Elucidating human beta cell transcriptional regulome with low-input genomic technologies

遗传学研究揭示了许多与糖尿病相关的非编码序列变异 或肥胖风险。在关键代谢细胞或组织类型中生成参考3D表观基因组数据，例如 因此，人胰腺β细胞对于理解疾病的病因学是非常重要的。 用于研究转录调控的常规基因组方法包括RNA-seq和ChIP-seq， 转录组和表观基因组的作图。这些技术通常需要~1 百万个细胞/测定。Hi-C是目前最流行的3D基因组结构定位方法， 一种无偏的方式，但需要数千万个细胞才能实现3D基因组的分辨率 分析.另一方面，β细胞研究社区依赖于死者的胰岛样本， 捐赠者，这是珍贵的，非常昂贵的。因此，低投入技术对β细胞至关重要 基因组研究该项目的总体目标是将联合收割机结合几种最先进的单电池和低成本电池。 输入基因组技术，以系统地表征来自队列的β细胞3D增强子调节组 40个新鲜的人类胰岛样本;一些关键技术是实验室的原始发明 我们的团队。在目标1中，我们将使用大规模并行单细胞RNA-seq方法（Drop-seq）来产生 单个胰岛细胞转录组的综合数据集，同时使用低输入 用芯片片段化方法对来自40名人类胰岛供体的增强子和启动子进行定位。这 将导致识别糖尿病和肥胖症的标志基因和可变增强子基因座（VEL） 与疾病状态相关。在目标2中，我们将以3D酶分辨率绘制人类β细胞3D基因组图谱， 使用高效的简易Hi-C（eHi-C）方法。地图将揭示所有的互动之间 单独的增强子和启动子。在目标3中，我们将首次对 在人INS基因座处的3D调节基因组，并使用免疫组织化学方法定量INS附近的所有增强子的活性。 单倍型分辨的人细胞系。我们还将检验MAU 2-NIPBL粘附素负载 复合物可能通过介导增强子-启动子DNA环调节胰岛素基因表达， 这个轨迹。最后，我们将使用一种新的高通量Mosaic-seq方法来验证体内活性 在单个细胞水平上的几十种β细胞增强剂。这个全面的3D规则组数据将提供一个 了解T2 D或肥胖症中非编码基因组功能的关键资源。

Genetic studies have revealed numerous non-coding sequence variants associated with diabetes or obesity risk. Generating reference 3D epigenome data in in key metabolic cell or tissue types, such as human pancreatic β cell, is therefore very important for the understanding of disease etiology. Conventional genomic methods to study transcriptional regulation include RNA-seq and ChIP-seq, for the mapping of transcriptome and epigenome, respectively. These technologies typically require ~1 million cells per assay. Hi-C is currently the most popular method to map the 3D genome organization in an unbiased fashion, but tens of millions cells are required to achieve kilobase resolution 3D genome analysis. On the other hand, the β cell research community is relying on islet samples from deceased donors, which are precious and very expensive. Low-input technologies are therefore essential for β cell genomic studies. The overall goal of this project is to combine several state-of-art single-cell and low- input genomic technologies to systematically characterize the β cell 3D enhancer regulome from a cohort of 40 fresh human islet samples; some key technologies are the original inventions from the laboratories of our team. In aim 1, we will use a massively parallel single cell RNA-seq method (Drop-seq) to generate a comprehensive dataset of single islet cell transcriptome, and simultaneously use a low-input ChIPmentation method to map enhancers and promoters from a cohort of 40 human islet donors. This will lead to the identification of diabetes and obesity signature genes and variable enhancer loci (VELs) correlated with disease status. In aim 2, we will map the human β cell 3D genome at kilobase resolution using a highly efficient easy Hi-C (eHi-C) method. The map will reveal all the interactions between individual enhancers and promoters. In aim 3, we will for the first time perform an in-depth study of the 3D regulome at the human INS locus, and quantify the activity of all enhancers near INS using a haplotype-resolved human cell line. We will also test a hypothesis that MAU2-NIPBL cohesin loading complex may regulate insulin gene expressions through mediating enhancer-promoter DNA looping at this locus. Finally, we will use a novel high-throughput Mosaic-seq method to validation the in vivo activity of dozens of β cell enhancers at single cell level. This comprehensive 3D regulome data will provide a key resource for the understanding of the functions of non-coding genome in T2D or obesity.