Clinical Validation of a Novel T-cell Receptor Sequencing Assay for Identification of Recent or Prior SARS-CoV-2 Infection.

BACKGROUND: While diagnostic, therapeutic, and vaccine development in the COVID-19 pandemic has proceeded at unprecedented speed, critical gaps in our understanding of the immune response to SARS-CoV-2 remain unaddressed by current diagnostic strategies. METHODS: A statistical classifier for identifying prior SARS-CoV-2 infection was trained using >4000 SARS-CoV-2-associated TCRß sequences identified by comparing 784 cases and 2447 controls from 5 independent cohorts. The T-Detect™ COVID assay applies this classifier to TCR repertoires sequenced from blood samples to yield a binary assessment of past infection. Assay performance was assessed in 2 retrospective (n = 346; n = 69) and 1 prospective cohort (n = 87) to determine positive percent agreement (PPA) and negative percent agreement (NPA). PPA was compared to 2 commercial serology assays, and pathogen cross-reactivity was evaluated. RESULTS: T-Detect COVID demonstrated high PPA in individuals with prior RT-PCR-confirmed SARS-CoV-2 infection (97.1% 15 + days from diagnosis; 94.5% 15 + days from symptom onset), high NPA (∼100%) in presumed or confirmed SARS-CoV-2 negative cases, equivalent or higher PPA than 2 commercial serology tests, and no evidence of pathogen cross-reactivity. CONCLUSION: T-Detect COVID is a novel T-cell immunosequencing assay demonstrating high clinical performance for identification of recent or prior SARS-CoV-2 infection from blood samples, with implications for clinical management, risk stratification, surveillance, and understanding protective immunity and long-term sequelae.

Case report: SARS-CoV-2 specific T-cells are associated with myocarditis after COVID-19 vaccination with mRNA-1273.

Vaccination of SARS-CoV-2 with BNT162b2 or mRNA-1273 both have a low incidence of induction of myocarditis. Here we report on utilizing adaptive immune receptor repertoire sequencing (AIRR-Seq) as a way to assess the specificity of tissue infiltrating immune cells.

Case report: Immunological characteristics of de novo ulcerative colitis in a child post COVID-19.

The pathological mechanisms of de novo inflammatory bowel disease (IBD) following SARS-CoV-2 infection are unknown. However, cases of coexisting IBD and multisystem inflammatory syndrome in children (MIS-C), which occurs 2-6 weeks after SARS-CoV-2 infection, have been reported, suggesting a shared underlying dysfunction of immune responses. Herein, we conducted the immunological analyses of a Japanese patient with de novo ulcerative colitis following SARS-CoV-2 infection based on the pathological hypothesis of MIS-C. Her serum level of lipopolysaccharide-binding protein, a microbial translocation marker, was elevated with T cell activation and skewed T cell receptor repertoire. The dynamics of activated CD8+ T cells, including T cells expressing the gut-homing marker α4ß7, and serum anti-SARS-CoV-2 spike IgG antibody titer reflected her clinical symptoms. These findings suggest that SARS-CoV-2 infection may trigger the de novo occurrence of ulcerative colitis by impairing intestinal barrier function, T cell activation with a skewed T cell receptor repertoire, and increasing levels of anti-SARS-CoV-2 spike IgG antibodies. Further research is needed to clarify the association between the functional role of the SARS-CoV-2 spike protein as a superantigen and ulcerative colitis.

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

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 αßTCRs 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.

Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor diversity.

T cells play a crucial role in combatting SARS-CoV-2 and forming long-term memory responses to this coronavirus. The emergence of SARS-CoV-2 variants that can evade T cell immunity has raised concerns about vaccine efficacy and the risk of reinfection. Some SARS-CoV-2 T cell epitopes elicit clonally restricted CD8+ T cell responses characterized by T cell receptors (TCRs) that lack structural diversity. Mutations in such epitopes can lead to loss of recognition by most T cells specific for that epitope, facilitating viral escape. Here, we studied an HLA-A2-restricted spike protein epitope (RLQ) that elicits CD8+ T cell responses in COVID-19 convalescent patients characterized by highly diverse TCRs. We previously reported the structure of an RLQ-specific TCR (RLQ3) with greatly reduced recognition of the most common natural variant of the RLQ epitope (T1006I). Opposite to RLQ3, TCR RLQ7 recognizes T1006I with even higher functional avidity than the WT epitope. To explain the ability of RLQ7, but not RLQ3, to tolerate the T1006I mutation, we determined structures of RLQ7 bound to RLQ-HLA-A2 and T1006I-HLA-A2. These complexes show that there are multiple structural solutions to recognizing RLQ and thereby generating a clonally diverse T cell response to this epitope that assures protection against viral escape and T cell clonal loss.

