Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination

Statistical analysis of repertoire data demonstrates the influence of microhomology in V(D)J recombination

V(D)J recombination generates the diverse B and T cell receptors essential for recognizing a wide array of antigens. This diversity arises from the combinatorial assembly of V(D)J genes and the junctional deletion and insertion of nucleotides. While previous in vitro studies have shown that microhomology-short stretches of sequence homology between gene ends-can bias the recombination process, the extent of microhomology's impact in vivo, particularly in humans, remains unknown. In this paper, we assess how germline-encoded microhomology influences trimming and ligation during V(D)J recombination using statistical inference on previously published high-throughput TCRα repertoire sequencing data. We find that microhomology increases both trimming and ligation probabilities, making it an important predictor of recombination outcomes. These effects are consistent across other receptor loci and sequence types. Further, we demonstrate that accounting for germline microhomology effects significantly alters sequence annotation probabilities and rankings, highlighting its practical importance for accurately inferring the V(D)J recombination events that generated an observed sequence. Together, these results enhance our understanding of how germline-encoded microhomologous nucleotides shape the human V(D)J recombination process.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

Single-cell analysis of cerebrospinal fluid reveals common features of neuroinflammation

Neuroinflammation is often characterized by immune cell infiltrates in the cerebrospinal fluid (CSF). Here, we apply single-cell RNA sequencing to explore the functional characteristics of these cells in patients with various inflammatory, infectious, and non-inflammatory neurological disorders. We show that CSF is distinct from the peripheral blood in terms of both cellular composition and gene expression. We report that the cellular and transcriptional landscape of CSF is altered in neuroinflammation but is strikingly similar across different neuroinflammatory disorders. We find clonal expansion of CSF lymphocytes in all disorders but most pronounced in inflammatory diseases, and we functionally characterize the transcriptional features of these cells. Finally, we explore the genetic control of gene expression in CSF lymphocytes. Our results highlight the common features of immune cells in the CSF compartment across diverse neurological diseases and may help to identify new targets for drug development or repurposing in multiple sclerosis (MS).

Learning predictive signatures of HLA type from T-cell repertoires

T cells recognize a wide range of pathogens using surface receptors that interact directly with peptides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

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

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

A general pHLA-CD80 scaffold fusion protein to promote efficient antigen-specific T cell-based immunotherapy

Inadequate antigen-specific T cells activation hampers immunotherapy due to complex antigen presentation. In addition, therapeutic in vivo T cell expansion is constrained by slow expansion rates and limited functionality. Herein, we introduce a model fusion protein termed antigen-presenting cell-mimic fusion protein (APC-mimic), designed to greatly mimicking the natural antigen presentation pattern of antigen-presenting cells and directly expand T cells both in vitro and in vivo. The APC-mimic comprises the cognate peptide-human leukocyte antigen (pHLA) complex and the co-stimulatory marker CD80, which are natural ligands on APCs. Following a single stimulation, APC-mimic leads to an approximately 400-fold increase in the polyclonal expansion of antigen-specific T cells compared with the untreated group in vitro without the requirement for specialized antigen-presenting cells. Through the combination of single-cell TCR sequencing (scTCR-seq) and single-cell RNA sequencing (scRNA-seq), we identify an approximately 600-fold monoclonal expansion clonotype among these polyclonal clonotypes. It also exhibits suitability for in vivo applications confirmed in the OT-1 mouse model. Furthermore, T cells expanded by APC-mimic effectively inhibits tumor growth in adoptive cell transfer (ACT) murine models. These findings pave the way for the versatile APC-mimic platform for personalized therapeutics, enabling direct expansion of polyfunctional antigen-specific T cell subsets in vitro and in vivo.

