T cells are defined by a heterodimeric surface receptor (the T cell receptor or TCR) that mediates recognition of pathogen-associated epitopes via interactions with peptide-major histocompatibility complexes (pMHC). TCRs are generated by genomic rearrangements of the germline TCR locus, a process termed V(D)J recombination that has the potential to generate a staggering diversity of TCRs (estimated to range from 1015 1 to as high as 1061 2 possible receptors). Despite this potential diversity, TCRs from T cells that recognize the same pMHC epitope often share conserved sequence features, suggesting that it may be possible to predictively model epitope specificity. Here we report the in-depth characterization of ten epitope-specific CD8+ TCR repertoires from mice and humans representing 4600+ in-frame, single cell-derived TCRαβ sequence pairs from 110 subjects. We developed novel analytical tools to characterize these epitope-specific repertoires: a distance measure on the space of TCRs that permits clustering and visualization (TCRdist), a robust repertoire diversity metric (TCRdiv) that accommodates the low number of paired public receptors observed when compared to single chain analyses, and a distance-based classifier capable of assigning previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Our analysis demonstrates that each epitope-specific repertoire contains a clustered group of receptors that share core sequence similarities, together with a dispersed set of diverse “outlier” sequences. By identifying shared motifs in core sequences, we were able to highlight key conserved residues driving essential elements of TCR recognition. These analyses provide insights into the generalizable, underlying features of epitope-specific repertoires and adaptive immune recognition.
We pursued a study of immune responses in coronavirus disease 2019 (COVID-19) and influenza patients. Compared to patients with influenza, patients with COVID-19 exhibited largely equivalent lymphocyte counts, fewer monocytes, and lower surface human leukocyte antigen (HLA)–class II expression on selected monocyte populations. Furthermore, decreased HLA-DR on intermediate monocytes predicted severe COVID-19 disease. In contrast to prevailing assumptions, very few (7 of 168) patients with COVID-19 exhibited cytokine profiles indicative of cytokine storm syndrome. After controlling for multiple factors including age and sample time point, patients with COVID-19 exhibited lower cytokine levels than patients with influenza. Up-regulation of IL-6, G-CSF, IL-1RA, and MCP1 predicted death in patients with COVID-19 but were not statistically higher than patients with influenza. Single-cell transcriptional profiling revealed profound suppression of interferon signaling among patients with COVID-19. When considered across the spectrum of peripheral immune profiles, patients with COVID-19 are less inflamed than patients with influenza.
Although mRNA vaccine efficacy against severe COVID-19 remains high, variant emergence has prompted booster immunizations. However, repeated antigen exposure effects on SARS-CoV-2 memory T cells are poorly understood. Here, we utilize MHC-multimers with scRNAseq to profile SARS-CoV-2-responsive T cells ex vivo from humans with one, two, or three antigen exposures, including vaccination, primary, and breakthrough infection. Exposure order determined the distribution between spike- and non-spike-specific responses, with vaccination after infection leading to expansion of spike-specific T cells and differentiation to CCR7-CD45RA+ effectors. In contrast, individuals after breakthrough infection mount vigorous non-spike-specific responses. Analysis of over 4,000 epitope-specific T cell receptor sequences demonstrates that all exposures elicit diverse repertoires characterized by shared TCR motifs, confirmed by monoclonal TCR characterization, with no evidence for repertoire narrowing from repeated exposure. Our findings suggest that breakthrough infections diversify the T cell memory repertoire and current vaccination protocols continue to expand and differentiate spike-specific memory.
T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes – groups of biochemically similar TCRs – that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.
Multi-modal single-cell technologies capable of simultaneously assaying gene expression and surface phenotype across large numbers of immune cells have described extensive heterogeneity within these complex populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX) profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph analysis or "CoNGA"---that identifies correlations between GEX profile and TCR sequence through statistical analysis of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a potential previously undescribed lineage of "natural lymphocytes"; a striking association between usage of a specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between TCR sequence and cellular state in single-cell applications..
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