Estimating T-cell repertoire diversity: limitations of classical estimators and a new approach

The DNA methylation landscape across the TCR loci in patients with acute myeloid leukemia

The capacity of T cells to initiate anti-leukemia immune responses is determined by the ability of their receptors (TCRs) to recognize leukemia neoantigens. Epigenetic mechanisms including DNA methylation contribute to shaping the TCR repertoire composition and diversity. The DNA hypomethylating agents (HMAs) have been widely used in the treatment of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Whether DNA HMAs directly influence TCR gene loci methylation patterns remains unknown. By analyzing public datasets, we compared methylation patterns across TCR loci in AML patients and healthy controls. We also explored how HMAs influence TCR loci DNA methylation in patients with AML. While methylation patterns are largely conserved across the TCR loci, certain V genes exhibit high interindividual variability. Although overall methylation levels within the TCR loci did not show significant differences, specific sites, including 32 TRAV and 12 TRBV sites exhibited distinct methylation patterns when comparing T cells from healthy donors to those from patients with AML. In leukemic cells, decitabine treatment demethylates sites across the TRAV and TRBV genes. While not as significant, a similar pattern of demethylation is observed in T cells. Pretreatment AML samples exhibit higher methylation beta values in differentially methylated positions (DMPs) compared with non-DMPs. Methylation levels of certain TRAV and TRBV genes in leukemic cells are associated with patients' risk status. The presence of disease specific TCR loci methylated signatures that are associated with clinical outcome presents an opportunity for therapeutic intervention. HMAs can modulate the TCR loci methylation patterns, yet whether they could reprogram the TCR repertoire composition remains to be explored.

Surrogate modeling and control of medical digital twins

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.

Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Circulating pre-treatment T-cell receptor repertoire as a predictive biomarker in advanced or metastatic non-small-cell lung cancer patients treated with pembrolizumab alone or in combination with chemotherapy

BACKGROUND

The circulating T-cell receptor (TCR) repertoire is a dynamic representation of overall immune responses in an individual.

MATERIALS AND METHODS

We prospectively collected baseline blood from patients treated with first-line pembrolizumab monotherapy or in combination with chemotherapy. TCR repertoire metrics were correlated with clinical benefit rate (CBR), progression-free survival (PFS), overall survival (OS) and immune-related adverse events (irAEs). We built a logistic regression classifier by fitting all four TCR-β repertoire metrics to the immune checkpoint inhibitor (ICI) CBR data. In the subsequent receiver operating characteristic (ROC) analysis of the resulting logistic regression model probabilities, the best cut-off value was selected to maximise sensitivity to predict CBR to ICI.

RESULTS

We observed an association between reduced number of unique clones and CBR among patients treated with pembrolizumab monotherapy (cohort 1) [risk ratio = 2.86, 95% confidence interval (CI) 1.04-8.73, P = 0.039]. For patients treated with pembrolizumab plus chemotherapy (cohort 2), increased number of unique clones [hazard ratio (HR) = 2.96, 95% CI 1.28-6.88, P = 0.012] and Shannon diversity (HR = 2.73, 95% CI 1.08-6.87, P = 0.033), and reduced evenness (HR = 0.43, 95% CI 0.21-0.90, P = 0.025) and convergence (HR = 0.41, 95% CI 0.19-0.90, P = 0.027) were associated with improved PFS, while only an increased number of unique clones (HR = 4.62, 95% CI 1.52-14.02, P = 0.007) were associated with improved OS. Logistic regression models combining the TCR repertoire metrics improved the prediction of CBR (cohorts 1 and 2) and were strongly associated with PFS (cohort 1, HR = 0.38, 95% CI 0.19-0.78, P = 0.009) and OS (cohort 2, HR = 0.20, 95% CI 0.05-0.76, P < 0.0001). Reduced TCR conversion was associated with increased frequency of irAEs needing systemic steroid treatment.

CONCLUSION

Combined pre-treatment circulating TCR metrics might serve as a predictive biomarker for clinical outcomes among patients with advanced non-small-cell lung cancer treated with pembrolizumab alone or in combination with chemotherapy.

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings

MOTIVATION

T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging.

RESULTS

We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models.

AVAILABILITY AND IMPLEMENTATION

https://github.com/DaniTheOrange/EPIC-TRACE.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.

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.

Numbers and odds: TCR repertoire size and its age changes impacting on T cell functions

A vast array of αβ T cell receptors (TCRs) is generated during T cell development in the thymus through V(D)J recombination, which involves the rearrangement of multiple V, D, and J genes and the pairing of α and β chains. These diverse TCRs provide protection to the human body against a multitude of foreign pathogens and internal cancer cells. The entirety of TCRs present in an individual's T cells is referred to as the TCR repertoire. Despite an estimated 4 × 1011 T cells in the adult human body, the lower bound estimate for the TCR repertoire is 3.8 × 108. While the number of circulating T cells may slightly decrease with age, the changes in the diversity of the TCR repertoire is more apparent. Here, I review recent advancements in TCR repertoire studies, the methods used to measure it, how richness and diversity change as humans age, and some of the known consequences associated with these changes.

Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

Maternal dendritic cells influence fetal allograft response following murine in-utero hematopoietic stem cell transplantation

BACKGROUND

Intrauterine hematopoietic stem cell transplantation (IUT), potentially curative in congenital haematological disease, is often inhibited by deleterious immune responses to donor cells resulting in subtherapeutic donor cell chimerism (DCC). Microchimerism of maternal immune cells (MMc) trafficked into transplanted recipients across the placenta may directly influence donor-specific alloresponsiveness, limiting DCC. We hypothesized that dendritic cells (DC) among trafficked MMc influence the development of tolerogenic or immunogenic responses towards donor cells, and investigated if maternal DC-depletion reduced recipient alloresponsiveness and enhanced DCC.

METHODS

Using transgenic CD11c.DTR (C57BL/6) female mice enabled transient maternal DC-depletion with a single dose of diphtheria toxin (DT). CD11c.DTR females and BALB/c males were cross-mated, producing hybrid pups. IUT was performed at E14 following maternal DT administration 24 h prior. Bone marrow-derived mononuclear cells were transplanted, obtained from semi-allogenic BALB/c (paternal-derived; pIUT), C57BL/6 (maternal-derived; mIUT), or fully allogenic (aIUT) C3H donor mice. Recipient F1 pups were analyzed for DCC, while maternal and IUT-recipient immune cell profile and reactivity were examined via mixed lymphocyte reactivity functional assays. T- and B-cell receptor repertoire diversity in maternal and recipient cells were examined following donor cell exposure.

RESULTS

DCC was highest and MMc was lowest following pIUT. In contrast, aIUT recipients had the lowest DCC and the highest MMc. In groups that were not DC-depleted, maternal cells trafficked post-IUT displayed reduced TCR & BCR clonotype diversity, while clonotype diversity was restored when dams were DC-depleted. Additionally, recipients displayed increased expression of regulatory T-cells and immune-inhibitory proteins, with reduced proinflammatory cytokine and donor-specific antibody production. DC-depletion did not impact initial donor chimerism. Postnatal transplantation without immunosuppression of paternal donor cells did not increase DCC in pIUT recipients; however there were no donor-specific antibody production or immune cell changes.

CONCLUSIONS

Though maternal DC depletion did not improve DCC, we show for the first time that MMc influences donor-specific alloresponsiveness, possibly by expanding alloreactive clonotypes, and depleting maternal DC promotes and maintains acquired tolerance to donor cells independent of DCC, presenting a novel approach to enhancing donor cell tolerance following IUT. This may have value when planning repeat HSC transplantations to treat haemoglobinopathies.

Myocardial Immune Cells: The Basis of Cardiac Immunology

The mammalian heart is characterized by the presence of striated myocytes, which allow continuous rhythmic contraction from early embryonic development until the last moments of life. However, the myocardium contains a significant contingent of leukocytes from every major class. This leukocyte pool includes both resident and nonresident immune cells. Over recent decades, it has become increasingly apparent that the heart is intimately sensitive to immune signaling and that myocardial leukocytes exhibit an array of critical functions, both in homeostasis and in the context of cardiac adaptation to injury. Here, we systematically review current knowledge of all major leukocyte classes in the heart, discussing their functions in health and disease. We also highlight the connection between the myocardium, immune cells, lymphoid organs, and both local and systemic immune responses.

Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities

Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

Linking antigen specific T-cell dynamics in a microfluidic chip to single cell transcription patterns

A new non-invasive screening profile has been realized that can aid in determining T-cell activation state at single-cell level. Production of activated T-cells with good specificity and stable proliferation is greatly beneficial for advancing adoptive immunotherapy as innate immunological cells are not effective in recognizing and eliminating cancer as expected. The screening method is realized by relating intracellular Ca²⁺ intensity and motility of T-cells interacting with APC (Antigen Presenting Cells) in a microfluidic chip. The system is tested using APC pulsed with OVA_257-264 peptide and its modified affinities (N4, Q4, T4 and V4), and the T-cells from OT-1 mice. In addition, single cell RNA sequencing reveals the activation states of the cells and the clusters from the derived profiles can be indicative of the T-cell activation state. The presented system here can be versatile for a comprehensive application to proceed with T-cell-based immunotherapy and screen the antigen-specific T-cells with excellent efficiency and high proliferation.

The pre-existing T cell landscape determines the response to bispecific T cell engagers in multiple myeloma patients

Bispecific T cell engagers (TCEs) have shown promise in the treatment of various cancers, but the immunological mechanism and molecular determinants of primary and acquired resistance to TCEs remain poorly understood. Here, we identify conserved behaviors of bone marrow-residing T cells in multiple myeloma patients undergoing BCMAxCD3 TCE therapy. We show that the immune repertoire reacts to TCE therapy with cell state-dependent clonal expansion and find evidence supporting the coupling of tumor recognition via major histocompatibility complex class I (MHC class I), exhaustion, and clinical response. We find the abundance of exhausted-like CD8⁺ T cell clones to be associated with clinical response failure, and we describe loss of target epitope and MHC class I as tumor-intrinsic adaptations to TCEs. These findings advance our understanding of the in vivo mechanism of TCE treatment in humans and provide the rationale for predictive immune-monitoring and conditioning of the immune repertoire to guide future immunotherapy in hematological malignancies.

