Dual TCR T Cells: Identity Crisis or Multitaskers?

scRNA+TCR+BCR-seq revealed the proportions and gene expression patterns of dual receptor T and B lymphocytes in NPC and NLH

While the relationship between single receptor lymphocytes and cancer has been deeply researched, the origin and biological roles of dual receptor lymphocytes in tumor microenvironment (TME) remain largely unknown. And since nasopharyngeal carcinoma (NPC) is a type of cancer closely associated with immune infiltration, studying the TME of NPC holds particular significance. Utilizing single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA + TCR + BCR-seq), we analyzed data from 7 patients with NPC and 3 patients with nasopharyngeal lymphatic hyperplasia (NLH). In our research, it was firstly found that the presence of dual receptor lymphocytes in both the TME of NPC and the inflammatory environment of NLH. We also confirmed their clonal expansion, suggesting their potential involvement in the immune response. Subsequently, we further discovered the lineage and the pairing characteristics. It was found that the dual receptor lymphocytes in NPC and NLH mainly originate from memory cells, and the predominant pairing type for dual TCR was β+α1+α2 and dual BCR was heavy+κ+λ. By further analyzing their gene expression, we compared the function of dual receptor cells with single receptor cells in the context of both NPC and NLH. This groundbreaking research has enhanced our comprehension of the features of dual-receptor cells and has contributed to a better understanding of the TME in NPC. By comparing with NLH, it illuminates part of the alterations in the process of malignant transformation in NPC. These findings present the potential to acquire improved diagnostic markers and treatment modalities.

Single-cell T-cell receptor repertoire profiling in dogs

Spontaneous cancers in companion dogs are robust models of human disease. Tracking tumor-specific immune responses in these models requires reagents to perform species-specific single cell T cell receptor sequencing (scTCRseq). scTCRseq and integration with scRNA data have not been demonstrated on companion dogs with cancer. Here, five healthy dogs, two dogs with T cell lymphoma and four dogs with melanoma are selected to demonstrate applicability of scTCRseq in a cancer immunotherapy setting. Single-cell suspensions of PBMCs or lymph node aspirates are profiled using scRNA and dog-specific scTCRseq primers. In total, 77,809 V(D)J-expressing cells are detected, with an average of 3498 (348 - 5,971) unique clonotypes identified per sample. In total, 29/34, 40/40, 22/22 and 9/9 known functional TRAV, TRAJ, TRBV and TRBJ gene segments are observed respectively. Pseudogene or otherwise defective gene segments are also detected supporting re-annotation of several as functional. Healthy dogs exhibit highly diverse repertoires, T cell lymphomas exhibit clonal repertoires, and vaccine-treated melanoma dogs are dominated by a small number of highly abundant clonotypes. scRNA libraries define large clusters of V(D)J-expressing CD8+ and CD4 + T cells. Dominant clonotypes observed in melanoma PBMCs are predominantly CD8 + T cells, with activated phenotypes, suggesting possible anti-tumor T cell populations.

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.

The Cyclin D3 Protein Enforces Monogenic TCRβ Expression by Mediating TCRβ Protein-Signaled Feedback Inhibition of Vβ Recombination

In jawed vertebrates, adaptive immunity depends on the process of V(D)J recombination creating vast numbers of T and B lymphocytes that each expresses unique Ag receptors of uniform specificity. The asynchronous initiation of V-to-(D)J rearrangement between alleles and the resulting protein from one allele signaling feedback inhibition of V recombination on the other allele ensures homogeneous receptor specificity of individual cells. Upon productive Vβ-to-DβJβ rearrangements in noncycling double-negative thymocytes, TCRβ protein signals induction of the cyclin D3 protein to accelerate cell cycle entry, thereby driving proliferative expansion of developing αβ T cells. Through undetermined mechanisms, the inactivation of cyclin D3 in mice causes an increased frequency of αβ T cells that express TCRβ proteins from both alleles, producing lymphocytes of heterogeneous specificities. To determine how cyclin D3 enforces monogenic TCRβ expression, we used our mouse lines with enhanced rearrangement of specific Vβ segments due to replacement of their poor-quality recombination signal sequence (RSS) DNA elements with a better RSS. We show that cyclin D3 inactivation in these mice elevates the frequencies of αβ T cells that display proteins from RSS-augmented Vβ segments on both alleles. By assaying mature αβ T cells, we find that cyclin D3 deficiency increases the levels of Vβ rearrangements that occur within developing thymocytes. Our data demonstrate that a component of the cell cycle machinery mediates TCRβ protein-signaled feedback inhibition in thymocytes to achieve monogenic TCRβ expression and resulting uniform specificity of individual αβ T cells.