Computational detection of antigen-specific B cell receptors following immunization

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Germline-like TCR-α chains shared between autoreactive T cells in blood and pancreas

Human type 1 diabetes (T1D) is caused by autoimmune attack on the insulin-producing pancreatic beta cells by islet antigen-reactive T cells. How human islet antigen-reactive (IAR) CD4+ memory T cells from peripheral blood affect T1D progression in the pancreas is poorly understood. Here, we aim to determine if IAR T cells in blood could be detected in pancreas. We identify paired αβ (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new-onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from healthy, at risk and T1D organ donors. We report extensive TRA junction sharing between IAR T cells and pancreas-infiltrating T cells (PIT), with perfect-match or single-mismatch TRA junction amino acid sequences comprising ~29% total unique IAR TRA junctions (942/3,264). PIT-matched TRA junctions were largely public and enriched for TRAV41 usage, showing significant nucleotide sequence convergence, increased use of germline-encoded versus non-templated residues in epitope engagement, and a potential for cross-reactivity. Our findings thus link T cells with distinctive germline-like TRA chains in the peripheral blood with T cells in the pancreas.

Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership

The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.

Principles and therapeutic applications of adaptive immunity

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

Learning predictive signatures of HLA type from T-cell repertoires

T cells recognize a wide range of pathogens using surface receptors that interact directly with pep-tides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity

The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that identifies population-level differences between TCRβ sequence sets. This approach revealed that weakly self-reactive T cell populations were enriched for longer CDR3β regions and acidic amino acids. We tested our ML predictions of self-reactivity using retrogenic mice with fixed TCRβ sequences. Extrapolating our analyses to independent datasets, we predicted high self-reactivity for regulatory T cells and slightly reduced self-reactivity for T cells responding to chronic infections. Our analyses suggest a potential trade-off between TCR repertoire diversity and self-reactivity. A record of this paper's transparent peer review process is included in the supplemental information.

An Interpretable Classification Model Using Gluten-Specific TCR Sequences Shows Diagnostic Potential in Coeliac Disease

Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4+ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4+ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.

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.

Tissue-specific features of the T cell repertoire after allogeneic hematopoietic cell transplantation in human and mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5 to 18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, and in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleens in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end stage of an inflammatory disorder after lymphocyte-directed therapy. These findings also underscore the importance of studying T cell in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and posttransplant T cell landscape.

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

SURROGATE SELECTION OVERSAMPLES EXPANDED T CELL CLONOTYPES

Inference from immunological data on cells in the adaptive immune system may benefit from modeling specifications that describe variation in the sizes of various clonal sub-populations. We develop one such specification in order to quantify the effects of surrogate selection assays, which we confirm may lead to an enrichment for amplified, potentially disease-relevant T cell clones. Our specification couples within-clonotype birth-death processes with an exchangeable model across clonotypes. Beyond enrichment questions about the surrogate selection design, our framework enables a study of sampling properties of elementary sample diversity statistics; it also points to new statistics that may usefully measure the burden of somatic genomic alterations associated with clonal expansion. We examine statistical properties of immunological samples governed by the coupled model specification, and we illustrate calculations in surrogate selection studies of melanoma and in single-cell genomic studies of T cell repertoires.

Inhibitory IL-10-producing CD4+ T cells are T-bet-dependent and facilitate cytomegalovirus persistence via coexpression of arginase-1

Inhibitory CD4+ T cells have been linked with suboptimal immune responses against cancer and pathogen chronicity. However, the mechanisms that underpin the development of these regulatory cells, especially in the context of ongoing antigen exposure, have remained obscure. To address this knowledge gap, we undertook a comprehensive functional, phenotypic, and transcriptomic analysis of interleukin (IL)-10-producing CD4+ T cells induced by chronic infection with murine cytomegalovirus (MCMV). We identified these cells as clonally expanded and highly differentiated TH1-like cells that developed in a T-bet-dependent manner and coexpressed arginase-1 (Arg1), which promotes the catalytic breakdown of L-arginine. Mice lacking Arg1-expressing CD4+ T cells exhibited more robust antiviral immunity and were better able to control MCMV. Conditional deletion of T-bet in the CD4+ lineage suppressed the development of these inhibitory cells and also enhanced immune control of MCMV. Collectively, these data elucidated the ontogeny of IL-10-producing CD4+ T cells and revealed a previously unappreciated mechanism of immune regulation, whereby viral persistence was facilitated by the site-specific delivery of Arg1.

