Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays
Dowon Moon, Seong‐Eun Kim, Chuangqi Wang, Kwonmoo Lee, Junsang Doh
Abstract
Abstract 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.