Macro CD5L+ deteriorates CD8+T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma

Intratumoral immune status influences tumor therapeutic response, but it remains largely unclear how the status determines therapies for patients with intrahepatic cholangiocarcinoma. Here, we examine the single-cell transcriptional and TCR profiles of 18 tumor tissues pre- and post- therapy of gemcitabine plus oxaliplatin, in combination with lenvatinib and anti-PD1 antibody for intrahepatic cholangiocarcinoma. We find that high CD8 GZMB+ and CD8 proliferating proportions and a low Macro CD5L+ proportion predict good response to the therapy. In patients with a poor response, the CD8 GZMB+ and CD8 proliferating proportions are increased, but the CD8 GZMK+ proportion is decreased after the therapy. Transition of CD8 proliferating and CD8 GZMB+ to CD8 GZMK+ facilitates good response to the therapy, while Macro CD5L+-CD8 GZMB+ crosstalk impairs the response by increasing CTLA4 in CD8 GZMB+. Anti-CTLA4 antibody reverses resistance of the therapy in intrahepatic cholangiocarcinoma. Our data provide a resource for predicting response of the combination therapy and highlight the importance of CD8+T-cell status conversion and exhaustion induced by Macro CD5L+ in influencing the response, suggesting future avenues for cancer treatment optimization.

TScan-II: A genome-scale platform for the de novo identification of CD4+ T cell epitopes

CD4+ T cells play fundamental roles in orchestrating immune responses and tissue homeostasis. However, our inability to associate peptide human leukocyte antigen class-II (HLA-II) complexes with their cognate T cell receptors (TCRs) in an unbiased manner has hampered our understanding of CD4+ T cell function and role in pathologies. Here, we introduce TScan-II, a highly sensitive genome-scale CD4+ antigen discovery platform. This platform seamlessly integrates the endogenous HLA-II antigen-processing machinery in synthetic antigen-presenting cells and TCR signaling in T cells, enabling the simultaneous screening of multiple HLAs and TCRs. Leveraging genome-scale human, virome, and epitope mutagenesis libraries, TScan-II facilitates de novo antigen discovery and deep exploration of TCR specificity. We demonstrate TScan-II's potential for basic and translational research by identifying a non-canonical antigen for a cancer-reactive CD4+ T cell clone. Additionally, we identified two antigens for clonally expanded CD4+ T cells in Sjögren's disease, which bind distinct HLAs and are expressed in HLA-II-positive ductal cells within affected salivary glands.

Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data

Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.

Mathematical Characterization of Private and Public Immune Receptor Sequences

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Dual receptor T cells mediate effective antitumor immune responses via increased recognition of tumor antigens

BACKGROUND

Discovery that ~16% of T cells naturally co-express two T-cell receptor (TCR) clonotypes prompts examining the role of dual TCR cells in immune functions.

METHODS

Using TCRα-reporter transgenic mice, enabling unambiguous identification of single-TCR and dual-TCR cells, we tested the role of dual TCR cells in antitumor immune responses against immune-responsive syngeneic 6727 sarcoma and immune-resistant B16F10 melanoma.

RESULTS

Dual TCR cells were specifically increased among tumor-infiltrating lymphocytes (TILs) in both models, indicating selective advantage in antitumor responses. Phenotype and single-cell gene expression analyses identified dual TCR are predominant during the effective antitumor response, demonstrating selectively increased activation in the TIL compartment and skewing toward an effector memory phenotype. Absence of dual TCR cells impaired immune response to B16F10 but not 6727, suggesting that dual TCR cells may be more influential in responses against poorly immunogenic tumors. Dual TCR cells demonstrated an advantage in recognition of B16F10-derived neoantigens in vitro, providing a mechanistic basis for their antitumor reactivity.

CONCLUSIONS

These results discover an unrecognized role for dual TCR cells in protective immune function and identify these cells and their TCRs as a potential resource for antitumor immunotherapy.

Dual TCR-Expressing T Cells in Cancer: How Single-Cell Technologies Enable New Investigation

TCR diversity measures are often used to understand the immune response in cancer. Traditional measures of diversity rely on bulk RNA sequencing (RNAseq) of the β-chain variable regions. However, the full αβ TCR repertoire is a combination of both the α- and β-chains, which are encoded by separate genes. In contrast with bulk RNAseq, single-cell RNAseq (scRNAseq) allows paired chain analyses, yielding a more accurate measure of the repertoire. Interestingly, ∼30% of mature peripheral T cells express multiple TCR alleles (e.g., two α-chains) and may exhibit dual Ag specificity. scRNAseq has become increasingly common, and data from both human and animal studies are publicly available. However, routine workflows discard secondary TCR alleles and focus on a single TCR clone per cell. This perspectives piece emphasizes why this may not be good practice and highlights unanswered questions in the field of T cell dual specificity.

