TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs

TCRCluster: a novel approach to T-cell receptor latent featurization and clustering using contrastive learning-guided two-stage variational autoencoders

T cells play a vital role in adaptive immunity by targeting pathogen-infected or cancerous cells, but predicting their specificity remains challenging. Encoding T-cell receptor (TCR) sequences into informative feature spaces is therefore crucial for advancing specificity prediction and downstream applications. For this, we developed a variational autoencoder (VAE)-based model trained on paired TCR α-β chain data, incorporating all six complementarity-determining regions. A semi-supervised 'two-stage VAE' framework, integrating cosine triplet loss and a classifier, was found to further refine peptide-specific latent representations, outperforming sequence-based methods in specificity prediction. Clustering analyses leveraging our VAE latent space were evaluated using K-means, agglomerative clustering, and a novel graph-based method. Agglomerative clustering achieved the most biologically relevant results, balancing cluster purity and retention despite noise in TCR specificity annotations. We extended these insights to evaluate TCR repertoire data. Across datasets, VAE-based models outperformed sequence-based methods, particularly in retention metrics, with notable improvements in the SARS-CoV-2 repertoire dataset. Moreover, the cancer repertoire analysis highlighted the generalizability of our approach, where the model displayed high performance despite minimal similarity between the training and test data. Collectively, these results demonstrate the potential of VAE-based latent representations to offer a robust framework for prediction, clustering, and repertoire analysis.

TCREMP: A Bioinformatic Pipeline for Efficient Embedding of T-cell Receptor Sequences from Immune Repertoire and Single-cell Sequencing Data

T-cell receptors (TCRs) expressed by T-lymphocytes can recognize antigens presented on the surface of our cells to tell normal cells from infected and malignant ones. An extremely diverse set of TCRs is generated during T-lymphocyte formation and can be surveyed using high-throughput sequencing to tell the story of past and ongoing immune responses. Proper handling of TCR sequencing data and using it to trace antigen specificities is a challenging bioinformatic task. Here we present TCREMP (TCR embedding via Prototypes), a pipeline to digitize TCR amino acid sequences based on their similarity to commonly observed sequences that can provide a reference point for comparative analysis and aid in decoding antigen specificity prediction across TCR repertoire samples that one may find useful for adaptive immunity studies. Our results show that TCREMP embeddings can efficiently distinguish distinct antigen-specific T-cell populations based on single-cell and Major Histocompatibility Complex (MHCs)-multimer-sorted repertoire sequencing data. TCREMP is freely available at https://github.com/antigenomics/tcremp.

Gradual changes within long-lived influenza virus-specific CD8+ T cells are associated with the loss of public TCR clonotypes in older adults

BACKGROUND

Susceptibility to life-threatening influenza increases with age, partly due to declining immunity. Frequency, phenotype and T-cell receptor (TCR) composition of influenza-specific CD8+ T-cells directed at the prominent A2/M158 influenza epitope change across the human lifespan.

METHODS

We investigated longevity and mechanisms underlying age-related changes in influenza-specific TCR repertoires by performing longitudinal analyses in young and older adults across 7-12 years within A2/M158+CD8+ T-cells using peptide-HLA tetramers directly ex vivo. Paired TCRαβ-chains were used to track clonotypes over time within individuals.

FINDINGS

Expanded public and private TCR clonotypes were long-lived but gradually declined over time. Loss of public clonotypes was initially compensated by expansions of clonotypes expressing public-associated features. Once these public-associated TCR clonotypes were abated in older adults, the void was filled by expansions of less similar private TCR clonotypes. Expanded older private TCR clonotypes also declined over time and were gradually replaced by other private TCR clonotypes with low similarity to public TCR clonotypes detected in adults.

INTERPRETATION

Despite our relatively small cohort, we provided conclusive evidence that CD8+ T-cells to a single HLA-A2-restricted influenza-epitope are long-lived. However, dynamic changes occur at the clonotypic level, which eventually result in loss of public clonotypes, indicating that T-cell-based influenza vaccines are likely more effective in adults than older adults.

FUNDING

This research was supported by the National Health and Medical Research Council (#1173871, #1159272), the Australian Research Council (#190102704), European Union's Horizon 2020 (#792532), the University of Melbourne. Funders had no role in design, analysis or reporting of the study.

