Abstract

Vascular dysfunction directly affects risk for arterial diseases (e.g. coronary artery disease and stroke), and manifestations of other diseases such as dementia, cancer, and diabetes. Single-cell analysis of the human vasculature has identified the basic mechanisms of vascular dysfunction in the large number of associated diseases. Our group, along with 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 heterogeneous cell populations exist in the arterial wall, but determining which populations play a causal role in early vascular dysfunction and disease risk remains a challenge.

The Human BioMolecular Atlas Program (HuBMAP) provides a rich source of data to begin establishing 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. To establish the cell types and transcriptional pathways associated with disease, it is 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 for integrating disease-associated genes from GWAS will 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 robust collection of diseased subjects we will share with HuBMAP. All data from vascular disease subjects is available for open-data sharing, and has been collected to include a diverse representation of subjects with respect to sex and ancestry. Our methods and statistical software for performing 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. 

Public health relevance statement

Vascular diseases account for half of the global morbidity and mortality rates. New single-cell data is available for vascular cells throughout the body, but it is unknown which cell populations contribute to vascular dysfunction and disease progression. We will build and share new computational software to integrate data from genetic association studies and single-cell analysis of diseased vascular tissue to identify the genes, pathways, and cell types that contribute to risk of multiple common diseases.