Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires

Abstract

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

Keywords: TCR repertoire analysis; cytomegalovirus (CMV); enrichment analysis; epitope specificity; immunoinformatics; infectious disease; vaccines; yellow fever virus (YFV).

Figure 1

Overview of the number of trained prediction models for each virus and cancer type (TCRex version 0.3.0). The bars show the number of epitopes for which a prediction model with sufficient performance (i.e., AUC ≥ 0.7 and average precision ≥0.35) was trained.

Figure 2

Percentage of unique identified LLWNGPMAV-specific TCRs pre- and post-vaccination. The boxplots show the proportion of unique LLWNGPMAV-specific T cells in pre- and post-vaccination PBMC samples for the nine volunteers from Dewitt et al. (1) (A) and the six volunteers from Pogorelyy et al. (2) (B). Epitope-specific T cells were identified with TCRex using a 0.01% BPR threshold. An increase in the number of unique epitope-specific cells was found for both studies.

Figure 3

Public vs. TCRex-identified unique LLWNGPMAV-specific TCRs in the post-vaccination samples of Dewitt et al. (1) and Pogorelyy et al. (2). (A) Overview of the number of unique LLWNGPMAV-specific TCRs that were identified with TCRex in the post-vaccination PBMC samples for all nine volunteers (V1–V9) of Dewitt et al. (1) (dark blue) and the total number of public LLWNGPMAV-specific TCRs that were present in these repertoires (light blue). (B) Overview of the number of unique LLWNGPMAV-specific TCRs that were identified with TCRex in the post-vaccination PBMC samples for all six volunteers (P1–S2) of Pogorelyy et al. (2) (dark orange) and the total number of public LLWNGPMAV-specific TCRs that were present in these repertoires (light orange).

Figure 4

Percentage of unique identified LLWNGPMAV-specific TCRs in the post-vaccination repertoires from Dewitt et al. (1). The box plots show the log-scaled proportion of unique LLWNGPMAV-specific T cells in post-vaccination PBMC samples and activated TCR repertoires [i.e., “CD3⁺ CD8⁺CD14⁻CD19⁻CD38⁺HLA-DR⁺ Ag-experienced, activated effector T cells” (1)] for the nine volunteers from Dewitt et al. (1). Epitope-specific TCRs were identified with TCRex using a 0.01% BPR threshold. An increase in the number of unique LLWNGPMAV-specific cells was found in the activated dataset.