Applicability of T cell receptor repertoire sequencing analysis to unbalanced clinical samples - comparing the T cell receptor repertoire of GATA2 deficient patients and healthy controls

T cell receptor repertoire sequencing (TCRseq) has become one of the major omic tools to study the immune system in health and disease. Multiple commercial solutions are currently available, greatly facilitating the implementation of this complex method into translational studies. However, the flexibility of these methods to react to suboptimal sample material is still limited. In a clinical research context, limited sample availability and/or unbalanced sample material can negatively impact the feasibility and quality of such analyses. We sequenced the T cell receptor repertoires of three healthy controls and four patients with GATA2 deficiency using a commercially available TCRseq kit and thereby (1) assessed the impact of suboptimal sample quality and (2) implemented a subsampling strategy to react to biased sample input quantity. Applying these strategies, we did not find significant differences in the global T cell receptor repertoire characteristics such as V and J gene usage, CDR3 junction length and repertoire diversity of GATA2-deficient patients compared with healthy control samples. Our results prove the adaptability of this TCRseq protocol to the analysis of unbalanced sample material and provide encouraging evidence for use of this method in future studies despite suboptimal patient samples.

The influences of microbial colonisation and germ-free status on the chicken TCRβ repertoire

Microbial colonisation is paramount to the normal development of the immune system, particularly at mucosal sites. However, the relationships between the microbiome and the adaptive immune repertoire have mostly been explored in rodents and humans. Here, we report a high-throughput sequencing analysis of the chicken TCRβ repertoire and the influences of microbial colonisation on tissue-resident TCRβ+ cells. The results reveal that the microbiome is an important driver of TCRβ diversity in both intestinal tissues and the bursa of Fabricius, but not in the spleen. Of note, public TCRβ sequences (shared across individuals) make a substantial contribution to the repertoire. Additionally, different tissues exhibit biases in terms of their V family and J gene usage, and these effects were influenced by the gut-associated microbiome. TCRβ clonal expansions were identified in both colonised and germ-free birds, but differences between the groups were indicative of an influence of the microbiota. Together, these findings provide an insight into the avian adaptive immune system and the influence of the microbiota on the TCRβ repertoire.

Are homeostatic mechanisms aiding the reconstitution of the T-cell pool during lymphopenia in humans?

A timely recovery of T-cell numbers following haematopoietic stem-cell transplantation (HSCT) is essential for preventing complications, such as increased risk of infection and disease relapse. In analogy to the occurrence of lymphopenia-induced proliferation in mice, T-cell dynamics in humans are thought to be homeostatically regulated in a cell density-dependent manner. The idea is that T cells divide faster and/or live longer when T-cell numbers are low, thereby helping the reconstitution of the T-cell pool. T-cell reconstitution after HSCT is, however, known to occur notoriously slowly. In fact, the evidence for the existence of homeostatic mechanisms in humans is quite ambiguous, since lymphopenia is often associated with infectious complications and immune activation, which confound the study of homeostatic regulation. This calls into question whether homeostatic mechanisms aid the reconstitution of the T-cell pool during lymphopenia in humans. Here we review the changes in T-cell dynamics in different situations of T-cell deficiency in humans, including the early development of the immune system after birth, healthy ageing, HIV infection, thymectomy and hematopoietic stem cell transplantation (HSCT). We discuss to what extent these changes in T-cell dynamics are a side-effect of increased immune activation during lymphopenia, and to what extent they truly reflect homeostatic mechanisms.

Perturbations in the T cell receptor β repertoire during malaria infection in children: A preliminary study

The changes occurring in the T cell repertoire during clinical malaria infection in children remain unknown. In this study, we undertook the first detailed comparative study of the T cell repertoire in African children with and without clinical malaria to test the hypothesis that clonotypic expansions that occur during P. falciparum infection will contribute to the generation of a T cell repertoire that is unique to each disease state. We profiled the complementarity-determining region 3 (CDR3) of the TCRβ chain sequences from children with Plasmodium falciparum infections (asymptomatic, uncomplicated and severe malaria) and compared these with sequences from healthy children. Interestingly, we discovered that children with symptomatic malaria have a lower TCR diversity and frequency of shared (or “public”) TCR sequences compared to asymptomatic children. Also, TCR diversity was inversely associated with parasitemia. Furthermore, by clustering TCR sequences based on their predicted antigen specificities, we identified a specificity cluster, with a 4-mer amino acid motif, that is overrepresented in the asymptomatic group compared to the diseased groups. Further investigations into this finding may help in delineating important antigenic targets for vaccine and therapeutic development. The results show that the T cell repertoire in children is altered during malaria, suggesting that exposure to P. falciparum antigens disrupts the adaptive immune response, which is an underlying feature of the disease.

