Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases

Immunosequencing identifies signatures of T cell responses for early detection of nasopharyngeal carcinoma

To identify nasopharyngeal carcinoma (NPC)-relevant T cell receptors (TCRs), we profile the repertoires of peripheral blood TCRβ chains from 228 NPC patients, 241 at-risk controls positive for serum Epstein-Barr virus (EBV) VCA-IgA antibody, and 251 seronegative controls. We develop a TCR-based signature (T-score) based on 208 NPC-enriched CDR3β sequences, which accurately diagnoses NPC in both the original and independent validation cohorts. Notably, a higher T-score, associated with a shorter time interval to NPC diagnosis, effectively identifies early-stage NPC among EBV-seropositive at-risk individuals prior to clinical diagnosis. These NPC-enriched TCRs react against not only EBV-specific antigens but also non-EBV antigens expressed by NPC cells, indicating a broad range of specificities. Moreover, the abundance of NPC-enriched CD8+ T cells in blood correlates with the infiltration of non-exhausted T cell counterparts in tumors and predicts prolonged survival, suggesting that these NPC-enriched T cells have significant potential for disease monitoring and therapeutic applications.

Disease diagnostics using machine learning of B cell and T cell receptor sequences

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.

Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM

Patients who have radiographically detectable lesions in their brain or other symptoms compatible with brain tumors pose challenges for diagnosis. The only definitive way to diagnose such patients is through brain biopsy, an obviously invasive and dangerous procedure. Here we present a new workflow termed "CSF-BAM" that simultaneously identifies B cell or T cell receptor rearrangements, A neuploidy, and M utations using PCR-mediated amplification of both strands of the DNA from CSF samples. We first describe the details of the molecular genetic assessments and then establish thresholds for positivity using training sets of libraries from patients with or without cancer. We then applied CSF-BAM to an independent set of 206 DNA samples from patients with common, aggressive cancer types as well as other forms of brain cancers. Among the 126 samples from patients with the most common aggressive cancer types (high grade gliomas, medulloblastomas, or metastatic cancers to the brain), the sensitivity of detection was >81%. None of 33 CSF-BAM assays (100% specificity, 90% to 100% credible interval) were positive in CSF samples from patients without brain cancers. The sensitivity of CSF-BAM was considerably higher than that achieved with cytology. CSF-BAM provides an integrated multi-analyte approach to identify neoplasia in the central nervous system, provides information about the immune environment in patients with or without cancer, and has the potential to inform the subsequent management of such patients.

Statement of significance

There is a paucity of technologies beyond surgical biopsy that can accurately diagnose central nervous system neoplasms. We developed a novel, sensitive and highly specific assay that can detect brain cancers by comprehensively identifying somatic mutations, chromosomal copy number changes, and adaptive immunoreceptor repertoires from samples of cerebrospinal fluid.

Advanced Polymeric Nanoparticles for Cancer Immunotherapy: Materials Engineering, Immunotherapeutic Mechanism and Clinical Translation

Cancer immunotherapy, which leverages immune system components to treat malignancies, has emerged as a cornerstone of contemporary therapeutic strategies. Yet, critical concerns about the efficacy and safety of cancer immunotherapies remain formidable. Nanotechnology, especially polymeric nanoparticles (PNPs), offers unparalleled flexibility in manipulation-from the chemical composition and physical properties to the precision control of nanoassemblies. PNPs provide an optimal platform to amplify the potency and minimize systematic toxicity in a broad spectrum of immunotherapeutic modalities. In this comprehensive review, the basics of polymer chemistry, and state-of-the-art designs of PNPs from a physicochemical standpoint for cancer immunotherapy, encompassing therapeutic cancer vaccines, in situ vaccination, adoptive T-cell therapies, tumor-infiltrating immune cell-targeted therapies, therapeutic antibodies, and cytokine therapies are delineated. Each immunotherapy necessitates distinctively tailored design strategies in polymeric nanoplatforms. The extensive applications of PNPs, and investigation of their mechanisms of action for enhanced efficacy are particularly focused on. The safety profiles of PNPs and clinical research progress are discussed. Additionally, forthcoming developments and emergent trends of polymeric nano-immunotherapeutics poised to transform cancer treatment paradigms into clinics are explored.

