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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.

项目总结