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..

Coronavirus disease 2019 (COVID-19) is characterized by a high incidence of acute respiratory failure. The underlying immunopathology of that failure and how it compares to other causes of severe respiratory distress, such as influenza virus infection, are not fully understood. Here we addressed this by developing a prospective observational cohort of COVID-19 and influenza subjects with varying degrees of disease severity and assessing the quality and magnitude of their immune responses at the cellular and protein level. Additionally, we performed single-cell RNA transcriptional profiling of peripheral blood mononuclear cells from select subjects. The cohort consists of 79 COVID-19 subjects, 26 influenza subjects, and 15 control subjects, including 35 COVID-19 and 7 influenza subjects with acute respiratory failure. While COVID-19 subjects exhibited largely equivalent or greater activated lymphocyte counts compared to influenza subjects, they had fewer monocytes and lower surface HLA-class II expression on monocytes compared to influenza subjects and controls. At least two distinct immune profiles were observed by cytokine levels in severe COVID-19 patients: 3 of 71 patients were characterized by extreme inflammation, with greater than or equal to ~50% of the 35 cytokines measured greater than 2 standard deviations from the mean level of other severe patients (both influenza and COVID-19); the other immune profile, which characterized 68 of 71 subjects, had a mixed inflammatory signature, where 28 of 35 cytokines in COVID-19 patients had lower mean cytokine levels, though not all were statistically significant. Only 2 cytokines were higher in COVID-19 subjects compared to influenza subjects (IL-6 and IL-8). Influenza and COVID-19 patients could be distinguished statistically based on cytokine module expression, particularly after controlling for the significant effects of age on cytokine expression, but again with lower levels of most cytokines in COVID-19 subjects. Further, high circulating levels of IL-1RA and IL-6 were associated with increased odds of intubation in the combined influenza and COVID-19 cohort [OR = 3.93 and 4.30, respectively] as well as among only COVID-19 patients. Single cell transcriptional profiling of COVID-19 and influenza subjects with respiratory failure identified profound suppression in type I and type II interferon signaling in COVID-19 patients across multiple clusters. In contrast, COVID-19 cell clusters were enriched for alterations in metabolic, stress, and apoptotic pathways. These alterations were consistent with an increased glucocorticoid response in COVID-19 patients compared to influenza. When considered across the spectrum of innate and adaptive immune profiles, the immune pathologies underlying severe influenza and COVID-19 are substantially distinct. The majority of COVID-19 patients with acute respiratory failure do not have a cytokine storm phenotype but instead exhibit profound type I and type II IFN immunosuppression when compared to patients with acute influenza. Upregulation of a small number of inflammatory mediators, including IL-6, predicts acute respiratory failure in both COVID-19 and influenza patients.

As the mechanistic basis of adaptive cellular antigen recognition, 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 antigen-enriched repertoires to form meta-clonotypes – groups of biochemically similar TCRs – that can be used to robustly quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1,915 public TCR meta-clonotypes from the 18 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 44% (845/1915) were significantly enriched among COVID-19 patients that expressed the putative restricting HLA allele, 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 workflows for distance-based TCR repertoire analysis.

Every T cell receptor (TCR) repertoire is shaped by a complex probabilistic tangle of genetically determined biases and immune exposures. T cells combine a random V(D)J recombination process with a selection process to generate highly diverse and functional TCRs. The extent to which an individual’s genetic background is associated with their resulting TCR repertoire diversity has yet to be fully explored. Using a previously published repertoire sequencing dataset paired with high-resolution genome-wide genotyping from a large human cohort, we infer specific genetic loci associated with V(D)J recombination probabilities using genome-wide association inference. We show that V(D)J gene usage profiles are associated with variation in the TCRB locus and, specifically for the functional TCR repertoire, variation in the major histocompatibility complex locus. Further, we identify specific variations in the genes encoding the Artemis protein and the TdT protein to be associated with biasing junctional nucleotide deletion and N-insertion, respectively. These results refine our understanding of genetically-determined TCR repertoire biases by confirming and extending previous studies on the genetic determinants of V(D)J gene usage and providing the first examples of trans genetic variants which are associated with modifying junctional diversity. Together, these insights lay the groundwork for further explorations into how immune responses vary between individuals.