The compositional behavior of the human T cell receptor repertoire in ovarian cancer compared to healthy donors

The distinctive characteristics of an individual's T cell receptor repertoire are crucial in recognizing and responding to a diverse array of antigens, contributing to immune specificity and adaptability. The repertoire, famously vast due to a series of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire of 85 women: ovarian cancer patients (OC) and healthy donors (HD), generating a dataset of T cell clones and their abundance. For the alpha chain we obtained 6.4·106 reads, with an average of 75936 clones per sample, and an average of 30607 clonotypes per sample. For the beta chain we obtained 13.6·106 reads, with an average of 160400 clones per sample, and an average of 70071 clonotypes per sample. The changes in dynamics of the repertoire can be observed in response to disease, with specific clones undergoing clonal expansion and contraction. The data provided here offers a unique view of immune system behavior in health and disease and can be used to stratify OC and HD.

A Robust Quantitative Multiplex PCR for T-cell Receptor Repertoire Sequencing from Formalin-Fixed Paraffin-Embedded Samples

The analysis of the T-cell receptor (TCR) is becoming an increasingly important tool in the study of the tumor microenvironment. Global features of the repertoire, including clonality and diversity, can give important insights into the immunological processes which drive cancer progression or regression, especially in the context of immune checkpoint inhibitors. Specific TCR sequences are increasingly being used as part of the computational or experimental antigen discovery pipeline. However, the clinical samples available from tumors frequently contain poor-quality nucleic acids, making the amplification of TCR libraries challenging. We describe a robust and inexpensive protocol which can be used in conjunction with our computational TCR annotation analysis pipeline, Decombinator, to generate TCR repertoires from a variety of RNA sources and concentrations, including poor-quality low-concentration RNA from formalin-fixed paraffin-embedded tissue. We call the method (FFPE-suitable Unique Molecular idEntifier-based TCRseq (FUME-TCRseq)). The method uses a multiplex PCR to amplify the TCRbeta CDR3 region, together with sufficient V and J gene sequence to allow accurate annotation. The method incorporates a 12-base pair unique molecular identifier into each cDNA molecule, allowing downstream correction for sequencing errors, as well as PCR bias. The method unlocks a vast collection of archived tumor samples for TCR repertoire analysis, potentially leading to a better understanding of the immunology of the tumor/host response, and the mode of action of immunotherapeutic intervention.

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.

Development and characterization of human T-cell receptor (TCR) alpha and beta clones' library as biological standards and resources for TCR sequencing and engineering

Characterization of T-cell receptors (TCRs) repertoire was revolutionized by next-generation sequencing technologies; however, standardization using biological controls to facilitate precision of current alignment and assembly tools remains a challenge. Additionally, availability of TCR libraries for off-the-shelf cloning and engineering TCR-specific T cells is a valuable resource for TCR-based immunotherapies. We established nine human TCR α and β clones that were evaluated using the 5'-rapid amplification of cDNA ends-like RNA-based TCR sequencing on the Illumina platform. TCR sequences were extracted and aligned using MiXCR, TRUST4, and CATT to validate their sensitivity and specificity and to validate library preparation methods. The correlation between actual and expected TCR ratios within libraries confirmed accuracy of the approach. Our findings established the development of biological standards and library of TCR clones to be leveraged in TCR sequencing and engineering. The remaining human TCR clones' libraries for a more diverse biological control will be generated.

Natural and revolutionary tumor-specific T-cell therapy

Recently the FDA conducted a risk investigation and labeled the Boxed Warning for all BCMA- and CD19-directed CAR-T cell therapy, so does it mean that the public must take risk of secondary cancer to receive cell therapy? Here, without lentivirus and professional antigen presenting cell application, a novel tumor-specific T-cell therapy was successfully developed only by co-culturing MHC+ cancer cells and Naïve-T cells under the CD28 co-stimulatory signals. These tumor-specific T-cells could be separated through cell size and abundantly produced from peripheral blood, and would spontaneously attack target cells that carrying the same tumor antigen while avoiding others in vitro test. Moreover, it markedly decreased 90% tumor nodules companying with greatly improving overall survival (76 days vs 30 days) after twice infusion back to mice. This work maximally avoided the risks of secondary cancer and non-specific killing, and might open a revolutionary beginning of natural tumor-specific T-cell therapy.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Microbiota dictate T cell clonal selection to augment graft-versus-host disease after stem cell transplantation

Allogeneic T cell expansion is the primary determinant of graft-versus-host disease (GVHD), and current dogma dictates that this is driven by histocompatibility antigen disparities between donor and recipient. This paradigm represents a closed genetic system within which donor T cells interact with peptide-major histocompatibility complexes (MHCs), though clonal interrogation remains challenging due to the sparseness of the T cell repertoire. We developed a Bayesian model using donor and recipient T cell receptor (TCR) frequencies in murine stem cell transplant systems to define limited common expansion of T cell clones across genetically identical donor-recipient pairs. A subset of donor CD4+ T cell clonotypes differentially expanded in identical recipients and were microbiota dependent. Microbiota-specific T cells augmented GVHD lethality and could target microbial antigens presented by gastrointestinal epithelium during an alloreactive response. The microbiota serves as a source of cognate antigens that contribute to clonotypic T cell expansion and the induction of GVHD independent of donor-recipient genetics.

