Building models of T cells' self/non-self discrimination from automatized/dynamic high-dimensional experimental profiling

We present an experimental/theoretical pipeline to build a quantitative model of antigen discrimination by T cells. We introduce a robotic platform to quantify the dynamics of cell differentiation and cytokine production/consumption by T cells ex vivo. These high-dimensional dynamics can be compressed into a 2D model using tools from machine learning. Our model highlights two modalities of T cell activation that enforce adaptive kinetic proofreading of antigen-TCR interactions, and that encode antigen discrimination. We test our model of antigen discrimination across varied immunological settings, including CAR-T and TCR-deficient T cells. To conclude, we highlight the power of lab automation, data integration, machine learning and theoretical modeling to usher new insights in systems immunology.