Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

Evolution of Cytomegalovirus-Responsive T Cell Clonality following Solid Organ Transplantation

CMV infection is a significant complication after solid organ transplantation. We used single cell TCR αβ sequencing to determine how memory inflation impacts clonality and diversity of the CMV-responsive CD8 and CD4 T cell repertoire in the first year after transplantation in human subjects. We observed CD8 T cell inflation but no changes in clonal diversity, indicating homeostatic stability in clones. In contrast, the CD4 repertoire was diverse and stable over time, with no evidence of CMV-responsive CD4 T cell expansion. We identified shared CDR3 TCR motifs among patients but no public CMV-specific TCRs. Temporal changes in clonality in response to transplantation and in the absence of detectable viral reactivation suggest changes in the repertoire immediately after transplantation followed by an expansion with stable clonal competition that may mediate protection.

Characteristics of T-cell receptor repertoire of stem cell-like memory CD4+ T cells

Stem cell-like memory T cells (Tscm) combine phenotypes of naïve and memory. However, it remains unclear how T cell receptor (TCR) characteristics contribute to heterogeneity in Tscm and other memory T cells. We compared the TCR-beta (TRB) repertoire characteristics of CD4+ Tscm with those of naïve and other CD4+ memory (Tm) in 16 human subjects. Compared with Tm, Tscm had an increased diversity across all stretches of TRB repertoire structure, a skewed gene usage, and a shorter length distribution of CDR3 region. These distinctions between Tscm and Tm were enlarged in top1000 abundant clonotypes. Furthermore, top1000 clonotypes in Tscm were more public than those in Tm and grouped in more clusters, implying more epitope types recognized by top1000 clonotypes in Tscm. Importantly, self-reactive clonotypes were public and enriched in Tscm rather than Tm, of type one diabetes patients. Therefore, this study highlights the unique features of Tscm different from those of other memory subsets and provides clues to understand the physiological and pathological functions of Tscm.

Antigen-driven clonal selection shapes the persistence of HIV-1-infected CD4+ T cells in vivo

Clonal expansion of infected CD4+ T cells is a major mechanism of HIV-1 persistence and a barrier to achieving a cure. Potential causes are homeostatic proliferation, effects of HIV-1 integration, and interaction with antigens. Here, we show that it is possible to link antigen responsiveness, the full proviral sequence, the integration site, and the T cell receptor β-chain (TCRβ) sequence to examine the role of recurrent antigenic exposure in maintaining the HIV-1 reservoir. We isolated CMV- and Gag-responding CD4+ T cells from 10 treated individuals. Proviral populations in CMV-responding cells were dominated by large clones, including clones harboring replication-competent proviruses. TCRβ repertoires showed high clonality driven by converging adaptive responses. Although some proviruses were in genes linked to HIV-1 persistence (BACH2, STAT5B, MKL1), the proliferation of infected cells under antigenic stimulation occurred regardless of the site of integration. Paired TCRβ and integration site analysis showed that infection could occur early or late in the course of a clone's response to antigen and could generate infected cell populations too large to be explained solely by homeostatic proliferation. Together, these findings implicate antigen-driven clonal selection as a major factor in HIV-1 persistence, a finding that will be a difficult challenge to eradication efforts.

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

Landscape of T-cell repertoires with public COVID-19-associated T-cell receptors in pre-pandemic risk cohorts

Objectives

T cells have an essential role in the antiviral defence. Public T-cell receptor (TCR) clonotypes are expanded in a substantial proportion of COVID-19 patients. We set out to exploit their potential use as read-out for COVID-19 T-cell immune responses.

Methods

We searched for COVID-19-associated T-cell clones with public TCRs, as defined by identical complementarity-determining region 3 (CDR3) beta chain amino acid sequence that can be reproducibly detected in the blood of COVID-19 patients. Of the different clonotype identification algorithms used in this study, deep sequencing of brain tissue of five patients with fatal COVID-19 delivered 68 TCR clonotypes with superior representation across 140 immune repertoires of unrelated COVID-19 patients.

Results

Mining of immune repertoires from subjects not previously exposed to the virus showed that these clonotypes can be found in almost 20% of pre-pandemic immune repertoires of healthy subjects, with lower representation in repertoires from risk groups like individuals above the age of 60 years or patients with cancer.

Conclusion

Together, our data show that at least a proportion of the SARS-CoV-2 T-cell response is mediated by public TCRs that are present in repertoires of unexposed individuals. The lower representation of these clones in repertoires of risk groups or failure to expand such clones may contribute to more unfavorable clinical COVID-19 courses.

