Treffer: 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.