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Defining gene regulatory networks controlling cell fate

定义控制细胞命运的基因调控网络

基本信息

批准号：
10530982
负责人：
Sushmita Roy
金额：
$ 32.91万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
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/10530982
关键词：
ATAC-seq Active Learning Address Affect Agreement Automobile Driving Basic Science Bayesian Network Biological Assay Biological Process 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 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 Translational Research Transposase base causal variant cell fate specification cell type computerized tools experimental study gene interaction gene regulatory network genetic regulatory protein improved insight molecular phenotype multiple omics multitask network models novel promoter single-cell RNA sequencing spatiotemporal tool transcription regulatory network transcriptome

项目摘要

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.

项目总结