Probing T-cell response by sequence-based probabilistic modeling

With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.

Impact of HLA type, age and chronic viral infection on peripheral T-cell receptor sharing between unrelated individuals

The human adaptive immune system must generate extraordinary diversity to be able to respond to all possible pathogens. The T-cell repertoire derives this high diversity through somatic recombination of the T-cell receptor (TCR) locus, a random process that results in repertoires that are largely private to each individual. However, factors such as thymic selection and T-cell proliferation upon antigen exposure can affect TCR sharing among individuals. By immunosequencing the TCRβ variable region of 426 healthy individuals, we find that, on average, fewer than 1% of TCRβ clones are shared between individuals, consistent with largely private TCRβ repertoires. However, we detect a significant correlation between increased HLA allele sharing and increased number of shared TCRβ clones, with each additional shared HLA allele contributing to an increase in ~0.01% of the total shared TCRβ clones, supporting a key role for HLA type in shaping the immune repertoire. Surprisingly, we find that shared antigen exposure to CMV leads to fewer shared TCRβ clones, even after controlling for HLA, indicative of a largely private response to major viral antigenic exposure. Consistent with this hypothesis, we find that increased age is correlated with decreased overall TCRβ clone sharing, indicating that the pattern of private TCRβ clonal expansion is a general feature of the T-cell response to other infectious antigens as well. However, increased age also correlates with increased sharing among the lowest frequency clones, consistent with decreased repertoire diversity in older individuals. Together, all of these factors contribute to shaping the TCRβ repertoire, and understanding their interplay has important implications for the use of T cells for therapeutics and diagnostics.

Clonally expanded, GPR15-expressing pathogenic effector TH2 cells are associated with eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is an allergic disorder characterized by the recruitment of eosinophils to the esophagus, resulting in chronic inflammation. We sought to understand the cellular populations present in tissue biopsies of patients with EoE and to determine how these populations are altered between active disease and remission. To this end, we analyzed cells obtained from esophageal biopsies, duodenal biopsies, and peripheral blood of patients with EoE diagnosed with active disease or remission with single-cell RNA and T cell receptor (TCR) sequencing. Pathogenic effector TH2 (peTH2) cells present in the esophageal biopsies of patients with active disease expressed distinct gene signatures associated with the synthesis of eicosanoids. The esophageal tissue-resident peTH2 population also exhibited clonal expansion, suggesting antigen-specific activation. Peripheral CRTH2+CD161- and CRTH2+CD161+ memory CD4+ T cells were enriched for either a conventional TH2 phenotype or a peTH2 phenotype, respectively. These cells also exhibited substantial clonal expansion and convergence of TCR sequences, suggesting that they are expanded in response to a defined set of antigens. The esophagus-homing receptor GPR15 was up-regulated by peripheral peTH2 clonotypes that were also detected in the esophagus. Finally, GPR15+ peTH2 cells were enriched among milk-reactive CD4+ T cells in patients with milk-triggered disease, suggesting that these cells are an expanded, food antigen-specific population with enhanced esophagus homing potential.

GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Grouping T-cell receptors (TCRs) by sequence similarity could lead to new immunological insights. Here, the authors propose a tool that allows the rapid clustering of millions of TCR sequences, identifying TCRs potentially associated with the response to cancer, infectious and autoimmune diseases.

TCR repertoire characteristics predict clinical response to adoptive CTL therapy against nasopharyngeal carcinoma

The past decade has witnessed the gradual and steady progress of adoptive T cell therapy in treating various types of cancer. In combination with gemcitabine and carboplatin chemotherapy, we previously conducted a clinical trial, NCT00690872, to treat Epstein-Barr virus (EBV)-positive nasopharyngeal carcinoma (NPC) patients with autologous EBV-expanded cytotoxic T lymphocytes (CTLs). While achieving a 2-year overall survival rate of 62.9%, this trial failed to induce an anti-tumor response in a sizable fraction of patients. Thus, the identification of benchmarks capable of evaluating CTL products and predicting clinical immunotherapeutic efficacy remains an urgent need. We conducted T cell receptor (TCR) repertoire sequencing to assess EBV-expanded infusion-ready CTL products. To depict the overall repertoire landscape, we evaluated the individual repertoire diversity by Shannon entropy, and, compared the inter-patient CDR3 similarity to estimate T cells expanded by common antigens. With a recently developed bioinformatics algorithm, termed Motif Analysis, we made a machine-learning prediction of structural regions within the CDR3 of TCRβ that associate with CTL therapy prognosis. We found that long term survivors, defined as patients surviving longer than two years, had a higher CTL repertoire diversity with reduced inter-patient similarity. Furthermore, TCR Motif Analysis identified 11 structural motifs distinguishing long term survivors from short term survivors. Specifically, two motifs with a high area under the curve (AUC) values were identified as potential predictive benchmarks for efficacious CTL production. Together, these results reveal that the presence of diverse TCR sequences containing a common core motif set is associated with a favorable response to CTL immunotherapy against EBV-positive NPC.