T cell receptor repertoire analysis in HTLV-1-associated diseases

Human T lymphotropic virus 1 (HTLV-1) is a human retrovirus identified as the causative agent in adult T-cell leukemia/lymphoma (ATL) and chronic-progressive neuroinflammatory disorder HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HTLV-1 is estimated to infect between 5-20 million people worldwide, although most infected individuals remain asymptomatic. HTLV-1 infected persons carry an estimated lifetime risk of approximately 5% of developing ATL, and between 0.25% and 1.8% of developing HAM/TSP. Most HTLV-1 infection is detected in CD4⁺ T cells in vivo which causes the aggressive malignancy in ATL. In HAM/TSP, the increase of HTLV-1 provirus induces immune dysregulation to alter inflammatory milieu, such as expansion of HTLV-1-specific CD8⁺ T cells, in the central nervous system of the infected subjects, which have been suggested to underlie the pathogenesis of HAM/TSP. Factors contributing to the conversion from asymptomatic carrier to disease state remain poorly understood. As such, the identification and tracking of HTLV-1-specific T cell biomarkers that may be used to monitor the progression from primary infection to immune dysfunction and disease are of great interest. T cell receptor (TCR) repertoires have been extensively investigated as a mechanism of monitoring adaptive T cell immune response to viruses and tumors. Breakthrough technologies such as single-cell RNA sequencing have increased the specificity with which T cell clones may be characterized and continue to improve our understanding of TCR signatures in viral infection, cancer, and associated treatments. In HTLV-1-associated disease, sequencing of TCR repertoires has been used to reveal repertoire patterns, diversity, and clonal expansions of HTLV-1-specific T cells capable of immune evasion and dysregulation in ATL as well as in HAM/TSP. Conserved sequence analysis has further been used to identify CDR3 motif sequences and exploit disease- or patient-specificity and commonality in HTLV-1-associated disease. In this article we review current research on TCR repertoires and HTLV-1-specific clonotypes in HTLV-1-associated diseases ATL and HAM/TSP and discuss the implications of TCR clonal expansions on HTLV-1-associated disease course and treatments.

Microfluidic T Cell Selection by Cellular Avidity

No T cell receptor (TCR) T cell therapies have obtained clinical approval. The lack of strategies capable of selecting and recovering potent T cell candidates may be a contributor to this. Existing protocols for selecting TCR T cell clones for cell therapies such as peptide multimer methods have provided effective measurements on TCR affinities. However, these methods lack the ability to measure the collective strength of intercellular interactions (i.e., cellular avidity) and markers of T cell activation such as immunological synapse formation. This study describes a novel microfluidic fluid shear stress-based approach to identify and recover highly potent T cell clones based on the cellular avidity between living T cells and tumor cells. This approach is capable of probing approximately up to 10 000 T cell-tumor cell interactions per run and can recover potent T cells with up to 100% purity from mixed populations of T cells within 30 min. Markers of cytotoxicity, activation, and avidity persist when recovered high cellular avidity T cells are subsequently exposed to fresh tumor cells. These results demonstrate how microfluidic probing of cellular avidity may fast track the therapeutic T cell selection process and move the authors closer to precision cancer immunotherapy.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Common trajectories of highly effective CD19-specific CAR T cells identified by endogenous T cell receptor lineages

Current chimeric antigen receptor-modified (CAR) T cell products are evaluated in bulk, without assessing functional heterogeneity. We therefore generated a comprehensive single-cell gene expression and T cell receptor (TCR) sequencing dataset using pre- and post-infusion CD19-CAR T cells from blood and bone marrow samples of pediatric patients with B cell acute lymphoblastic leukemia (B-ALL). We identified cytotoxic post-infusion cells with identical TCRs to a subset of pre-infusion CAR T cells. These effector precursor cells exhibited a unique transcriptional profile compared to other pre-infusion cells, corresponding to an unexpected surface phenotype (TIGIT+, CD62Llo, CD27-). Upon stimulation, these cells showed functional superiority and decreased expression of the exhaustion-associated transcription factor, TOX. Collectively, these results demonstrate diverse effector potentials within pre-infusion CAR T cell products, which can be exploited for therapeutic applications. Furthermore, we provide an integrative experimental and analytical framework for elucidating the mechanisms underlying effector development in CAR T cell products.

DECODE: a computational pipeline to discover T cell receptor binding rules

Motivation

Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope.

Results

We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding.

Availability and implementation

Code is available publicly at https://github.com/phineasng/DECODE.

Supplementary information

Supplementary data are available at Bioinformatics online.

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

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

Advances in Our Understanding of the Interaction of Drugs with T-cells: Implications for the Discovery of Biomarkers in Severe Cutaneous Drug Reactions

Drugs can activate different cells of the immune system and initiate an immune response that can lead to life-threatening diseases collectively known as severe cutaneous adverse reactions (SCARs). Antibiotics, anticonvulsants, and antiretrovirals are involved in the development of SCARs by the activation of αβ naïve T-cells. However, other subsets of lymphocytes known as nonconventional T-cells with a limited T-cell receptor repertoire and innate and adaptative functions also recognize drugs and drug-like molecules, but their role in the pathogenesis of SCARs has only just begun to be explored. Despite 30 years of advances in our understanding of the mechanisms in which drugs interact with T-cells and the pathways for tissue injury seen during T-cell activation, at present, the development of useful clinical biomarkers for SCARs or predictive preclinical in vitro assays that could identify immunogenic moieties during drug discovery is an unmet goal. Therefore, the present review focuses on (i) advances in the understanding of the pathogenesis of SCARs reactions, (ii) a description of the interaction of drugs with conventional and nonconventional T-cells, and (iii) the current state of soluble blood circulating biomarker candidates for SCARs.

