Effect of Cell–Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System

Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail to disclose the heterogeneity at a single-cell resolution. Single-cell coculture systems that isolate and cultivate single-cell pairs can help reveal heterogeneous interactions between different types of individual cells. However, precise and high-throughput pairing of individual cells for long-term coculture remains challenging. Meanwhile, tools for analyzing single-cell data sets have lagged due to the increased data throughput. Herein, we report a deep learning-assisted high-throughput addressable single-cell coculture system (DL-HASCCS), enabling fast pairing of individual heterogeneous cells and quantitative analysis of single-cell interactions in a high-throughput manner by integrating high-throughput single-cell cocultivation and automated data processing. By analyzing the interaction between single breast cancer cells and single endothelial cells under normal and chemotherapy conditions, the effect of cell-cell interactions on cell proliferation and migration is revealed at the single-cell level, providing valuable insights into cellular heterogeneity.