T Cell Receptor Sequences Amplified during Severe COVID-19 and Multisystem Inflammatory Syndrome in Children Mimic SARS-CoV-2, Its Bacterial Co-Infections and Host Autoantigens.

Published hypervariable region V-beta T cell receptor (TCR) sequences were collected from people with severe COVID-19 characterized by having various autoimmune complications, including blood coagulopathies and cardiac autoimmunity, as well as from patients diagnosed with the Kawasaki disease (KD)-like multisystem inflammatory syndrome in children (MIS-C). These were compared with comparable published v-beta TCR sequences from people diagnosed with KD and from healthy individuals. Since TCR V-beta sequences are supposed to be complementary to antigens that induce clonal expansion, it was surprising that only a quarter of the TCR sequences derived from severe COVID-19 and MIS-C patients mimicked SARS-CoV-2 proteins. Thirty percent of the KD-derived TCR mimicked coronaviruses other than SARS-CoV-2. In contrast, only three percent of the TCR sequences from healthy individuals and those diagnosed with autoimmune myocarditis displayed similarities to any coronavirus. In each disease, significant increases were found in the amount of TCRs from healthy individuals mimicking specific bacterial co-infections (especially Enterococcus faecium, Staphylococcal and Streptococcal antigens) and host autoantigens targeted by autoimmune diseases (especially myosin, collagen, phospholipid-associated proteins, and blood coagulation proteins). Theoretical explanations for these surprising observations and implications to unravel the causes of autoimmune diseases are explored.

CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes.

Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.

pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research.

T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries.

Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19.

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV-2-specific naïve T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. A novel population of naïve interferon-activated T cells is expanded in acute COVID-19 and is recruited into the memory compartment during convalescence in adults but not children. This was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases.

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Haploidentical CD3+ TCR αß/CD19+ depleted HSCT for MHC Class II deficiency and persistent SARS-CoV-2 pneumonitis.

Background & Objectives: SARS-CoV-2 infection leads to coronavirus disease 2019 (COVID-19), which can range from a mild illness to a severe phenotype characterised by acute respiratory distress, needing mechanical ventilation. Children with combined immunodeficiencies might be unable to mount a sufficient cellular and humoral immune response against Covid-19 and have persistent disease. The authors describe a child with combined immunodeficiency, with favorable post-HSCT course following a haploidentical haematopoietic stem cell transplant in the presence of persistent SARS-CoV-2 infection. Methods & results: A 13-month-old girl with MHC class II deficiency developed persistent pre-HSCT SARS-CoV-2 infection. Faced with a significant challenge of balancing the risk of progressive infection due to incompetent immune system with the danger of inflammatory pneumonitis peri-immune reconstitution post-HSCT, she underwent a maternal (with a recent history of Covid-19 infection) haploidentical haematopoietic stem cell transplant. The patient received Regdanvimab® (post stem cell infusion) and Remdesivir (pre and post stem cell infusion). We noted a gradual increase in the Ct (cycle threshold) values, implying reduction in viral RNA with concomitant expansion in the CD3 lymphocyte subset and clinical/radiological improvement. Conclusions: Combination of adoptive transfer of maternal CD45RO+ memory add-back T-lymphocytes after haploidentical HSCT, use of Regdanvimab® (SARS-CoV-2 neutralising monoclonal antibody) and Remdesivir may have led to the successful outcome in our patient with severe immunodeficiency, undergoing HSCT. Our case highlights the role of novel antiviral strategies (monoclonal antibodies and CD45RO+ memory T-lymphocytes) in contributing to viral clearance in a challenging clinical scenario.

Immunological characteristics of severe acute hepatitis of unknown origin in a child post SARS-CoV-2 infection.