Expansion of mammary intraepithelial lymphocytes and intestinal inputs shape T cell dynamics in lactogenesis

Pregnancy brings about profound changes in the mammary gland to prepare for lactation, yet immunocyte changes that accompany this rapid remodeling are incompletely understood. We comprehensively analyzed mammary T cells, revealing a marked increase in CD4+ and CD8+ T effector cells, including an expansion of TCRαβ+CD8αα+ cells, in pregnancy and lactation. T cells were localized in the mammary epithelium, resembling intraepithelial lymphocytes (IELs) typically found in mucosal tissues. Similarity to mucosal tissues was substantiated by demonstrating partial dependence on microbial cues, T cell migration from the intestine to the mammary gland in late pregnancy, and shared TCR clonotypes between intestinal and mammary tissues, including intriguing public TCR families. Putative counterparts of mammary IELs were found in human breast and milk. Mammary T cells are thus poised to manage the transition from a non-mucosal tissue to a mucosal barrier during lactogenesis.

Longitudinal single cell profiling of epitope specific memory CD4+ T cell responses to recombinant zoster vaccine

Vaccination leads to rapid expansion of antigen-specific T cells within in the first few days. However, understanding of transcriptomic changes and fates of antigen-specific T cells upon vaccination remains limited. Here, we investigate the fate of memory CD4+ T cells upon reactivation to recombinant zoster vaccine for shingles at cellular and transcriptional levels. We show that glycoprotein E-specific memory CD4+ T cells respond strongly, their frequencies remain high, and they retain markers of cell activation one year following vaccination. Memory T cells with the most dominant TCR clonotype pre-vaccination remain prevalent at year one post-vaccination. These data implicate a major role for pre-existing memory T cells in perpetuating immune repertoires upon re-encountering cognate antigens. Differential gene expression indicates that cells post-vaccination are distinct from cells at baseline, suggesting committed memory T cells display transcriptional changes upon vaccination that could alter their responses against cognate immunogens.

Comprehensive Analysis of TCR and BCR Repertoires: Insights into Methodologies, Challenges, and Applications

The diversity of T-cell receptors (TCRs) and B-cell receptors (BCRs) underpins the adaptive immune system's ability to recognize and respond to a wide array of antigens. Recent advancements in RNA sequencing have expanded its application beyond transcriptomics to include the analysis of immune repertoires, enabling the exploration of TCR and BCR sequences across various physiological and pathological contexts. This review highlights key methodologies and considerations for TCR and BCR repertoire analysis, focusing on the technical aspects of receptor sequence extraction, data processing, and clonotype identification. We compare the use of bulk and single-cell sequencing, discuss computational tools and pipelines, and evaluate the implications of examining specific receptor regions such as CDR3. By integrating immunology, bioinformatics, and clinical research, immune repertoire analysis provides valuable insights into immune function, therapeutic responses, and precision medicine approaches, advancing our understanding of health and disease.

Altered immune landscape of cervical lymph nodes reveals Epstein-Barr virus signature in multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, and Epstein-Barr virus (EBV) infection is a prerequisite for developing the disease. However, the pathogenic mechanisms that lead to MS remain to be determined. Here, we characterized the immune landscape of deep cervical lymph nodes (dcLNs) in newly diagnosed untreated patients with MS (pwMS) using fine-needle aspirations. By combining single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing, we observed increased memory B cells and reduced germinal center B cells with decreased clonality in pwMS. Double-negative memory B cells were increased in pwMS that transcriptionally resembled B cells with a lytic EBV infection. Moreover, EBV-targeting memory CD8 T cells were detected in a subset of pwMS. We also detected increased EBV DNA in dcLNs and elevated viral loads in patient saliva. These findings suggest that EBV-driven B cell dysregulation is a critical mechanism in MS pathogenesis.

Identification of indirect CD4+ T cell epitopes associated with transplant rejection provides a target for donor-specific tolerance induction

Antibodies against the donor human leukocyte antigen (HLA) molecules drive late transplant failure, with HLA-DQ donor-specific antibodies (DSAs) posing the highest rejection risk. Here, we investigated the role of indirect CD4+ T cell epitopes-donor-derived peptides presented by recipient major histocompatibility complex (MHC) class II-in DSA formation. Antigen mapping of samples from HLA-DQ DSA-positive kidney and heart transplant recipients revealed two polymorphic hotspots in donor HLA-DQ that generated alloreactive peptides. Antigen mapping of indirect CD4+ T cell epitopes in a mouse model of fully MHC mismatched skin graft transplantation (BALB/c to C57BL/6) identified a similar epitope (amino acids 287-301) derived from the donor H2-Kd. Tetramer-binding Kd287+ CD4+ T cells were detected during rejection and their transfer into T cell-deficient mice induced DSA. Systemic delivery of high-dose donor H2-Kd peptides combined with CTLA4-Ig reduced the frequencies of Kd287+ CD4+ T cells and DSA formation. Thus, targeting a narrow range of donor antigens may prevent DSA formation and improve transplant outcomes.

