Microfluidics guided by deep learning for cancer immunotherapy screening
dc.contributor.author | Ao, Zheng | |
dc.contributor.author | Cai, Hongwei | |
dc.contributor.author | Wu, Zhuhao | |
dc.contributor.author | Hu, Liya | |
dc.contributor.author | Nunez, Asael | |
dc.contributor.author | Zhou, Zhuolong | |
dc.contributor.author | Liu, Hongcheng | |
dc.contributor.author | Bondesson, Maria | |
dc.contributor.author | Lu, Xiongbin | |
dc.contributor.author | Lu, Xin | |
dc.contributor.author | Dao, Ming | |
dc.contributor.author | Guo, Feng | |
dc.contributor.department | Medical and Molecular Genetics, School of Medicine | |
dc.date.accessioned | 2024-01-03T09:15:09Z | |
dc.date.available | 2024-01-03T09:15:09Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Immune-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. | |
dc.eprint.version | Final published version | |
dc.identifier.citation | Ao 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.2214569119 | |
dc.identifier.uri | https://hdl.handle.net/1805/37560 | |
dc.language.iso | en_US | |
dc.publisher | National Academy of Science | |
dc.relation.isversionof | 10.1073/pnas.2214569119 | |
dc.relation.journal | Proceedings of the National Academy of Sciences of the United States of America | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | PMC | |
dc.subject | Cancer immunotherapy | |
dc.subject | Deep learning | |
dc.subject | Drug screening | |
dc.subject | Immune infiltration | |
dc.subject | Microfluidics | |
dc.title | Microfluidics guided by deep learning for cancer immunotherapy screening | |
dc.type | Article |