Yeast display of MHC-II enables rapid identification of peptide ligands from protein antigens (RIPPA)

CD4+ T cells orchestrate adaptive immune responses via binding of antigens to their receptors through specific peptide/MHC-II complexes. To study these responses, it is essential to identify protein-derived MHC-II peptide ligands that constitute epitopes for T cell recognition. However, generating cells expressing single MHC-II alleles and isolating these proteins for use in peptide elution or binding studies is time consuming. Here, we express human MHC alleles (HLA-DR4 and HLA-DQ6) as native, noncovalent αβ dimers on yeast cells for direct flow cytometry-based screening of peptide ligands from selected antigens. We demonstrate rapid, accurate identification of DQ6 ligands from pre-pro-hypocretin, a narcolepsy-related immunogenic target. We also identify 20 DR4-binding SARS-CoV-2 spike peptides homologous to SARS-CoV-1 epitopes, and one spike peptide overlapping with the reported SARS-CoV-2 epitope recognized by CD4+ T cells from unexposed individuals carrying DR4 subtypes. Our method is optimized for immediate application upon the emergence of novel pathogens.

Antigen-Specific Treg Therapy in Type 1 Diabetes - Challenges and Opportunities

Regulatory T cells (Tregs) are key mediators of peripheral self-tolerance and alterations in their frequencies, stability, and function have been linked to autoimmunity. The antigen-specific induction of Tregs is a long-envisioned goal for the treatment of autoimmune diseases given reduced side effects compared to general immunosuppressive therapies. However, the translation of antigen-specific Treg inducing therapies for the treatment or prevention of autoimmune diseases into the clinic remains challenging. In this mini review, we will discuss promising results for antigen-specific Treg therapies in allergy and specific challenges for such therapies in autoimmune diseases, with a focus on type 1 diabetes (T1D). We will furthermore discuss opportunities for antigen-specific Treg therapies in T1D, including combinatorial strategies and tissue-specific Treg targeting. Specifically, we will highlight recent advances in miRNA-targeting as a means to foster Tregs in autoimmunity. Additionally, we will discuss advances and perspectives of computational strategies for the detailed analysis of tissue-specific Tregs on the single-cell level.

Targeting Neoepitopes to Treat Solid Malignancies: Immunosurgery

Successful outcome of immune checkpoint blockade in patients with solid cancers is in part associated with a high tumor mutational burden (TMB) and the recognition of private neoantigens by T-cells. The quality and quantity of target recognition is determined by the repertoire of ‘neoepitope’-specific T-cell receptors (TCRs) in tumor-infiltrating lymphocytes (TIL), or peripheral T-cells. Interferon gamma (IFN-γ), produced by T-cells and other immune cells, is essential for controlling proliferation of transformed cells, induction of apoptosis and enhancing human leukocyte antigen (HLA) expression, thereby increasing immunogenicity of cancer cells. TCR αβ-dependent therapies should account for tumor heterogeneity and availability of the TCR repertoire capable of reacting to neoepitopes and functional HLA pathways. Immunogenic epitopes in the tumor-stroma may also be targeted to achieve tumor-containment by changing the immune-contexture in the tumor microenvironment (TME). Non protein-coding regions of the tumor-cell genome may also contain many aberrantly expressed, non-mutated tumor-associated antigens (TAAs) capable of eliciting productive anti-tumor immune responses. Whole-exome sequencing (WES) and/or RNA sequencing (RNA-Seq) of cancer tissue, combined with several layers of bioinformatic analysis is commonly used to predict possible neoepitopes present in clinical samples. At the ImmunoSurgery Unit of the Champalimaud Centre for the Unknown (CCU), a pipeline combining several tools is used for predicting private mutations from WES and RNA-Seq data followed by the construction of synthetic peptides tailored for immunological response assessment reflecting the patient’s tumor mutations, guided by MHC typing. Subsequent immunoassays allow the detection of differential IFN-γ production patterns associated with (intra-tumoral) spatiotemporal differences in TIL or peripheral T-cells versus TIL. These bioinformatics tools, in addition to histopathological assessment, immunological readouts from functional bioassays and deep T-cell ‘adaptome’ analyses, are expected to advance discovery and development of next-generation personalized precision medicine strategies to improve clinical outcomes in cancer in the context of i) anti-tumor vaccination strategies, ii) gauging mutation-reactive T-cell responses in biological therapies and iii) expansion of tumor-reactive T-cells for the cellular treatment of patients with cancer.