Anti-tumor memory CD4 and CD8 T-cells quantified by bulk T-cell receptor (TCR) clonal analysis

Simple, reliable methods to detect anti-tumor memory T-cells are necessary to develop a clinical tumor vaccination program. A mouse model of curative viral onco-immunotherapy found that peritoneal tumor challenge following cure identified an oligoclonal anti-tumor memory CD4 and CD8 T-cell response. Clonotypes differed among the challenged animals but were congruent in blood, spleen and peritoneal cells (PC) of the same animal. Adoptive transfer demonstrated that the high-frequency responding T-cells were tumor specific. Tetramer analysis confirmed that clonotype frequency determined by T-cell receptor (TCR)- chain (TRB) analysis closely approximated cell clone frequency. The mean frequency of resting anti-tumor memory CD4 T-cells in unchallenged spleen was 0.028% and of memory CD8 T-cells was 0.11% which was not high enough to distinguish them from background. Stimulation produced a mean ~10-fold increase in splenic and 100-fold increase in peritoneal anti-tumor memory T-cell clonotypes. This methodology can be developed to use blood and tissue sampling to rapidly quantify the effectiveness of a tumor vaccine or any vaccine generating therapeutic T-cells.

Canine peripheral blood TCRαβ T cell atlas: Identification of diverse subsets including CD8A+ MAIT-like cells by combined single-cell transcriptome and V(D)J repertoire analysis

The dog is valued as a companion animal and increasingly recognized as a model for human disorders. Given the importance of T cells in health and disease, comprehensive knowledge of canine T cells can contribute to our understanding of pathogenesis mechanisms and inform the development of new treatment strategies. However, the diversity of canine T cells is still poorly understood mainly due to the lack of species-reactive antibodies for use in flow cytometry. The aim of this study was to generate a detailed atlas of peripheral blood TCRαβ⁺ T cells of healthy dogs using single-cell RNA-sequencing (scRNAseq) combined with immune repertoire sequencing. A total of 22 TCRαβ⁺ T cell clusters were identified, which were classified into three major groups: CD4-dominant (11 clusters), CD8A-dominant (8 clusters), and CD4/CD8A-mixed (3 clusters). Based on differential gene expression, distinct differentiation states (naïve, effector, memory, exhausted) and lineages (e.g. CD4 T helper and regulatory T cells) could be distinguished. Importantly, several T cell populations were identified, which have not been described in dogs before. Of particular note, our data provide first evidence for the existence of canine mucosa-associated invariant T cell (MAIT)-like cells, representing one of three newly identified FCER1G⁺ innate-like CD8A⁺ T cell populations in the peripheral blood of healthy dogs. In conclusion, using scRNAseq combined with immune repertoire sequencing we were able to resolve canine TCRαβ⁺ T cell populations at unprecedented resolution. The peripheral blood TCRαβ⁺ T cell atlas of healthy dogs generated here represents an important reference data set for future studies and is of relevance for identifying new targets for T cell-specific therapies.

Genetic Strategies to Study T Cell Development

Genetics approaches have been instrumental to deciphering T cell development in the thymus, including gene disruption by homologous recombination and more recently Crispr-based gene editing and transgenic gene expression, especially of specific T cell antigen receptors (TCR). This brief chapter describes commonly used tools and strategies to modify the genome of thymocytes, including mouse strains with lineage- and stage-specific expression of the Cre recombinase used for conditional allele inactivation or expressing unique antigen receptor specificities.

Diversity and Clonality of T Cell Receptor Repertoire and Antigen Specificities in Small Joints of Early Rheumatoid Arthritis

OBJECTIVE

CD4+ T cells are implicated in rheumatoid arthritis (RA) pathology from the strong association between RA and certain HLA class II gene variants. This study was undertaken to examine the synovial T cell receptor (TCR) repertoire, T cell phenotypes, and T cell specificities in small joints of RA patients at time of diagnosis before therapeutic intervention.

METHODS

Sixteen patients, of whom 11 patients were anti-citrullinated protein antibody (ACPA)-positive and 5 patients were ACPA-, underwent ultrasound-guided synovial biopsy of a small joint (n = 13) or arthroscopic synovial biopsy of a large joint (n = 3), followed by direct sorting of single T cells for paired sequencing of the αβ TCR together with flow cytometry analysis. TCRs from expanded CD4+ T cell clones of 4 patients carrying an HLA-DRB1*04:01 allele were artificially reexpressed to study antigen specificity.

