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

纳米地形 巨噬细胞极化 纳米技术 巨噬细胞 材料科学 计算机科学 机械转化 生物 细胞生物学 体外 生物化学
作者
Yannan Hou,Brandon Conklin,Hye Kyu Choi,Letao Yang,Ki‐Bum Lee
出处
期刊:ACS Nano [American Chemical Society]
卷期号:18 (37): 25465-25477 被引量:15
标识
DOI:10.1021/acsnano.4c04406
摘要

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.
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