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

通过将单细胞数据与多基因风险相结合来识别器官型和疾病特异性血管细胞群

• 批准号: 10652639
• 负责人: RAJAT M GUPTA
• 金额: $ 51.15万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652639
• 关键词: Affect; Aorta; Aortic Aneurysm; Biological Process; Biology; Blood Vessels; Cell physiology; Cells; Clinical; Collection; Communities; Complex; Computational Biology; Computer software; Computing Methodologies; Coronary Arteriosclerosis; Data; Data Set; Dementia; Diabetes Mellitus; Disease; Disease Progression; Disease susceptibility; Effect Modifiers (Epidemiology); Endothelial Cells; Ensure; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Risk; Genetic Transcription; Genotype; Heterogeneity; Human; Human BioMolecular Atlas Program; Human body; Integrins; Laboratories; Link; Lipids; Location; Malignant Neoplasms; Methodology; Methods; Morbidity - disease rate; Mus; Myofibroblast; Normal Cell; Organ; Pathogenicity; Pathway interactions; Play; Population; Process; Research Personnel; Risk; Role; Sampling; Signal Transduction; Smooth Muscle Myocytes; Source; Stroke; System; Temporal Arteritis; Tissues; United States; Vascular Diseases; Vascular Smooth Muscle; Work; angiogenesis; autoencoder; cell type; data integration; data sharing; disorder risk; genetic association; genetic information; genome wide association study; genomic data; genomic profiles; human data; human tissue; mortality; multiple data types; novel; open data; programs; public health relevance; sex; single cell analysis; single nucleus RNA-sequencing; single-cell RNA sequencing; trait

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.

血管细胞存在于整个人体，并导致多种疾病的风险。 血管功能障碍直接影响动脉疾病的风险（例如冠状动脉疾病和中风） 作为痴呆症，癌症和糖尿病等其他疾病的表现。人类的单细胞分析 脉管系统已经开始确定大量血管功能障碍的基本机制 相关疾病。我们的小组和其他几个实验室已经使用了单细胞RNA-sequest（SCRNA-SEQ） 鉴定血管细胞异质性。我们进行了主动脉的SCRNA-SEQ，以识别功能上不同的 内皮细胞（EC）亚群，多组已经鉴定出活化的肌纤维细胞 小鼠和人血管组织。这些研究证明了动脉壁中存在异质细胞群， 但仍未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。 人类生物分子图集计划（Hubmap）提供了丰富的数据来源，以开始建立 特异性血管细胞亚群的因果关系。在Hubmap数据中，EC和血管平滑 肌肉细胞（VSMC）包括从每个器官鉴定的大部分单细胞。但是，要 建立与疾病相关的细胞类型和转录途径，必须合并 我们在此应用程序中提出的新数据集和计算方法。我们的目标是使用新的计算 将患病血管组织数据与正常的Hubmap数据相结合的方法，以确定疾病 - 血管细胞的相关特征。整合GWAS疾病相关基因的新方法也将 帮助研究人员优先考虑多种常见疾病的因果细胞。为了实现这一目标，我们将：1）使用新 在Hubmap参考数据中识别血管细胞的器官特征的软件； 2）识别特异性疾病 血管细胞特征通过将Hubmap参考数据与血管疾病的样品进行比较； 3）构建 并共享一项计算计划，以通过 与遗传关联数据集成。这些分析利用现有的血管疾病SnRNA-Seq 我们可以与Hubmap共享的大量患病受试者的数据。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收藏 关于性和祖先。我们执行多个集成的方法和统计软件 具有遗传关联的单细胞数据集将建立一种可概括的方法，以迅速发现 与遗传性疾病有关的脉管系统和所有其他细胞类型的疾病的细胞和过程 风险和可用的GWA。我们的团队立即准备进行拟议的研究，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，以及 拥有一个多元化的团队，具有血管生物学，统计遗传学和计算生物学方面的专业知识。