T cell repertoire breadth is associated with the number of acute respiratory infections in the LoewenKIDS birth cohort

We set out to gain insight into peripheral blood B and T cell repertoires from 120 infants of the LoewenKIDS birth cohort to investigate potential determinants of early life respiratory infections. Low antigen-dependent somatic hypermutation of B cell repertoires, as well as low T and B cell repertoire clonality, high diversity, and high richness especially in public T cell clonotypes reflected the immunological naivety at 12 months of age when high thymic and bone marrow output are associated with relatively few prior antigen encounters. Infants with inadequately low T cell repertoire diversity or high clonality showed higher numbers of acute respiratory infections over the first 4 years of life. No correlation of T or B cell repertoire metrics with other parameters such as sex, birth mode, older siblings, pets, the onset of daycare, or duration of breast feeding was noted. Together, this study supports that-regardless of T cell functionality-the breadth of the T cell repertoire is associated with the number of acute respiratory infections in the first 4 years of life. Moreover, this study provides a valuable resource of millions of T and B cell receptor sequences from infants with available metadata for researchers in the field.

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnostic tool for predicting SARS-CoV-2 status from repertoire data.

Measures of epitope binding degeneracy from T cell receptor repertoires

Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.

Counting is almost all you need

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases

BACKGROUND

Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite-stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumor-infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T-cell infiltration in CLM using T-cell receptor (TCR) repertoire sequencing. Eighty-five resected CLMs from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T-cell fractions were calculated, repertoire clonality was analyzed based on Hill evenness curves, and TCR sequence convergence was assessed using network analysis.

RESULTS

Increased T-cell fractions (10.6% vs. 6.3%) were detected in CLMs exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT-exposed tumors independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence.

CONCLUSIONS

TCR repertoire sequencing can be used to quantify T-cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T-cell clonal expansion in CLM in a clinical context.

A template for multitudes: Germline immune polymorphism of the T cell receptor loci

Short-read next-generation sequencing has failed to adequately genotype the T cell receptor (TCR) loci, limiting our ability to characterize the role of germline TCR variation. In this issue of Cell Genomics, Rodriguez et al.1 describe how a probe-based hybrid capture approach coupled with long-read sequencing can resolve fully phased TCR locus haplotypes from diploid human genomes.

The splenic T cell receptor repertoire during an immune response against a complex antigen: Expanding private clones accumulate in the high and low copy number region

Large cellular antigens comprise a variety of different epitopes leading to a T cell response of extreme diversity. Therefore, tracking such a response by next generation sequencing of the T cell receptor (TCR) in order to identify common TCR properties among the expanding T cells represents an enormous challenge. In the present study we adapted a set of established indices to elucidate alterations in the TCR repertoire regarding sequence similarities between TCRs including VJ segment usage and diversity of nucleotide coding of a single TCR. We combined the usage of these indices with a new systematic splitting strategy regarding the copy number of the extracted clones to divide the repertoire into multiple fractions for separate analysis. We implemented this new analytic approach using the splenic TCR repertoire following immunization with sheep red blood cells (SRBC) in mice. As expected, early after immunization presumably antigen-specific clones accumulated in high copy number fractions, but at later time points similar accumulation of specific clones occurred within the repertoire fractions of lowest copy number. For both repertoire regions immunized animals could reliably be distinguished from control in a classification approach, demonstrating the robustness of the two effects at the individual level. The direction in which the indices shifted after immunization revealed that for both the early and the late effect alterations in repertoire parameters were caused by antigen-specific private clones displacing non-specific public clones. Taken together, tracking antigen-specific clones by their displacement of average TCR repertoire characteristics in standardized repertoire fractions ensures that our analytical approach is fairly independent from the antigen in question and thus allows the in-depth characterization of a variety of immune responses.

Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k -mer Feature Extraction

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

Homologous recombination repair deficient prostate cancer represents an immunologically distinct subtype

Homologous recombination repair deficiency (HRD) is observed in 10% of patients with castrate-resistant prostate cancer (PCa). Preliminary data suggest that HRD-PCa might be more responsive to immune checkpoint inhibitors (ICIs). In this study, we compare the tumor immune landscape and peripheral T cell receptor (TCR) repertoire of patients with and without HRD-PCa to gain further insight into the immunogenicity of HRD-PCa. Immunohistochemistry was performed on tumor tissue of 81 patients, including 15 patients with HRD-PCa. Peripheral TCR sequencing was performed in a partially overlapping cohort of 48 patients, including 16 patients with HRD-PCa. HRD patients more frequently had intratumoral CD3⁺, CD3⁺CD8⁻FoxP3⁻ or Foxp3⁺ TILs above median compared to patients without DNA damage repair alterations (DDRwt; CD3⁺ and Foxp3⁺: 77% vs 35%, p = .013; CD3⁺CD8⁻FoxP3⁻: 80% vs 44%, p = .031). No significant difference in CD8⁺ TILs or PD-L1 expression was observed. In peripheral blood, HRD patients displayed a more diverse TCR repertoire compared to DDRwt patients (p = .014). Additionally, HRD patients shared TCR clusters with low generation probability, suggesting patient-overlapping T cell responses. A pooled analysis of clinical data from 227 patients with molecularly characterized PCa suggested increased efficacy of ICIs in HRD-PCa. In conclusion, patients with HRD-PCa display increased TIL density and an altered peripheral TCR repertoire. Further research into the efficacy of ICIs and the presence of shared neoantigens in HRD-PCa is warranted.

Naive and memory T cells TCR-HLA-binding prediction

T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR-MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR-pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home.

Pinpointing the tumor-specific T-cells via TCR clusters

Adoptive cell transfer (ACT) is a promising approach to cancer immunotherapy, but its efficiency fundamentally depends on the extent of tumor-specific T cell enrichment within the graft. This can be estimated via activation with identifiable neoantigens, tumor-associated antigens (TAAs), or living or lysed tumor cells, but these approaches remain laborious, time-consuming, and functionally limited, hampering clinical development of ACT. Here, we demonstrate that homology cluster analysis of T cell receptor (TCR) repertoires efficiently identifies tumor-reactive TCRs allowing to: (1) detect their presence within the pool of tumor-infiltrating lymphocytes (TILs); (2) optimize TIL culturing conditions, with IL-2_low/IL-21/anti-PD-1 combination showing increased efficiency; (3) investigate surface marker-based enrichment for tumor-targeting T cells in freshly isolated TILs (enrichment confirmed for CD4⁺ and CD8⁺ PD-1⁺/CD39⁺ subsets), or re-stimulated TILs (informs on enrichment in 4-1BB-sorted cells). We believe that this approach to the rapid assessment of tumor-specific TCR enrichment should accelerate T cell therapy development.

TCR-sequencing in cancer and autoimmunity: barcodes and beyond

The T cell receptor (TCR) endows T cells with antigen specificity and is central to nearly all aspects of T cell function. Each naïve T cell has a unique TCR sequence that is stably maintained during cell division. In this way, the TCR serves as a molecular barcode that tracks processes such as migration, differentiation, and proliferation of T cells. Recent technological advances have enabled sequencing of the TCR from single cells alongside deep molecular phenotypes on an unprecedented scale. In this review, we discuss strengths and limitations of TCR sequences as molecular barcodes and their application to study immune responses following Programmed Death-1 (PD-1) blockade in cancer. Additionally, we consider applications of TCR data beyond use as a barcode.

