SUMMARY T cells can discriminate between diseased and non-diseased states. To make this distinction, the information provided by extracellular receptors must be interpreted by signaling networks within the T cell. Most notable is T cell antigen receptor (TCR) signaling network because it combines a receptor that scans the surface of human cells for threats and signaling proteins that ensure a highly sensitive and specific response. This TCR signaling machinery can detect small quantities of an antigenic agonist and discriminate it from a background of structurally similar endogenous ligands. The overarching goal of my research group is to elucidate how the TCR signaling machinery discriminates between TCR ligands by producing distinct signaling outcomes. The past few decades have revealed how the TCR is coupled to signaling proteins within the T cell, but much less is known about how these signaling proteins are coordinated to produce different cellular responses, such as whether a TCR ligand should be ignored, or cause the T cell to become activated. The underlying mechanisms used to produce context- specific TCR signals must be determined to fully realize the potential of T cells as therapeutic entities for the treatment of human disease. Regulatory mechanisms can diversify signaling by controlling the activity of signaling proteins, such as kinases, and their assembly into protein complexes. We propose to determine how TCR signal diversification can arise from (1) negative feedback loops, (2) differential assembly of signaling complexes, and (3) adaptive desensitization of TCR signaling. To interrogate these mechanisms of signal diversification and their effect on a T cell response, we will combine chemical tools with immunological approaches. We will use proximity labeling and mass spectrometry analyses to determine how the composition of signaling complexes is altered by kinase-responsive negative feedback and TCR-ligand binding properties. We will also evaluate more long-term mechanisms of receptor desensitization caused by adaptive transcriptional changes by RNA sequencing. These datasets will be generated and analyzed with our collaborators at the University of Michigan Medical School, which includes the Proteomics Resource Facility and Advanced Genomics Core.