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通过集成机器学习和组合生物物理线索映射探究纳米形貌介导的巨噬细胞极化。

Probing Nanotopography-Mediated Macrophage Polarization via Integrated Machine Learning and Combinatorial Biophysical Cue Mapping.

机构信息

Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.

Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital affiliated to Tongji University, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, Tongji University, Shanghai 200065, China.

出版信息

ACS Nano. 2024 Sep 17;18(37):25465-25477. doi: 10.1021/acsnano.4c04406. Epub 2024 Sep 3.

Abstract

Inflammatory responses, leading to fibrosis and potential host rejection, significantly hinder the long-term success and widespread adoption of biomedical implants. The ability to control and investigated macrophage inflammatory responses at the implant-macrophage interface would be critical for reducing chronic inflammation and improving tissue integration. Nonetheless, the systematic investigation of how surface topography affects macrophage polarization is typically complicated by the restricted complexity of accessible nanostructures, difficulties in achieving exact control, and biased preselection of experimental parameters. In response to these problems, we developed a large-scale, high-content combinatorial biophysical cue (CBC) array for enabling high-throughput screening (HTS) of the effects of nanotopography on macrophage polarization and subsequent inflammatory processes. Our CBC array, created utilizing the dynamic laser interference lithography (DLIL) technology, contains over 1 million nanotopographies, ranging from nanolines and nanogrids to intricate hierarchical structures with dimensions ranging from 100 nm to several microns. Using machine learning (ML) based on the Gaussian process regression algorithm, we successfully identified certain topographical signals that either repress (pro-M2) or stimulate (pro-M1) macrophage polarization. The upscaling of these nanotopographies for further examination has shown mechanisms such as cytoskeletal remodeling and ROCK-dependent epigenetic activation to be critical to the mechanotransduction pathways regulating macrophage fate. Thus, we have also developed a platform combining advanced DLIL nanofabrication techniques, HTS, ML-driven prediction of nanobio interactions, and mechanotransduction pathway evaluation. In short, our developed platform technology not only improves our ability to investigate and understand nanotopography-regulated macrophage inflammatory responses but also holds great potential for guiding the design of nanostructured coatings for therapeutic biomaterials and biomedical implants.

摘要

炎症反应导致纤维化和潜在的宿主排斥,严重阻碍了生物医学植入物的长期成功和广泛应用。控制和研究植入物-巨噬细胞界面处巨噬细胞炎症反应的能力对于减少慢性炎症和改善组织整合至关重要。尽管如此,系统研究表面形貌如何影响巨噬细胞极化通常受到以下因素的限制:可访问纳米结构的复杂性有限、难以实现精确控制以及实验参数的有偏预选。针对这些问题,我们开发了一种大规模、高通量组合生物物理线索(CBC)阵列,用于实现高通量筛选(HTS)纳米形貌对巨噬细胞极化和随后的炎症过程的影响。我们的 CBC 阵列是利用动态激光干涉光刻(DLIL)技术创建的,其中包含超过 100 万个纳米形貌,范围从纳米线和纳米网格到具有 100nm 到几微米尺寸的复杂分层结构。我们使用基于高斯过程回归算法的机器学习(ML),成功地识别出某些抑制(M2 极化)或刺激(M1 极化)巨噬细胞极化的拓扑信号。对这些纳米形貌的放大研究表明,细胞骨架重塑和 ROCK 依赖性表观遗传激活等机制对于调节巨噬细胞命运的力学转导途径至关重要。因此,我们还开发了一个结合先进的 DLIL 纳米制造技术、HTS、基于 ML 的纳米生物相互作用预测以及力学转导途径评估的平台。简而言之,我们开发的平台技术不仅提高了我们研究和理解纳米形貌调节的巨噬细胞炎症反应的能力,而且对于指导治疗性生物材料和生物医学植入物的纳米结构涂层的设计具有很大的潜力。

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