RESULTS

T cell analysis demonstrated CD4+ dominance and the presence of peripheral helper T-like cells in both patient groups. We identified >4,000 unique TCR sequences, as well as 225 clonal expansions. Additionally, T cells with double α-chains were a recurring feature. We identified a biased gene usage of the V_β chain segment TRBV20-1 in CD4+ cells from ACPA+ patients. In vitro stimulation of T cell lines expressing selected TCRs with an extensive panel of citrullinated and viral peptides identified several different virus-specific TCRs (e.g., human cytomegalovirus and human herpesvirus 2). Still, the majority of clones remained orphans with unknown specificity.

CONCLUSION

Minimally invasive biopsies of the RA synovium allow for single-cell TCR sequencing and phenotyping. Clonally expanded, viral-reactive T cells account for part of the diverse CD4+ T cell repertoire. TRBV20-1 bias in ACPA+ patients suggests recognition of common antigens.

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.

Monogenic TCRβ Assembly and Expression Are Paramount for Uniform Antigen Receptor Specificity of Individual αβ T Lymphocytes

The ability of individual T and B cells to display Ag receptors of unique uniform specificity is the molecular basis of adaptive immunity. Most αβ T cells achieve uniform specificity by assembling in-frame genes on only one allelic copy of TCRβ and TCRα loci, while others prevent incorporation of TCRα protein from both alleles into TCRs. Analysis of mice expressing TCR proteins from a restricted combination of transgenes showed that TCR protein pairing restrictions achieve uniform specificity of cells expressing two types of TCRβ protein. However, whether this mechanism operates in the physiological context where each dual-TCRβ cell expresses one set of a vast number of different TCRβ proteins remains an open question, largely because there is a low, but significant, portion of cells carrying two in-frame TCRβ genes. To resolve this issue, we inactivated one allelic copy of the TCRα locus in a new mouse strain that assembles two in-frame TCRβ genes in an elevated fraction of cells. This genetic manipulation has no effect on the frequency of cells that display multiple types of αβ TCR, yet increases the representation of cells displaying TCRβ proteins that generate more highly expressed TCRs. Our data demonstrate that some TCRβ proteins exhibit differential functional pairing with TCRα proteins, but these restrictions have negligible contribution for ensuring uniform specificity of cells that express two types of TCRβ protein. Therefore, we conclude that mechanisms governing monogenic assembly and expression of TCRβ genes in individual cells are paramount for uniform specificity of αβ T lymphocytes.

Longitudinal analysis reveals age-related changes in the T cell receptor repertoire of human T cell subsets

A diverse T cell receptor (TCR) repertoire is essential for protection against a variety of pathogens, and TCR repertoire size is believed to decline with age. However, the precise size of human TCR repertoires, in both total and subsets of T cells, as well as their changes with age, are not fully characterized. We conducted a longitudinal analysis of the human blood TCRα and TCRβ repertoire of CD4⁺ and CD8⁺ T cell subsets using a unique molecular identifier–based (UMI-based) RNA-seq method. Thorough analysis of 1.9 × 10⁸ T cells yielded the lower estimate of TCR repertoire richness in an adult at 3.8 × 10⁸. Alterations of the TCR repertoire with age were observed in all 4 subsets of T cells. The greatest reduction was observed in naive CD8⁺ T cells, while the greatest clonal expansion was in memory CD8⁺ T cells, and the highest increased retention of TCR sequences was in memory CD8⁺ T cells. Our results demonstrated that age-related TCR repertoire attrition is subset specific and more profound for CD8⁺ than CD4⁺ T cells, suggesting that aging has a more profound effect on cytotoxic as opposed to helper T cell functions. This may explain the increased susceptibility of older adults to novel infections.

Single-Cell Analysis and Tracking of Antigen-Specific T Cells: Integrating Paired Chain AIRR-Seq and Transcriptome Sequencing: A Method by the AIRR Community

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) offers the possibility to access the nucleotide sequences of paired receptor chains from T-cell receptors (TCR) or B-cell receptors (BCR ). Here we describe two protocols and the downstream bioinformatic approaches that facilitate the integrated analysis of paired T-cell receptor (TR ) alpha/beta (TRA /TRB ) AIRR-seq, RNA sequencing (RNAseq), immunophenotyping, and antigen-binding information. To illustrate the methodologies with a use case, we describe how to identify, characterize, and track SARS-CoV-2-specific T cells over multiple time points following infection with the virus. The first method allows the analysis of pools of memory CD8⁺ cells, identifying expansions and contractions of clones of interest. The second method allows the study of rare or antigen-specific cells and allows studying their changes over time.

