Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery

Abstract

The current strategy to detect immunodominant T cell responses focuses on the antigen, employing large peptide pools to screen for functional cell activation. However, these approaches are labor and sample intensive and scale poorly with increasing size of the pathogen peptidome. T cell receptors (TCRs) recognizing the same epitope frequently have highly similar sequences, and thus, the presence of large sequence similarity clusters in the TCR repertoire likely identify the most public and immunodominant responses. Here, we perform a meta-analysis of large, publicly available single-cell and bulk TCR datasets from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals to identify public CD4⁺ responses. We report more than 1,200 αβTCRs forming six prominent similarity clusters and validate histocompatibility leukocyte antigen (HLA) restriction and epitope specificity predictions for five clusters using transgenic T cell lines. Collectively, these data provide information on immunodominant CD4⁺ T cell responses to SARS-CoV-2 and demonstrate the utility of the reverse epitope discovery approach.

Keywords: CD4 T cells; COVID-19; T cell receptor; TCR repertoire; epitope discovery; public T cell response.

Graphical abstract

Figure 1

Merged analysis of single-cell CD4⁺ SARS-CoV-2-reactive T cell public datasets (A) Uniform Manifold Approximation and Projection (UMAP) of single cells from merged datasets containing SARS-CoV-2-antigen-enriched CD4 T cells. Colors indicate clusters of cells with distinct gene expression profiles. (B) Differentially expressed genes in each gene expression (GEX) cluster. (C) Distribution of cells between GEX clusters is plotted for each donor; clusters of healthy donors do not contain Tfh cells (populations 1 and 2). (D) Boxplots depicting the fraction of cells among functional clusters for each participant (Mann-Whitney U test; Bonferroni multiple comparison correction, ∗p < 0.05, ∗∗p < 0.005, ∗∗∗p < 0.0005).

Figure 2

Reverse epitope discovery of SARS-Cov-2-reactive public CD4⁺ T cell clonotypes (A) Volcano plot shows enrichment of TCRbeta chains from merged single-cell TCR sequencing (scTCR-seq) datasets in a large (n = 1,414) collection of bulk TCRbeta repertoires from COVID-19 patients (purple) in comparison to the healthy donor cohort from Emerson et al. (n = 786) (x axis) versus p value (y axis). ns, not significant. (B) Barplot showing the distribution of COVID-enriched (purple) and COVID-depleted (green) TCR clonotypes in GEX clusters. Fisher’s exact test was used for the comparison, with Bonferroni multiple comparison correction. ∗p < 0.05, ∗∗p < 0.005, ∗∗∗p < 0.0005, ns, not significant. (C) The boxplots show the fraction of donors from healthy and COVID-19 cohorts sharing significantly COVID-depleted (green) and COVID-enriched (purple) clonotypes. (D) A similarity network of COVID-associated public TCR clonotypes. Each vertex represents a TCR alpha/beta clonotype, and edges connect vertices with <120 TCRdist units. Colors show predicted specificity to SARS-CoV-2 peptide pools from the MIRA class II dataset. Bottom: TCRdist logos for the most prominent clonotype clusters with predicted peptide specificity and HLA restriction are shown. (E) Manhattan plot for association of representative clonotypes from cluster 2 with various HLA types. (F) A tree map showing the fraction of T cells of the merged single-cell dataset carrying clonotypes from the prominent TCR similarity clusters from (C). (G) Occurrence of TCRbeta from six large clusters from (C) prior to and following SARS-CoV-2 vaccination with the ChAdOx1 (AstraZeneca) vaccine. Significantly more TCRs from spike specific (clusters 2 and 5) are found after vaccination (one-sided Wilcoxon rank-sum test with Benjamini-Hochberg multiple testing correction).

Figure 3

Results of TCR specificity validation experiment (A) Gating strategy. Jurkat activation is tracked by GFP expression under NFAT control. (B) Peptides triggering the response for each analyzed cell line are shown on the corresponding regions of SARS-CoV-2 M, N, and S proteins. The height of the bars indicates the percentage of antigen-specific response of the NFAT-GFP TCR transgenic Jurkat cell lines in co-culture with PBMCs from healthy donors pulsed with overlapping 17-mer peptides covering the predicted antigenic region. Dashed lines show the background activation level of the corresponding transgenic Jurkat cell line in an unstimulated sample. The previously computationally predicted epitope and HLA are indicated above each plot in blue or red lines for weak and strong predicted HLA binders, respectively. (C) HLA restriction prediction by NetMHC: the identified immunogenic peptides are computationally tested for HLA binding against the HLA alleles present on the donor PBMCs used in the experiment; colors show peptides overlapping with weak (blue) and strong (red) HLA-binding cores.