Single cell analysis of the human pancreas in type 1 diabetes

项目概要/摘要 1型糖尿病（T1 D）的特征是胰腺中产生胰岛素的β细胞的自身免疫性破坏。 小岛在T1 D中，胰岛小生境中免疫细胞、内皮细胞和内分泌细胞之间的相互作用导致β细胞 功能障碍和/或破坏;然而，对启动和 驱动免疫介导的β细胞破坏。最近的单细胞RNA-seq（scRNA-seq）分析研究 来自非糖尿病供体的人胰腺和胰岛缺乏表征免疫细胞的分辨率。 此外，尚未全面分析胰岛细胞库中T1 D相关的变化，并且细胞凋亡可能与T1 D相关。 对于T1 D相关的细胞类型，基因调控元件的类型分辨表观基因组图谱仍有待产生。 当与来自全基因组T1 D关联研究的遗传变异相结合时，这样的图谱可能会有所帮助。 找出在T1 D中起因果作用的细胞和基因。为了填补这些知识空白，我们组建了一个团队， 在胰岛生物学和糖尿病（桑德），遗传学和糖尿病基因组学方面有很高成就的研究人员 （Gaulton）和功能基因组学（Ren，UCSD Center for Epigenomics）。该项目将采用新的 单核（sn）技术来表征表观基因组（Aim 1）和转录组（Aim 2）的概况， 非糖尿病和T1 D个体胰腺中的单个T1 D相关细胞。最能丰富细胞类型 与T1 D发病机制相关的细胞（即内分泌、免疫和内皮细胞），我们将从 全胰制剂。从这些富集的细胞制备物中，我们将生成可访问的 染色质（snATAC-seq）和基因表达（snRNA-seq）。首先，我们将使用fresh 非糖尿病供体的胰腺组织，然后使用我们最近改编的snATAC-seq和snRNA- seq技术用于分析来自非糖尿病、T1 D抗体阳性和T1 D供体的冷冻存档胰腺 糖尿病胰腺器官捐赠者网络（NPOD）在目标3中，我们将整合 目的1和2中生成的snATAC-seq和snRNA-seq数据与T1 D遗传关联数据，以鉴定 胰腺细胞类型和调控程序参与T1 D发病机制。该分析将1）定义单元格 类型和亚型以及它们在非糖尿病胰腺中的调节程序，2）识别T1 D依赖性 胰腺细胞类型的存在、组成、调节和相互连接的变化，以及3）鉴定 通过将snATAC-seq和snRNA-seq与T1 D整合，在T1 D中具有可能因果作用的细胞、网络和基因 遗传关联数据。通过生成胰腺细胞中染色质和基因表达的参考图谱 从非糖尿病和T1 D个体，该提案将识别产生的常驻免疫和其他细胞， T1 D期间的变化，可以作为疾病的新生物标志物，并将为早期治疗策略提供信息。 干预与遗传数据的进一步整合将揭示细胞，网络和基因上的因果关系 疾病的途径，这将为治疗靶点的发现提供信息。我们的发现将为我们提供新的见解 进入胰腺微环境中细胞的致病过程，导致T1 D中β细胞损失。

PROJECT SUMMARY/ABSTRACT Type 1 diabetes (T1D) is characterized by autoimmune destruction of insulin-producing beta cells in pancreatic islets. In T1D the interplay between immune, endothelial, and endocrine cells in the islet niche leads to beta cell dysfunction and/or destruction; however, there is limited knowledge of the molecular blueprint that initiates and drives immune-mediated beta cell destruction. Recent single cell RNA-seq (scRNA-seq) profiling studies of human pancreas and islets from non-diabetic donors lack the resolution to characterize immune cells. Furthermore, T1D-related changes in the islet cell repertoire have not been comprehensively analyzed, and cell type-resolved epigenomic maps of gene regulatory elements remain to be generated for T1D-relevant cell types. When intersected with genetic variants from genome-wide T1D association studies, such maps could help pinpoint cells and genes with causal roles in T1D. To fill these knowledge gaps, we have assembled a team of highly accomplished researchers in islet biology and diabetes (Sander), genetics and genomics of diabetes (Gaulton) and functional genomics (Ren, UCSD Center for Epigenomics). The proposed project will apply novel single nuclei (sn) technologies to characterize the epigenomic (Aim 1) and transcriptomic (Aim 2) profiles of individual T1D-relevant cells in the pancreas of non-diabetic and T1D individuals. To enrich cell types most relevant for T1D pathogenesis (i.e. endocrine, immune and endothelial cells), we will deplete acinar cells from whole pancreas preparations. From these enriched cell preparations, we will generate maps of accessible chromatin (snATAC-seq) and gene expression (snRNA-seq). First, we will generate reference maps using fresh pancreatic tissue from non-diabetic donors, and then employ our recent adaptions of snATAC-seq and snRNA- seq technology to profile frozen, archived pancreata from non-diabetic, T1D antibody-positive, and T1D donors in the Network for Pancreatic Organ Donors with Diabetes (nPOD) biorepository. In Aim 3, we will integrate snATAC-seq and snRNA-seq data generated in Aims 1 and 2 with T1D genetic association data to identify pancreatic cell types and regulatory programs involved in T1D pathogenesis. This analysis will 1) define cell types and subtypes and their regulatory programs in the non-diabetic pancreas, 2) identify T1D-dependent changes in the existence, composition, regulation and inter-connectivity of pancreatic cell types, and 3) identify cells, networks and genes with likely causal roles in T1D by integrating snATAC-seq and snRNA-seq with T1D genetic association data. By generating reference maps of chromatin and gene expression in pancreatic cells from non-diabetic and T1D individuals, this proposal will identify resident immune and other cells that arise and change during T1D that can serve as novel biomarkers of disease and which will inform strategies for early intervention. Further integration with genetic data will reveal cells, networks and genes that are on the causal pathway to disease, which will inform therapeutic target discovery. Together our findings will provide novel insight into the pathogenic processes of cells in the pancreatic micro-environment that lead to beta cells loss in T1D.