Identifying specificity groups in the T cell receptor repertoire

Area of Science:

• Immunology
• Bioinformatics
• Structural Biology

Background:

• T cell receptor (TCR) sequences exhibit vast diversity, exceeding the number of T cells in an individual.
• Understanding TCR antigen specificity is crucial for deciphering immune responses.

Purpose of the Study:

• To define the minimal requirements for TCR antigen specificity.
• To develop a computational tool for clustering TCRs with shared specificity.

Main Methods:

• Analysis of TCR sequences using peptide-major histocompatibility complex (pMHC)-tetramer-sorted cells and structural data.
• Development and application of the GLIPH (grouping of lymphocyte interactions by paratope hotspots) algorithm.
• Independent validation using TCR sequences from individuals with Mycobacterium tuberculosis infection.

Main Results:

• The GLIPH algorithm reliably groups TCRs of common specificity from different donors.
• Conserved complementarity-determining region 3 (CDR3) motifs within TCRs were identified as critical for antigen recognition.
• Analysis of Mycobacterium tuberculosis-reactive T cells identified 141 TCR specificity groups, including 16 shared across multiple individuals.

Conclusions:

• The GLIPH algorithm effectively analyzes large TCR sequence datasets to define shared specificity groups.
• This approach accelerates the analysis of T cell responses and the identification of specific peptide-major histocompatibility complex (pMHC) ligands.
• GLIPH facilitates the discovery of TCRs recognizing specific antigens, aiding in the development of immunotherapies and diagnostics.