Ao, ZhengCai, HongweiWu, ZhuhaoHu, LiyaNunez, AsaelZhou, ZhuolongLiu, HongchengBondesson, MariaLu, XiongbinLu, XinDao, MingGuo, Feng2024-01-032024-01-032022Ao Z, Cai H, Wu Z, et al. Microfluidics guided by deep learning for cancer immunotherapy screening. Proc Natl Acad Sci U S A. 2022;119(46):e2214569119. doi:10.1073/pnas.2214569119https://hdl.handle.net/1805/37560Immune-cell infiltration and cytotoxicity to pathogens and diseased cells are ubiquitous in health and disease. To better understand immune-cell behavior in a 3D environment, we developed an automated high-throughput microfluidic platform that enables real-time imaging of immune-cell infiltration dynamics and killing of the target cancer cells. We trained a deep learning algorithm using clinical data and integrated the algorithm with our microfluidic platform to effectively identify epigenetic drugs that promote T cell tumor infiltration and enhance cancer immunotherapy efficacy both in vitro and in vivo. Our platform provides a unique method to investigate immune-tissue interactions, which can be widely applied to oncology, immunology, neurology, microbiology, tissue engineering, regenerative medicine, translational medicine, and so on.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalCancer immunotherapyDeep learningDrug screeningImmune infiltrationMicrofluidicsArticle