Identifying immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors (preprint)

Memory T cells are records of clonal expansion from prior immune exposures, such as infections, vaccines and chronic diseases like cancer. A subset of the receptors of these expanded T cells in a typical immune repertoire are highly public, i.e., present in many individuals exposed to the same exposure. For the most part, the exposures associated with these public T cells are unknown. To identify public T-cell receptor signatures of immune exposures, we mined the immunosequencing repertoires of tens of thousands of donors to define clusters of co-occurring T cells. We first built co-occurrence clusters of T cells responding to antigens presented by the same Human Leukocyte Antigen (HLA) and then combined those clusters across HLAs. Each cross-HLA cluster putatively represents the public T-cell signature of a single prevalent exposure. Using repertoires from donors with known serological status for 7 prevalent exposures (HSV-1, HSV-2, EBV, Parvovirus, Toxoplasma gondii, Cytomegalovirus and SARS CoV-2), we identified a single T-cell cluster strongly associated with each exposure and used it to construct a highly sensitive and specific diagnostic model for the exposure. These T-cell clusters constitute the public immune responses to prevalent exposures, 7 known and many others unknown. By learning the exposure associations for more T cell clusters, this approach could be used to derive a ledger of a person's past and present immune exposures.

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires (preprint)

Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-specific TCRs. Population genetics and sequencing depth can also have strong systematic effects on repertoires, which requires careful consideration when developing diagnostic models. We present an Adaptive Immune Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that learns a low-dimensional, interpretable, and compositional representation of TCR repertoires to disentangle such systematic effects in repertoires. We apply AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically show that we can disentangle the individual disease signals. We further demonstrate AIRIVA's capability to: learn from unlabelled samples; generate in-silico TCR repertoires by intervening on the latent factors; and identify disease-associated TCRs validated using TCR annotations from external assay data.

CD8+ T cell clonotypes from prior SARS-CoV-2 infection predominate during the cellular immune response to mRNA vaccination (preprint)

Almost three years into the SARS-CoV-2 pandemic, hybrid immunity is highly prevalent worldwide and more protective than vaccination or prior infection alone. Given emerging resistance of variant strains to neutralizing antibodies (nAb), it is likely that T cells contribute to this protection. To understand how sequential SARS-CoV-2 infection and mRNA-vectored SARS-CoV-2 spike (S) vaccines affect T cell clonotype-level expansion kinetics, we identified and cross-referenced TCR sequences from thousands of S-reactive single cells against deeply sequenced peripheral blood TCR repertoires longitudinally collected from persons during COVID-19 convalescence through booster vaccination. Successive vaccinations recalled memory T cells and elicited antigen-specific T cell clonotypes not detected after infection. Vaccine-related recruitment of novel clonotypes and the expansion of S-specific clones were most strongly observed for CD8+ T cells. Severe COVID-19 illness was associated with a more diverse CD4+ T cell response to SARS-CoV-2 both prior to and after mRNA vaccination, suggesting imprinting of CD4+ T cells by severe infection. TCR sequence similarity search algorithms revealed myriad public TCR clusters correlating with human leukocyte antigen (HLA) alleles. Selected TCRs from distinct clusters functionally recognized S in the predicted HLA context, with fine viral peptide requirements differing between TCRs. Most subjects tested had S-specific T cells in the nasal mucosa after a 3rd mRNA vaccine dose. The blood and nasal T cell responses to vaccination revealed by clonal tracking were more heterogeneous than nAb boosts. Analysis of bulk and single cell TCR sequences reveals T cell kinetics and diversity at the clonotype level, without requiring prior knowledge of T cell epitopes or HLA restriction, providing a roadmap for rapid assessment of T cell responses to emerging pathogens.

Immunosequencing and epitope mapping reveal substantial preservation of the T cell immune response to Omicron generated by SARS-CoV-2 vaccines (preprint)

The Omicron SARS-CoV-2 variant contains 34 mutations in the spike gene likely impacting protective efficacy from vaccines. We evaluated the potential impact of these mutations on the cellular immune response. Combining epitope mapping to SARS-CoV-2 vaccines that we have determined from past experiments along with T cell receptor (TCR) repertoire sequencing from thousands of vaccinated or naturally infected individuals, we estimate the abrogation of the cellular immune response in Omicron. Although 20% of CD4+ T cell epitopes are potentially affected, the loss of immunity mediated by CD4+ T cells is estimated to be slightly above 30% as some of the affected epitopes are relatively more immunogenic. For CD8+ T cells, we estimate a loss of approximately 20%. These reductions in T cell immunity are substantially larger than observed in other widely distributed variants. Combined with the expected substantial loss of neutralization from antibodies, the overall protection provided by SARS-CoV-2 vaccines could be impacted adversely. From analysis of prior variants, the efficacy of vaccines against symptomatic infection has been largely maintained and is strongly correlated with the T cell response but not as strongly with the neutralizing antibody response. We expect the remaining 70% to 80% of on-target T cells induced by SARS-CoV-2 vaccination to reduce morbidity and mortality from infection with Omicron.