A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 Patients

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8⁺ T cell population. Highly expanded CD8⁺ and CD4⁺ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

Evaluating T-cell cross-reactivity between tumors and immune-related adverse events with TCR sequencing: pitfalls in interpretations of functional relevance

T-cell receptor sequencing (TCRseq) enables tracking of T-cell clonotypes recognizing the same antigen over time and across biological compartments. TCRseq has been used to test if cross-reactive antitumor T cells are responsible for development of immune-related adverse events (irAEs) following immune checkpoint blockade. Prior studies have interpreted T-cell clones shared among the tumor and irAE as evidence supporting this, but interpretations of these findings are challenging, given the constraints of TCRseq. Here we capitalize on a rare opportunity to understand the impact of potential confounders, such as sample size, tissue compartment, and collection batch/timepoint, on the relative proportion of shared T-cell clones between an irAE and tumor specimens. TCRseq was performed on tumor-involved and -uninvolved tissues, including an irAE, that were obtained throughout disease progression and at the time of rapid autopsy from a patient with renal cell carcinoma treated with programmed death-1 (PD-1) blockade. Our analyses show significant effects of these confounders on our ability to understand T-cell receptor overlap, and we present mitigation strategies and study design recommendations to reduce these errors. Implementation of these strategies will enable more rigorous TCRseq-based studies of immune responses in human tissues, particularly as they relate to antitumor T-cell cross-reactivity in irAEs following checkpoint blockade.

CD8+ T cells specific for conserved coronavirus epitopes correlate with milder disease in COVID-19 patients

A central feature of the SARS-CoV-2 pandemic is that some individuals become severely ill or die, whereas others have only a mild disease course or are asymptomatic. Here we report development of an improved multimeric αβ T cell staining reagent platform, with each maxi-ferritin "spheromer" displaying 12 peptide-MHC complexes. Spheromers stain specific T cells more efficiently than peptide-MHC tetramers and capture a broader portion of the sequence repertoire for a given peptide-MHC. Analyzing the response in unexposed individuals, we find that T cells recognizing peptides conserved amongst coronaviruses are more abundant and tend to have a "memory" phenotype, compared to those unique to SARS-CoV-2. Significantly, CD8+ T cells with these conserved specificities are much more abundant in COVID-19 patients with mild disease versus those with a more severe illness, suggesting a protective role.

ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity

MOTIVATION

The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large data sets exceeding 1 million sequences. To account for this limitation, we developed ClusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences, without knowledge about their antigen specificity.

RESULTS

Benchmarking comparisons revealed similar accuracy of ClusTCR as compared to other TCR clustering methods, as measured by cluster retention, purity and consistency. ClusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through ultra-efficient similarity searching and sequence hashing.

AVAILABILITY

ClusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4+ T cell repertoire

The CD4⁺ T cell response is critical to host protection against helminth infection. How this response varies across different hosts and tissues remains an important gap in our understanding. Using IL-4-reporter mice to identify responding CD4⁺ T cells to Nippostrongylus brasiliensis infection, T cell receptor sequencing paired with novel clustering algorithms revealed a broadly reactive and clonally diverse CD4⁺ T cell response. While the most prevalent clones and clonotypes exhibited some tissue selectivity, most were observed to reside in both the lung and lung-draining lymph nodes. Antigen-reactivity of the broader repertoires was predicted to be shared across both tissues and individual mice. Transcriptome, trajectory, and chromatin accessibility analysis of lung and lymph-node repertoires revealed three unique but related populations of responding IL-4⁺ CD4⁺ T cells consistent with T follicular helper, T helper 2, and a transitional population sharing similarity with both populations. The shared antigen reactivity of lymph node and lung repertoires combined with the adoption of tissue-specific gene programs allows for the pairing of cellular and humoral responses critical to the orchestration of anti-helminth immunity.

