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利用新型体外筛选方法发现协同的材料-形貌组合,以实现具有免疫调节成骨作用的生物材料:ChemoTopoChip。

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

机构信息

University of Nottingham, Nottingham, NG7 2RD, United Kingdom.

Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands.

出版信息

Biomaterials. 2021 Apr;271:120740. doi: 10.1016/j.biomaterials.2021.120740. Epub 2021 Mar 1.

DOI:10.1016/j.biomaterials.2021.120740
PMID:33714019
Abstract

Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers.

摘要

人骨髓间充质干细胞(hMSCs)由于其与自体植入的相容性,在再生医学临床策略中得到广泛应用。有效的骨再生涉及巨噬细胞和 hMSCs 之间的串扰,巨噬细胞在 hMSCs 的募集和分化中起着关键作用。然而,能够同时指导 hMSC 命运和调节巨噬细胞表型的工程生物材料尚未被识别。这里使用了一种新的组合化学-形貌筛选平台 ChemoTopoChip,通过在每个实验中筛选 1008 种组合来筛选用于人永生化间充质干细胞(hiMSCs)和人巨噬细胞反应的材料,从而确定适合骨再生的材料。在基础培养基中培养的 hiMSCs 上培养的“命中”材料可实现与在成骨培养基中培养的 hiMSCs 相当的成骨诱导,这表明这些材料为骨再生中的成骨诱导补充剂提供了一种材料诱导的替代方法。这些相同的化学-微形貌组合中的一些还表现出免疫调节刺激作用,使巨噬细胞向促愈合表型极化。当化学物质和形貌都被招募来指导所需的细胞表型时,可以实现对细胞反应的最大控制,从而协同作用。大型组合文库使我们首次能够探测微形貌和材料化学对细胞的相对指令作用,我们发现这两种线索都能提供类似的细胞调节范围。机器学习用于生成结构-活性关系,这些关系确定了增强两种细胞类型反应的关键化学和形貌特征,为更好地理解细胞对微形貌图案聚合物的反应提供了基础。

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