Contrastive learning of T cell receptor representations

Computational prediction of the interaction of T cell receptors (TCRs) and their ligands is a grand challenge in immunology. Despite advances in high-throughput assays, specificity-labeled TCR data remain sparse. In other domains, the pre-training of language models on unlabeled data has been successfully used to address data bottlenecks. However, it is unclear how to best pre-train protein language models for TCR specificity prediction. Here, we introduce a TCR language model called SCEPTR (simple contrastive embedding of the primary sequence of T cell receptors), which is capable of data-efficient transfer learning. Through our model, we introduce a pre-training strategy combining autocontrastive learning and masked-language modeling, which enables SCEPTR to achieve its state-of-the-art performance. In contrast, existing protein language models and a variant of SCEPTR pre-trained without autocontrastive learning are outperformed by sequence alignment-based methods. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity. A record of this paper's transparent peer review process is included in the supplemental information.

Exploring the Relationship Between Humoral and Cellular T Cell Responses Against SARS-CoV-2 in Exposed Individuals From Emilia Romagna Region and COVID-19 Severity

COVID-19 remains a significant global health problem with uncertain long-term consequences for convalescents. We investigated the relationships between anti-N protein antibody levels, severe acute respiratory syndrome (SARS)-CoV-2-associated TCR repertoire parameters, HLA type and epidemiological information from three cohorts of 524 SARS-CoV-2-infected subjects subgrouped in acute phase, seronegative and seropositive convalescents from the Emilia Romagna region. Epidemiological information and anti-N antibody index were associated with TCR repertoire data. HLA type was inferred from TCR repertoire using the HLA3 tool and its association with clonal breadth (CB) and clonal depth (CD) was assessed. Age above 58 years, male and COVID-19 hospitalisation were significantly and independently associated with seropositivity (p = 0.004; p = 0.004; p = 0.04), suggesting an association between high antibody titres and symptoms' severity. As for the TCR repertoire analysis, we found no difference in CB among the cohorts, while CD was higher in seronegative than acute (p = 0.04). However, clustering analysis supported that seronegative patients are endowed with broader CB and deeper CD indicating a compensatory mechanism without effective seroconversion. The CD calculated on the TCRs associated with the single SARS-CoV-2 ORFs in convalescents is higher when compared to the acute. Lastly, we identified and reported on novel HLAs significantly associated with increased risk of hospitalisation such as HLA-C*07:02 carriers (OR = 3.9, CI = 1.1-13.4, p = 0.03) and on HLAs that associate significantly with lower or higher TCR repertoire parameters in a population exposed for the first time to SARS-CoV-2.

NK cells restrain cytotoxic CD8+ T cells in the submandibular gland via PD-1-PD-L1

The increasing use of anti-programmed cell death 1 (PD-1) immune checkpoint blockade has led to the emergence of immune-related adverse events (irAEs), including dysfunction of the submandibular gland (SMG). In this study, we investigated the immunoregulatory mechanism contributing to the susceptibility of the SMG to irAEs. We found that the SMGs of PD-1-deficient mice and anti-programmed cell death ligand 1 (PD-L1)-treated mice harbor an expanded population of CD8+ T cells. We demonstrate that natural killer (NK) cells expressing PD-L1 tightly regulate CD8+ T cells in the SMG. When this immunoregulation is disrupted, CD8+ T cells clonally expand and acquire a unique transcriptional profile consistent with T cell receptor (TCR) activation. These clonally expanded cells phenotypically overlapped with cytotoxic GzmK+ CD8+ T autoimmune cells identified in patients with primary Sjögren's syndrome. Understanding how NK cells modulate CD8+ T cell activity in the SMG opens new avenues for preventing irAEs in patients undergoing checkpoint blockade therapies.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Single cell and TCR analysis of immune cells from AAV gene therapy-dosed Duchenne muscular dystrophy patients

Clinical trials for Duchenne muscular dystrophy (DMD) are assessing the therapeutic efficacy of systemically delivered adeno-associated virus (AAV) carrying a modified DMD transgene. High vector doses (>1E14 vg/kg) are needed to globally transduce skeletal muscles; however, such doses trigger immune-related adverse events. Mitigating these immune responses is crucial for widespread application of AAV-based therapies. We used single-cell RNA sequencing and T cell receptor (TCR) sequencing on peripheral blood mononuclear cells from five participants prior to, and after, dosing. One subject in the high-dose cohort experienced thrombotic microangiopathy (TMA). Few changes in cell frequencies occurred after treatment; however, differential gene expression demonstrated induction of interferon response genes in most T cell types. T cell clonotype and clumping analysis showed the expansion or appearance of groups of related TCR sequences in the post-treatment samples. Three of these expanded clumps could be assigned to prior human herpesvirus infections, two of which were present in the participant that exhibited TMA. These data provide insight on the mechanistic basis of human immune-AAV interactions and lay a foundation for improved understanding of why TMA arises in some patients and not others.