Using various “omic” approaches, the CD4⁺ T cell receptor (TCR) repertoire was explored after primary helminth infection. Infection generated a broadly reactive and clonally diverse CD4⁺ T cell response with the most prevalent clonotypes and predicted antigen specificities residing in both the lung and lung-draining lymph nodes. Tissue-specific programming of responding CD4⁺ T cells directed the establishment of committed Tfh and Th2 cells, both critical for driving distinct hallmarks of type-2 inflammation. These datasets help to explore the diverse yet tissue-specific nature of anti-helminth immunity.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community's Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.

Rapid T cell receptor interaction grouping with ting

MOTIVATION

Clustering T cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences.

RESULTS

We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3β sequences using both an implementation of our method called ting, GLIPH and its successsor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in naïve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters.

AVAILABILITY

Our method has been implemented in Python as a tool called ting. It is available from GitHub (https://github.com/FelixMoelder/ting) or PyPI under the MIT license.

Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrated, but the effect of immune heterogeneity on clinical outcome in ccRCC has not been fully characterized. Here we perform paired single-cell RNA (scRNA) and T cell receptor (TCR) sequencing of 167,283 cells from multiple tumor regions, lymph node, normal kidney, and peripheral blood of two immune checkpoint blockade (ICB)-naïve and four ICB-treated patients to map the ccRCC immune landscape. We detect extensive heterogeneity within and between patients, with enrichment of CD8A+ tissue-resident T cells in a patient responsive to ICB and tumor-associated macrophages (TAMs) in a resistant patient. A TCR trajectory framework suggests distinct T cell differentiation pathways between patients responding and resistant to ICB. Finally, scRNA-derived signatures of tissue-resident T cells and TAMs are associated with response to ICB and targeted therapies across multiple independent cohorts. Our study establishes a multimodal interrogation of the cellular programs underlying therapeutic efficacy in ccRCC.

Profiling of T Cell Repertoire in SARS-CoV-2-Infected COVID-19 Patients Between Mild Disease and Pneumonia

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a public health emergency. The most common symptoms of COVID-19 are fever, cough, and fatigue. While most patients with COVID-19 present with mild illness, some patients develop pneumonia, an important risk factor for mortality, at early stage of viral infection, putting these patients at increased risk of death. So far, little has been known about differences in the T cell repertoires between COVID-19 patients with and without pneumonia during SARS-CoV-2 infection. Herein, we aimed to investigate T cell receptor (TCR) repertoire profiles and patient-specific SARS-CoV-2-associated TCR clusters between COVID-19 patients with mild disease (no sign of pneumonia) and pneumonia. The TCR sequencing was conducted to characterize the peripheral TCR repertoire profile and diversity. The TCR clustering and CDR3 annotation were exploited to further discover groups of patient-specific TCR clonotypes with potential SARS-CoV-2 antigen specificities. Our study indicated a slight decrease in the TCR repertoire diversity and a skewed CDR3 length usage in patients with pneumonia compared to those with mild disease. The SARS-CoV-2-associated TCR clusters enriched in patients with mild disease exhibited significantly higher TCR generation probabilities and most of which were highly shared among patients, compared with those from pneumonia patients. Importantly, using similarity network-based clustering followed by the sequence conservation analysis, we found different patterns of CDR3 sequence motifs between mild disease- and pneumonia-specific SARS-CoV-2-associated public TCR clusters. Our results showed that characteristics of overall TCR repertoire and SARS-CoV-2-associated TCR clusters/clonotypes were divergent between COVID-19 patients with mild disease and patients with pneumonia. These findings provide important insights into the correlation between the TCR repertoire and disease severity in COVID-19 patients.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

TRB sequences targeting ORF1a/b are associated with disease severity in hospitalized COVID-19 patients

The potential protective or pathogenic role of the adaptive immune response to SARS‐CoV‐2 infection has been vigorously debated. While COVID‐19 patients consistently generate a T lymphocyte response to SARS‐CoV‐2 antigens, evidence of significant immune dysregulation in these patients continues to accumulate. In this study, next generation sequencing of the T cell receptor beta chain (TRB) repertoire was conducted in hospitalized COVID‐19 patients to determine if immunogenetic differences of the TRB repertoire contribute to disease course severity. Clustering of highly similar TRB CDR3 amino acid sequences across COVID‐19 patients yielded 781 shared TRB sequences. The TRB sequences were then filtered for known associations with common diseases such as EBV and CMV. The remaining sequences were cross‐referenced to a publicly accessible dataset that mapped COVID‐19 specific TCRs to the SARS‐CoV‐2 genome. We identified 158 SARS‐CoV‐2 specific TRB sequences belonging to 134 clusters in our COVID‐19 patients. Next, we investigated 113 SARS‐CoV‐2 specific clusters binding only one peptide target in relation to disease course. Distinct skewing of SARS‐CoV‐2 specific TRB sequences toward the nonstructural proteins (NSPs) encoded within ORF1a/b of the SARS‐CoV‐2 genome was observed in clusters associated with critical disease course when compared to COVID‐19 clusters associated with a severe disease course. These data imply that T‐lymphocyte reactivity towards peptides from NSPs of SARS‐CoV‐2 may not constitute an effective adaptive immune response and thus may negatively affect disease severity.