Cross-tissue immune cell analysis reveals tissue-specific features in humans

Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.

High-efficiency nonviral CRISPR/Cas9-mediated gene editing of human T cells using plasmid donor DNA

Genome engineering of T lymphocytes, the main effectors of antitumor adaptive immune responses, has the potential to uncover unique insights into their functions and enable the development of next-generation adoptive T cell therapies. Viral gene delivery into T cells, which is currently used to generate CAR T cells, has limitations in regard to targeting precision, cargo flexibility, and reagent production. Nonviral methods for effective CRISPR/Cas9-mediated gene knock-out in primary human T cells have been developed, but complementary techniques for nonviral gene knock-in can be cumbersome and inefficient. Here, we report a convenient and scalable nonviral method that allows precise gene edits and transgene integration in primary human T cells, using plasmid donor DNA template and Cas9-RNP. This method is highly efficient for single and multiplex gene manipulation, without compromising T cell function, and is thus valuable for use in basic and translational research.

Dual TCR-α Expression on Mucosal-Associated Invariant T Cells as a Potential Confounder of TCR Interpretation

Mucosal-associated invariant T (MAIT) cells are innate-like T cells that are highly abundant in human blood and tissues. Most MAIT cells have an invariant TCRα-chain that uses T cell receptor α-variable 1-2 (TRAV1-2) joined to TRAJ33/20/12 and recognizes metabolites from bacterial riboflavin synthesis bound to the Ag-presenting molecule MHC class I related (MR1). Our attempts to identify alternative MR1-presented Ags led to the discovery of rare MR1-restricted T cells with non-TRAV1-2 TCRs. Because altered Ag specificity likely alters affinity for the most potent known Ag, 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU), we performed bulk TCRα- and TCRβ-chain sequencing and single-cell-based paired TCR sequencing on T cells that bound the MR1-5-OP-RU tetramer with differing intensities. Bulk sequencing showed that use of V genes other than TRAV1-2 was enriched among MR1-5-OP-RU tetramer^low cells. Although we initially interpreted these as diverse MR1-restricted TCRs, single-cell TCR sequencing revealed that cells expressing atypical TCRα-chains also coexpressed an invariant MAIT TCRα-chain. Transfection of each non-TRAV1-2 TCRα-chain with the TCRβ-chain from the same cell demonstrated that the non-TRAV1-2 TCR did not bind the MR1-5-OP-RU tetramer. Thus, dual TCRα-chain expression in human T cells and competition for the endogenous β-chain explains the existence of some MR1-5-OP-RU tetramer^low T cells. The discovery of simultaneous expression of canonical and noncanonical TCRs on the same T cell means that claims of roles for non-TRAV1-2 TCR in MR1 response must be validated by TCR transfer-based confirmation of Ag specificity.

Single-cell transcriptomics links malignant T cells to the tumor immune landscape in cutaneous T cell lymphoma

Cutaneous T cell lymphoma (CTCL) represents a heterogeneous group of non-Hodgkin lymphoma distinguished by the presence of clonal malignant T cells. The heterogeneity of malignant T cells and the complex tumor microenvironment remain poorly characterized. With single-cell RNA analysis and bulk whole-exome sequencing on 19 skin lesions from 15 CTCL patients, we decipher the intra-tumor and inter-lesion diversity of CTCL patients and propose a multi-step tumor evolution model. We further establish a subtyping scheme based on the molecular features of malignant T cells and their pro-tumorigenic microenvironments: the T_CyEM group, demonstrating a cytotoxic effector memory T cell phenotype, shows more M2 macrophages infiltration, while the T_CM group, featured by a central memory T cell phenotype and adverse patient outcome, is infiltrated by highly exhausted CD8⁺ reactive T cells, B cells and Tregs with suppressive activities. Our results establish a solid basis for understanding the nature of CTCL and pave the way for future precision medicine for CTCL patients.

Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate

T cells acquire a regulatory phenotype when their T cell receptors (TCRs) experience an intermediate-to-high affinity interaction with a self-peptide presented via the major histocompatibility complex (MHC). Using TCRβ sequences from flow-sorted human cells, we identified TCR features that promote regulatory T cell (T_reg) fate. From these results, we developed a scoring system to quantify TCR-intrinsic regulatory potential (TiRP). When applied to the tumor microenvironment, TiRP scoring helped to explain why only some T cell clones maintained the T_conv phenotype through expansion. To elucidate drivers of these predictive TCR features, we then examined the two elements of the T_reg TCR ligand separately: the self-peptide, and the human MHC II molecule. These analyses revealed that hydrophobicity in the third complementarity determining region (CDR3β) of the TCR promotes reactivity to self-peptides, while TCR variable gene (TRBV gene) usage shapes the TCR’s general propensity for human MHC II-restricted activation.