How Naive T-Cell Clone Counts Are Shaped By Heterogeneous Thymic Output and Homeostatic Proliferation

The specificity of T cells is that each T cell has only one T cell receptor (TCR). A T cell clone represents a collection of T cells with the same TCR sequence. Thus, the number of different T cell clones in an organism reflects the number of different T cell receptors (TCRs) that arise from recombination of the V(D)J gene segments during T cell development in the thymus. TCR diversity and more specifically, the clone abundance distribution, are important factors in immune functions. Specific recombination patterns occur more frequently than others while subsequent interactions between TCRs and self-antigens are known to trigger proliferation and sustain naive T cell survival. These processes are TCR-dependent, leading to clone-dependent thymic export and naive T cell proliferation rates. We describe the heterogeneous steady-state population of naive T cells (those that have not yet been antigenically triggered) by using a mean-field model of a regulated birth-death-immigration process. After accounting for random sampling, we investigate how TCR-dependent heterogeneities in immigration and proliferation rates affect the shape of clone abundance distributions (the number of different clones that are represented by a specific number of cells, or “clone counts”). By using reasonable physiological parameter values and fitting predicted clone counts to experimentally sampled clone abundances, we show that realistic levels of heterogeneity in immigration rates cause very little change to predicted clone-counts, but that modest heterogeneity in proliferation rates can generate the observed clone abundances. Our analysis provides constraints among physiological parameters that are necessary to yield predictions that qualitatively match the data. Assumptions of the model and potentially other important mechanistic factors are discussed.

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.

Role of the hematopoietic stem cells in immunological memory

Purpose of review

Immunological memory is an important evolutionary adaptation of the immune system. Previously restricted to the adaptive immune system, the concept of memory has recently been broadened to the innate immune system. This review summarizes recent studies that highlight the contribution of the hematopoietic stem cells (HSCs) in supporting immunological memory.

Recent findings

Short-lived innate immune cells can build a long-lasting memory of infection to improve their response to secondary challenges. Studies show that these unexpected properties of the innate immune system are sustained by epigenetic and metabolic changes in the HSC compartment.

Summary

HSCs are durably altered in response to pathogens and serve as long-term support for innate immune memory. Many questions remain regarding the mechanisms contributing to the induction and the maintenance of this immune memory in HSCs. Answering these questions will open new perspectives to understand how environmental factors shape the HSC activity over time.

Haematopoietic Stem Cell Transplantation Results in Extensive Remodelling of the Clonal T Cell Repertoire in Multiple Sclerosis

Autologous haematopoietic stem cell transplantation (AHSCT) is a vital therapeutic option for patients with highly active multiple sclerosis (MS). Rates of remission suggest AHSCT is the most effective form of immunotherapy in controlling the disease. Despite an evolving understanding of the biology of immune reconstitution following AHSCT, the mechanism by which AHSCT enables sustained disease remission beyond the period of lymphopenia remains to be elucidated. Auto-reactive T cells are considered central to MS pathogenesis. Here, we analyse T cell reconstitution for 36 months following AHSCT in a cohort of highly active MS patients. Through longitudinal analysis of sorted naïve and memory T cell clones, we establish that AHSCT induces profound changes in the dominant T cell landscape of both CD4+ and CD8+ memory T cell clones. Lymphopenia induced homeostatic proliferation is followed by clonal attrition; with only 19% of dominant CD4 (p <0.025) and 13% of dominant CD8 (p <0.005) clones from the pre-transplant repertoire detected at 36 months. Recovery of a thymically-derived CD4 naïve T cell repertoire occurs at 12 months and is ongoing at 36 months, however diversity of the naïve populations is not increased from baseline suggesting the principal mechanism of durable remission from MS after AHSCT relates to depletion of putative auto-reactive clones. In a cohort of MS patients expressing the MS risk allele HLA DRB1*15:01, public clones are probed as potential biomarkers of disease. AHSCT appears to induce sustained periods of disease remission with dynamic changes in the clonal T cell repertoire out to 36 months post-transplant.

T-Cell Receptor Repertoire Analysis with Computational Tools-An Immunologist's Perspective

Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth a revolution in the characterization of immune repertoires. T-cell receptor (TCR) repertoires carry comprehensive information on the history of an individual’s antigen exposure. They serve as correlates of host protection and tolerance, as well as biomarkers of immunological perturbation by natural infections, vaccines or immunotherapies. Their interrogation yields large amounts of data. This requires a suite of highly sophisticated bioinformatics tools to leverage the meaning and complexity of the large datasets. Many different tools and methods, specifically designed for various aspects of immunological research, have recently emerged. Thus, researchers are now confronted with the issue of having to choose the right kind of approach to analyze, visualize and ultimately solve their task at hand. In order to help immunologists to choose from the vastness of available tools for their data analysis, this review addresses and compares commonly used bioinformatics tools for TCR repertoire analysis and illustrates the advantages and limitations of these tools from an immunologist’s perspective.

Expansion of Human Papillomavirus-Specific T Cells in Periphery and Cervix in a Therapeutic Vaccine Recipient Whose Cervical High-Grade Squamous Intraepithelial Lesion Regressed

Advances in high-throughput sequencing have revolutionized the manner with which we can study T cell responses. We describe a woman who received a human papillomavirus (HPV) therapeutic vaccine called PepCan, and experienced complete resolution of her cervical high-grade squamous intraepithelial lesion. By performing bulk T cell receptor (TCR) β deep sequencing of peripheral blood mononuclear cells before and after 4 vaccinations, 70 putatively vaccine-specific clonotypes were identified for being significantly increased using a beta-binomial model. In order to verify the vaccine-specificity of these clonotypes, T cells with specificity to a region, HPV 16 E6 91-115, previously identified to be vaccine-induced using an interferon-γ enzyme-linked immunospot assay, were sorted and analyzed using single-cell RNA-seq and TCR sequencing. HPV specificity in 60 of the 70 clonotypes identified to be vaccine-specific was demonstrated. TCR β bulk sequencing of the cervical liquid-based cytology samples and cervical formalin-fixed paraffin-embedded samples before and after 4 vaccinations demonstrated the presence of these HPV-specific T cells in the cervix. Combining traditional and cutting-edge immunomonitoring techniques enabled us to demonstrate expansion of HPV-antigen specific T cells not only in the periphery but also in the cervix. Such an approach should be useful as a novel approach to assess vaccine-specific responses in various anatomical areas.

