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.

基于活细胞成像的免疫细胞的细胞毒性测定提供了单细胞水平的全面信息，但数据采集和分析是劳动密集型的。为了克服这一限制，我们之前开发了单癌细胞阵列，将癌细胞作为单细胞阵列固定在微孔中，从而实现高通量数据采集。在这项研究中，我们利用深度学习在单个癌细胞阵列的背景下自动分析 NK 细胞的细胞毒性。明确的癌细胞位置以及 NK 细胞和癌细胞沿不同光学平面的分离有助于通过深度学习进行分割和分类。评估各种深度学习模型以确定最合适的模型。基于深度学习的自动化数据分析结果与之前的手动分析结果一致。微孔平台和深度学习的集成将为细胞间相互作用的分析提供新的机会。