Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency

项目摘要 仅通过四个转录将人体细胞重编程为诱导多能干细胞（iPSC） 转录因子Oct 4、Sox 2、Klf 4和cMyc（OSKM）是基因调控的最显著的重塑之一， 网络. OSKM将不同的体细胞类型重编程为iPSC的显着能力， 在功能上与胚胎干细胞无法区分，这表明OSKM利用了一种基本的 这是一种用于网络重塑的机制，其通常可适用于所有细胞命运转变。以前的研究 已经确定了合作TF结合在抑制体细胞程序中的关键作用， 激活多能细胞。然而，将TF结合动力学和表观基因组重塑与关键的 在重编程期间的分叉事件被细胞的高度异质性所混淆。 重编程过程以及缺乏关于如何从体细胞向多能细胞转变的知识 调节程序发生在单个细胞中。在这个项目中，我们的目标是建立监管网络的模型， 使用三种类型的单细胞多组学谱的重编程的细胞命运改变， 重新编程期间的时间点。我们将询问网络利用自然扰动 通过遗传变异进行重编程和多能性。众所周知，遗传变异可以调节 多能性网络，并有助于细胞表型和分化能力的变异性， iPSC系。我们将产生群体规模的RNA和DNA甲基化的单细胞联合分析（snmCT- seq）、RNA和染色质可及性的联合分析（scRNA + ATAC-seq）和 染色质构象和DNA甲基化（sn-m3 C-seq），允许细胞类型特异性确定 转录组、染色质可及性和调控元件的甲基化状态，以及增强子基因 将非编码变体连接到它们的调节靶标。为了将OSKM结合与单细胞 转录组学和表观基因组学动力学，我们将确定TF和组蛋白的等位基因特异性结合 使用合并的等位基因ChIP-seq策略进行修饰。我们将使用动态监管事件挖掘器（DREM） 通过整合转录因子-基因相互作用信息与时间-和 伪时间序列基因组学数据。为了确定重编程网络的遗传调控，我们将 应用新的统计方法FastGxE来区分细胞类型特异性和共享遗传成分 的基因表达调控，以提高识别细胞类型特异性数量性状基因座的灵敏度 （QTL）。为了测试调控网络，我们将通过实验确定网络枢纽基因的功能， 使用高通量CRISPR干扰和精确的变体替换实验来检测非编码变体。 我们提出的项目整合了多种方法，包括单细胞多组学，计算建模， 和基因工程，并可能提供新的见解的机制，其中转录因子重塑调控 网络的细胞类型的身份，并作为一个模型，在其他系统中的类似分析。

PROJECT SUMMARY The reprogramming of somatic human cells to induced pluripotent stem cells (iPSCs) by only four transcription factors (TFs) Oct4, Sox2, Klf4, and cMyc (OSKM) is one of the most striking remodelings of gene regulatory networks. The remarkable ability of OSKM to reprogram diverse somatic cell types into iPSCs that are functionally indistinguishable from embryonic stem cells indicates that OSKM leverages a fundamental mechanism for network remodeling that may be generally applicable to all cell fate transitions. Previous studies of reprogramming have identified the crucial role of cooperative TF binding in repressing somatic programs and activating pluripotent ones. However, associating TF binding dynamics and epigenomic remodeling with key bifurcation events during reprogramming is confounded by the highly heterogeneous nature of the reprogramming process and the lack of knowledge regarding how the transition from somatic to pluripotent regulatory programs occurs in individual cells. In this project, we aim to model the regulatory network underlying the cell fate change of reprogramming using three types of single-cell multi-omic profiles generated from critical time points during reprogramming. We will interrogate the network leveraging natural perturbation of reprogramming and pluripotency by genetic variants. Genetic variation is well known to modulate the regulatory network of pluripotency and contributes to the variability of cellular phenotypes and differentiation capacity of iPSC lines. We will generate population-scale single-cell joint profiling of RNA and DNA methylation (snmCT- seq), joint profiling of RNA and chromatin accessibility (scRNA + ATAC-seq) and single-nucleus joint profiling of chromatin conformation and DNA methylation (sn-m3C-seq), allowing the cell-type-specific determination of transcriptome, chromatin accessibility and methylation states at regulatory elements, as well as enhancer-gene looping to connect non-coding variants to their regulatory target. To integrate OSKM binding with the single-cell transcriptomic and epigenomic dynamics, we will determine the allele-specific binding of TFs and histone modifications using a pooled-alleles ChIP-seq strategy. We will use Dynamic Regulatory Events Miner (DREM) to construct predictive models by integrating transcription factor-gene interaction information with time- and pseudotime-series genomics data. To determine the genetic regulation of the reprogramming network, we will apply the novel statistical method FastGxE to distinguish cell-type-specific from the shared genetic component of gene expression regulation, to enhance the sensitivity for identifying cell-type-specific quantitative trait loci (QTLs). To test the regulatory network, we will experimentally determine the function of network hub genes and non-coding variants using high-throughput CRISPR interference and precise variant replacement experiments. Our proposed project integrates diverse approaches including single-cell multi-omics, computational modeling, and genetic engineering, and will likely provide new insights into the mechanism by which TFs remodel regulatory networks of cell type identity and serve as a model for similar analyses in other systems.