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.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

FUME-TCRseq Enables Sensitive and Accurate Sequencing of the T-cell Receptor from Limited Input of Degraded RNA

Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth, and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low-quality, degraded nucleic acids. Here, we developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. Using RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods demonstrated excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enabled quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. Spatially resolved FUME-TCRseq analysis of colorectal cancers using macrodissected archival samples revealed the shifting T-cell landscapes at the transition to an invasive phenotype and between tumor subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive, and low-cost tool for the characterization of T-cell repertoires, particularly in samples with low-quality RNA that have not been accessible using existing methodology.

SIGNIFICANCE

FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.

Autoimmune CD8+ T cells in type 1 diabetes: from single-cell RNA sequencing to T-cell receptor redirection

Type 1 diabetes (T1D) is an organ-specific autoimmune disease caused by pancreatic β cell destruction and mediated primarily by autoreactive CD8+ T cells. It has been shown that only a small number of stem cell-like β cell-specific CD8+ T cells are needed to convert normal mice into T1D mice; thus, it is likely that T1D can be cured or significantly improved by modulating or altering self-reactive CD8+ T cells. However, stem cell-type, effector and exhausted CD8+ T cells play intricate and important roles in T1D. The highly diverse T-cell receptors (TCRs) also make precise and stable targeted therapy more difficult. Therefore, this review will investigate the mechanisms of autoimmune CD8+ T cells and TCRs in T1D, as well as the related single-cell RNA sequencing (ScRNA-Seq), CRISPR/Cas9, chimeric antigen receptor T-cell (CAR-T) and T-cell receptor-gene engineered T cells (TCR-T), for a detailed and clear overview. This review highlights that targeting CD8+ T cells and their TCRs may be a potential strategy for predicting or treating T1D.

T-cell receptor sequencing reveals selected donor-reactive CD8+ T cell clones resist antithymocyte globulin depletion after kidney transplantation

High frequencies of donor-reactive memory T cells in the periphery of transplant candidates prior to transplantation are linked to the development of posttransplant acute rejection episodes and reduced allograft function. Rabbit antithymocyte globulin (rATG) effectively depletes naïve CD4+ and CD8+ T cells for >6 months posttransplant, but rATG's effects on human donor-reactive T cells have not been carefully determined. To address this, we performed T cell receptor β-chain sequencing on peripheral blood mononuclear cells aliquots collected pretransplant and serially posttransplant in 7 kidney transplant recipients who received rATG as induction therapy. We tracked the evolution of the donor-reactive CD4+ and CD8+ T cell repertoires and identified stimulated pretransplant, CTV-(surface dye)-labeled, peripheral blood mononuclear cells from each patient with donor cells or third-party cells. Our analyses showed that while rATG depleted CD4+ T cells in all tested subjects, a subset of donor-reactive CD8+ T cells that were present at high frequencies pretransplant, consistent with expanded memory cells, resisted rATG depletion, underwent posttransplant expansion and were functional. Together, our data support the conclusion that a subset of human memory CD8+ T cells specifically reactive to donor antigens expand in vivo despite induction therapy with rATG and thus have the potential to mediate allograft damage.

Enhancing comparative T cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice

Accurate characterization and comparison of T cell receptor (TCR) repertoires from small biological samples present significant challenges. The main challenge is the low material input, which compromises the quality of bulk sequencing and hinders the recovery of sufficient TCR sequences for robust analyses. We aimed to address this limitation by implementing a strategic approach to pool homologous biological samples. Our findings demonstrate that such pooling indeed enhances the TCR repertoire coverage, particularly for cell subsets of constrained sizes, and enables accurate comparisons of TCR repertoires at different levels of complexity across T cell subsets with different sizes. This methodology holds promise for advancing our understanding of T cell repertoires in scenarios where sample size constraints are a prevailing concern.

Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires

An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Ovarian cancer is detectable from peripheral blood using machine learning over T-cell receptor repertoires

The extraordinary diversity of T cells and B cells is critical for body maintenance. This diversity has an important role in protecting against tumor formation. In humans, the T-cell receptor (TCR) repertoire is generated through a striking stochastic process called V(D)J recombination, in which different gene segments are assembled and modified, leading to extensive variety. In ovarian cancer (OC), an unfortunate 80% of cases are detected late, leading to poor survival outcomes. However, when detected early, approximately 94% of patients live longer than 5 years after diagnosis. Thus, early detection is critical for patient survival. To determine whether the TCR repertoire obtained from peripheral blood is associated with tumor status, we collected blood samples from 85 women with or without OC and obtained TCR information. We then used machine learning to learn the characteristics of samples and to finally predict, over a set of unseen samples, whether the person is with or without OC. We successfully stratified the two groups, thereby associating the peripheral blood TCR repertoire with the formation of OC tumors. A careful study of the origin of the set of T cells most informative for the signature indicated the involvement of a specific invariant natural killer T (iNKT) clone and a specific mucosal-associated invariant T (MAIT) clone. Our findings here support the proposition that tumor-relevant signal is maintained by the immune system and is coded in the T-cell repertoire available in peripheral blood. It is also possible that the immune system detects tumors early enough for repertoire technologies to inform us near the beginning of tumor formation. Although such detection is made by the immune system, we might be able to identify it, using repertoire data from peripheral blood, to offer a pragmatic way to search for early signs of cancer with minimal patient burden, possibly with enhanced sensitivity.

Nivolumab plus ipilimumab in advanced salivary gland cancer: a phase 2 trial

Salivary gland cancers (SGCs) are rare, aggressive cancers without effective treatments when metastasized. We conducted a phase 2 trial evaluating nivolumab (nivo, anti-PD-1) and ipilimumab (ipi, anti-CTLA-4) in 64 patients with metastatic SGC enrolled in two histology-based cohorts (32 patients each): adenoid cystic carcinoma (ACC; cohort 1) and other SGCs (cohort 2). The primary efficacy endpoint (≥4 objective responses) was met in cohort 2 (5/32, 16%) but not in cohort 1 (2/32, 6%). Treatment safety/tolerability and progression-free survival (PFS) were secondary endpoints. Treatment-related adverse events grade ≥3 occurred in 24 of 64 (38%) patients across both cohorts, and median PFS was 4.4 months (95% confidence interval (CI): 2.4, 8.3) and 2.2 months (95% CI: 1.8, 5.3) for cohorts 1 and 2, respectively. We present whole-exome, RNA and T cell receptor (TCR) sequencing data from pre-treatment and on-treatment tumors and immune cell flow cytometry and TCR sequencing from peripheral blood at serial timepoints. Responding tumors universally demonstrated clonal expansion of pre-existing T cells and mutational contraction. Responding ACCs harbored neoantigens, including fusion-derived neoepitopes, that induced T cell responses ex vivo. This study shows that nivo+ipi has limited efficacy in ACC, albeit with infrequent, exceptional responses, and that it could be promising for non-ACC SGCs, particularly salivary duct carcinomas. ClinicalTrials.gov identifier: NCT03172624 .

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.

Comprehensive landscape of the T and B-cell repertoires of newly diagnosed gestational diabetes mellitus

This study conducted a high-throughput sequencing analysis of the T- and B- repertoires in the newly diagnosed GDM patients and evaluated the association between abnormal adaptive immunity and GDM. The unique TCR CDR3 clonotypes were mildly decreased in GDM patients, and the similarity of TCR V-J distributions was higher in the GDM group. Moreover, the usages of the V gene and V-J pair and the frequency distributions of some CDR3 amino acids (AAs) both in BCR and TCR were significantly different between groups. Moreover, the cytokines including IL-4, IL-6, IFN-γ and IL-17A were synchronously elevated in the GDM cases. Our findings provide a comprehensive view of BCR and TCR repertoires at newly diagnosed GDM patients, revealing the mild reduction in unique TCRB CDR3 sequences and slight alteration of the V gene, V-J combination and CDR3 (AA) usages of BCR and TCR. This work provides deep insight into the mechanism of maternal adaptive immunity in GDM and provides novel diagnostic biomarkers and potential immunotherapy targets for GDM.

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.

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

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

TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs

T cell receptor (TCR) technologies, including repertoire analyses and T cell engineering, are increasingly important in the clinical management of cellular immunity in cancer, transplantation, and other immune diseases. However, sensitive and reliable methods for repertoire analyses and TCR cloning are still lacking. Here, we report on SEQTR, a high-throughput approach to analyze human and mouse repertoires that is more sensitive, reproducible, and accurate as compared with commonly used assays, and thus more reliably captures the complexity of blood and tumor TCR repertoires. We also present a TCR cloning strategy to specifically amplify TCRs from T cell populations. Positioned downstream of single-cell or bulk TCR sequencing, it allows time- and cost-effective discovery, cloning, screening, and engineering of tumor-specific TCRs. Together, these methods will accelerate TCR repertoire analyses in discovery, translational, and clinical settings and permit fast TCR engineering for cellular therapies.

DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences. DagSim is available as a Python package at PyPI. Source code and documentation are available at: https://github.com/uio-bmi/dagsim.

Potential of TCR sequencing in graft-versus-host disease

Graft-versus-host disease (GvHD) remains one of the major complications following allogeneic haematopoietic stem cell transplantation (allo-HSCT). GvHD can occur in almost every tissue, with the skin, liver, and intestines being the mainly affected organs. T cells are implicated in initiating GvHD. T cells identify a broad range of antigens and mediate the immune response through receptors on their surfaces (T cell receptors, TCRs). The composition of TCRs within a T cell population defines the TCR repertoire of an individual, and this repertoire represents exposure to self and non-self proteins. Monitoring the changes in the TCR repertoire using TCR sequencing can provide an indication of the dynamics of a T cell population. Monitoring the frequency and specificities of specific TCR clonotypes longitudinally in different conditions and specimens (peripheral blood, GvHD-affected tissue samples) can provide insights into factors modulating immune reactions following allogeneic transplantation and will help to understand the underlying mechanisms mediating GvHD. This review provides insights into current studies of the TCR repertoire in GvHD and potential future clinical implications of TCR sequencing.

TCR sequencing: applications in immuno-oncology research

•T-cell receptor (TCR) interaction with major histocompatibility complex-antigen complexes leads to antitumour responses.•TCR sequencing analysis allows characterisation of T cells that recognise tumour neoantigens.•T-cell clonal revival and clonal replacement potentially underpin immunotherapy responses.

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 use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling

High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies are still challenging. Here, we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.

Immunogenic self-peptides - the great unknowns in autoimmunity: Identifying T-cell epitopes driving the autoimmune response in autoimmune diseases

HLA-associated autoimmune diseases likely arise from T-cell-mediated autoimmune responses against certain self-peptides from the broad HLA-presented immunopeptidomes. The limited knowledge of the autoimmune target peptides has so far compromised the basic understanding of autoimmune pathogenesis. This is due to the complexity of antigen processing and presentation as well as the polyspecificity of T-cell receptors (TCRs), which pose high methodological challenges on the discovery of immunogenic self-peptides. HLA-class I molecules present peptides to CD8⁺ T cells primarily derived from cytoplasmic proteins. Therefore, HLA-class I-restricted autoimmune responses should be directed against target cells expressing the corresponding parental protein. In HLA-class II-associated diseases, the origin of immunogenic peptides is not pre-specified, because peptides presented by HLA-class II molecules to CD4⁺ T cells may originate from both extracellular and cellular self-proteins. The different origins of HLA-class I and class II presented peptides determine the respective strategy for the discovery of immunogenic self-peptides in approaches based on the TCRs isolated from clonally expanded pathogenic T cells. Both involve identifying the respective restricting HLA allele as well as determining the recognition motif of the TCR under investigation by peptide library screening, which is required to search for homologous immunogenic self-peptides. In HLA-class I-associated autoimmune diseases, identification of the target cells allows for defining the restricting HLA allotype from the 6 different HLA-class I alleles of the individual HLA haplotype. It furthermore limits the search for immunogenic self-peptides to the transcriptome or immunopeptidome of the target cells, although neoepitopes generated by peptide splicing or translational errors may complicate identification. In HLA class II-associated autoimmune diseases, the lack of a defined target cell and differential antigen processing in different antigen-presenting cells complicate identification of the HLA restriction of autoreactive TCRs from CD4⁺ T cells. To avoid that all corresponding HLA-class II allotypes have to be included in the peptide discovery, autoantigens defined by autoantibodies can guide the search for immunogenic self-peptides presented by the respective HLA-class II risk allele. The objective of this article is to highlight important aspects to be considered in the discovery of immunogenic self-peptides in autoimmune diseases.

Unique and common TCR repertoire features of Ni2+ -, Co2+ -, and Pd2+ -specific human CD154 + CD4+ T cells

BACKGROUND

Apart from Ni²⁺ , Co²⁺ , and Pd²⁺ ions commonly trigger T cell-mediated allergic contact dermatitis. However, in vitro frequencies of metal-specific T cells and the mechanisms of antigen recognition remain unclear.

