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

• 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
• 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

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 生物学中的一个基本问题是了解遗传变异是如何影响基因组功能的 表型。大多数与人类疾病相关的遗传变异位于非编码范围内 基因组区域，并可能通过调节顺式酶的活性来影响基因组的功能和表型。 调控元件和细胞类型特异性基因调控网络(GRN)。然而，我们知道的关于 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)和相关的顺式调节因子 众所周知，根据细胞类型和状态，基因组变异如何影响，这些元素具有不同的功能 功能元件和表型的细胞类型/状态特异性活性在许多方面仍有待表征 更多细节。 这项提案旨在利用一组多种族、性别平衡的人类诱导的多能干细胞。 (HiPSC)系列(欧洲、非裔美国人和非洲狩猎采集者)以及在单一- 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 和高通量单细胞微扰方法研究基因组变异对细胞功能的影响 调节网络，细胞表型。一是建立健全部署试点框架。 用于检测功能性遗传变异的先进的时间分辨和多组单细胞技术 单细胞水平。接下来，我们将开发新的计算方法，用于跨 不同的模式并用于驱动细胞识别的GRN的准确重建和预测建模， 发育动力学(心脏和神经谱系细胞命运转换)。最后，我们将应用高吞吐量 组合遗传或表观遗传扰动方法，以调节关键基因或假定的顺式基因的活性。 单细胞级别的监管元素，以提高我们对网络级别关系的理解 基因组变异和表型。