Bystander T cells in cancer immunology and therapy

Cancer-specific T cells are required for effective anti-cancer immunity and have a central role in cancer immunotherapy. However, emerging evidence suggests that only a small fraction of tumor-infiltrating T cells are cancer specific, and T cells that recognize cancer-unrelated antigens (so-called 'bystanders') are abundant. Although the role of cancer-specific T cells in anti-cancer immunity has been well established, the implications of bystander T cells in tumors are only beginning to be understood. It is becoming increasingly clear that bystander T cells are not a homogeneous group of cells but, instead, they differ in their specificities, their activation states and effector functions. In this Perspective, we discuss recent studies of bystander T cells in tumors, including experimental and computational approaches that enable their identification and functional analysis and viewpoints on how these insights could be used to develop new therapeutic approaches for cancer immunotherapy.

Analysis of T-Cell Receptor Repertoire in Transplantation: Fingerprint of T Cell-mediated Alloresponse

T cells play a key role in determining allograft function by mediating allogeneic immune responses to cause rejection, and recent work pointed their role in mediating tolerance in transplantation. The unique T-cell receptor (TCR) expressed on the surface of each T cell determines the antigen specificity of the cell and can be the specific fingerprint for identifying and monitoring. Next-generation sequencing (NGS) techniques provide powerful tools for deep and high-throughput TCR profiling, and facilitate to depict the entire T cell repertoire profile and trace antigen-specific T cells in circulation and local tissues. Tailing T cell transcriptomes and TCR sequences at the single cell level provides a full landscape of alloreactive T-cell clones development and biofunction in alloresponse. Here, we review the recent advances in TCR sequencing techniques and computational tools, as well as the recent discovery in overall TCR profile and antigen-specific T cells tracking in transplantation. We further discuss the challenges and potential of using TCR sequencing-based assays to profile alloreactive TCR repertoire as the fingerprint for immune monitoring and prediction of rejection and tolerance.

HLA-DR3 restricted environmental epitopes from the bacterium Clostridium tetani have T cell cross-reactivity to the SLE-related autoantigen SmD

HLA-DR3 (DR3) is one of the dominant HLA-DR alleles associated with systemic lupus erythematosus (SLE) susceptibility. Our previous studies showed multiple intramolecular DR3 restricted T cell epitopes in the Smith D (SmD) protein, from which we generated a non-homologous, bacterial epitope mimics library. From this library we identified ABC_247-261 Mimic as one new DR3 restricted bacterial T cell epitope from the ABC transporter ATP-binding protein in Clostridium tetani. It activated and induced autoreactive SmD_66-80-specific T cells and induced autoantibodies to lupus-related autoantigens in vivo. Compared to healthy donors, SLE patients have a greater percentage of cross-reactive T cells to ABC_247-261 Mimic and SmD_66-80. In addition, we analyzed the ability of single DR3 restricted Tetanus toxoid (TT) T cell epitopes to induce autoimmune T cells. We found that the immunodominant TT epitope TT_826-845 stimulated SmD_66-80 reactive T cells but failed to induce persistent anti-SmD autoantibodies compared to the ABC_247-261 Mimic. Thus, exposure to the ABC_247-261 Mimic epitope may contribute to autoimmunity in susceptible DR3 individuals.

Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes

Human islet antigen reactive CD4⁺ memory T cells (IAR T cells) play a key role in the pathogenesis of autoimmune type 1 diabetes (T1D). Using single-cell RNA sequencing (scRNA-Seq) to identify T cell receptors (TCRs) in IAR T cells, we have identified a class of TCRs that share TCRα chains between individuals (“public” chains). We isolated IAR T cells from blood of healthy, new-onset T1D and established T1D donors using multiplexed CD154 enrichment and identified paired TCRαβ sequences from 2767 individual cells. More than a quarter of cells shared TCR junctions between 2 or more cells (“expanded”), and 29/47 (~62%) of expanded TCRs tested showed specificity for islet antigen epitopes. Public TCRs sharing TCRα junctions were most prominent in new-onset T1D. Public TCR sequences were more germline like than expanded unique, or “private,” TCRs, and had shorter junction sequences, suggestive of fewer random nucleotide insertions. Public TCRα junctions were often paired with mismatched TCRβ junctions in TCRs; remarkably, a subset of these TCRs exhibited cross-reactivity toward distinct islet antigen peptides. Our findings demonstrate a prevalent population of IAR T cells with diverse specificities determined by TCRs with restricted TCRα junctions and germline-constrained antigen recognition properties. Since these “innate-like” TCRs differ from previously described immunodominant TCRβ chains in autoimmunity, they have implications for fundamental studies of disease mechanisms. Self-reactive restricted TCRα chains and their associated epitopes should be considered in fundamental and translational investigations of TCRs in T1D.

