Learning to Operate T cells

T cells orchestrate immune responses crucial for the elimination of infections and cancers.

They do this by initiating diverse effector responses when their T cell receptors (TCRs) recognise antigens sourced from these threats. It is now appreciated that many other, “accessory”, receptors shape these responses.

Despite extensive molecular work highlighting differences between these receptors, they are largely grouped into binary co-stimulation or co- inhibition categories.

Here, we hypothesise that different accessory receptors will have a different impact on important quantitative T cell phenotypes including antigen sensitivity (ability to recognise few antigens), discrimination (ability to selectively respond to higher-affinity antigens), and adaptation (ability to maintain responses to constant antigen stimulation).

We will produce a large functional atlas for how accessory receptors impact these phenotypes alone or in different combination, and use an algorithm to learn an operational model that mechanistically explains how their signals are integrated (or not) with TCR signalling.

The predictive power of the model will be used to manipulate antigen sensitivity, discrimination, and adaptation to improve T cell-based therapies.

The work will move the field beyond the current binary paradigm of accessory receptors and offers a new way to quantitatively study how surface receptors control cellular responses.