Identifying T-cell clubs by embracing the local harmony between TCR and gene expressions

T cell receptors (TCR) and gene expression provide two complementary and essential aspects in T cell understanding, yet their diversity presents challenges in integrative analysis. We introduce TCRclub, a novel method integrating single-cell RNA sequencing data and single-cell TCR sequencing data using local harmony to identify functionally similar T cell groups, termed 'clubs'. We applied TCRclub to 298,106 T cells across seven datasets encompassing various diseases. First, TCRclub outperforms the state-of-the-art methods in clustering T cells on a dataset with over 400 verified peptide-major histocompatibility complex categories. Second, TCRclub reveals a transition from activated to exhausted T cells in cholangiocarcinoma patients. Third, TCRclub discovered the pathways that could intervene in response to anti-PD-1 therapy for patients with basal cell carcinoma by analyzing the pre-treatment and post-treatment samples. Furthermore, TCRclub unveiled different T-cell responses and gene patterns at different severity levels in patients with COVID-19. Hence, TCRclub aids in developing more effective immunotherapeutic strategies for cancer and infectious diseases.

T cell receptor-centric perspective to multimodal single-cell data analysis

The T cell receptor (TCR), despite its importance, is underutilized in single-cell analysis, with gene expression features solely driving current strategies. Here, we argue for a TCR-first approach, more suited toward T cell repertoires. To this end, we curated a large T cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head and neck cancer, blood cancer, and lung transplantation. Here, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We showcase the utility of a TCR-first approach through application of the STEGO.R tool for the identification of treatment-related dynamics and previously unknown public T cell clusters with potential antigen-specific properties. Thus, the paradigm shift to a TCR-first can highlight overlooked key T cell features that have the potential for improvements in immunotherapy and diagnostics.

Features of Highly Homologous T-Cell Receptor Repertoire in the Immune Response to Mutations in Immunogenic Epitopes

CD8+ T-cell immunity, mediated through interactions between human leukocyte antigen (HLA) and the T-cell receptor (TCR), plays a pivotal role in conferring immune memory and protection against viral infections. The emergence of SARS-CoV-2 variants presents a significant challenge to the existing population immunity. While numerous SARS-CoV-2 mutations have been associated with immune evasion from CD8+ T cells, the molecular effects of most mutations on epitope-specific TCR recognition remain largely unexplored, particularly for epitope-specific repertoires characterized by common TCRs. In this study, we investigated an HLA-A*24-restricted NYN epitope (Spike448-456) that elicits broad and highly homologous CD8+ T cell responses in COVID-19 patients. Eleven naturally occurring mutations in the NYN epitope, all of which retained cell surface presentation by HLA, were tested against four transgenic Jurkat reporter cell lines. Our findings demonstrate that, with the exception of L452R and the combined mutation L452Q + Y453F, these mutations have minimal impact on the avidity of recognition by NYN peptide-specific TCRs. Additionally, we observed that a similar TCR responded differently to mutant epitopes and demonstrated cross-reactivity to the unrelated VYF epitope (ORF3a112-120). The results contradict the idea that immune responses with limited receptor diversity are insufficient to provide protection against emerging variants.

tcrBLOSUM: an amino acid substitution matrix for sensitive alignment of distant epitope-specific TCRs

