An imaging flow cytometry dataset for profiling the immunological synapse of therapeutic antibodies

Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune cells and act within the immunological synapse, an essential cell-to-cell interaction to direct the humoral immune response. Although many antibody designs are generated and evaluated, a high-throughput tool for systematic antibody characterization and function prediction is lacking. Here, we generate the largest publicly available imaging flow cytometry (IFC) data set of the human immunological synapse containing over 2.8 million images. This dataset is used to analyze class frequency and morphological changes under different immune stimulation. In addition to the dataset, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI, https://github.com/marrlab/scifAI), for preprocessing, feature engineering, and explainable, predictive machine learning IFC data. Using scifAI, we analyze class frequency- and morphological changes under different immune stimulation. scifAI is universally applicable to IFC data and, given its modular architecture, straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.