huARdb: human Antigen Receptor database for interactive clonotype-transcriptome analysis at the single-cell level

T-cell receptors (TCRs) and B-cell receptors (BCRs) are critical in recognizing antigens and activating the adaptive immune response. Stochastic V(D)J recombination generates massive TCR/BCR repertoire diversity. Single-cell immune profiling with transcriptome analysis allows the high-throughput study of individual TCR/BCR clonotypes and functions under both normal and pathological settings. However, a comprehensive database linking these data is not yet readily available. Here, we present the human Antigen Receptor database (huARdb), a large-scale human single-cell immune profiling database that contains 444 794 high confidence T or B cells (hcT/B cells) with full-length TCR/BCR sequence and transcriptomes from 215 datasets. All datasets were processed in a uniform workflow, including sequence alignment, cell subtype prediction, unsupervised cell clustering, and clonotype definition. We also developed a multi-functional and user-friendly web interface that provides interactive visualization modules for biologists to analyze the transcriptome and TCR/BCR features at the single-cell level. HuARdb is freely available at https://huarc.net/database with functions for data querying, browsing, downloading, and depositing. In conclusion, huARdb is a comprehensive and multi-perspective atlas for human antigen receptors.

TCRpair: prediction of functional pairing between HLA-A*02:01-restricted T cell receptor α and β chains

SUMMARY

The ability of a T cell to recognize foreign peptides is defined by a single α and a single β hypervariable complementarity determining region (CDR3), which together form the T cell receptor (TCR) heterodimer. In ∼30%-35% of T cells, two α chains are expressed at the mRNA level but only one α chain is part of the functional TCR. This effect can also be observed for β chains, although it is less common. The identification of functional α/β chain pairs is instrumental in high-throughput characterization of therapeutic TCRs. TCRpair is the first method that predicts whether an α and β chain pair forms a functional, HLA-A*02:01 specific TCR without requiring the sequence of a recognized peptide. By taking additional amino acids flanking the CDR3 regions into account, TCRpair achieves an AUC of 0.71.

AVAILABILITY

TCRpair is implemented in Python using TensorFlow 2.0 and is freely available at https://www.github.com/amoesch/TCRpair.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clonotypic architecture of a Gag-specific CD8+ T-cell response in chronic human HIV-2 infection

The dynamics of T-cell receptor (TCR) selection in chronic HIV-1 infection, and its association with clinical outcome, is well documented for an array of MHC-peptide complexes and disease stages. However, the factors that may contribute to the selection and expansion of CD8+ T-cells in chronic HIV-2 infection, especially at clonal level remain unclear. To address this question, we undertook a detailed molecular characterization of the clonotypic architecture of an HLA-B*3501 restricted Gag -specific CD8+ T-cell response in donors chronically infected with HIV-2 using a combination of flow cytometry, tetramer-specific CD8+ TCR clonotyping and in vitro assays. We show that the response to the NY9 epitope is hierarchical and narrow in terms of T-cell receptor alpha (TCRA) and beta (TCRB) gene usage yet clonotypically diverse. Furthermore, clonotypic dominance in shared origin cytotoxic T lymphocyte (CTL) clones was associated with a greater magnitude of cytokine production and antigen sensitivity at limiting antigen dilution as well as enhanced cross-reactivity for known HIV-2 variants. Hence, our data suggest that effector mobilization and expansion in human chronic HIV-2 infection may be linked to the qualitative features of specific CD8+ T-cell clonotypes, which could have implications for viral control and disease outcome. This article is protected by copyright. All rights reserved.