Azacitidine-induced reconstitution of the bone marrow T cell repertoire is associated with superior survival in AML patients

Hypomethylating agents (HMA) like azacitidine are licensed for the treatment of acute myeloid leukemia (AML) patients ineligible for allogeneic hematopoietic stem cell transplantation. Biomarker-driven identification of HMA-responsive patients may facilitate the choice of treatment, especially in the challenging subgroup above 60 years of age. Since HMA possesses immunomodulatory functions that constitute part of their anti-tumor effect, we set out to analyze the bone marrow (BM) immune environment by next-generation sequencing of T cell receptor beta (TRB) repertoires in 51 AML patients treated within the RAS-AZIC trial. Patients with elevated pretreatment T cell diversity (11 out of 41 patients) and those with a boost of TRB richness on day 15 after azacitidine treatment (12 out of 46 patients) had longer event-free and overall survival. Both pretreatment and dynamic BM T cell metrics proved to be better predictors of outcome than other established risk factors. The favorable broadening of the BM T cell space appeared to be driven by antigen since these patients showed significant skewing of TRBV gene usage. Our data suggest that one course of AZA can cause reconstitution to a more physiological T cell BM niche and that the T cell space plays an underestimated prognostic role in AML.

Trial registration: DRKS identifier: DRKS00004519

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

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

Individualized VDJ recombination predisposes the available Ig sequence space

The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.

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

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 experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. 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 (false discovery rate [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.

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.

T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19

Objective

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health emergency. T-cell receptors (TCRs) are crucial mediators of antiviral adaptive immunity. This study sought to comprehensively characterize the TCR repertoire changes in patients with COVID-19.

Methods

A large sample size multi-center randomized controlled trial was implemented to study the features of the TCR repertoire and identify COVID-19 disease-related TCR sequences.

Results

It was found that some T-cell receptor beta chain (TCRβ) features differed markedly between COVID-19 patients and healthy controls, including decreased repertoire diversity, longer complementarity-determining region 3 (CDR3) length, skewed utilization of the TCRβ variable gene/joining gene (TRBV/J), and a high degree of TCRβ sharing in COVID-19 patients. Moreover, this analysis showed that TCR repertoire diversity declines with aging, which may be a cause of the higher infection and mortality rates in elderly patients. Importantly, a set of TCRβ clones that can distinguish COVID-19 patients from healthy controls with high accuracy was identified. Notably, this diagnostic model demonstrates 100% specificity and 82.68% sensitivity at 0–3 days post diagnosis.

Conclusions

This study lays the foundation for immunodiagnosis and the development of medicines and vaccines for COVID-19 patients.

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.

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.

TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood

BACKGROUND

T cells play a key role in the pathogenesis of multiple sclerosis (MS), a chronic, inflammatory, demyelinating disease of the central nervous system (CNS). Although several studies recently investigated the T-cell receptor (TCR) repertoire in cerebrospinal fluid (CSF) of MS patients by high-throughput sequencing (HTS), a deep analysis on repertoire similarities and differences among compartments is still missing.

METHODS

We performed comprehensive bioinformatics on high-dimensional TCR Vβ sequencing data from published and unpublished MS and healthy donors (HD) studies. We evaluated repertoire polarization, clone distribution, shared CDR3 amino acid sequences (CDR3s-a.a.) across repertoires, clone overlap with public databases, and TCR similarity architecture.

FINDINGS

CSF repertoires showed a significantly higher public clones percentage and sequence similarity compared to peripheral blood (PB). On the other hand, we failed to reject the null hypothesis that the repertoire polarization is the same between CSF and PB. One Primary-Progressive MS (PPMS) CSF repertoire differed from the others in terms of TCR similarity architecture. Cluster analysis splits MS from HD.

INTERPRETATION

In MS patients, the presence of a physiological barrier, the blood-brain barrier, does not impact clone prevalence and distribution, but impacts public clones, indicating CSF as a more private site. We reported a high Vβ sequence similarity in the CSF-TCR architecture in one PPMS. If confirmed it may be an interesting insight into MS progressive inflammatory mechanisms. The clustering of MS repertoires from HD suggests that disease shapes the TCR Vβ clonal profile.

FUNDING

This study was partly financially supported by the Italian Multiple Sclerosis Foundation (FISM), that contributed to Ballerini-DB data collection (grant #2015 R02).

Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19

Individuals with the 2019 coronavirus disease (COVID-19) show varying severity of the disease, ranging from asymptomatic to requiring intensive care. Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, we still lack an understanding of the overall landscape of B cell receptor (BCR) repertoires in individuals with COVID-19. We use high-throughput sequencing of bulk and plasma B cells collected at multiple time points during infection to characterize signatures of the B cell response to SARS-CoV-2 in 19 individuals. Using principled statistical approaches, we associate differential features of BCRs with different disease severity. We identify 38 significantly expanded clonal lineages shared among individuals as candidates for responses specific to SARS-CoV-2. Using single-cell sequencing, we verify the reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identify the natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in some individuals. Our results provide insights important for development of rational therapies and vaccines against COVID-19.

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.

Age and CMV-Infection Jointly Affect the EBV-Specific CD8+ T-Cell Repertoire

CD8+ T cells play an important role in protection against viral infections. With age, changes in the T-cell pool occur, leading to diminished responses against both new and recurring infections in older adults. This is thought to be due to a decrease in both T-cell numbers and T-cell receptor (TCR) diversity. Latent infection with cytomegalovirus (CMV) is assumed to contribute to this age-associated decline of the immune system. The observation that the level of TCR diversity in the total memory T-cell pool stays relatively stable during aging is remarkable in light of the constant input of new antigen-specific memory T cells. What happens with the diversity of the individual antigen-specific T-cell repertoires in the memory pool remains largely unknown. Here we studied the effect of aging on the phenotype and repertoire diversity of CMV-specific and Epstein-Barr virus (EBV)-specific CD8+ T cells, as well as the separate effects of aging and CMV-infection on the EBV-specific T-cell repertoire. Antigen-specific T cells against both persistent viruses showed an age-related increase in the expression of markers associated with a more differentiated phenotype, including KLRG-1, an increase in the fraction of terminally differentiated T cells, and a decrease in the diversity of the T-cell repertoire. Not only age, but also CMV infection was associated with a decreased diversity of the EBV-specific T-cell repertoire. This suggests that both CMV infection and age can impact the T-cell repertoire against other antigens.

STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets

Single-cell RNA sequencing is a powerful tool to examine cellular heterogeneity, novel markers and target genes, and therapeutic mechanisms in human cancers and animal models. Here, we analyzed single-cell RNA sequencing data of T cells obtained from multiple mouse tumor models by PCA-based subclustering coupled with TCR tracking using the STARTRAC algorithm. This approach revealed various differentiated T cell subsets and activation states, and a correspondence of T cell subsets between human and mouse tumors. STARTRAC analyses demonstrated peripheral T cell subsets that were developmentally connected with tumor-infiltrating CD8⁺ cells, CD4⁺ Th1 cells, and T reg cells. In addition, large amounts of paired TCRα/β sequences enabled us to identify a specific enrichment of paired public TCR clones in tumor. Finally, we identified CCR8 as a tumor-associated T reg cell marker that could preferentially deplete tumor-associated T reg cells. We showed that CCR8-depleting antibody treatment provided therapeutic benefit in CT26 tumors and synergized with anti–PD-1 treatment in MC38 and B16F10 tumor models.

Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.

Selection influences naive CD8+ TCR-β repertoire sharing

Within each individual, the adaptive immune system generates a repertoire of cells expressing receptors capable of recognizing diverse potential pathogens. The theoretical diversity of the T-cell receptor (TCR) repertoire exceeds the actual size of the T-cell population in an individual by several orders of magnitude - making the observation of identical TCRs in different individuals extremely improbable if all receptors were equally likely. Despite this disparity between the theoretical and the realized diversity of the repertoire, these 'public' receptor sequences have been identified in autoimmune, cancer and pathogen interaction contexts. Biased generation processes explain the presence of public TCRs in the naive repertoire, but do not adequately explain the different abundances of these public TCRs. We investigate and characterize the distribution of genomic TCR-β sequences of naive CD8+ T cells from three genetically identical mice, comparing non-productive (non-functional sequences) and productive sequences. We find public TCR-β sequences at higher abundances compared with unshared sequences in the productive, but not in the non-productive, repertoire. We show that neutral processes such as recombination biases, codon degeneracy and generation probability do not fully account for these differences, and conclude that thymic or peripheral selection plays an important role in increasing the abundances of public TCR-β sequences.

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.