Popis: |
Extracellular vesicles (EVs) are nano-sized carriers that reflect the parent cell's information and are known to mediate cell-cell communication. In order to overcome the disadvantages of mesenchymal stem cells (MSCs) in cell therapy, such as unexpected differentiation leading to tumorization, immune rejection, and other side effects, EVs derived from MSCs (MSC-EVs) with the tissue regenerative function have been studied as new cell-free therapeutics. However, therapeutic applications of EVs require overcoming several challenges. First, the production efficiency of MSC-EVs should be increased at least as much as the quantity of them are required to their clinical application; second, MSC-EVs needs to show various functionality further, thereby increasing tissue regeneration efficiency. In this study, we treated tauroursodeoxycholic acid (TUDCA), a biological derivative known to regulate cholesterol, to MSCs and investigated whether TUDCA treatment would be able to increase EV production efficiency and tissue regenerative capacity of EVs. Indeed, it appears that TUDCA priming to MSC increases the yield of MSC-EVs2 times by reducing the cellular cholesterol level in MSCs and increasing the exocytosis-related CAV1 expression. Interestingly, it was found that the EVs derived from TUDCA-primed MSCs (T-EV) contained higher amounts of anti-inflammatory cytokines (IL1RN, IL6, IL10, and IL11) and osteogenic proteins (ALP, RUNX2, BMP2, BMPR1, and BMPR2) than those in control MSC-EVs (C-EV). Besides, it was shown that T-EV not only regulated M1/M2 macrophages differentiation of monocytes, also effectively increased the osteogenic differentiation of MSCs as well as bone tissue regeneration in a bone defect rat model. Based on these results, it is concluded that TUDCA treatment to MSC as a new approach endows EV with high-yield production and functionality. Thus, we strongly believe T-EV would be a powerful therapeutic material for bone tissue regeneration and potentially could be expanded to other types of tissue regeneration for clinical applications. |