Single-cell Multimodal integration links granular vascular cell states to coronary artery disease risk 7 views

Coronary artery disease (CAD) and related cardiovascular disorders remain the leading cause of death worldwide. The primary underlying cause of CAD is atherosclerosis, a chronic inflammatory disease characterized by the accumulation of lipid-rich plaques within the arterial wall. As these lesions progress, they can restrict blood flow and, in advanced stages, become unstable and rupture, triggering thrombosis and acute clinical events such as myocardial infarction and stroke. Genome-wide association studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), the vast majority of which reside within non-coding regions of the genome. These variants are thought to influence disease risk through regulatory effects on gene expression, but pinpointing the causal variants and their underlying mechanisms of action remains challenging. Such efforts require integration of information on disease-relevant cell types and tissues and genome properties such as chromatin accessibility and transcription factor (TF) binding. Although approaches such as quantitative trait locus (QTL) mapping have substantially advanced fine-mapping of CAD loci, both bulk tissue profiling and in vitro models have important limitations. Bulk assays can obscure regulatory signals arising from rare or specialized cell populations, while cultured cell models may not fully capture the transcriptional and cis-regulatory complexity present in vivo. These challenges are particularly relevant for smooth muscle cells (SMCs) and endothelial cells (ECs), whose extensive phenotypic plasticity plays a central role in atherosclerosis progression. Consequently, the field still lacks a comprehensive cell-resolved transcriptomic and epigenomic characterization of SMCs and ECs to further refine GWAS associations. In this dissertation, we address this gap by generating a multimodal atlas of human atherosclerosis encompassing more than one million cells across single-cell transcriptomic, epigenomic, and high-resolution spatial transcriptomic datasets. We developed an adaptive computational framework capable of integrating highly heterogeneous datasets, enabling the most extensive survey of vascular and immune cell heterogeneity in human atherosclerotic lesions to date. Spatial mapping of these cellular states within their native tissue context revealed key processes underlying disease progression, including the gradual loss of SMC differentiation programs as cells migrate from the medial layer and undergo phenotypic modulation within the plaque. We leveraged this atlas to define the transcriptomic and epigenomic landscapes of phenotypically modulated SMCs and ECs, uncovering novel markers of vascular cell state transitions, including LTBP1 and CRTAC1. Integration of these molecular profiles with GWAS data enabled quantification of specific vascular cell states' contributions to CAD heritability and related traits such as coronary artery calcification, revealing a previously unrecognized role for transitional SMCs in mediating genetic risk. We further constructed multimodal gene regulatory networks to identify key drivers of disease-associated programs, nominating BNC2 as a regulator of osteogenic SMC transitions. By combining single-cell and spatial datasets with human genetics, we also fine-mapped CAD risk loci with unprecedented cellular and anatomical resolution, identifying candidate EC-specific mechanisms at less characterized CAD loci such as FGD5 and MCF2L. Together, this dissertation establishes a framework for translating genetic associations into cell-specific regulatory mechanisms, which can inform the development of more efficient pre-clinical models for therapeutic pipelines.

PHD (Doctor of Philosophy)

Genomics; Single-cell; Atherosclerosis; Epigenomics

English

2026-07-21