Defining causal roles of genomic variants on gene regulatory networks with spatiotemporally-resolved single-cell multiomics

通过时空解析的单细胞多组学定义基因组变异对基因调控网络的因果作用

基本信息

批准号：
10474569
负责人：
Sreeram Kannan
金额：
$ 121万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10474569
关键词：
3-Dimensional ATAC-seq Adherent Culture Affect African African American Automobile Driving Bar Codes Basic Science Benchmarking Biology Brain Cardiac Cell Communication Cell Differentiation process Cell Line Cell Lineage Cells Cellular Assay Chromatin Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Computational algorithm Computer Models Computer software Computing Methodologies DNA DNA Methylation Data Data Set Databases Development Developmental Gene Elements Epigenetic Process European Gender Gene Expression Gene Expression Regulation Genes Genetic Genetic Variation Genome Genomic Segment Genomics Human Human Development Human Genetics Infrastructure Knowledge Label Machine Learning Measurement Measures Messenger RNA Metabolic Methods Modality Modeling Multipotent Stem Cells Organism Organoids Outcome Pennsylvania Performance Phenotype Population Heterogeneity RNA Regulatory Element Research Personnel Role Technology Testing Time Translating Translational Research Universities Untranslated RNA Washington base biological systems causal variant cell type combinatorial computer framework computerized tools data integration data sharing deep learning disorder risk epigenome editing epigenomics gene regulatory network genetic variant genome editing genomic tools genomic variation human disease improved induced pluripotent stem cell insight mRNA sequencing member methylome multi-ethnic multimodal data multimodality multiple omics network models novel open source predictive modeling reconstruction relating to nervous system risk variant single cell technology single molecule single-cell RNA sequencing spatiotemporal stem cell differentiation therapeutic target transcription factor transcriptomics

项目摘要

PROJECT SUMMARY A fundamental question in biology is to understand how genetic variation affects genome function to influence phenotypes. The majority of genetic variants associated with human diseases are located within non-coding genomic regions and may affect genome functions and phenotypes through modulating the activity of cis- regulatory elements and cell-type specific gene regulatory networks (GRNs). However, our knowledge about the impact of genomic variants (alone or as combinations) on gene expression, GRN activity and ultimately cellular phenotypes are rather limited. Further, because transcription factors (TFs) and related cis-regulatory elements are known to have distinct functions based on cell-type and state, how genomic variants influence cell-type/state-specific activity of functional elements and phenotypes remains to be characterized in much greater details. This proposal aims to leverage a panel of multi-ethnic, gender-balanced human induced pluripotent stem cell (hiPSC) lines (European, African American and African hunter gatherers) as well as recent advances in single- cell time-resolved or multi-omics technologies, predictive modeling of regulatory networks by machine learning and high throughput single-cell perturbation methods to study the functional impact of genomic variations on regulatory network, cellular phenotypes. First, we will establish a robust experimental framework of deploying advanced time-resolved and multi-omic single-cell technologies for detecting functional genetic variants at single-cell level. Next, we will develop novel computational methods for integration of single-cell data across different modalities and for accurate reconstruction and predictive modeling of GRNs driving cellular identify, developmental dynamics (cardiac and neural lineage cell fate transition). Finally, we will apply high-throughput combinatorial genetic or epigenetic perturbation approaches to modulate activity of key genes or putative cis- regulatory elements at single-cell levels to improve our understanding of network level relationships among genomic variants and phenotypes.

项目概要生物学的一个基本问题是了解遗传变异如何影响基因组功能以影响表型。与人类疾病相关的大多数遗传变异位于非编码区域基因组区域，并可能通过调节顺式-的活性影响基因组功能和表型调控元件和细胞类型特异性基因调控网络（GRN）。然而，我们的知识基因组变异（单独或组合）对基因表达、GRN 活性以及最终的影响细胞表型相当有限。此外，由于转录因子（TF）和相关的顺式调节已知元素根据细胞类型和状态具有不同的功能，基因组变异如何影响功能元件和表型的细胞类型/状态特异性活性仍有待表征更多细节。该提案旨在利用一组多种族、性别平衡的人类诱导多能干细胞（hiPSC）线（欧洲、非裔美国人和非洲狩猎采集者）以及单细胞的最新进展细胞时间分辨或多组学技术，通过机器学习对调控网络进行预测建模和高通量单细胞扰动方法来研究基因组变异对功能的影响调节网络，细胞表型。首先，我们将建立一个强大的部署实验框架先进的时间分辨和多组学单细胞技术，用于检测功能性遗传变异单细胞水平。接下来，我们将开发新的计算方法来整合单细胞数据不同的模式以及驱动细胞识别的 GRN 的精确重建和预测建模，发育动力学（心脏和神经谱系细胞命运转变）。最后，我们将应用高通量组合遗传或表观遗传扰动方法来调节关键基因或假定的顺式活性单细胞水平的调控元件，以提高我们对网络水平关系的理解基因组变异和表型。