通过晶格自组装颗粒环境(GELS)系统构建前列腺肿瘤微环境

Developing physiologically relevant in vitro tumor microenvironment models demands faithful reproduction of tissue architecture, mechanical properties, and vascularization. To meet these requirements, I have developed a technique called Granular Environment for Lattice Self-assembly (GELS), which leverages granular hydrogel as modular building blocks that both support and constrain cell adhesion, migration, and collective remodeling. GELS is particularly well suited to prostate cancer engineering due to its emergent architecture resembling branching glands and ducts. In GELS, cells remodel their surroundings in proportion to the energy landscape required to displace neighboring granules. When nearby energy barriers are low, cells drive extensive reorganization, whereas high barriers restrict them to localized adjustments. By tuning the spatial distribution and magnitude of these barriers through granule size, surface chemistry, and connectivity, we can implement broad patterns, while allowing cells to contribute fine structural detail by adaptively responding to native chemical cues that guide tissue formation. Preliminary results demonstrate that altering granule size and composition induces a transition from disconnected, organoid-like aggregates to continuous, tissue-like arrangements with interconnected microchannels. We aim to use these insights to derive predictive design rules for tissue self-assembly, enabling the creation of in vitro models for specific tumor microenvironments, including the prostate. By seeding fibroblasts in well-defined patterns within GELS, first we established a healthy stromal environment into which prostate cancer spheroids can be introduced. The subsequent transition of fibroblasts into cancer-associated fibroblasts (CAFs) will be monitored through traction force microscopy for changes in alignment and ELISA readouts of TGF-beta levels and localization. These measurements will link mechanical remodeling to biochemical signaling in a dynamically evolving TME. This method holds the potential to significantly advance the field of cancer engineering by enabling the creation of more complex and viable TME models with live readouts of key signals.