METHODS

Here, we utilized a CD154 upregulation assay to quantify Ni²⁺ -, Co²⁺ -, and Pd²⁺ -specific CD4+ T cells in peripheral blood mononuclear cells (PBMC). Involved αβ T cell receptor (TCR) repertoires were analyzed by high-throughput sequencing.

RESULTS

Peripheral blood mononuclear cells incubation with NiSO₄ , CoCl₂ , and PdCl₂ increased frequencies of CD154 + CD4+ memory T cells that peaked at ~400 μM. Activation was TCR-mediated as shown by the metal-specific restimulation of T cell clones. Most abundant were Pd²⁺ -specific T cells (mean 3.5%, n = 19), followed by Co²⁺ - and Ni²⁺ -specific cells (0.6%, n = 18 and 0.3%, n = 20) in both allergic and non-allergic individuals. A strong overrepresentation of the gene segment TRAV9-2 was unique for Ni²⁺ -specific TCR (28% of TCR) while Co²⁺ and Pd²⁺ -specific TCR favorably expressed TRAV2 (8%) and the TRBV4 gene segment family (21%), respectively. As a second, independent mechanism of metal ion recognition, all analyzed metal-specific TCR showed a common overrepresentation of a histidine in the complementarity determining region 3 (CDR3; 15% of α-chains, 34% of β-chains). The positions of the CDR3 histidine among metal-specific TCR mirrored those in random repertoires and were conserved among cross-reactive clonotypes.

CONCLUSIONS

Induced CD154 expression allows a fast and comprehensive detection of Ni²⁺ -, Co²⁺ -, and Pd²⁺ -specific CD4+ T cells. Distinct TCR repertoire features underlie the frequent activation and cross-reactivity of human metal-specific T cells.

NoisET: Noise Learning and Expansion Detection of T-Cell Receptors

High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.

T-cell receptor variable region usage in Chagas disease: A systematic review of experimental and human studies

T cells recognize their ligand, the peptide major histocompatibility complex (MHC), via the T-cell receptor (TCR), which is composed of covalently linked α and β or γ and δ chains. This recognition is critical for T-cell ontogeny and controls the selection, activation, and function of T lymphocytes. Specific TCR αβ variable regions have been associated with immunopathogenesis of Chagas disease. Here, we present a systematic review that compiles experimental in vivo and human data regarding the preferential expression of variable alpha (Vα) and variable beta (Vβ) chain regions in Trypanosoma cruzi infection. The original studies indexed in PubMed/Medline, Scopus, and Web of Science databases were screened according to the PRISMA strategy. The analysis showed that expression of TCR Vα subfamilies were evaluated in one human study, and, unlike TCR Vβ, TCR Vα presented a more restricted usage. Despite the great variability in the usage of TCR Vβ regions in human Chagas disease, a down-regulation of TCR Vβ5 expression by T cells from patients in the acute phase of the disease was shown. Opposingly, this TCR region was found overly expressed in CD4+ T cells from chronic Chagas patients. It was also demonstrated that murine Vβ9+ T cells derived from nonlymphoid organs of T. cruzi-infected animals had a modulatory profile, while splenic Vβ9+ T cells produced inflammatory cytokines, indicating that although they display the same TCR Vβ region usage, these cells are functionally distinct. Despite the limitations of few papers and year of publication of the studies, compiling the data derived from them reveals that further investigation of TCR usage will point to their potential role in protective or pathogenic responses, as biomarkers of disease progression, and in the search for dominant peptides potentially useful for the development of vaccines or therapies.

Chagas disease is a neglected tropical disease, caused by infection with Trypanosoma cruzi. Differential expression of certain T-cell receptor (TCR) variable regions has been associated with the immunopathogenesis of Chagas disease. Here, we present a systematic review that compiled experimental in vivo and human data regarding the preferential expression of TCR alpha and beta chain variable regions in Chagas disease. The original studies indexed in the PubMed/Medline, Scopus, and Web of Science databases were screened according to the PRISMA strategy. Despite the great variability in the use of TCR Vβ in T. cruzi infection, the outcomes indicate that there is a down-regulation of TCR Vβ5 expression in T cells from patients in the acute phase of Chagas disease. However, this region is preferentially expressed by CD4+ T cells from chronic Chagas patients. Additionally, it has been demonstrated that murine Vβ9+ T cells derived from nonlymphoid organs displayed a modulatory profile, while splenic Vβ9+ T cells produced inflammatory cytokines, indicating that although they express the same TCR Vβ region, these cells are functionally distinct. Information on TCR expression, specificity and function have critical impact on vaccine design.