Response to Hypomethylating Agents in Myelodysplastic Syndrome Is Associated With Emergence of Novel TCR Clonotypes

Aberrant T-cell function is implicated in the pathogenesis of myelodysplastic syndrome (MDS). Monitoring the T-cell receptor (TCR) repertoire can provide insights into T-cell adaptive immunity. Previous studies found skewed TCR repertoires in MDS compared to healthy patients; however these studies that leverage mRNA-based spectratyping have limitations. Furthermore, evaluating the TCR repertoire in context of hypomethylating agents (HMAs) treatment can provide insights into the dynamics of T-cell mediated responses in MDS. We conducted immunosequencing of the CDR3 regions of TCRβ chains in bone marrows of 11 MDS patients prior to treatment (n=11 bone marrows prior to treatment), and in at least 2 timepoints for each patient following treatment (n=26 bone marrow aspirates post-treatment) with (HMA), alongside analyzing bone marrows from 4 healthy donors as controls. TCR repertoires in MDS patients were more clonal and less diverse than healthy donors. However, unlike previous reports, we did not observe significant skewness in CDR3 length or spectratyping. The global metrics of TCR profiling including richness, clonality, overlaps were not significantly changed in responders or non-responders following treatment with HMAs. However, we found an emergence of novel clonotypes in MDS patients who responded to treatment, while non-responders had a higher frequency of contracted clonotypes following treatment. By applying GLIPH2 for antigen prediction, we found rare TCR specificity clusters shared by TCR clonotypes from different patients at pre- or following treatment. Our data show clear differences in TCR repertoires of MDS compared with healthy patients and that novel TCR clonotype emergence in response to HMA therapy was correlated with response. This suggests that response to HMA therapy may be partially driven by T-cell mediated immunity and that the immune-based therapies, which target the adaptive immune system, may play a significant role in select patients with MDS.

TCR meta-clonotypes for biomarker discovery with tcrdist3: identification of public, HLA-restricted SARS-CoV-2 associated TCR features

As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages antigen-enriched repertoires to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly identify and quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the 17 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (FDR < 0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

The ability to monitor anti-tumor CD8⁺ T cell responses in the blood has tremendous therapeutic potential. Here, we used paired single-cell RNA and TCR sequencing to detect and characterize “tumor-matching” (TM) CD8⁺ T cells in the blood of mice with MC38 tumors or melanoma patients using the TCR as a molecular barcode. TM cells showed increased activation compared with nonmatching T cells in blood and were less exhausted than matching cells in tumors. Importantly, PD-1, which has been used to identify putative circulating anti-tumor CD8⁺ T cells, showed poor sensitivity for identifying TM cells. By leveraging the transcriptome, we identified candidate cell surface markers for TM cells in mice and patients and validated NKG2D, CD39, and CX3CR1 in mice. These data show that the TCR can be used to identify tumor-relevant cells for characterization, reveal unique transcriptional properties of TM cells, and develop marker panels for tracking and analysis of these cells.

Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery

To identify disease-relevant T cell receptors (TCRs) with shared antigen specificity, we analyzed 778,938 TCRβ chain sequences from 178 non-small cell lung cancer patients using the GLIPH2 (grouping of lymphocyte interactions with paratope hotspots 2) algorithm. We identified over 66,000 shared specificity groups, of which 435 were clonally expanded and enriched in tumors compared to adjacent lung. The antigenic epitopes of one such tumor-enriched specificity group were identified using a yeast peptide-HLA A^∗02:01 display library. These included a peptide from the epithelial protein TMEM161A, which is overexpressed in tumors and cross-reactive epitopes from Epstein-Barr virus and E. coli. Our findings suggest that this cross-reactivity may underlie the presence of virus-specific T cells in tumor infiltrates and that pathogen cross-reactivity may be a feature of multiple cancers. The approach and analytical pipelines generated in this work, as well as the specificity groups defined here, present a resource for understanding the T cell response in cancer.

