An Acellular Scaffold Facilitates Endometrial Regeneration and Fertility Restoration via Recruiting Endogenous Mesenchymal Stem Cells.
Liaobing XinXiaowen ZhengJianmin ChenSentao HuYilun LuoQunzi GeXiaoying JinLie MaSongying ZhangPublished in: Advanced healthcare materials (2022)
Severe intrauterine adhesions (IUAs), characterized by inadequate endometrial repair and fibrosis, can lead to infertility. Stem cell-based therapies, which deliver mesenchymal stem cells (MSCs) to the wound site, hold a considerable promise for endometrium regeneration. However, some notable hurdles, such as stemness loss, immunogenicity, low retention and survival rate, limit their clinical application. Evidence shows a strategy of mobilizing endogenous MSCs recruitment can overcome the traditional limitations of exogenous stem cell-based therapies. Here, an acellular biomaterial named stromal derived factor-1 alpha (SDF-1α)/E7-modified collagen scaffold (CES) is explored. CES based on harnessing the innate regenerative potential of the body enables near-complete endometrium regeneration and fertility restoration both in a rat endometrium acute damage model and a rat IUA model. Mechanistically, the CES implantation promotes endogenous MSCs recruitment via a macrophage-coordinated strategy; then the homing MSCs exert the function of immunomodulation and altered local microenvironments toward regeneration. To conclude, CES, which can harness endogenous MSCs and overcome the traditional limitations of cell-based therapies, can serve as a clinically feasible and cell-free strategy with high therapeutic efficiency for IUA treatment.
Keyphrases
- stem cells
- mesenchymal stem cells
- umbilical cord
- cell therapy
- bone marrow
- cell free
- tissue engineering
- oxidative stress
- immune response
- adipose tissue
- wound healing
- single cell
- endometrial cancer
- drug induced
- intensive care unit
- early onset
- type diabetes
- combination therapy
- signaling pathway
- epithelial mesenchymal transition
- climate change
- skeletal muscle
- machine learning
- hepatitis b virus
- surgical site infection
- acute respiratory distress syndrome