To derive a “TCR fingerprint” (amino acid residues that determine interactions between the TCR and the peptide-MHC [pMHC] complex), Bentzen and Such et al. measured the relative affinities of a TCR to DNA barcode-labeled pMHC multimers. Fingerprints differed substantially among TCRs recognizing the same epitope (even when isolated from the same patient) and the number of potential pMHC targets for a given TCR inversely correlated with the functional avidity of the corresponding T cell clone. TCR fingerprinting could be used to assess potential cross-recognition of TCRs selected for clinical development to reduce the risk of autoimmune reactions.
The promiscuous nature of T-cell receptors (TCRs) allows T cells to recognize a large variety of pathogens, but makes it challenging to understand and control T-cell recognition. Existing technologies provide limited information about the key requirements for T-cell recognition and the ability of TCRs to cross-recognize structurally related elements. Here we present a 'one-pot' strategy for determining the interactions that govern TCR recognition of peptide-major histocompatibility complex (pMHC). We measured the relative affinities of TCRs to libraries of barcoded peptide-MHC variants and applied this knowledge to understand the recognition motif, here termed the TCR fingerprint. The TCR fingerprints of 16 different TCRs were identified and used to predict and validate cross-recognized peptides from the human proteome. The identified fingerprints differed among TCRs recognizing the same epitope, demonstrating the value of this strategy for understanding T-cell interactions and assessing potential cross-recognition before selection of TCRs for clinical development.
Author Info: (1) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. (2) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, D
Author Info: (1) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. (2) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. (3) Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark. (4) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. (5) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark. (6) Department of Medicine, Divisions of Dermatology, University of Washington, Seattle, Washington, USA. (7) Department of Medicine, Divisions of Dermatology, University of Washington, Seattle, Washington, USA. (8) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark. (9) Department of Medicine, University of Washington, Seattle, Washington, USA. Division of Vaccine and Infectious Diseases, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. (10) Translational Skin Cancer Research, University Hospital Essen and University of Duisburg-Essen, Essen, Germany. (11) Department of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (12) Department of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (13) Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark. (14) Department of Medicine, Divisions of Dermatology, University of Washington, Seattle, Washington, USA. (15) Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark. Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina. (16) Department of Micro and Nanotechnology, Technical University of Denmark, Lyngby, Denmark.
