Models of Single Blood Vessels and Vascular Networks in Health and Disease: From Blood-Derived Patient-Specific Organ-On-Chips to Computational Transport Phenomena

Abstract

Advances in tissue engineering and microfabrication techniques have skyrocketed the adoption of contemporary organ-chip biotechnology as an alternative disease and drug screening model compared to animals and traditional in vitro systems. Vascular organ-chips, specifically, have allowed researchers to mimic the microphysiological and hemodynamic conditions prevalent in the vascular niche and have hence contributed to increasing our understanding of the underlying complex cellular and molecular mechanisms. However contemporary vessel-chip models still lack the inclusion of a physiologically relevant, patient-derived endothelium. Advancements in stem cell biotechnology have ushered a new wave of patient-derived tissues, however, these methods are difficult to adopt in labs, and often result in impurity and heterogeneity of cells. This limits the power of organ-chips in making accurate physiological predictions. Here, we report the use of blood-derived endothelial cells (or BOECs) as alternatives to primary and iPSC-derived endothelial cells. The study begins with a comparative analysis of the genotype, phenotype, and organ-chip functional characteristics of blood-derived outgrowth endothelial cells against commercially available and most used primary human umbilical vein endothelial cells (or HUVECs) and iPSC-derived endothelial cells. BOECs exhibit similar levels of pro-endothelial markers, response to exogenous stimulation and fluid shear stress, growth kinetics in vitro, vasculogenesis and also thromboinflammation. We then investigate the disease-phenotype expressing ability of patient-derived BOECs by evaluating the endothelial dysfunction observed in Type 1 diabetes. Using a microphysiological vessel-chip model, we demonstrate that diseased BOECs exhibit hallmarks of endothelial activation, oxidative stress, and reduced proliferation in vitro. We further demonstrate that diabetic BOECs induce thromboinflammation without exogenous stimulation with cytokines. In addition to disease-specificity, we investigate the patient-specificity of autologous BOECs by examining the endothelial activation in sickle cell disease (SCD). BOECs from two distinct SCD patients with known differences in clinical severity are isolated, and their transcriptomic profiles are compared to healthy controls. Interestingly, BOECs from the more severe patient express more differentially expressed genes (DEGs) as compared to the mild patient, and the mild patient in turn expressed more DEGs than the controls. Both SCD patients had expression of some common genes which contributed to the endothelial activation and vascular dysfunction pathways. Finally, we compare the functional characteristics of patient derived BOECs by evaluating thromboinflammation using vessel-chips. In addition to modeling patient-specific pathophysiology, their ease of fabrication and real-time imaging allows organ-chips to generate high throughput datasets for sophisticated machine learning applications. We demonstrate one such application using high throughput imaging data of in vitro microvascular networks for evaluating oxygen transport. Here, we demonstrate AngioMT, a MATLAB based finite element method (FEM) mass transport solver. The image-to-physics methodology allows AngioMT to predict the oxygen distribution in a spatially aware manner. These microphysiological and in silico approaches can significantly improve the predictive power of organ-chip technology and allow the development of more robust and personalized disease-models.