些网络需要大量的分析。单细胞组学的进展 技术，例如单细胞 RNA-seq (scRNA-seq) 和单细胞 ATAC-seq (scATAC-seq)，提供了新的 由于其能够全面分析细胞类型的调控网络，因此有机会定义细胞类型特异性调控网络 数千个单个细胞的转录组和可访问性。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异性调控网络是有限的。 大多数方法仅使用一种类型的测定，重点关注顺式或反式成分，并且没有 建模多样本数据集的时间或层次相关性。最后，计算性能 与实验检测到的网络相比，网络推理方法仍然很低。致地址 这些挑战，我们将开发新颖的计算方法和强大的资源来绘制基因图谱 驱动细胞类型特异性的调控网络动态。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（目标 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（目标 2），(b) 识别重新连接的网络 细胞重编程等动态过程中的组成部分（目标 2），以及 (c) 开发一种主动的 基于学习的方法来推断因果监管网络并应用该框架来完善监管 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： 该项目的一部分。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 动态过程（例如重新编程或开发）期间的规范。我们的主动学习方法 将使用 Perturb-Seq 执行调节器扰动，以验证预测的网络以及 为对转化和基础研究具有重要意义的系统建立改进的黄金标准。这 该项目生成的工具和数据集将公开可用，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。