Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies

Sayedali Shetab Boushehri, Katharina Essig, Nikolaos-Kosmas Chlis, Sylvia Herter, Marina Bacac, Fabian J. Theis, Elke Glasmacher (), Carsten Marr () and Fabian Schmich ()
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Sayedali Shetab Boushehri: German Research Center for Environmental Health
Katharina Essig: Roche Innovation Center Munich
Nikolaos-Kosmas Chlis: Roche Innovation Center Munich
Sylvia Herter: Roche Pharma Research and Early Development (pRED)
Marina Bacac: Roche Pharma Research and Early Development (pRED)
Fabian J. Theis: German Research Center for Environmental Health
Elke Glasmacher: Roche Innovation Center Munich
Carsten Marr: German Research Center for Environmental Health
Fabian Schmich: Roche Innovation Center Munich

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract 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 prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering, and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC dataset of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. scifAI is universally applicable to IFC data. Given its modular architecture, it is straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.

Date: 2023
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DOI: 10.1038/s41467-023-43429-2

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