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

项目总结 只需4次转录即可将体细胞重编程为诱导的多能干细胞 因子Oct4、Sox2、Klf4和cMyc(OSKM)是基因调控中最引人注目的改造之一 网络。OSKM将不同的体细胞类型重新编程为IPSCs的非凡能力 与胚胎干细胞在功能上无法区分表明OSKM利用了一种基本的 可普遍适用于所有细胞命运转换的网络重塑机制。以前的研究 已经确定了协同转铁蛋白结合在抑制体细胞程序中的关键作用 激活多能者。然而，将转铁蛋白结合动力学和表观基因组重构与关键字联系起来 事件的高度异构性使重新编程过程中的分叉事件变得混乱 重新编程过程以及缺乏关于如何从体细胞向多能性转变的知识 调节程序发生在单个细胞中。在这个项目中，我们的目标是对潜在的监管网络进行建模 使用三种类型的单细胞多组型重新编程的细胞命运变化 重新编程期间的时间点。我们将利用自然扰动来询问网络 通过基因变异进行重新编程和多能性。众所周知，基因变异可以调节 多能性网络，并有助于细胞表型和分化能力的多样性 IPSC系列。我们将产生群体规模的单细胞RNA和DNA甲基化联合分析(SNmCT- 、核糖核酸和染色质可及性联合分析(scRNA+atac-seq)和单核联合分析 染色质构象和DNA甲基化(sn-m3c-seq)，允许特定细胞类型的测定 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 循环将非编码变体连接到它们的调控目标。将OSKM绑定与单元格集成 转录和表观基因组动力学，我们将确定转铁蛋白和组蛋白的等位基因特异性结合 使用混合等位基因芯片序列策略的修改。我们将使用动态监管事件挖掘器(DREM) 通过整合转录因子-基因相互作用信息和时间来构建预测模型 伪时间序列基因组学数据。为了确定重新编程网络的遗传调节，我们将 应用新的统计方法FastGxE区分特定细胞类型和共有遗传成分 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敏感性 (QTL)。为了测试调控网络，我们将通过实验确定网络中枢基因和 使用高通量CRISPR干扰的非编码变体和精确的变体替换实验。 我们建议的项目集成了多种方法，包括单细胞多组学、计算建模、 和基因工程，并可能为转录因子重塑监管机制提供新的见解 细胞类型识别的网络，并作为其他系统中类似分析的模型。

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.