Dataset related to article "Single-Cell and Single-Nucleus Multiomics Reveal Cardiac Endothelial Cell Heterogeneity and Novel Pathological Processes in Pressure Overload-Induced Hypertrophy"

This record contains raw data related to article “Single-Cell and Single-Nucleus Multiomics Reveal Cardiac Endothelial Cell Heterogeneity and Novel Pathological Processes in Pressure Overload-Induced Hypertrophy"

Endothelial cells, the most prevalent cellular population in the heart, line the internal walls of coronary and lymphatic vessels and form the endocardium and cardiac valves. The heterogeneity of cardiac endothelium poses a challenge in fully understanding endothelial states. However, to better grasp the plasticity of endothelial cell phenotypes, it's crucial to distinguish the varied responses of endothelial populations to physiological and pathological stimuli. Endothelial dysfunction plays a significant role in the development and progression of heart failure, a condition impacting millions globally. For the heart to sustain pressure-overload-induced remodelling, endothelial cells must proliferate and generate new blood vessels. When cardiomyocyte hypertrophy and angiogenesis become uncoupled, it leads to decompensated heart failure. Despite this, the molecular mechanisms governing cardiac vascularization during pathological hypertrophy remain unclear.

Transcriptional phenotyping methods have been widely used to capture endothelial cell plasticity and heterogeneity. However, only single-cell profiling has effectively resolved distinct phenotypes. While single-cell RNA sequencing can create detailed cellular maps and cell-to-cell communication networks, it does not fully elucidate how gene regulatory programs are established or how cell states, functions, and responses are specified. To address these gaps, multimodal omics approaches have been developed, allowing for simultaneous profiling of chromatin accessibility and gene expression within the same cell.

In our study, we aimed to characterize the transcriptional and epigenetic profiles of cardiac endothelial cells in a mouse model of pressure-overload-induced hypertrophy using multiomics bioinformatic approaches at single-cell resolution. This innovative technology enabled us to uncover the regulatory mechanisms that drive endothelial cell sub-population specifications following banding. Our resulting atlas is intended to serve as a valuable reference and resource for future studies and the development of therapeutic strategies targeting endothelial cells in cardiovascular diseases.