Deciphering the specificity of T-cell receptor (TCR) repertoires is crucial for monitoring adaptive immune responses and developing targeted immunotherapies and vaccines. To elucidate the specificity of previously unseen TCRs, many methods employ the BLOSUM62 matrix to find TCRs with similar amino acid (AA) sequences. However, while BLOSUM62 reflects the AA substitutions within conserved regions of proteins with similar functions, the remarkable diversity of TCRs means that both TCRs with similar and dissimilar sequences can bind the same epitope. Therefore, reliance on BLOSUM62 may bias detection towards epitope-specific TCRs with similar biochemical properties, overlooking those with more diverse AA compositions. In this study, we introduce tcrBLOSUMa and tcrBLOSUMb, specialized AA substitution matrices for CDR3 alpha and CDR3 beta TCR chains, respectively. The matrices reflect AA frequencies and variations occurring within TCRs that bind the same epitope, revealing that both CDR3 alpha and CDR3 beta display tolerance to a wide range of AA substitutions and differ noticeably from the standard BLOSUM62. By accurately aligning distant TCRs employing tcrBLOSUMb, we were able to improve clustering performance and capture a large number of epitope-specific TCRs with diverse AA compositions and physicochemical profiles overlooked by BLOSUM62. Utilizing both the general BLOSUM62 and specialized tcrBLOSUM matrices in existing computational tools will broaden the range of TCRs that can be associated with their cognate epitopes, thereby enhancing TCR repertoire analysis.

A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions

The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis. We assessed these models utilizing eight downstream classifiers and five downstream clustering methods, with the performance measured by a diverse range of metrics for precision, robustness, and usability. Overall, handcrafted embeddings outperformed data-driven ones in modeling TCR-epitope interactions. To further refine our comparative findings, we developed an all-in-one TCR CDR3 embedding package comprising all evaluated embedding models. This package will assist users in easily selecting suitable embedding models for their data.

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist

Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

Low-frequency CD8+ T cells induced by SIGN-R1+ macrophage-targeted vaccine confer SARS-CoV-2 clearance in mice

Vaccine-induced T cells and neutralizing antibodies are essential for protection against SARS-CoV-2. Previously, we demonstrated that an antigen delivery system, pullulan nanogel (PNG), delivers vaccine antigen to lymph node medullary macrophages and thereby enhances the induction of specific CD8+ T cells. In this study, we revealed that medullary macrophage-selective delivery by PNG depends on its binding to a C-type lectin SIGN-R1. In a K18-hACE2 mouse model of SARS-CoV-2 infection, vaccination with a PNG-encapsulated receptor-binding domain of spike protein decreased the viral load and prolonged the survival in the CD8+ T cell- and B cell-dependent manners. T cell receptor repertoire analysis revealed that although the vaccine induced T cells at various frequencies, low-frequency specific T cells mainly promoted virus clearance. Thus, the induction of specific CD8+ T cells that respond quickly to viral infection, even at low frequencies, is important for vaccine efficacy and can be achieved by SIGN-R1+ medullary macrophage-targeted antigen delivery.

Predicting T cell receptor functionality against mutant epitopes

Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph nodes and blood following mRNA vaccination in humans

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and mRNA vaccination induce robust CD4+ T cell responses. Using single-cell transcriptomics, here, we evaluated CD4+ T cells specific for the SARS-CoV-2 spike protein in the blood and draining lymph nodes (dLNs) of individuals 3 months and 6 months after vaccination with the BNT162b2 mRNA vaccine. We analyzed 1,277 spike-specific CD4+ T cells, including 238 defined using Trex, a deep learning-based reverse epitope mapping method to predict antigen specificity. Human dLN spike-specific CD4+ follicular helper T (TFH) cells exhibited heterogeneous phenotypes, including germinal center CD4+ TFH cells and CD4+IL-10+ TFH cells. Analysis of an independent cohort of SARS-CoV-2-infected individuals 3 months and 6 months after infection found spike-specific CD4+ T cell profiles in blood that were distinct from those detected in blood 3 months and 6 months after BNT162b2 vaccination. Our findings provide an atlas of human spike-specific CD4+ T cell transcriptional phenotypes in the dLNs and blood following SARS-CoV-2 vaccination or infection.

Human TCR repertoire in cancer

BACKGROUND

T cells, the "superstar" of the immune system, play a crucial role in antitumor immunity. T-cell receptors (TCR) are crucial molecules that enable T cells to identify antigens and start immunological responses. The body has evolved a unique method for rearrangement, resulting in a vast diversity of TCR repertoires. A healthy TCR repertoire is essential for the particular identification of antigens by T cells.

METHODS

In this article, we systematically summarized the TCR creation mechanisms and analysis methodologies, particularly focusing on the application of next-generation sequencing (NGS) technology. We explore the TCR repertoire in health and cancer, and discuss the implications of TCR repertoire analysis in understanding carcinogenesis, cancer progression, and treatment.

RESULTS

The TCR repertoire analysis has enormous potential for monitoring the emergence and progression of malignancies, as well as assessing therapy response and prognosis. The application of NGS has dramatically accelerated our comprehension of TCR diversity and its role in cancer immunity.