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

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

Immunodominance complexity: lessons yet to be learned from dominant T cell responses to SARS-COV-2

Immunodominance is a complex and highly debated topic of T cell biology. The current SARS-CoV-2 pandemic has provided the opportunity to profile adaptive immune responses and determine molecular factors contributing to emerging responses towards immunodominant viral epitopes. Here, we discuss parameters that alter the dynamics of CD8 viral epitope processing, generation and T-cell responses, and how immunodominance counteracts viral immune escape mechanisms that develop in the context of emerging SARS-CoV-2 variants.

VariantStore: an index for large-scale genomic variant search

Efficiently scaling genomic variant search indexes to thousands of samples is computationally challenging due to the presence of multiple coordinate systems to avoid reference biases. We present VariantStore, a system that indexes genomic variants from multiple samples using a variation graph and enables variant queries across any sample-specific coordinate system. We show the scalability of VariantStore by indexing genomic variants from the TCGA project in 4 h and the 1000 Genomes project in 3 h. Querying for variants in a gene takes between 0.002 and 3 seconds using memory only 10% of the size of the full representation.

TITAN: T-cell receptor specificity prediction with bimodal attention networks

MOTIVATION

The activity of the adaptive immune system is governed by T-cells and their specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent advances in experimental techniques have enabled sequencing of TCRs and their antigenic targets (epitopes), allowing to research the missing link between TCR sequence and epitope binding specificity. Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance. Here, we establish a k-nearest-neighbor (K-NN) classifier as a strong baseline and then propose Tcr epITope bimodal Attention Networks (TITAN), a bimodal neural network that explicitly encodes both TCR sequences and epitopes to enable the independent study of generalization capabilities to unseen TCRs and/or epitopes.

RESULTS

By encoding epitopes at the atomic level with SMILES sequences, we leverage transfer learning and data augmentation to enrich the input data space and boost performance. TITAN achieves high performance in the prediction of specificity of unseen TCRs (ROC-AUC 0.87 in 10-fold CV) and surpasses the results of the current state-of-the-art (ImRex) by a large margin. Notably, our Levenshtein-based K-NN classifier also exhibits competitive performance on unseen TCRs. While the generalization to unseen epitopes remains challenging, we report two major breakthroughs. First, by dissecting the attention heatmaps, we demonstrate that the sparsity of available epitope data favors an implicit treatment of epitopes as classes. This may be a general problem that limits unseen epitope performance for sufficiently complex models. Second, we show that TITAN nevertheless exhibits significantly improved performance on unseen epitopes and is capable of focusing attention on chemically meaningful molecular structures.

AVAILABILITY AND IMPLEMENTATION

The code as well as the dataset used in this study is publicly available at https://github.com/PaccMann/TITAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drug and Chemical Allergy: A Role for a Specific Naive T-Cell Repertoire?

Allergic reactions to drugs and chemicals are mediated by an adaptive immune response involving specific T cells. During thymic selection, T cells that have not yet encountered their cognate antigen are considered naive T cells. Due to the artificial nature of drug/chemical-T-cell epitopes, it is not clear whether thymic selection of drug/chemical-specific T cells is a common phenomenon or remains limited to few donors or simply does not exist, suggesting T-cell receptor (TCR) cross-reactivity with other antigens. Selection of drug/chemical-specific T cells could be a relatively rare event accounting for the low occurrence of drug allergy. On the other hand, a large T-cell repertoire found in multiple donors would underline the potential of a drug/chemical to be recognized by many donors. Recent observations raise the hypothesis that not only the drug/chemical, but also parts of the haptenated protein or peptides may constitute the important structural determinants for antigen recognition by the TCR. These observations may also suggest that in the case of drug/chemical allergy, the T-cell repertoire results from particular properties of certain TCR to recognize hapten-modified peptides without need for previous thymic selection. The aim of this review is to address the existence and the role of a naive T-cell repertoire in drug and chemical allergy. Understanding this role has the potential to reveal efficient strategies not only for allergy diagnosis but also for prediction of the immunogenic potential of new chemicals.

De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection

The adaptive immune system recognizes tumor antigens at an early stage to eradicate cancer cells. This process is accompanied by systemic proliferation of the tumor antigen-specific T lymphocytes. While detection of asymptomatic early-stage cancers is challenging due to small tumor size and limited somatic alterations, tracking peripheral T cell repertoire changes may provide an attractive solution to cancer diagnosis. Here, we developed a deep learning method called DeepCAT to enable de novo prediction of cancer-associated T cell receptors (TCRs). We validated DeepCAT using cancer-specific or non-cancer TCRs obtained from multiple major histocompatibility complex I (MHC-I) multimer-sorting experiments and demonstrated its prediction power for TCRs specific to cancer antigens. We blindly applied DeepCAT to distinguish over 250 patients with cancer from over 600 healthy individuals using blood TCR sequences and observed high prediction accuracy, with area under the curve (AUC) ≥ 0.95 for multiple early-stage cancers. This work sets the stage for using the peripheral blood TCR repertoire for noninvasive cancer detection.

TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb .

Using Domain Based Latent Personal Analysis of B Cell Clone Diversity Patterns to Identify Novel Relationships Between the B Cell Clone Populations in Different Tissues

The B cell population is highly diverse and very skewed. It is divided into clones (B cells with a common mother cell). It is thought that each clone represents an initial B cell receptor specificity. A few clones are very abundant, comprised of hundreds or thousands of B cells while the majority have only a few cells per clone. We suggest a novel method - domain-based latent personal analysis (LPA), a method for spectral exploration of entities in a domain, which can be used to find the spectral spread of sub repertoires within a person. LPA defines a domain-based spectral signature for each sub repertoire. LPA signatures consist of the elements, in our case - the clones, that most differentiate the sub repertoire from the person’s abundance of clones. They include both positive elements, which describe overabundant clones, and negative elements that describe missing clones. The signatures can also be used to compare the sub repertoires they represent to each other. Applying LPA to compare the repertoires found in different tissues, we reiterated previous findings that showed that gut and blood tissues have separate repertoires. We further identify a third branch of clonal patterns typical of the lymphatic organs (Spleen, MLN, and bone marrow) separated from the other two categories. We developed a python version of LPA analysis that can easily be applied to compare clonal distributions - https://github.com/ScanLab-ossi/LPA. It could also be easily adapted to study other skewed sequence populations used in the analysis of B cell receptor populations, for instance, k-mers and V gene usage. These analysis types should allow for inter and intra-repertoire comparisons of diversity, which could revolutionize the way we understand repertoire changes and diversity.

Host Components That Modulate the Disease Caused by hMPV

Human metapneumovirus (hMPV) is one of the main pathogens responsible for acute respiratory infections in children up to 5 years of age, contributing substantially to health burden. The worldwide economic and social impact of this virus is significant and must be addressed. The structural components of hMPV (either proteins or genetic material) can be detected by several receptors expressed by host cells through the engagement of pattern recognition receptors. The recognition of the structural components of hMPV can promote the signaling of the immune response to clear the infection, leading to the activation of several pathways, such as those related to the interferon response. Even so, several intrinsic factors are capable of modulating the immune response or directly inhibiting the replication of hMPV. This article will discuss the current knowledge regarding the innate and adaptive immune response during hMPV infections. Accordingly, the host intrinsic components capable of modulating the immune response and the elements capable of restricting viral replication during hMPV infections will be examined.

Immunopathogenic CSF TCR repertoire signatures in virus-associated neurologic disease

In this study, we examined and characterized disease-specific TCR signatures in cerebrospinal fluid (CSF) of patients with HTLV-1–associated myelopathy/tropical spastic paraparesis (HAM/TSP). TCR β libraries using unique molecular identifier–based methodologies were sequenced in paired peripheral blood mononuclear cells (PBMCs) and CSF cells from HAM/TSP patients and normal healthy donors (NDs). The sequence analysis demonstrated that TCR β repertoires in CSF of HAM/TSP patients were highly expanded and contained both TCR clonotypes shared with PBMCs and uniquely enriched within the CSF. In addition, we analyzed TCR β repertoires of highly expanded and potentially immunopathologic HTLV-1 Tax11-19–specific CD8⁺ T cells from PBMCs of HLA-A*0201⁺ HAM/TSP and identified a conserved motif (PGLAG) in the CDR3 region. Importantly, TCR β clonotypes of expanded clones in HTLV-1 Tax11-19–specific CD8⁺ T cells were also expanded and enriched in the CSF of the same patient. These results suggest that exploring TCR repertoires of CSF and antigen-specific T cells may provide a TCR repertoire signature in virus-associated neurologic disorders.

Dynamics of thymus function and T cell receptor repertoire breadth in health and disease

T cell recognition of unknown antigens relies on the tremendous diversity of the T cell receptor (TCR) repertoire; generation of which can only occur in the thymus. TCR repertoire breadth is thus critical for not only coordinating the adaptive response against pathogens but also for mounting a response against malignancies. However, thymic function is exquisitely sensitive to negative stimuli, which can come in the form of acute insult, such as that caused by stress, infection, or common cancer therapies; or chronic damage such as the progressive decline in thymic function with age. Whether it be prolonged T cell deficiency after hematopoietic cell transplantation (HCT) or constriction in the breadth of the peripheral TCR repertoire with age; these insults result in poor adaptive immune responses. In this review, we will discuss the importance of thymic function for generation of the TCR repertoire and how acute and chronic thymic damage influences immune health. We will also discuss methods that are used to measure thymic function in patients and strategies that have been developed to boost thymic function.

Characterization of human T cell receptor repertoire data in eight thymus samples and four related blood samples

T cell receptor (TCR) is a heterodimer consisting of TCRα and TCRβ chains that are generated by somatic recombination of multiple gene segments. Nascent TCR repertoire undergoes thymic selections where non-functional and potentially autoreactive receptors are removed. During the last years, the development of high-throughput sequencing technology has allowed a large scale assessment of TCR repertoire and multiple analysis tools are now also available.

