Regeneration of the rotator cuff tendon-to-bone interface using umbilical cord-derived mesenchymal stem cells and gradient extracellular matrix scaffolds from adipose tissue in a rat model

Abstract

Regeneration of the gradient structure of the tendon-to-bone interface (TBI) is a crucial goal after rotator cuffrepair. The purpose of this study was to investigate the efficacy of a biomimetic hydroxyapatite-gradient scaffold (HA-G scaffold) isolated from adipose tissue (AD) with umbilical cord derived mesenchymal stem cells (UC MSCs) on the regeneration of the structure of the TBI by analyzing the histological and biomechanical changes in a rat repair model. As a result, the HA-G scaffold had progressively increased numbers of hydroxyapatite (HA) particles from the tendon to the bone phase. After seeding UC MSCs to the scaffold, specific matrices, such as collagen, glycoaminoglycan, and calcium, were synthesized with respect to the HA density. In a rat repair model, compared to the repair group, the UC MSCs seeded HA-G scaffold group had improved collagen organization and cartilage formation by 52% at 8 weeks and 262.96% at 4 weeks respectively. Moreover, ultimate failure load also increased by 30.71% at 4 weeks in the UC MSCs seeded HA-G scaffold group compared to the repair group. Especially, the improved values were comparable to values in normal tissue. This study demonstrated that HA-G scaffold isolated from AD induced UC MSCs to form tendon, cartilage and bone matrices similar to the TBI structure according to the HA density. Furthermore, UC MSC-seeded HA-G scaffold regenerated the TBI of the rotator cuffin a rat repair model in terms of histological and biomechanical properties similar to the normal TBI.

Statement of Significance

We found specific extracellular matrix (ECM) formation in the biomimetic-hydroxyapatite-gradient-scaffold (HA-G-scaffold) in vitro as well as improved histological and biomechanical results of repaired rotator cuffafter the scaffold implantation in a rat model. This study has four strengths