去细胞化
细胞外基质
生物相容性
伤口愈合
材料科学
再生(生物学)
生物医学工程
组织工程
纳米技术
细胞生物学
医学
生物
免疫学
冶金
作者
Pu Luo,Ruoxuan Huang,You Wu,Xingchen Liu,Zhengjie Shan,L. Gong,Shudan Deng,Haiwen Liu,Jinghan Fang,Shiyu Wu,Xiayi Wu,Quan Liu,Zetao Chen,K.W.K. Yeung,Wei Qiao,Shoucheng Chen,Zhuofan Chen
标识
DOI:10.1016/j.bioactmat.2023.05.011
摘要
With the discovery of the pivotal role of macrophages in tissue regeneration through shaping the tissue immune microenvironment, various immunomodulatory strategies have been proposed to modify traditional biomaterials. Decellularized extracellular matrix (dECM) has been extensively used in the clinical treatment of tissue injury due to its favorable biocompatibility and similarity to the native tissue environment. However, most reported decellularization protocols may cause damage to the native structure of dECM, which undermines its inherent advantages and potential clinical applications. Here, we introduce a mechanically tunable dECM prepared by optimizing the freeze-thaw cycles. We demonstrated that the alteration in micromechanical properties of dECM resulting from the cyclic freeze-thaw process contributes to distinct macrophage-mediated host immune responses to the materials, which are recently recognized to play a pivotal role in determining the outcome of tissue regeneration. Our sequencing data further revealed that the immunomodulatory effect of dECM was induced via the mechnotrasduction pathways in macrophages. Next, we tested the dECM in a rat skin injury model and found an enhanced micromechanical property of dECM achieved with three freeze-thaw cycles significantly promoted the M2 polarization of macrophages, leading to superior wound healing. These findings suggest that the immunomodulatory property of dECM can be efficiently manipulated by tailoring its inherent micromechanical properties during the decellularization process. Therefore, our mechanics-immunomodulation-based strategy provides new insights into the development of advanced biomaterials for wound healing.
科研通智能强力驱动
Strongly Powered by AbleSci AI