Repertoire-scale T cell antigen identification via peptide-MHC lentivirus display

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Project Abstract T cell receptor (TCR) recognition of peptide-Major Histocompatibility Complex (pMHC) antigens is an essential component of adaptive immunity, impacting infectious disease, cancer, transplantation, and autoimmunity. Maintaining antigen specificity requires the detection, generation, and control of immense molecular diversity. Each individual’s T cell repertoire encodes millions of unique TCRs to specifically identify the trillions of possible pMHC antigens that can be encountered in one’s lifetime. The molecular diversity that makes adaptive immunity so effective also complicates its study. Emerging technologies have aided in the study of T cell recognition, but each focuses upon assessing large numbers of either pMHC or TCR sequences while severely limiting the number of examined variants for the other due to the inability to directly link TCR-pMHC information. In order to understand complex immune responses to large pathogens or cancer, or responses to many antigens on a systems level, a technique enabling ‘repertoire vs repertoire’ study of TCR-pMHC interactions is required. To achieve this, we have discovered that pseudotyped lentiviruses can use pMHCs displayed on the virus surface to direct viral entry specifically to T cells that express a cognate TCR. We therefore propose creating ‘lentivirus displayed’ pMHC libraries, where each virus displays and encodes a unique pMHC to enable trackable, antigen-specific infection of T cells. Upon interacting with their cognate receptors on primary, polyclonal T cells, these viruses will integrate the genetic information of the antigen that enabled viral entry, providing linked TCR- pMHC sequence pairs that can be directly recovered via single-cell sequencing. Here, we propose developing this technique to study three currently intractable application areas in human immune recognition: responses to rhinoviruses, tracking the human vaccinome, and generating broad datasets of TCR-pMHC interactions to aid in the computational prediction of T cell responses. If successful, our work can be readily generalized and deployed for use in areas of key unmet clinical need, such as infectious disease and cancer, as well as for fundamental questions in human immunology.