Atherosclerosis is a chronic inflammatory disease that remains the leading cause of morbidity and mortality worldwide. Despite decades of research into the development and progression of this disease, current management and treatment approaches remain unsatisfactory and further studies are required to understand the exact pathophysiology. This review aims to provide a comprehensive assessment of currently published data utilizing single-cell and next-generation sequencing techniques to identify key cellular and molecular contributions to atherosclerosis and vascular inflammation.
Electronic searches of Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE databases were undertaken from inception until February 2022. A narrative synthesis of all included studies was performed for all included studies. Quality assessment and risk of bias analysis was evaluated using the ARRIVE and SYRCLE checklist tools.
Thirty-four studies were eligible for narrative synthesis, with 16 articles utilizing single-cell exclusively, 10 utilizing next-generation sequencing and 8 using a combination of these approaches. Studies investigated numerous targets, ranging from exploratory tissue and plaque analysis, cell phenotype investigation and physiological/hemodynamic contributions to disease progression at both the single-cell and whole genome level. A significant area of focus was placed on smooth muscle cell, macrophage, and stem/progenitor contributions to disease, with little focus placed on contributions of other cell types including lymphocytes and endothelial cells. A significant level of heterogeneity exists in the outcomes from single-cell sequencing of similar samples, leading to inter-sample and inter-study variation.
Single-cell and next-generation sequencing methodologies offer novel means of elucidating atherosclerosis with significantly higher resolution than previous methodologies. These approaches also show significant potential for translatability into other vascular disease states, by facilitating cell-specific gene expression profiles between disease states. Implementation of these technologies may offer novel approaches to understanding the disease pathophysiology and improving disease prevention, management, and treatment.