Moon, Dowon Kim, Seong-Eun Wang, Chuangqi Lee, Kwonmoo Doh, Junsang 2024-07-11T06:30:48Z 2024-07-11T06:30:48Z 2024-07-11 2024-09 1976-0280 https://pubs.kist.re.kr/handle/201004/150223 The cytotoxicity assay of immune cells based on live cell imaging offers comprehensive information at the single cell-level information, but the data acquisition and analysis are labor-intensive. To overcome this limitation, we previously developed single cancer cell arrays that immobilize cancer cells in microwells as single cell arrays, thus allow high-throughput data acquisition. In this study, we utilize deep learning to automatically analyze NK cell cytotoxicity in the context of single cancer cell arrays. Defined cancer cell position and the separation of NK cells and cancer cells along distinct optical planes facilitate segmentation and classification by deep learning. Various deep learning models are evaluated to determine the most appropriate model. The results of the deep learning-based automated data analysis are consistent with those of the previous manual analysis. The integration of the microwell platform and deep learning would present new opportunities for the analysis of cell-cell interactions. English 한국바이오칩학회 Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays Article 10.1007/s13206-024-00158-y 1 BioChip Journal, v.18, no.3, pp.453 - 463 BioChip Journal 18 3 453 463 Y scie scopus kci ART003119082 001347341700010 Biochemical Research Methods Chemistry, Analytical Nanoscience & Nanotechnology Biochemistry & Molecular Biology Chemistry Science & Technology - Other Topics Article LACTATE-DEHYDROGENASE ASSAY IMMUNOTHERAPY Deep learning NK cell cytotoxicity Single cell array Cell-cell interaction Cancer immunotherapy