한빛사논문
Yannan Hou 1, Brandon Conklin 1, Hye Kyu Choi 1, Letao Yang 1,2*, Ki-Bum Lee 1*
1Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.
2Key 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.
*Corresponding authors: Letao Yang, Ki-Bum Lee
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.
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