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.

项目摘要 细胞类型特异性的转录网络是动态重新配置的基因调节网络 基因的精确时空表达模式。这些网络是细胞类型特异性的核心，并且是 经常在许多疾病中受到干扰。这些网络的结构由指定的反式组件定义 哪个调节蛋白控制基因的表达和种类调节区域的顺式成分 这可以通过近端和分别调节基因的表达。确定这些监管网络具有 由于基因的潜在调节剂数量，对于哺乳动物细胞类型是一个重大挑战 以及准确定义这些网络所需的大量测定。单细胞幻数的进步 单细胞RNA-Seq（SCRNA-SEQ）和单细胞ATAC-SEQ（SCATAC-SEQ）等技术提供了新 定义特定特定细胞类型的调节网络的机会，因为它们具有全面概况的能力 成千上万个单个单元的转录组和可访问性。但是，用于计算方法 集成这些数据以定义细胞谱系结构和细胞类型的特定调节网络是有限的。 大多数方法仅使用一种聚焦于顺式或反式组件的一种类型的测定 建模多样本数据集的临时或分层相关性。最后，计算的性能 与实验检测到的网络相比，网络推断方法仍然很低。解决 这些挑战，我们将开发新颖的计算方法和绘制基因的强大资源 调节网络动力学驱动细胞类型特异性。我们的目的是（a）开发一个计算工具包 集成的SCRNA-SEQ和SCATAC-SEQ数据集可推断细胞类型谱系（AIM 1）和特定于细胞类型的谱系 来自SCRNA-SEQ和ATAC-SEQ数据的转录调节网络（AIM 2），（b）识别REING网络 动态进展过程中的组件，例如细胞重编程（AIM 2），（c）发展一个活动 基于学习的方法来推断因果监管网络并应用此框架来完善监管 细胞重编程的网络（AIM 3）。我们将把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CI和反式调节网络组件 在动态过程中的规范，例如重编程或开发。我们积极学习的方法 将使用worturb-seq执行调节器扰动，以验证预测的网络以及 建立改进的金标准，以使对翻译和基础研究具有很高意义的系统。这 该项目生成的工具和数据集将公开可用，并将作为强大资源 了解细胞脂肪规范中的调节网络动态。我们的工具应广泛适用于 定义潜水生物学过程的监管网络动态。