CONCLUSIONS

To substantiate the significance of TCR repertoires as biomarkers, more thorough and exhaustive research should be conducted. The TCR repertoire analysis, enabled by advanced sequencing technologies, is poised to become a crucial tool in the future of cancer diagnosis, monitoring, and therapy evaluation.

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

Human SARS-CoV-2 challenge uncovers local and systemic response dynamics

The COVID-19 pandemic is an ongoing global health threat, yet our understanding of the dynamics of early cellular responses to this disease remains limited1. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

Expansion of the HSV-2-specific T cell repertoire in skin after immunotherapeutic HSV-2 vaccine

The skin at the site of HSV-2 reactivation is enriched for HSV-2-specific T cells. To evaluate whether an immunotherapeutic vaccine could elicit skin-based memory T cells, we studied skin biopsies and HSV-2-reactive CD4+ T cells from PBMCs by T cell receptor (TCR) β chain (TRB) sequencing before and after vaccination with a replication-incompetent whole-virus HSV-2 vaccine candidate (HSV529). The representation of HSV-2-reactive CD4+ TRB sequences from PBMCs in the skin TRB repertoire increased after the first vaccine dose. We found sustained expansion after vaccination of unique, skin-based T cell clonotypes that were not detected in HSV-2-reactive CD4+ T cells isolated from PBMCs. In one participant, a switch in immunodominance occurred with the emergence of a TCR αβ pair after vaccination that was not detected in blood. This TCRαβ was shown to be HSV-2 reactive by expression of a synthetic TCR in a Jurkat-based NR4A1 reporter system. The skin in areas of HSV-2 reactivation possessed an oligoclonal TRB repertoire that was distinct from the circulation. Defining the influence of therapeutic vaccination on the HSV-2-specific TRB repertoire requires tissue-based evaluation.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

Designing meaningful continuous representations of T cell receptor sequences with deep generative models

T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.

MR1-restricted T cell clonotypes are associated with "resistance" to Mycobacterium tuberculosis infection

T cells are required for protective immunity against Mycobacterium tuberculosis. We recently described a cohort of Ugandan household contacts of tuberculosis cases who appear to "resist" M. tuberculosis infection (resisters; RSTRs) and showed that these individuals harbor IFN-γ-independent T cell responses to M. tuberculosis-specific peptide antigens. However, T cells also recognize nonprotein antigens via antigen-presenting systems that are independent of genetic background, known as donor-unrestricted T cells (DURTs). We used tetramer staining and flow cytometry to characterize the association between DURTs and "resistance" to M. tuberculosis infection. Peripheral blood frequencies of most DURT subsets were comparable between RSTRs and latently infected controls (LTBIs). However, we observed a 1.65-fold increase in frequency of MR1-restricted T (MR1T) cells among RSTRs in comparison with LTBIs. Single-cell RNA sequencing of 18,251 MR1T cells sorted from 8 donors revealed 5,150 clonotypes that expressed a common transcriptional program, the majority of which were private. Sequencing of the T cell receptor α/T cell receptor δ (TCRα/δ) repertoire revealed several DURT clonotypes were expanded among RSTRs, including 2 MR1T clonotypes that recognized mycobacteria-infected cells in a TCR-dependent manner. Overall, our data reveal unexpected donor-specific diversity in the TCR repertoire of human MR1T cells as well as associations between mycobacteria-reactive MR1T clonotypes and resistance to M. tuberculosis infection.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Principles and therapeutic applications of adaptive immunity

Adaptive immunity provides protection against infectious and malignant diseases. These effects are mediated by lymphocytes that sense and respond with targeted precision to perturbations induced by pathogens and tissue damage. Here, we review key principles underlying adaptive immunity orchestrated by distinct T cell and B cell populations and their extensions to disease therapies. We discuss the intracellular and intercellular processes shaping antigen specificity and recognition in immune activation and lymphocyte functions in mediating effector and memory responses. We also describe how lymphocytes balance protective immunity against autoimmunity and immunopathology, including during immune tolerance, response to chronic antigen stimulation, and adaptation to non-lymphoid tissues in coordinating tissue immunity and homeostasis. Finally, we discuss extracellular signals and cell-intrinsic programs underpinning adaptive immunity and conclude by summarizing key advances in vaccination and engineering adaptive immune responses for therapeutic interventions. A deeper understanding of these principles holds promise for uncovering new means to improve human health.