In our recent manuscript, Human thymic T cell repertoire is imprinted with strong convergence to shared sequences[1], we show highly overlapping thymic TCR repertoires in unrelated individuals. In the current Data in Brief article, we provide a more detailed characterization of the basic features of these thymic and related peripheral blood TCR repertoires. The thymus samples were collected from eight infants undergoing corrective cardiac surgery, two of whom were monozygous twins [2]. In parallel with the surgery, a small aliquot of peripheral blood was drawn from four of the donors. Genomic DNA was extracted from mechanically released thymocytes and circulating leukocytes. The sequencing of TCRα and TCRβ repertoires was performed at ImmunoSEQ platform (Adaptive Biotechnologies). The obtained repertoire data were analysed applying relevant features from immunoSEQ® 3.0 Analyzer (Adaptive Biotechnologies) and a freely available VDJTools software package for programming language R [3].

The current data analysis displays the basic features of the sequenced repertoires including observed TCR diversity, various descriptive TCR diversity measures, and V and J gene usage. In addition, multiple methods to calculate repertoire overlap between two individuals are applied. The raw sequence data provide a large database of reference TCRs in healthy individuals at an early developmental stage. The data can be exploited to improve existing computational models on TCR repertoire behaviour as well as in the generation of new models.

Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status

In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasionally including aggressive T-cell lymphoma. Diagnosis, based primarily on histopathological examination of duodenal biopsies, is confounded by poor concordance between pathologists and minimal histological abnormality if insufficient gluten is consumed. CeD pathogenesis involves both CD4⁺ T-cell-mediated gluten recognition and CD8⁺ and γδ T-cell-mediated inflammation, with a previous study demonstrating a permanent change in γδ T-cell populations in CeD. We leveraged this understanding and explored the diagnostic utility of bulk T-cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD. Genomic DNA extracted from duodenal biopsies underwent sequencing for TCR-δ (TRD) (CeD, n = 11; non-CeD, n = 11) and TCR-γ (TRG) (CeD, n = 33; non-CeD, n = 21). We developed a novel machine learning-based analysis of the TCR repertoire, clustering samples by diagnosis. Leave-one-out cross-validation (LOOCV) was performed to validate the classification algorithm. Using TRD repertoire, 100% (22/22) of duodenal biopsies were correctly classified, with a LOOCV accuracy of 91%. Using TCR-γ (TRG) repertoire, 94.4% (51/54) of duodenal biopsies were correctly classified, with LOOCV of 87%. Duodenal biopsy TRG repertoire analysis permitted accurate classification of biopsies from patients with CeD following a strict gluten-free diet for at least 6 months, who would be misclassified by current tests. This result reflects permanent changes to the duodenal γδ TCR repertoire in CeD, even in the absence of gluten consumption. Our method could complement or replace histopathological diagnosis in CeD and might have particular clinical utility in the diagnostic testing of patients unable to tolerate dietary gluten, and for assessing duodenal biopsies with equivocal features. This approach is generalisable to any TCR/BCR locus and any sequencing platform, with potential to predict diagnosis or prognosis in conditions mediated or modulated by the adaptive immune response. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Improved T-cell Receptor Diversity Estimates Associate with Survival and Response to Anti-PD-1 Therapy

T-cell receptor (TCR) repertoire profiling has emerged as a powerful tool for biological discovery and biomarker development in cancer immunology and immunotherapy. A key statistic derived from repertoire profiling data is diversity, which summarizes the frequency distribution of TCRs within a mixed population. Despite the growing use of TCR diversity metrics in clinical trial correlative studies in oncology, their accuracy has not been validated using published ground-truth datasets. Here, we reported the performance characteristics of methods for TCR repertoire profiling from RNA-sequencing data, showed undersampling as a prominent source of bias in diversity estimates, and derived a model via statistical learning that attenuates bias to produce corrected diversity estimates. This modeled diversity improved discrimination in The Cancer Genome Atlas data and associated with survival and treatment response in patients with melanoma treated with anti-PD-1 therapy, where the commonly used diversity normalizations did not. These findings have the potential to increase our understanding of the tumor immune microenvironment and improve the accuracy of predictions of patient responses to immunotherapy.

Needle in a Haystack: The Naïve Repertoire as a Source of T Cell Receptors for Adoptive Therapy with Engineered T Cells

T cell engineering with antigen-specific T cell receptors (TCRs) has allowed the generation of increasingly specific, reliable, and versatile T cell products with near-physiological features. However, a broad applicability of TCR-based therapies in cancer is still limited by the restricted number of TCRs, often also of suboptimal potency, available for clinical use. In addition, targeting of tumor neoantigens with TCR-engineered T cell therapy moves the field towards a highly personalized treatment, as tumor neoantigens derive from somatic mutations and are extremely patient-specific. Therefore, relevant TCRs have to be de novo identified for each patient and within a narrow time window. The naïve repertoire of healthy donors would represent a reliable source due to its huge diverse TCR repertoire, which theoretically entails T cells for any antigen specificity, including tumor neoantigens. As a challenge, antigen-specific naïve T cells are of extremely low frequency and mostly of low functionality, making the identification of highly functional TCRs finding a "needle in a haystack." In this review, we present the technological advancements achieved in high-throughput mapping of patient-specific neoantigens and corresponding cognate TCRs and how these platforms can be used to interrogate the naïve repertoire for a fast and efficient identification of rare but therapeutically valuable TCRs for personalized adoptive T cell therapy.