The T-cell clonal response to SARS-CoV-2 vaccination in inflammatory bowel disease patients is augmented by anti-TNF therapy and often deficient in antibody-responders (preprint)

BackgroundVaccination against SARS-CoV-2 is a highly effective strategy to protect against infection, which is predominantly mediated by vaccine-induced antibodies. Postvaccination antibodies are robustly produced by those with inflammatory bowel disease (IBD) even on immune-modifying therapies but are blunted by anti-TNF therapy. In contrast, T-cell response which primarily determines long-term efficacy against disease progression,, is less well understood. We aimed to assess the post-vaccination T-cell response and its relationship to antibody responses in patients with inflammatory bowel disease (IBD) on immune-modifying therapies. MethodsWe evaluated IBD patients who completed SARS-CoV-2 vaccination using samples collected at four time points (dose 1, dose 2, 2 weeks after dose 2, 8 weeks after dose 2). T-cell clonal analysis was performed by T-cell Receptor (TCR) immunosequencing. The breadth (number of unique sequences to a given protein) and depth (relative abundance of all the unique sequences to a given protein) of the T-cell clonal response were quantified using reference datasets and were compared to antibody responses. ResultsOverall, 303 subjects were included (55% female; 5% with prior COVID) (Table). 53% received BNT262b (Pfizer), 42% mRNA-1273 (Moderna) and 5% Ad26CoV2 (J&J). The Spike-specific clonal response peaked 2 weeks after completion of the vaccine regimen (3- and 5-fold for breadth and depth, respectively); no changes were seen for non-Spike clones, suggesting vaccine specificity. Reduced T-cell clonal depth was associated with chronologic age, male sex, and immunomodulator treatment. It was preserved by non-anti-TNF biologic therapies, and augmented clonal depth was associated with anti-TNF treatment. TCR depth and breadth were associated with vaccine type; after adjusting for age and gender, Ad26CoV2 (J&J) exhibited weaker metrics than mRNA-1273 (Moderna) (p=0.01 for each) or BNT262b (Pfizer) (p=0.056 for depth). Antibody and T-cell responses were only modestly correlated. While those with robust humoral responses also had robust TCR clonal expansion, a substantial fraction of patients with high antibody levels had only a minimal T-cell clonal response. ConclusionAge, sex and select immunotherapies are associated with the T-cell clonal response to SARS-CoV-2 vaccines, and T-cell responses are low in many patients despite high antibody levels. These factors, as well as differences seen by vaccine type may help guide reimmunization vaccine strategy in immune-impaired populations. Further study of the effects of anti-TNF therapy on vaccine responses are warranted.

T-cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibody titers and disease severity (preprint)

Measuring the adaptive immune response to SARS-CoV-2 can enable the assessment of past infection as well as protective immunity and the risk of reinfection. While neutralizing antibody (nAb) titers are one measure of protection, such assays are challenging to perform at a large scale and the longevity of the SARS-CoV-2 nAb response is not fully understood. Here, we apply a T-cell receptor (TCR) sequencing assay that can be performed on a small volume standard blood sample to assess the adaptive T-cell response to SARS-CoV-2 infection. Samples were collected from a cohort of 302 individuals recovered from COVID-19 up to 6 months after infection. Previously published findings in this cohort showed that two commercially available SARS-CoV-2 serologic assays correlate well with nAb testing. We demonstrate that the magnitude of the SARS-CoV-2-specific T-cell response strongly correlates with nAb titer, as well as clinical indicators of disease severity including hospitalization, fever, or difficulty breathing. While the depth and breadth of the T-cell response declines during convalescence, the T-cell signal remains well above background with high sensitivity up to at least 6 months following initial infection. Compared to serology tests detecting binding antibodies to SARS-CoV-2 spike and nucleoprotein, the overall sensitivity of the TCR-based assay across the entire cohort and all timepoints was approximately 5% greater for identifying prior SARS-CoV-2 infection. Notably, the improved performance of T-cell testing compared to serology was most apparent in recovered individuals who were not hospitalized and were sampled beyond 150 days of their initial illness, suggesting that antibody testing may have reduced sensitivity in individuals who experienced less severe COVID-19 illness and at later timepoints. Finally, T-cell testing was able to identify SARS-CoV-2 infection in 68% (55/81) of convalescent samples having nAb titers below the lower limit of detection, as well as 37% (13/35) of samples testing negative by all three antibody assays. These results demonstrate the utility of a TCR-based assay as a scalable, reliable measure of past SARS-CoV-2 infection across a spectrum of disease severity. Additionally, the TCR repertoire may be useful as a surrogate for protective immunity with additive clinical value beyond serologic or nAb testing methods.

Diagnosis and Tracking of Past SARS-CoV-2 Infection in a Large Study of Vo', Italy Through T-Cell Receptor Sequencing (preprint)

Measuring the adaptive immune response after SARS-CoV-2 infection may improve our understanding of COVID-19 exposure and potential future protection or immunity. We analyzed T-cell and antibody signatures in a large population study of over 2,200 individuals from the municipality of Vo', Italy, including 70 PCR-confirmed COVID cases (24 asymptomatic, 37 symptomatic, 9 hospitalized). Blood samples taken 60 days after PCR diagnosis demonstrated 97% (68/70) of the latter subjects had a positive T-cell test result, higher than an antibody serology assay (77%; 54/70 of subjects) performed on the same samples. The depth and breadth of the T-cell response was associated with disease severity, with symptomatic and hospitalized COVID cases having significantly higher response than asymptomatic cases. In contrast, antibody levels at this convalescent time point were less informative as they did not correlate with disease severity. 45 additional suspected infections were identified based on T-cell response from the 2,220 subjects without confirmatory PCR tests. Among these, notably, subjects who reported symptoms or had household exposure to a PCR-confirmed infection presented a higher T-cell test positive rate. Taken together, these results establish that T cells are a sensitive, reliable and persistent measure of past SARS-CoV-2 infection.

Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels (preprint)

T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,015 samples (from 827 COVID-19 subjects) as well as 3,500 controls to identify shared "public" T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3-7 = 83.8% [95% CI = 77.6-89.4]; Day 8-14 = 92.4% [87.6-96.6]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 96.7% [93.0-99.2]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in vaccine development as well as clinical diagnostics and monitoring.