Regulatory T cells constrain the TCR repertoire of antigen-stimulated conventional CD4 T cells.

To analyze the potential role of Tregs in controlling the TCR repertoire breadth to a non-self-antigen, a TCRß transgenic mouse model (EF4.1) expressing a limited, yet polyclonal naïve T-cell repertoire was used. The response of EF4.1 mice to an I-Ab-associated epitope of the F-MuLV envelope protein is dominated by clones expressing a Vα2 gene segment, thus allowing a comprehensive analysis of the TCRα repertoire in a relatively large cohort of mice. Control and Treg-depleted EF4.1 mice were immunized, and the extent of the Vα2-bearing, antigen-specific TCR repertoire was characterized by high-throughput sequencing and spectratyping analysis. In addition to increased clonal expansion and acquisition of effector functions, Treg depletion led to the expression of a more diverse TCR repertoire comprising several private clonotypes rarely observed in control mice or in the pre-immune repertoire. Injection of anti-CD86 antibodies in vivo led to a strong reduction in TCR diversity, suggesting that Tregs may influence TCR repertoire diversity by modulating costimulatory molecule availability. Collectively, these studies illustrate an additional mechanism whereby Tregs control the immune response to non-self-antigens.

T Cell Repertoire Abnormality in Immunodeficiency Patients with DNA Repair and Methylation Defects.

Both DNA damage response and methylation play a crucial role in antigen receptor recombination by creating a diverse repertoire in developing lymphocytes, but how their defects relate to T cell repertoire and phenotypic heterogeneity of immunodeficiency remains obscure. We studied the TCR repertoire in patients with the mutation in different genes (ATM, DNMT3B, ZBTB24, RAG1, DCLRE1C, and JAK3) and uncovered distinct characteristics of repertoire diversity. We propose that early aberrancies in thymus T cell development predispose to the heterogeneous phenotypes of the immunodeficiency spectrum. Shorter CDR3 lengths in ATM-deficient patients, resulting from a decreased number of nucleotide insertions during VDJ recombination in the pre-selected TCR repertoire, as well as the increment of CDR3 tyrosine residues, lead to the enrichment of pathology-associated TCRs, which may contribute to the phenotypes of ATM deficiency. Furthermore, patients with DNMT3B and ZBTB24 mutations who exhibit discrepant phenotypes present longer CDR3 lengths and reduced number of known pathology-associated TCRs.

Clustering based approach for population level identification of condition-associated T-cell receptor ß-chain CDR3 sequences.

BACKGROUND: Deep immune receptor sequencing, RepSeq, provides unprecedented opportunities for identifying and studying condition-associated T-cell clonotypes, represented by T-cell receptor (TCR) CDR3 sequences. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has mostly been limited to either "public" CDR3 sequences or to comparisons of CDR3 frequencies observed in a single individual. A methodology for the identification of condition-associated TCR CDR3s by direct population level comparison of RepSeq samples is currently lacking. RESULTS: We present a method for direct population level comparison of RepSeq samples using immune repertoire sub-units (or sub-repertoires) that are shared across individuals. The method first performs unsupervised clustering of CDR3s within each sample. It then finds matching clusters across samples, called immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. It finally ranks CDR3s in differentially abundant sub-repertoires for relevance to the condition. We applied the method on total TCR CDR3ß RepSeq datasets of celiac disease patients, as well as on public datasets of yellow fever vaccination. The method successfully identified celiac disease associated CDR3ß sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3ß sequences with previously known CDR3ßs specific to gluten in celiac disease. It also successfully recovered significantly high numbers of previously known CDR3ß sequences relevant to each condition than would be expected by chance. CONCLUSION: We conclude that immune sub-repertoires of similar immuno-genomic features shared across unrelated individuals can serve as viable units of immune repertoire comparison, serving as proxy for identification of condition-associated CDR3s.

Distinctive properties of identical twins' TCR repertoires revealed by high-throughput sequencing.

Adaptive immunity in humans is provided by hypervariable Ig-like molecules on the surface of B and T cells. The final set of these molecules in each organism is formed under the influence of two forces: individual genetic traits and the environment, which includes the diverse spectra of alien and self-antigens. Here we assess the impact of individual genetic factors on the formation of the adaptive immunity by analyzing the T-cell receptor (TCR) repertoires of three pairs of monozygous twins by next-generation sequencing. Surprisingly, we found that an overlap between the TCR repertoires of monozygous twins is similar to an overlap between the TCR repertoires of nonrelated individuals. However, the number of identical complementary determining region 3 sequences in two individuals is significantly increased for twin pairs in the fraction of highly abundant TCR molecules, which is enriched by the antigen-experienced T cells. We found that the initial recruitment of particular TCR V genes for recombination and subsequent selection in the thymus is strictly determined by individual genetic factors. J genes of TCRs are selected randomly for recombination; however, the subsequent selection in the thymus gives preference to some α but not ß J segments. These findings provide a deeper insight into the mechanism of TCR repertoire generation.

