Identifying the organotypic and disease-specific vascular cell populations by integrating single cell data with polygenic risk

血管细胞存在于人体各处，与多种疾病的风险有关。 血管功能障碍也直接影响动脉疾病(如冠状动脉疾病和中风)的风险。 作为痴呆症、癌症和糖尿病等其他疾病的表现。人体的单细胞分析 血管系统已经开始识别大量血管功能障碍的基本机制 相关疾病。我们的团队和其他几个实验室已经使用单细胞RNA测序(scRNA-seq)来 鉴定血管细胞异质性。我们对大动脉进行了scrna-seq检查，以确定不同的功能。 内皮细胞(EC)亚群，多个组织已经在疾病中发现激活的肌成纤维细胞 小鼠和人类的血管组织。这些研究证明动脉壁中存在异质细胞群， 但目前还不确定哪些人群在早期血管功能障碍和疾病风险中起因果作用。 人类生物分子图谱计划(HuBMAP)提供了丰富的数据来源，以便开始建立 特定血管细胞亚群与疾病的因果联系。在HuBMAP数据中，内皮细胞和血管平滑 肌肉细胞(VSMC)包括从每个器官鉴定的单个细胞的很大一部分。然而，为了 建立与疾病相关的细胞类型和转录途径，这将是必要的 我们在本申请中提出的新的数据集和计算方法。我们的目标是使用新的计算 方法将病变血管组织的数据与正常HuBMAP数据相结合，以识别疾病- 血管细胞的相关特征。整合来自GWAS的疾病相关基因的新方法也将 帮助调查人员确定多种常见疾病的因果细胞的优先顺序。为了实现这一点，我们将：1)使用新的 在HUBMAP参考数据中识别血管细胞器官型特征的软件；2)识别特定疾病 通过将HUBMAP参考数据与血管疾病样本进行比较来确定血管细胞特征；以及3)建立 并共享一个计算程序来识别与疾病相关的细胞群体和基因模块 与遗传关联数据的集成。这些分析利用了现有的血管疾病SnRNA-seq 我们可以与HuBMAP分享来自丰富的疾病受试者的数据。所有来自血管疾病的数据 主题可用于开放数据共享，并已收集到包括各种主题的集合 在性别和血统方面。我们的方法和统计软件来执行这一整合 具有遗传关联的单细胞数据集将建立一种可推广的方法学，以快速发现 与疾病相关的细胞和血管系统的突起，以及所有其他类型的细胞，对于任何具有遗传性的疾病 风险和可用的GWA值。我们的团队立即准备好进行拟议的研究，并分享 HuBMAP社区的软件。我们有快速共享单细胞RNA-seq数据的记录，并且 拥有一支在血管生物学、统计遗传学和计算生物学方面具有专业知识的多元化团队。

Vascular cells are present throughout the human body and contribute to risk of multiple diseases. Vascular dysfunction directly affects risk for arterial diseases (e.g. coronary artery disease and stroke) as well as manifestations of other diseases such as dementia, cancer, and diabetes. Single cell analysis of the human vasculature has already begun to identify the basic mechanisms of vascular dysfunction in the large number of associated diseases. Our group, and several other labs, have used single cell RNA-sequencing (scRNA-seq) to identify vascular cell heterogeneity. We performed scRNA-seq of the aorta to identify functionally distinct endothelial cell (EC) subpopulations, and multiple groups have identified activated myofibroblasts in diseased mouse and human vascular tissue. These studies prove heterogenous cell populations exist in the arterial wall, but it remains undetermined which populations play a causal role in early vascular dysfunction and disease risk. The Human BioMolecular Atlas Program (HuBMAP) provides a rich source of data to begin to establish a causal link for specific vascular cell subpopulations with disease. In HuBMAP data, ECs and vascular smooth muscle cells (VSMCs) comprise a large portion of the single cells identified from each organ. However, to establish the cell types and transcriptional pathways associated with disease it will be necessary to incorporate the new datasets and computational methods we propose in this application. We aim to use new computational methods to integrate data from diseased vascular tissue with normal HuBMAP data, to identify the disease- relevant features of vascular cells. New methods to integrate disease associated genes from GWAS will also help investigators prioritize causal cells for multiple common diseases. To achieve this, we will: 1) Use new software to identify organotypic features of vascular cells in HUBMAP reference data; 2) Identify disease-specific vascular cell signature by comparing HUBMAP reference data with samples from vascular disease; and 3) Build and share a computational program to identify disease-relevant cell populations and gene modules through integration with genetic association data. These analyses make use of existing vascular disease snRNA-seq data from a rich collection of diseased subjects we can share with the HuBMAP. All data from vascular disease subjects is available for open data sharing, and has been collected to include a diverse collection of subjects with respect to sex and ancestry. Our methods and statistical software to perform this integration of multiple single cell datasets with genetic associations will establish a generalizable methodology to rapidly discover the disease-relevant cells and processes of the vasculature, and all other cell-types, for any diseases with genetic risk and available GWAS. Our team is immediately ready to undertake the proposed studies and share the software with the HuBMAP community. We have a track record of rapidly sharing single cell RNA-seq data, and have a diverse team with expertise in vascular biology, statistical genetics, and computational biology.