TCR function analysis using a novel system reveals the multiple unconventional tumor-reactive T cells in human breast cancer-infiltrating lymphocytes

Tumor-infiltrating lymphocytes (TILs) are a potent source for obtaining tumor-reactive T cell receptors (TCRs). Although comprehensive methods to analyze the TCR repertoire in TILs have been reported, the evaluation system for TCR-reactivity to endogenously expressed antigen in tumor cells remains laborious and time consuming. Consequently, very limited numbers of TCRs in TILs have been analyzed for their reactivity to tumor cells. In this study, we developed an efficient evaluation system for TCR function designated c-FIT (comprehensive functional investigation of TCRs) to analyze TCR reactivity. The c-FIT system enabled us to analyze up to 90 TCRs for their reactivity to tumor cells by a single assay within a month. Using c-FIT, we analyzed 70 TCRs of CD8⁺ TILs derived from two breast cancer patients and obtained 23 TCRs that reacted to tumor cells. Surprisingly, although two TCRs were HLA class I-restricted, the remaining 21 TCRs were non-HLA-restricted. Thus, c-FIT can be applied for monitoring multiple conventional and unconventional antigen-specific killer T cells in TILs, leading to the development of new designs for more effective T-cell-based immunotherapies.

Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

SUMMARY

Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Regulation of T-cell Receptor Gene Expression by Three-Dimensional Locus Conformation and Enhancer Function

The adaptive immune response in vertebrates depends on the expression of antigen-specific receptors in lymphocytes. T-cell receptor (TCR) gene expression is exquisitely regulated during thymocyte development to drive the generation of αβ and γδ T lymphocytes. The TCRα, TCRβ, TCRγ, and TCRδ genes exist in two different configurations, unrearranged and rearranged. A correctly rearranged configuration is required for expression of a functional TCR chain. TCRs can take the form of one of three possible heterodimers, pre-TCR, TCRαβ, or TCRγδ which drive thymocyte maturation into αβ or γδ T lymphocytes. To pass from an unrearranged to a rearranged configuration, global and local three dimensional (3D) chromatin changes must occur during thymocyte development to regulate gene segment accessibility for V(D)J recombination. During this process, enhancers play a critical role by modifying the chromatin conformation and triggering noncoding germline transcription that promotes the recruitment of the recombination machinery. The different signaling that thymocytes receive during their development controls enhancer activity. Here, we summarize the dynamics of long-distance interactions established through chromatin regulatory elements that drive transcription and V(D)J recombination and how different signaling pathways are orchestrated to regulate the activity of enhancers to precisely control TCR gene expression during T-cell maturation.

Molecular Mimicry and Uveitis

Molecular or antigenic mimicry is a term for the similarity of different antigens, which can be confused by the immune system. Antigen recognition by antibodies and T cell receptors is specific, but not restricted to a single antigen. Both types of receptors specifically recognize antigens and are expressed with a very high but still restricted variability compared to the number of different antigens they potentially could bind. T cell receptors only can bind to antigen peptides presented on certain self-MHC-molecules by screening only some amino acid side chains on both the presented peptides and the MHC molecule. The other amino acids of the peptide are not directly perceived by the T cell, offering the opportunity for a single T cell to recognize a variety of different antigens with the same receptor, which significantly increases the immune repertoire. The immune system is usually tolerant to autoantigens, especially to those of immune privileged sites, like the eye. Therefore, autoimmune diseases targeting these organs were hard to explain, unless a T cell is activated by an environmental peptide (e.g. pathogen) that is similar, but not necessarily identical with an autoantigen. Here we describe antigenic mimicry of retinal autoantigens with a variety of non-ocular antigens resulting in the induction of intraocular inflammation. T cells that are activated by mimotopes outside of the eye can pass the blood-retina barrier and enter ocular tissues. When reactivated in the eye by crossreaction with autoantigens they induce uveitis by recruiting inflammatory cells.

Breaking Bottlenecks for the TCR Therapy of Cancer

Immune checkpoint inhibitors have redefined the treatment of cancer, but their efficacy depends critically on the presence of sufficient tumor-specific lymphocytes, and cellular immunotherapies develop rapidly to fill this gap. The paucity of suitable extracellular and tumor-associated antigens in solid cancers necessitates the use of neoantigen-directed T-cell-receptor (TCR)-engineered cells, while prevention of tumor evasion requires combined targeting of multiple neoepitopes. These can be currently identified within 2 weeks by combining cutting-edge next-generation sequencing with bioinformatic pipelines and used to select tumor-reactive TCRs in a high-throughput manner for expeditious scalable non-viral gene editing of autologous or allogeneic lymphocytes. "Young" cells with a naive, memory stem or central memory phenotype can be additionally armored with "next-generation" features against exhaustion and the immunosuppressive tumor microenvironment, where they wander after reinfusion to attack heavily pretreated and hitherto hopeless neoplasms. Facilitated by major technological breakthroughs in critical manufacturing steps, based on a solid preclinical rationale, and backed by rapidly accumulating evidence, TCR therapies break one bottleneck after the other and hold the promise to become the next immuno-oncological revolution.