Identification of TCR repertoire patterns linked with anti-cancer immunotherapy.

The highly diverse T cell receptor (TCR) repertoire is a crucial component of the adaptive immune system that aids in the protection against a wide variety of pathogens. This TCR repertoire, comprising the collection of all TCRs in an individual, is a valuable source of information on both recent and ongoing T cell activation. Cancer cells, like pathogens, have the ability to trigger an adaptive immune response. However, because cancer cells use a variety of strategies to escape immune responses, this is often insufficient to completely eradicate them. As a result, immunotherapy is a promising treatment option for cancer patients. This treatment is expected to increase T cell activation and subsequently alter the TCR repertoire composition in these patients. Monitoring TCR repertoires before and after immunotherapy can therefore provide additional insight into T cell responses and might identify cancer-associated TCR sequences. Here we present a computational strategy to identify those changes in the TCR repertoire that occur after treatment with immunotherapy. Since this method allows the identification of TCR patterns that might be treatment-associated, it can help future research by revealing those patterns that are related with response. This TCR analysis workflow is illustrated using public data from three different cancer patients who received anti-PD-1 treatment.

Chicken γδ T cells proliferate upon IL-2 and IL-12 treatment and show a restricted receptor repertoire in cell culture.

In chickens, γδ T cells represent a large fraction of peripheral T cells; however, their function remains largely unknown. Here, we describe the selective in vitro expansion of γδ T cells from total splenocytes by stimulation with the cytokines IL-2 and IL-12. Under these conditions, γδ T cells proliferated preferentially and reached frequencies of >95% within three weeks. Although IL-2 alone also triggered proliferation, an increased proliferation rate was observed in combination with IL-12. Most of the expanded cells were γδ TCR and CD8 double-positive. Splenocytes sorted into TCR1+CD8+, TCR1highCD8-, and TCR1lowCD8- subsets proliferated well upon dual stimulation with IL-2/IL-12, indicating that none of the three γδ T cell subsets require bystander activation for proliferation. TCR1+CD8+ cells maintained CD8 surface expression during stimulation, whereas CD8- subpopulations showed varied levels of CD8 upregulation, with the highest upregulation observed in the TCR1high subset. Changes in the γδ T-cell receptor repertoire during cell culture from day 0 to day 21 were analyzed by next-generation sequencing of the γδ variable regions. Overall, long-term culture led to a restricted γ and δ chain repertoire, characterized by a reduced number of unique variable region clonotypes, and specific V genes were enriched at day 21. On day 0, the δ chain repertoire was highly diverse, and the predominant clonotypes differed between animals, while the most frequent γ-chain clonotypes were shared between animals. However, on day 21, the most frequent clonotypes in both the γ and δ chain repertoires were different between animals, indicating that selective expansion of dominant clonotypes during stimulation seems to be an individual outcome. In conclusion, IL-2 and IL-12 were sufficient to stimulate the in vitro outgrowth of γδ T cells. Analyses of the TCR repertoire indicate that the culture leads to an expansion of individual T cell clones, which may reflect previous in vivo activation. This system will be instrumental in studying γδ T cell function.

Pipeline to characterize antigen-specific TCR repertoires in tumors: Examples from an HPV16 tumor model.

Immunotherapies that improve T cell-based anti-tumor immunity have revolutionized cancer. However, the underlying mechanisms of cancer immune responsiveness are still not fully understood. Using immune competent mice for preclinical development of novel mono and combination therapies is a common strategy, and to monitor the T cell response inside tumors and in the periphery offers valuable insight. T cells recognize target cells by based on the binding between the T cell receptor (on T cells) and peptides presented on MHC-I (on tumor cells). As such, the T cell receptor can be used as a "barcode" for a specific T cell clone. Via TCR sequencing, the sequence of this "barcode" can be identified, and eventually, the TCR repertoire in a sample can be assessed as a whole. This information can be useful in multiple ways, including but not excluded to: (i) tracing specific clones in tissues and in blood, and (ii) determine clonal expansion of a specific clone in the tumor microenvironment which suggest anti-tumor activity of the clone in question. This protocol can be used as a guide from experimental design through TCR-sequencing to analysis of the repertoire. Instead of being specifically focused on one type of TCR-sequencing, this protocol can be used as a resource and contains links and references to useful information that has to be considered. Lastly, certain common metrics when analyzing the TCR repertoire are given and discussed.

