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

项目摘要 生物学中的一个基本问题是了解遗传变异如何影响基因组功能， 表型大多数与人类疾病相关的遗传变异位于非编码区。 基因组区域，并可能影响基因组功能和表型，通过调节顺式- 调控元件和细胞类型特异性基因调控网络（GRNs）。然而，我们对 基因组变体（单独或作为组合）对基因表达、GRN活性和最终 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（cis-regulatory factor，cis- 已知元件具有基于细胞类型和状态的不同功能，基因组变体如何影响 功能元件和表型的细胞类型/状态特异性活性仍有待于在许多方面进行表征。 更多细节 该提案旨在利用多种族，性别平衡的人类诱导多能干细胞小组， （hiPSC）系（欧洲人，非洲裔美国人和非洲狩猎采集者）以及单- 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 调控网络，细胞表型。首先，我们将建立一个强大的实验框架， 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，用于整合单细胞数据， 不同的模态，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推定的顺式- 在单细胞水平的调控元件，以提高我们的网络水平的关系， 基因组变体和表型。

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.