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

Innate-like T cell subset commitment in the murine thymus is independent of TCR characteristics and occurs during proliferation

How T-cell receptor (TCR) characteristics determine subset commitment during T-cell development is still unclear. Here, we addressed this question for innate-like T cells, mucosal-associated invariant T (MAIT) cells, and invariant natural killer T (iNKT) cells. MAIT and iNKT cells have similar developmental paths, leading in mice to two effector subsets, cytotoxic (MAIT1/iNKT1) and IL17-secreting (MAIT17/iNKT17). For iNKT1 vs iNKT17 fate choice, an instructive role for TCR affinity was proposed but recent data argue against this model. Herein, we examined TCR role in MAIT and iNKT subset commitment through scRNAseq and TCR repertoire analysis. In our dataset of thymic MAIT cells, we found pairs of T-cell clones with identical amino acid TCR sequences originating from distinct precursors, one of which committed to MAIT1 and the other to MAIT17 fates. Quantitative in silico simulations indicated that the number of such cases is best explained by lineage choice being independent of TCR characteristics. Comparison of TCR features of MAIT1 and MAIT17 clonotypes demonstrated that the subsets cannot be distinguished based on TCR sequence. To pinpoint the developmental stage associated with MAIT sublineage choice, we demonstrated that proliferation takes place both before and after MAIT fate commitment. Altogether, we propose a model of MAIT cell development in which noncommitted, intermediate-stage MAIT cells undergo a first round of proliferation, followed by TCR characteristics-independent commitment to MAIT1 or MAIT17 lineage, followed by an additional round of proliferation. Reanalyzing a published iNKT TCR dataset, we showed that this model is also relevant for iNKT cell development.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-β regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-β sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-β sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

CD8+ T cell memory induced by successive SARS-CoV-2 mRNA vaccinations is characterized by shifts in clonal dominance

mRNA vaccines against the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) elicit strong T cell responses. However, a clonal-resolution analysis of T cell responses to mRNA vaccination has not been performed. Here, we temporally track the CD8+ T cell repertoire in individuals who received three shots of the BNT162b2 mRNA vaccine through longitudinal T cell receptor sequencing with peptide-human leukocyte antigen (HLA) tetramer analysis. We demonstrate a shift in T cell responses between the clonotypes with different kinetics: from early responders that expand rapidly after the first shot to main responders that greatly expand after the second shot. Although the main responders re-expand after the third shot, their clonal diversity is skewed, and newly elicited third responders partially replace them. Furthermore, this shift in clonal dominance occurs not only between, but also within, clonotypes specific for spike epitopes. Our study will be a valuable resource for understanding vaccine-induced T cell responses in general.

DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma

Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.

Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.

Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.

A comparison of clustering models for inference of T cell receptor antigen specificity

The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.

Circulating cancer-specific CD8 T cell frequency is associated with response to PD-1 blockade in Merkel cell carcinoma

Understanding cancer immunobiology has been hampered by difficulty identifying cancer-specific T cells. Merkel cell polyomavirus (MCPyV) causes most Merkel cell carcinomas (MCCs). All patients with virus-driven MCC express MCPyV oncoproteins, facilitating identification of virus (cancer)-specific T cells. We studied MCPyV-specific T cells from 27 patients with MCC using MCPyV peptide-HLA-I multimers, 26-color flow cytometry, single-cell transcriptomics, and T cell receptor (TCR) sequencing. In a prospective clinical trial, higher circulating MCPyV-specific CD8 T cell frequency before anti-PD-1 treatment was strongly associated with 2-year recurrence-free survival (75% if detectable, 0% if undetectable, p = 0.0018; ClinicalTrial.gov: NCT02488759). Intratumorally, such T cells were typically present, but their frequency did not significantly associate with response. Circulating MCPyV-specific CD8 T cells had increased stem/memory and decreased exhaustion signatures relative to their intratumoral counterparts. These results suggest that cancer-specific CD8 T cells in the blood may play a role in anti-PD-1 responses. Thus, strategies that augment their number or mobilize them into tumors could improve outcomes.

Merkel cell polyomavirus-specific and CD39+CLA+ CD8 T cells as blood-based predictive biomarkers for PD-1 blockade in Merkel cell carcinoma

Merkel cell carcinoma is a skin cancer often driven by Merkel cell polyomavirus (MCPyV) with high rates of response to anti-PD-1 therapy despite low mutational burden. MCPyV-specific CD8 T cells are implicated in anti-PD-1-associated immune responses and provide a means to directly study tumor-specific T cell responses to treatment. Using mass cytometry and combinatorial tetramer staining, we find that baseline frequencies of blood MCPyV-specific cells correlated with response and survival. Frequencies of these cells decrease markedly during response to therapy. Phenotypes of MCPyV-specific CD8 T cells have distinct expression patterns of CD39, cutaneous lymphocyte-associated antigen (CLA), and CD103. Correspondingly, overall bulk CD39+CLA+ CD8 T cell frequencies in blood correlate with MCPyV-specific cell frequencies and similarly predicted favorable clinical outcomes. Conversely, frequencies of CD39+CD103+ CD8 T cells are associated with tumor burden and worse outcomes. These cell subsets can be useful as biomarkers and to isolate blood-derived tumor-specific T cells.

