Abstract
Atherosclerosis is a disease of the cardiovascular system characterized by local chronic inflammation. The disease’s hallmarks are vessel stenosis, stiffening, and hyperplasia, which are the most prominent underlying causes of cardiovascular complications. Cells from the innate immune response have a central role in disease development as they orchestrate inflammatory events leading to the deposition of fatty streaks in the sub-endothelium. The negative remodeling of atherosclerotic vessels is exacerbated by local variations of intraluminal hemodynamic load, where disturbed blood flow aggravates plaque deposition. Despite pioneering efforts to explore the relationship between inflammation and hemodynamics in the disease framework, interactions between these two elements have never been investigated in vitro before due to the lack of modeling systems with an adequate degree of complexity. Here, we employed a multifaced approach combining computational fluid dynamics (CFD) and tissue-engineering to achieve, for the first time in vitro, the full development of human atherosclerotic plaques within a one-month timeframe. We established the atherosclerosis-on-a-chip model using human induced pluripotent stem cells-derived populations assembled into tissue-engineered arterial vessels and cultured in atheroprone conditions. We reliably predicted regions of plaque deposition within the vessels via tailored CFD modeling. Using machine-learning-aided immunophenotyping and molecular analyses, we found that immune cell populations and extracellular matrix (ECM) components from modeled plaques were comparable to those in human carotid lesions. Furthermore, we discovered similarities between the ECM tensional state of tissue-engineered and native plaques by performing nanoprobe-based tensile analyses. Our results provide the in vitro proof of the link between hemodynamics and inflammation in atherosclerosis and present a personalized, up-scalable tool to study human arterial atherosclerosis onset and progression. We anticipate our work to represent a milestone in the atherosclerosis modeling and precision medicine arena and to serve as a starting point for in-depth analyses targeted at more specific disease progression stages.
Competing Interest Statement
The authors have declared no competing interest.