Leveraging T-cell receptor - epitope recognition models to disentangle unique and cross-reactive T-cell response to SARS-CoV-2 during COVID-19 progression/resolution.

Despite the general agreement on the significance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain. Understanding this aspect could provide insights for adjusting vaccines and maintaining robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to SARS-CoV-2 epitopes unique to the virus (SC2-unique) or shared with other coronaviruses (CoV-common), we trained a large number of T-cell receptor (TCR) - epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. These models were then applied to longitudinal CD8+ TCR repertoires from critical and non-critical COVID-19 patients. In spite of comparable initial CoV-common TCR repertoire depth and CD8+ T-cell depletion, the temporal dynamics of SC2-unique TCRs differed depending on the disease severity. Specifically, while non-critical patients demonstrated a large and diverse SC2-unique TCR repertoire by the second week of the disease, critical patients did not. Furthermore, only non-critical patients exhibited redundancy in the CD8+ T-cell response to both groups of epitopes, SC2-unique and CoV-common. These findings indicate a valuable contribution of the SC2-unique CD8+ TCR repertoires. Therefore, a combination of specific and cross-reactive CD8+ T-cell responses may offer a stronger clinical advantage. Besides tracking the specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire, our analytical framework can be expanded to more epitopes and assist in the assessment and monitoring of CD8+ T-cell response to other infections.

Clustering and Annotation of T Cell Receptor Repertoires.

Immunological protection against a wide variety of pathogens is largely mediated by the diverse and dynamic T cell receptor (TCR) repertoire, a crucial component of the adaptive immune system. An encounter with infectious agents stimulates specific T cells to initiate a direct immune response to combat intruders. Hence, the TCR repertoire may conceal crucial information regarding current and past infections and might assist in the development and monitoring of vaccines. To unlock its knowledge, we describe a computational workflow involving both supervised and unsupervised machine learning techniques to analyze and annotate full TCR repertoire data. The method is explained using data from a published yellow fever virus (YFV) vaccination study in healthy individuals. The TCR repertoire of one individual is studied before and 2 weeks after vaccination, using an efficient clustering method and identification of YFV-specific TCRs.

Public T-Cell Receptors (TCRs) Revisited by Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires of Healthy Individuals.

Since multiple different T-cell receptor (TCR) sequences can bind to the same peptide-MHC combination and the number of TCR-sequences that can theoretically be generated even exceeds the number of T cells in a human body, the likelihood that many public identical (PUB-I) TCR-sequences frequently contribute to immune responses has been estimated to be low. Here, we quantitatively analyzed the TCR-repertoires of 190 purified virus-specific memory T-cell populations, directed against 21 epitopes of Cytomegalovirus, Epstein-Barr virus and Adenovirus isolated from 29 healthy individuals, and determined the magnitude, defined as prevalence within the population and frequencies within individuals, of PUB-I TCR and of TCR-sequences that are highly-similar (PUB-HS) to these PUB-I TCR-sequences. We found that almost one third of all TCR nucleotide-sequences represented PUB-I TCR amino-acid (AA) sequences and found an additional 12% of PUB-HS TCRs differing by maximally 3 AAs. We illustrate that these PUB-I and PUB-HS TCRs were structurally related and contained shared core-sequences in their TCR-sequences. We found a prevalence of PUB-I and PUB-HS TCRs of up to 50% among individuals and showed frequencies of virus-specific PUB-I and PUB-HS TCRs making up more than 10% of each virus-specific T-cell population. These findings were confirmed by using an independent TCR-database of virus-specific TCRs. We therefore conclude that the magnitude of the contribution of PUB-I and PUB-HS TCRs to these virus-specific T-cell responses is high. Because the T cells from these virus-specific memory TCR-repertoires were the result of successful control of the virus in these healthy individuals, these PUB-HS TCRs and PUB-I TCRs may be attractive candidates for immunotherapy in immunocompromised patients that lack virus-specific T cells to control viral reactivation.

Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction.

Introduction: Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding. Methods: We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction. Results and Discussion: ERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires.

Recognition of cancer epitopes by T cells is fundamental for the activation of targeted antitumor responses. As such, the identification and study of epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) directly from TCR repertoire data and perform epitope-specificity enrichment analyses. This chapter details the use of the TCRex web tool.

Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.