Recent studies have reported that pediatric acute liver failure of unknown origin is immune-mediated, with CD8+ T cells playing a key role. Moreover, investigation of superantigen-mediated T-cell activation by the SARS-CoV-2 spike protein in pediatric severe acute hepatitis is needed in the context of the proposed mechanism of multisystem inflammatory syndrome in children (MIS-C). We investigated the immunological characteristics of a Japanese pediatric patient with severe acute hepatitis post SARS-CoV-2 infection. The patient demonstrated autoimmune hepatitis-like liver histology with CD8+ lymphocyte-predominant infiltration. There was Th1-type immune skewing, including remarkable peripheral CD8+ T-cell activation and a skewed T cell receptor repertoire. We also found elevated plasma levels of the anti-SARS-CoV-2 spike-specific IgG antibody, and the titer peaked after treatment, as seen with MIS-C. These findings support that immunological activation involving SARS-CoV-2 spike protein plays a crucial role in a pediatric patient with acute severe hepatitis post SARS-CoV-2 infection.

Toward a fractalomic idiotype/anti-idiotypic paradigm.

The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.

Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses.

The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.

Antibody and T-cell responses by ultra-deep T-cell receptor immunosequencing after COVID-19 vaccination in patients with plasma cell dyscrasias.

We investigated antibody and coronavirus disease 2019 (COVID-19)-specific T-cell mediated responses via ultra-deep immunosequencing of the T-cell receptor (TCR) repertoire in patients with plasma cell dyscrasias (PCD). We identified 364 patients with PCD who underwent spike antibody testing using commercially available spike-receptor binding domain immunoglobulin G antibodies ≥2 weeks after completion of the initial two doses of mRNA vaccines or one dose of JNJ-78436735. A total of 56 patients underwent TCR immunosequencing after vaccination. Overall, 86% tested within 6 months of vaccination had detectable spike antibodies. Increasing age, use of anti-CD38 or anti-B-cell maturation antigen therapy, and receipt of BNT162b2 (vs. mRNA-1273) were associated with lower antibody titres. We observed an increased proportion of TCRs associated with surface glycoprotein regions of the COVID-19 genome after vaccination, consistent with spike-specific T-cell responses. The median spike-specific T-cell breadth was 3.11 × 10^-5 , comparable to those in healthy populations after vaccination. Although spike-specific T-cell breadth correlated with antibody titres, patients without antibody responses also demonstrated spike-specific T-cell responses. Patients receiving mRNA-1273 had higher median spike-specific T-cell breadth than those receiving BNT162b2 (p = 0.01). Although patients with PCD are often immunocompromised due to underlying disease and treatments, COVID-19 vaccination can still elicit humoral and T-cell responses and remain an important intervention in this patient population.

Machine Learning Approaches to TCR Repertoire Analysis.

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

SARS-CoV-2-specific T cell responses in patients with multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infections that occurs in the pediatric population. We sought to characterize T cell responses in MIS-C compared to COVID-19 and pediatric hyperinflammatory syndromes. MIS-C was distinct from COVID-19 and hyperinflammatory syndromes due to an expansion of T cells expressing TRBV11-2 that was not associated with HLA genotype. Children diagnosed with MIS-C, but who were negative for SARS-CoV-2 by PCR and serology, did not display Vß skewing. There was no difference in the proportion of T cells that became activated after stimulation with SARS-CoV-2 peptides in children with MIS-C compared to convalescent COVID-19. The frequency of SARS-CoV-2-specific TCRs and the antigens recognized by these TCRs were comparable in MIS-C and COVID-19. Expansion of Vß11-2⁺ T cells was a specific biomarker of MIS-C patients with laboratory confirmed SARS-CoV-2 infections. Children with MIS-C had robust antigen-specific T cell responses to SARS-CoV-2.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis.

Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.

Broadly recognized, cross-reactive SARS-CoV-2 CD4 T cell epitopes are highly conserved across human coronaviruses and presented by common HLA alleles.

Sequence homology between SARS-CoV-2 and common-cold human coronaviruses (HCoVs) raises the possibility that memory responses to prior HCoV infection can affect T cell response in COVID-19. We studied T cell responses to SARS-CoV-2 and HCoVs in convalescent COVID-19 donors and identified a highly conserved SARS-CoV-2 sequence, S811-831, with overlapping epitopes presented by common MHC class II proteins HLA-DQ5 and HLA-DP4. These epitopes are recognized by low-abundance CD4 T cells from convalescent COVID-19 donors, mRNA vaccine recipients, and uninfected donors. TCR sequencing revealed a diverse repertoire with public TCRs. T cell cross-reactivity is driven by the high conservation across human and animal coronaviruses of T cell contact residues in both HLA-DQ5 and HLA-DP4 binding frames, with distinct patterns of HCoV cross-reactivity explained by MHC class II binding preferences and substitutions at secondary TCR contact sites. These data highlight S811-831 as a highly conserved CD4 T cell epitope broadly recognized across human populations.