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: 10852399
• 负责人: RAJAT M GUPTA
• 金额: $ 8.96万
• 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/10852399
• 关键词: Affect; Aorta; Biology; Blood Vessels; Cells; Collection; Communities; Computational Biology; Computer software; Computing Methodologies; Coronary Arteriosclerosis; Data; Data Set; Dementia; Diabetes Mellitus; Disease; Disease Progression; Endothelial Cells; Genes; Genetic; Genetic Risk; Genetic Transcription; Heterogeneity; Human; Human BioMolecular Atlas Program; Human body; Link; Malignant Neoplasms; Methodology; Methods; Morbidity - disease rate; Mus; Myofibroblast; Organ; Pathway interactions; Play; Population; Process; Research Personnel; Risk; Role; Sampling; Smooth Muscle Myocytes; Source; Stroke; Tissues; United States; Vascular Diseases; Vascular Smooth Muscle; cell type; data integration; data sharing; disorder risk; genetic association; genome wide association study; mortality; open data; programs; public health relevance; sex; single cell analysis; single nucleus RNA-sequencing; single-cell RNA sequencing

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.

血管细胞存在于人体的各个部位，并导致多种疾病的发生。