Transplantation of mesenchymal stem cell sheet to treat acute hind-limb ischemia: A preclinical study

Introduction: Mesenchymal stem cell (MSC) transplantation has been reported as a promising therapy for acute limb ischemia (ALI). However, the treatment efficacy is limited to only certain improvements. Therefore, this study aims to improve the treatment efficacy of MSC transplantation through the use of MSC sheets produced from MSCs' cultured in fibrin scaffold (Fi-MSCs) in ALI models. Methods: MSCs were isolated and expanded from human umbilical cord tissue. The fibril scaffold was produced from human umbilical cord blood. Fi-MSCs were prepared by mixing MSCs with fibril according to a published protocol, and the Fi-MSC sheets were implanted directly into ligated and transected sites in the hind limbs of ischemic models (treatment group — group I). The results were compared with that of the control group (group II) in which mice were injected with saline. The treatment efficacy was recorded and evaluated through the following assays: limb morphology, SpO2, blood perfusion, angiogenesis, and histological morphology on days 7, 14, and 28 after treatment. Results: The results indicate that the transplantation of Fi-MSC sheets positively affected the acute ischemia hind-limb mouse models. On day 7 post-transplantation, the SpO2 index recorded at feet in group I (treatment) significantly increased from 79.24% + 1.43% to 89.40% + 1.65% (p-value < 0.05), while in group II (control), the SpO2 index slightly increased from 76.52% + 1.63% to 77.00 + 1.15 (p-value > 0.05). Besides, there were 60.00% (auto-recovery), 13.33%, and 26.67% mice at damage grades 0, I, and II, respectively, in the control group compared to 80%, 20%, and 0% mice at damage grades 0, I, and II, respectively. Moreover, in group I, all mice showed improved blood reperfusion, neovascular, and repaired muscle tissue compared to group II. Conclusion: Fi-MSC sheet transplantation positively reduced injury and improved blood perfusion ALI in the Swiss mouse model.