AI-aided high-throughput profiling of single-cell migration and proliferation on addressable dual-nested microwell arrays

The assessment of cell migration and proliferation is essential in the field of oncology. It has been widely performed for cancer prognosis and the prediction of treatment outcome. The microdevice-based methods have enabled the assessment of these two processes at single-cell resolution, which could acquire unique information on cell heterogeneity and subtypes. However, most of the current platforms show limited throughput due to design defects or lack of modules for high-speed data analysis, which greatly hampers the extraction of statistically unbiased biological data. To address this challenge, we propose a high-throughput system consisting of an addressable dual-nested microwell array chip (DNMA chip) and an artificial intelligence (AI)-based image analysis algorithm. Our DNMA chip allows single-cell trapping, label-free encoding, and long-term incubation. Combined with AI-aided data processing, the migration and proliferation of single tumor cells under normal culture or chemotherapy are quantitatively analyzed in a high-throughput and non-destructive manner.