Mesenchymal stem cell-derived exosomes: The dawn of diabetic wound healing
Chronic wound healing has long been an unmet medical need in the field of wound repair, with diabetes being one of the major etiologies. Diabetic chronic wounds (DCWs), especially diabetic foot ulcers, are one of the most threatening chronic complications of diabetes. Although the treatment strategies, drugs, and dressings for DCWs have made great progress, they remain ineffective in some patients with refractory wounds. Stem cell-based therapies have achieved specific efficacy in various fields, with mesenchymal stem cells (MSCs) being the most widely used. Although MSCs have achieved good feedback in preclinical studies and clinical trials in the treatment of cutaneous wounds or other situations, the potential safety concerns associated with allogeneic/autologous stem cells and unknown long-term health effects need further attention and supervision. Recent studies have reported that stem cells mainly exert their trauma repair effects through paracrine secretion, and exosomes play an important role in intercellular communication as their main bioactive component. MSC-derived exosomes (MSC-Exos) inherit the powerful inflammation and immune modulation, angiogenesis, cell proliferation and migration promotion, oxidative stress alleviation, collagen remodeling imbalances regulation of their parental cells, and can avoid the potential risks of direct stem cell transplantation to a large extent, thus demonstrating promising performance as novel "cell-free" therapies in chronic wounds. This review aimed to elucidate the potential mechanism and update the progress of MSC-Exos in DCW healing, thereby providing new therapeutic directions for DCWs that are difficult to be cured using conventional therapy.Keywords: Diabetic wounds; Exosomes; Mesenchymal stem cells; Pre-conditioning; Preclinical translation; Wound and injuries.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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