Taking a systems biology approach to model how antigens of different sequences impact T cell biology, Achar, Bourassa, and Rademaker et al. used a robotics platform to dynamically monitor multiple secreted cytokines as a measure of T cell activation, utilizing murine and human antigen:TCR pairs, a CAR, and multiple perturbations. Dimensionality reduction, modeling, and machine learning deduced at least 6 classes of antigens, which varied in signal direction (agonism vs antagonism), strength, and “latent space”, a parameter reflecting antigen quality. CAR-19 insertion into OT-I cells revealed the complex impact of co-delivering TCR and CAR signals.
Contributed by Ed Fritsch
ABSTRACT: Systems immunology lacks a framework with which to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8(+) T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called "antigen encoding"). We used antigen encoding to model and reconstruct patterns of T cell immune activation. The model delineated six classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies.