Luminal endothelialization of small caliber silk tubular graft for vascular constructs engineering

The constantly increasing incidence of coronary artery disease worldwide makes necessary to set advanced therapies and tools such as tissue engineered vessel grafts (TEVGs) to surpass the autologous grafts [(i.e., mammary and internal thoracic arteries, saphenous vein (SV)] currently employed in coronary artery and vascular surgery. To this aim, in vitro cellularization of artificial tubular scaffolds still holds a good potential to overcome the unresolved problem of vessel conduits availability and the issues resulting from thrombosis, intima hyperplasia and matrix remodeling, occurring in autologous grafts especially with small caliber (<6 mm). The employment of silk-based tubular scaffolds has been proposed as a promising approach to engineer small caliber cellularized vascular constructs. The advantage of the silk material is the excellent manufacturability and the easiness of fiber deposition, mechanical properties, low immunogenicity and the extremely high in vivo biocompatibility. In the present work, we propose a method to optimize coverage of the luminal surface of silk electrospun tubular scaffold with endothelial cells. Our strategy is based on seeding endothelial cells (ECs) on the luminal surface of the scaffolds using a low-speed rolling. We show that this procedure allows the formation of a nearly complete EC monolayer suitable for flow-dependent studies and vascular maturation, as a step toward derivation of complete vascular constructs for transplantation and disease modeling.

Automated summary

The increasing incidence of coronary artery disease worldwide has prompted the development of advanced therapies such as tissue engineered vessel grafts (TEVGs) to replace autologous grafts currently used in surgery. In this study, the authors propose a method to optimize the coverage of silk electrospun tubular scaffold with endothelial cells, aiming to derive complete vascular constructs for transplantation and disease modeling.