T cell help shapes B cell tolerance

T cell help is a crucial component of the normal humoral immune response, yet whether it promotes or restrains autoreactive B cell responses remains unclear. Here, we observe that autoreactive germinal centers require T cell help for their formation and persistence. Using retrogenic chimeras transduced with candidate TCRs, we demonstrate that a follicular T cell repertoire restricted to a single autoreactive TCR, but not a foreign antigen-specific TCR, is sufficient to initiate autoreactive germinal centers. Follicular T cell specificity influences the breadth of epitope spreading by regulating wild-type B cell entry into autoreactive germinal centers. These results demonstrate that TCR-dependent T cell help can promote loss of B cell tolerance and that epitope spreading is determined by TCR specificity.

Expansion of the HSV-2-specific T cell repertoire in skin after immunotherapeutic HSV-2 vaccine

The skin at the site of HSV-2 reactivation is enriched for HSV-2-specific T cells. To evaluate whether an immunotherapeutic vaccine could elicit skin-based memory T cells, we studied skin biopsies and HSV-2-reactive CD4+ T cells from peripheral blood mononuclear cells (PBMCs) by T cell receptor β (TRB) sequencing before and after vaccination with a replication-incompetent whole virus HSV-2 vaccine candidate (HSV529). The representation of HSV-2-reactive CD4+ TRB sequences from PBMCs in the skin TRB repertoire increased after the first vaccine dose. We found sustained expansion after vaccination of unique, skin-based T-cell clonotypes that were not detected in HSV-2-reactive CD4+ T cells isolated from PBMCs. In one participant a switch in immunodominance occurred with the emergence of a T cell receptor (TCR) αβ pair after vaccination that was not detected in blood. This TCRαβ was shown to be HSV-2-reactive by expression of a synthetic TCR in a Jurkat-based NR4A1 reporter system. The skin in areas of HSV-2 reactivation possesses an oligoclonal TRB repertoire that is distinct from the circulation. Defining the influence of therapeutic vaccination on the HSV-2-specific TRB repertoire requires tissue-based evaluation.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

In Silico Tools for Predicting Novel Epitopes

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Repeated mRNA vaccination sequentially boosts SARS-CoV-2-specific CD8+ T cells in persons with previous COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hybrid immunity is more protective than vaccination or previous infection alone. To investigate the kinetics of spike-reactive T (TS) cells from SARS-CoV-2 infection through messenger RNA vaccination in persons with hybrid immunity, we identified the T cell receptor (TCR) sequences of thousands of index TS cells and tracked their frequency in bulk TCRβ repertoires sampled longitudinally from the peripheral blood of persons who had recovered from coronavirus disease 2019 (COVID-19). Vaccinations led to large expansions in memory TS cell clonotypes, most of which were CD8+ T cells, while also eliciting diverse TS cell clonotypes not observed before vaccination. TCR sequence similarity clustering identified public CD8+ and CD4+ TCR motifs associated with spike (S) specificity. Synthesis of longitudinal bulk ex vivo single-chain TCRβ repertoires and paired-chain TCRɑβ sequences from droplet sequencing of TS cells provides a roadmap for the rapid assessment of T cell responses to vaccines and emerging pathogens.

Local and Global Variability in Developing Human T-Cell Repertoires

The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T-cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T-cell maturation in the thymus. Characterizing TCR repertoires across individuals and T-cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies. Analyzing a dataset of human TCR repertoires from thymocyte subsets, we find that the variability between individuals generated during the TCR V(D)J recombination is maintained through all stages of T-cell maturation and differentiation. The interindividual variability of repertoires of the same cell type is of comparable magnitude to the variability across cell types within the same individual. To zoom in on smaller scales than whole repertoires, we defined a distance measuring the relative overlap of locally similar sequences in repertoires. We find that the whole repertoire models correctly predict local similarity networks, suggesting a lack of forbidden T-cell receptor sequences. The local measure correlates well with distances calculated using whole repertoire traits and carries information about cell types.

Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences

IMPORTANCE

Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.