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 细胞转录调节组

基本信息

批准号：
10400115
负责人：
Yan Li
金额：
$ 44.27万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10400115
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 Hi-C 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

项目摘要

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.

遗传研究揭示了许多与糖尿病相关的非编码序列变体或肥胖风险。在关键代谢细胞或组织类型中生成参考3D表观组数据，例如因此，人胰腺β细胞对于理解疾病病因非常重要。用于研究转录调控的常规基因组方法包括RNA-seq和ChIP-Seq，用于转录组和表观基因组的映射。这些技术通常需要〜1 每个测定法。 HI-C是目前最受欢迎的绘制3D基因组组织的方法一种公正的时尚，但是需要数千万个细胞才能实现千目标分辨率3D基因组分析。另一方面，β细胞研究界依靠已故的胰岛样品捐助者，非常珍贵，非常昂贵。因此，低输入技术对于β细胞至关重要基因组研究。该项目的总体目标是结合几个最先进的单细胞和低 - 输入基因组技术，以系统地表征来自队列的β细胞3D增强子调节体 40个新鲜的人类胰岛样品；一些关键技术是实验室的原始事件我们的团队。在AIM 1中，我们将使用大量平行的单细胞RNA-seq方法（drop-seq）生成单胰岛单元转录组的综合数据集，只需使用低输入从40个人类胰岛供体组成的队列中绘制增强子和启动子的芯片化方法。这将导致鉴定糖尿病和肥胖特征基因和可变增强子基因座（VELS）与疾病状况相关。在AIM 2中，我们将以千目标分辨率绘制人β细胞3D基因组使用高效的简单HI-C（EHI-C）方法。该地图将揭示之间的所有相互作用在AIM 3中，我们将首次对在人INS基因座上的3D调节组，并使用A量化所有增强子的活性单倍型分辨的人类细胞系。我们还将检验一个假设MAU2-NIPBL粘着蛋白加载复合物可以通过介导增强子促销DNA循环来调节胰岛素基因表达这个基因座。最后，我们将使用一种新型的高通量镶嵌式方法来验证体内活性在单细胞水平上的数十个β细胞增强子这个全面的3D调节数据将提供理解T2D或肥胖中非编码基因组功能的关键资源。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Easy Hi-C: A Low-Input Method for Capturing Genome Organization.

DOI：
10.1007/978-1-0716-2847-8_9
发表时间：
2023
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
0
作者：
通讯作者：

Identifying differential regulatory control of APOE ɛ4 on African versus European haplotypes as potential therapeutic targets.

DOI：
10.1002/alz.12534
发表时间：
2022-10
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
0
作者：
通讯作者：

Single-cell lineage analysis reveals extensive multimodal transcriptional control during directed beta-cell differentiation.

DOI：
10.1038/s42255-020-00314-2
发表时间：
2020-12
期刊：
Nature metabolism
影响因子：
20.8
作者：
Weng C;Xi J;Li H;Cui J;Gu A;Lai S;Leskov K;Ke L;Jin F;Li Y
通讯作者：
Li Y

Novel correlative analysis identifies multiple genomic variations impacting ASD with macrocephaly.

新颖的相关分析确定了影响自闭症谱系障碍和大头畸形的多种基因组变异。

DOI：
10.1093/hmg/ddac300
发表时间：
2023
期刊：
Human molecular genetics
影响因子：
3.5
作者：
Fu,Chen;Ngo,Justine;Zhang,Shanshan;Lu,Leina;Miron,Alexander;Schafer,Simon;Gage,FredH;Jin,Fulai;Schumacher,FredrickR;Wynshaw-Boris,Anthony
通讯作者：
Wynshaw-Boris,Anthony