Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

Abstract

CD4 + T cells are critical to fighting pathogens, but a comprehensive analysis of human T cell specificities is hindered by the diversity of HLA alleles (>20,000) and the complexity of many pathogen genomes. We previously described GLIPH, an algorithm to cluster T cell receptors (TCRs) that recognize the same epitope and to predict their HLA restriction, but this method loses efficiency and accuracy when analyzing >10,000 TCRs. Here we describe an improved algorithm, GLIPH2, that can process millions of TCR sequences. We used GLIPH2 to analyze 19,044 unique TCRβsequences from 58 individuals latently infected with Mycobacterium tuberculosis (Mtb) and to group them according to their specificity. To identify the epitopes targeted by clusters of Mtb -specific T cells, we carried out a screen of 3,724 distinct proteins covering 95% of Mtb protein-coding genes using artificial antigen presenting cells (aAPC) and reporter T cells. We found that at least five PPE (Pro-Pro-Glu) proteins are targets for T cell recognition in Mtb .

Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

“…Using an exact sequence match for both the alpha and beta chain (see Methods) is a highly stringent definition, and it is possible that some TM cells may have been missed. To address this issue, we utilized two additional TCR clustering tools, GLIPH2 (Huang et al, 2020) and iSMART (Zhang et al, 2020) to identify tumor-matching cells in blood. Across all patients, we observed an average increase of 8.2% in the number of TM cells (5.26% -12.9%) ( Fig.…”

The immune roadmap for understanding multi-system inflammatory syndrome in children: opportunities and challenges

“…Mark Davis (Stanford University School of Medicine) has taken a broad approach to identify T cell specificities that might be clinically relevant in the response to SARS-CoV-2. T cell receptor (TCR) sequence analysis with the GLIPH2 algorithm 22,23 identified large numbers of TCRs from different people on the basis of sequence and shared motifs and organized them into clusters indicative of peptide-major histocompatibility complex specificities. The analysis of millions of TCR sequences from patients with a range of COVID-19 disease severities allows investigation of the repertoire for unique TCR motifs shared by people with mild illness that could be important in controlling the disease.…”

Methods for sequence and structural analysis of B and T cell receptor repertoires

“…Based on the observation that there are specific positions in TCR CDR3 regions that contact antigen peptides and that the presence of particular sequence motifs can define TCR clusters, Glanville et al, developed the GLIPH (grouping of lymphocyte interactions by paratope hotspots) algorithm [63] , [64] . This algorithm clusters TCRs based on local sequence motifs, as well as on other parameters such as global CDR3 similarity, V gene usage, CDR3 length, MHC profile of donor(s) and clone size.…”