Nanobiomaterial-based vaccination immunotherapy of cancer

Cancer immunotherapies including cancer vaccines, immune checkpoint blockade or chimeric antigen receptor T cells have been exploited as the attractive treatment modalities in recent years. Among these approaches, cancer vaccines that designed to deliver tumor antigens and adjuvants to activate the antigen presenting cells (APCs) and induce antitumor immune responses, have shown significant efficacy in inhibiting tumor growth, preventing tumor relapse and metastasis. Despite the potential of cancer vaccination strategies, the therapeutic outcomes in preclinical trials are failed to promote their clinical translation, which is in part due to their inefficient vaccination cascade of five critical steps: antigen identification, antigen encapsulation, antigen delivery, antigen release and antigen presentation to T cells. In recent years, it has been demonstrated that various nanobiomaterials hold great potential to enhance cancer vaccination cascade and improve their antitumor performance and reduce the off-target effect. We summarize the cutting-edge advances of nanobiomaterials-based vaccination immunotherapy of cancer in this review. The various cancer nanovaccines including antigen peptide/adjuvant-based nanovaccines, nucleic acid-based nanovaccines as well as biomimetic nanobiomaterials-based nanovaccines are discussed in detail. We also provide some challenges and perspectives associated with the clinical translation of cancer nanovaccines.

Antigen receptor repertoires of one of the smallest known vertebrates

The rules underlying the structure of antigen receptor repertoires are not yet fully defined, despite their enormous importance for the understanding of adaptive immunity. With current technology, the large antigen receptor repertoires of mice and humans cannot be comprehensively studied. To circumvent the problems associated with incomplete sampling, we have studied the immunogenetic features of one of the smallest known vertebrates, the cyprinid fish Paedocypris sp. "Singkep" ("minifish"). Despite its small size, minifish has the key genetic facilities characterizing the principal vertebrate lymphocyte lineages. As described for mammals, the frequency distributions of immunoglobulin and T cell receptor clonotypes exhibit the features of fractal systems, demonstrating that self-similarity is a fundamental property of antigen receptor repertoires of vertebrates, irrespective of body size. Hence, minifish achieve immunocompetence via a few thousand lymphocytes organized in robust scale-free networks, thereby ensuring immune reactivity even when cells are lost or clone sizes fluctuate during immune responses.

A Conserved TCRβ Signature Dominates a Highly Polyclonal T-Cell Expansion During the Acute Phase of a Murine Malaria Infection

CD4+ αβ T-cells are key mediators of the immune response to a first Plasmodium infection, undergoing extensive activation and splenic expansion during the acute phase of an infection. However, the clonality and clonal composition of this expansion has not previously been described. Using a comparative infection model, we sequenced the splenic CD4+ T-cell receptor repertoires generated over the time-course of a Plasmodium chabaudi infection. We show through repeat replicate experiments, single-cell RNA-seq, and analyses of independent RNA-seq data, that following a first infection - within a highly polyclonal expansion - T-effector repertoires are consistently dominated by TRBV3 gene usage. Clustering by sequence similarity, we find the same dominant clonal signature is expanded across replicates in the acute phase of an infection, revealing a conserved pathogen-specific T-cell response that is consistently a hallmark of a first infection, but not expanded upon re-challenge. Determining the host or parasite factors driving this conserved response may uncover novel immune targets for malaria therapeutic purposes.

Epitope prediction and identification- adaptive T cell responses in humans

Epitopes, in the context of T cell recognition, are short peptides typically derived by antigen processing, and presented on the cell surface bound to MHC molecules (HLA molecules in humans) for TCR scrutiny. The identification of epitopes is a context-dependent process, with consideration given to, for example, the source pathogen and protein, the host organism, and state of the immune reaction (e.g., following natural infection, vaccination, etc.). In the following review, we consider the various approaches used to define T cell epitopes, including both bioinformatic and experimental approaches, and discuss the concepts of immunodominance and immunoprevalence. We also discuss HLA polymorphism and epitope restriction, and the resulting impact on the identification of, and potential population coverage afforded by, epitopes or epitope-based vaccines. Finally, some examples of the practical application of T cell epitope identification are provided, showing how epitopes have been valuable for deriving novel immunological insights in the context of the immune response to various pathogens and allergens.

Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Advances in reading, writing, and editing DNA are providing unprecedented insights into the complexity of immunological systems. This combination of systems and synthetic biology methods is enabling the quantitative and precise understanding of molecular recognition in adaptive immunity, thus providing a framework for reprogramming immune responses for translational medicine. In this review, we will highlight state-of-the-art methods such as immune repertoire sequencing, immunoinformatics, and immunogenomic engineering and their application toward adaptive immunity. We showcase novel and interdisciplinary approaches that have the promise of transforming the design and breadth of molecular and cellular immunotherapies.

Ultra-efficient sequencing of T Cell receptor repertoires reveals shared responses in muscle from patients with Myositis

BACKGROUND

Myositis, or idiopathic inflammatory myopathy (IIM), is a group disorders of unknown etiology characterized by the inflammation of skeletal muscle. The role of T cells and their antigenic targets in IIM initiation and progression is poorly understood. T cell receptor (TCR) repertoire sequencing is a powerful approach for characterizing complex T cell responses. However, current TCR sequencing methodologies are complex, expensive, or both, greatly limiting the scale of feasible studies.

METHODS

Here we present Framework Region 3 AmplifiKation sequencing ("FR3AK-seq"), a simplified multiplex PCR-based approach for the ultra-efficient and quantitative analysis of TCR complementarity determining region 3 (CDR3) repertoires. By using minimal primer sets targeting a conserved region immediately upstream of CDR3, undistorted amplicons are analyzed via short read, single-end sequencing. We also introduce the novel algorithm Inferring Sequences via Efficiency Projection and Primer Incorporation ("ISEPPI") for linking CDR3s to their associated variable genes.

FINDINGS

We find that FR3AK-seq is sensitive and quantitative, performing comparably to two different industry standards. FR3AK-seq and ISEPPI were used to efficiently and inexpensively characterize the T cell infiltrates of surgical muscle biopsies obtained from 145 patients with IIM and controls. A cluster of closely related TCRs was identified in samples from patients with sporadic inclusion body myositis (IBM).

INTERPRETATION

The ease and minimal cost of FR3AK-seq removes critical barriers to routine, large-scale TCR CDR3 repertoire analyses, thereby democratizing the quantitative assessment of human TCR repertoires in disease-relevant target tissues. Importantly, discovery of closely related TCRs in muscle from patients with IBM provides evidence for a shared antigen-driven T cell response in this disease of unknown pathogenesis.

FUNDING

This work was supported by NIH grant U24AI118633 and a Prostate Cancer Foundation Young Investigator Award.

Next-Generation Sequencing of T and B Cell Receptor Repertoires from COVID-19 Patients Showed Signatures Associated with Severity of Disease

We profiled adaptive immunity in COVID-19 patients with active infection or after recovery and created a repository of currently >14 million B and T cell receptor (BCR and TCR) sequences from the blood of these patients. The B cell response showed converging IGHV3-driven BCR clusters closely associated with SARS-CoV-2 antibodies. Clonality and skewing of TCR repertoires were associated with interferon type I and III responses, early CD4+ and CD8+ T cell activation, and counterregulation by the co-receptors BTLA, Tim-3, PD-1, TIGIT, and CD73. Tfh, Th17-like, and nonconventional (but not classical antiviral) Th1 cell polarizations were induced. SARS-CoV-2-specific T cell responses were driven by TCR clusters shared between patients with a characteristic trajectory of clonotypes and traceability over the disease course. Our data provide fundamental insight into adaptive immunity to SARS-CoV-2 with the actively updated repository providing a resource for the scientific community urgently needed to inform therapeutic concepts and vaccine development.

Systems immunology

Systems biological approaches to immunology have grown exponentially in the past decade, especially as broad approaches to data collection have become more accessible. It is still in its infancy; however, largely descriptive, and looking for the main drivers of particular phenomena, such as vaccination effects or pregnancy. But this lays the ground work for an increasingly sophisticated appreciation of subsystems and interactions and will lead to predictive modeling and a deeper understanding of human diseases and interactions with pathogens.

T cell analysis in vaccination

A major problem in the analysis of vaccine candidates is the lack of any agreed upon surrogates of efficacy, which means that for diseases that depend on a strong T cell response (HIV, TB especially) the only option is to perform an efficacy trial, involving thousands of subjects, enormous costs, and years before the results are known [1]. We also know that T cell responses are an important part of most pathogen responses, and so identifying key T cell response metrics in early vaccine trials would be generally useful. Given our ignorance of what the most important variables are, what would we like to measure and how can this be accomplished, especially given the explosion of new technologies that are available? What follows is a consideration of what should be measured, with the caveat that some of these will be more important than others.

Methods for sequence and structural analysis of B and T cell receptor repertoires

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.