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

利用低输入基因组技术阐明人类 β 细胞转录调控组

• 批准号: 9906888
• 负责人: Yan Li
• 金额: $ 44.22万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906888
• 关键词: 3-Dimensional; Affect; Beta Cell; Biological Assay; Biology; CRISPR screen; Cell Nucleus; Cell Separation; Cell physiology; Cells; ChIP-seq; Chromatin Loop; Clinical; Communities; Complex; DNA; Data; Data Set; Development; Diabetes Mellitus; Diagnosis; Disease; Elements; Enhancers; Environmental Risk Factor; Etiology; Gene Expression; Genes; Genetic; Genetic Transcription; Genetic study; Genome; Genome Mappings; Genomics; Goals; Grant; Haplotypes; Human; Human Cell Line; Human body; INS gene; Individual; Insulin; Islet Cell; Knowledge; Laboratories; Lead; Libraries; Maps; Mediating; Medical; Metabolic; Methods; Mosaicism; Obesity; Prevention; Research; Resolution; Resources; Role; Sampling; Structure of beta Cell of islet; Technology; Testing; Time; Tissues; Transcriptional Regulation; Translating; Type 2 diabetic; Untranslated RNA; Validation; Variant; Work; blood glucose regulation; cohesin; cohort; cost; cost effectiveness; data analysis pipeline; diabetes risk; droplet sequencing; epigenome; genetic signature; genome analysis; genome wide association study; genomic tools; human embryonic stem cell; improved; in vivo; insulin regulation; invention; islet; lifestyle factors; new technology; novel; obesity risk; promoter; risk variant; single-cell RNA sequencing; transcriptome; transcriptome sequencing

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.

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细胞对于了解疾病的病因学非常重要。 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 每份化验结果为百万个细胞。HI-C是目前最流行的绘制3D基因组组织图的方法 一种不偏不倚的方式，但需要数千万个细胞才能达到千碱基的3D基因组分辨率 分析。另一方面，β细胞研究界依赖于死者的胰岛样本 捐赠者，这是珍贵和非常昂贵的。因此，低投入技术对于β信元是必不可少的 基因组研究。该项目的总体目标是将几种最先进的单电池和低成本 输入基因组技术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创发明 我们队中的一员。在目标1中，我们将使用大规模并行的单细胞RNA-seq方法(Drop-seq)来生成 一个完整的单个胰岛细胞转录组数据集，同时使用低输入 从40名人类胰岛供者队列中定位增强子和启动子的化学染色方法。这 将导致识别糖尿病和肥胖症的特征基因和可变增强子基因(VEL) 与疾病状态相关。在目标2中，我们将以千碱基分辨率绘制人类β细胞3D基因组图 使用高效的简易Hi-C(eHi-C)方法。这张地图将揭示 单独的增强剂和促进剂。在目标3中，我们将首次深入研究 人类INS基因座的3D调节组，并使用一种 单倍型分辨的人类细胞系。我们还将测试MAU2-NIPBL粘附素负荷的假设 复合体可能通过介导增强子-启动子DNA环调节胰岛素基因的表达 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 单细胞水平的数十种β细胞增强剂。这一全面的3D Regulome数据将提供 了解非编码基因组在T2D或肥胖中的功能的关键资源。