Profiling genes encoding the adaptive immune receptor repertoire with gAIRR Suite

Adaptive immune receptor repertoire (AIRR) is encoded by T cell receptor (TR) and immunoglobulin (IG) genes. Profiling these germline genes encoding AIRR (abbreviated as gAIRR) is important in understanding adaptive immune responses but is challenging due to the high genetic complexity. Our gAIRR Suite comprises three modules. gAIRR-seq, a probe capture-based targeted sequencing pipeline, profiles gAIRR from individual DNA samples. gAIRR-call and gAIRR-annotate call alleles from gAIRR-seq reads and annotate whole-genome assemblies, respectively. We gAIRR-seqed TRV and TRJ of seven Genome in a Bottle (GIAB) DNA samples with 100% accuracy and discovered novel alleles. We also gAIRR-seqed and gAIRR-called the TR and IG genes of a subject from both the peripheral blood mononuclear cells (PBMC) and oral mucosal cells. The calling results from these two cell types have a high concordance (99% for all known gAIRR alleles). We gAIRR-annotated 36 genomes to unearth 325 novel TRV alleles and 29 novel TRJ alleles. We could further profile the flanking sequences, including the recombination signal sequence (RSS). We validated two structural variants for HG002 and uncovered substantial differences of gAIRR genes in references GRCh37 and GRCh38. gAIRR Suite serves as a resource to sequence, analyze, and validate germline TR and IG genes to study various immune-related phenotypes.

Reference-based comparison of adaptive immune receptor repertoires

B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and functionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes.

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Over 10⁸ TCR CDR3 sequences from ∼10³ individuals and 7 cohorts were analyzed

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The TCR repertoire is composed of two layers: neonatal and TDT-dependent layer

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∼70% of frequent cord blood TCRs are associated with common pathogens

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Acute graft-vs-host disease correlates with a high proportion of TDT-dependent TCRs

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

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.

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.

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

Revealing Clonal Responses of Tumor-Reactive T-Cells Through T Cell Receptor Repertoire Analysis

CD8⁺ T cells are the key effector cells that contribute to the antitumor immune response. They comprise various T-cell clones with diverse antigen-specific T-cell receptors (TCRs). Thus, elucidating the overall antitumor responses of diverse T-cell clones is an emerging challenge in tumor immunology. With the recent advancement in next-generation DNA sequencers, comprehensive analysis of the collection of TCR genes (TCR repertoire analysis) is feasible and has been used to investigate the clonal responses of antitumor T cells. However, the immunopathological significance of TCR repertoire indices is still undefined. In this review, we introduce two approaches that facilitate an immunological interpretation of the TCR repertoire data: inter-organ clone tracking analysis and single-cell TCR sequencing. These approaches for TCR repertoire analysis will provide a more accurate understanding of the response of tumor-specific T cells in the tumor microenvironment.

Frequencies and TCR Repertoires of Human 2,4,6-Trinitrobenzenesulfonic Acid-specific T Cells

Allergic contact dermatitis is a widespread T cell-mediated inflammatory skin disease, but in vitro monitoring of chemical-specific T cells remains challenging. We here introduce short-term CD154/CD137 upregulation to monitor human T cell responses to the experimental sensitizer 2,4,6-trinitrobenzenesulfonic acid (TNBS). Peripheral blood mononuclear cells (PBMC) from healthy donor buffy coats were TNBS-modified and incubated with unmodified PBMC. After 5 and 16 h, we detected TNBS-specific activated CD154+CD4+ and CD137+CD8+ T cells by multi-parameter flow cytometry, respectively. Activated cells were sorted for restimulation and bulk T cell receptor (TCR) high-throughput sequencing (HTS). Stimulation with TNBS-modified cells (3 mM) induced CD154 expression on 0.04% of CD4+ and CD137 expression on 0.60% of CD8+ memory T cells, respectively (means, n = 11-17 donors). CD69 co-expression argued for TCR-mediated activation, which was further supported by TNBS-specific restimulation of 10/13 CD154+CD4+ and 11/15 CD137+CD8+ T cell clones and lines. Major histocompatibility complex (MHC) blocking antibodies prevented activation, illustrating MHC restriction. The high frequencies of TNBS-specific T cells were associated with distinct common changes in the TCR β-chain repertoire. We observed an overrepresentation of tryptophan and lysine in the complementarity determining regions 3 (CDR3) (n = 3-5 donors), indicating a preferential interaction of these amino acids with the TNBS-induced epitopes. In summary, the detection of TNBS-specific T cells by CD154/CD137 upregulation is a fast, comprehensive and quantitative method. Combined with TCR HTS, the mechanisms of chemical allergen recognition that underlie unusually frequent T cell activation can be assessed. In the future, this approach may be adapted to detect T cells activated by additional chemical sensitizers.

Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome

BACKGROUND

Next-generation sequencing (NGS) can identify mutations in the human genome that cause disease and has been widely adopted in clinical diagnosis. However, the human genome contains many polymorphic, low-complexity, and repetitive regions that are difficult to sequence and analyze. Despite their difficulty, these regions include many clinically important sequences that can inform the treatment of human diseases and improve the diagnostic yield of NGS.

RESULTS

To evaluate the accuracy by which these difficult regions are analyzed with NGS, we built an in silico decoy chromosome, along with corresponding synthetic DNA reference controls, that encode difficult and clinically important human genome regions, including repeats, microsatellites, HLA genes, and immune receptors. These controls provide a known ground-truth reference against which to measure the performance of diverse sequencing technologies, reagents, and bioinformatic tools. Using this approach, we provide a comprehensive evaluation of short- and long-read sequencing instruments, library preparation methods, and software tools and identify the errors and systematic bias that confound our resolution of these remaining difficult regions.

CONCLUSIONS

This study provides an analytical validation of diagnosis using NGS in difficult regions of the human genome and highlights the challenges that remain to resolve these difficult regions.

Immunological causes of obsessive-compulsive disorder: is it time for the concept of an "autoimmune OCD" subtype?

Obsessive-compulsive disorder (OCD) is a highly disabling mental illness that can be divided into frequent primary and rarer organic secondary forms. Its association with secondary autoimmune triggers was introduced through the discovery of Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal infection (PANDAS) and Pediatric Acute onset Neuropsychiatric Syndrome (PANS). Autoimmune encephalitis and systemic autoimmune diseases or other autoimmune brain diseases, such as multiple sclerosis, have also been reported to sometimes present with obsessive-compulsive symptoms (OCS). Subgroups of patients with OCD show elevated proinflammatory cytokines and autoantibodies against targets that include the basal ganglia. In this conceptual review paper, the clinical manifestations, pathophysiological considerations, diagnostic investigations, and treatment approaches of immune-related secondary OCD are summarized. The novel concept of "autoimmune OCD" is proposed for a small subgroup of OCD patients, and clinical signs based on the PANDAS/PANS criteria and from recent experience with autoimmune encephalitis and autoimmune psychosis are suggested. Red flag signs for "autoimmune OCD" could include (sub)acute onset, unusual age of onset, atypical presentation of OCS with neuropsychiatric features (e.g., disproportionate cognitive deficits) or accompanying neurological symptoms (e.g., movement disorders), autonomic dysfunction, treatment resistance, associations of symptom onset with infections such as group A streptococcus, comorbid autoimmune diseases or malignancies. Clinical investigations may also reveal alterations such as increased levels of anti-basal ganglia or dopamine receptor antibodies or inflammatory changes in the basal ganglia in neuroimaging. Based on these red flag signs, the criteria for a possible, probable, and definite autoimmune OCD subtype are proposed.

AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data

SUMMARY

T cells participate directly in the body's immune response to cancer, allowing immunotherapy treatments to effectively recognize and target cancer cells. We previously developed DeepCAT to demonstrate that T cells serve as a biomarker of immune response in cancer patients and can be utilized as a diagnostic tool to differentiate healthy and cancer patient samples. However, DeepCAT's reliance on tumor bulk RNA-seq samples as training data limited its further performance improvement. Here, we benchmarked a new approach, AutoCAT, to predict tumor-associated TCRs from targeted TCR-seq data as a new form of input for DeepCAT, and observed the same level of predictive accuracy.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at https://github.com/cew88/AutoCAT, and data is available at 10.5281/zenodo.5176884.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Circulating T-Cell Repertoires Correlate With the Tumor Response in Patients With Breast Cancer Receiving Neoadjuvant Chemotherapy

PURPOSE

Neoadjuvant chemotherapy (NAC) has been widely used in patients with breast cancer to minish tumor burden and increase resection rate of cancer. T-cell repertoire has been believed to be able to monitor antitumor immune responses. This study aimed to explore the dynamic change of T-cell repertoire and its clinical value in evaluating the tumor response in patients with breast cancer receiving NAC.

MATERIALS AND METHODS

Ninety-four patients who underwent NAC before surgery were recruited, and peripheral blood samples were collected at multiple time points during NAC. High-throughput T-cell receptor (TCR)-β sequencing was used to characterize the T-cell repertoire of every sample and analyzed the changes in circulating T-cell repertoire during NAC.

RESULTS

We found that the diversity of TCR repertoires was associated with age and clinical stage of the patients with breast cancer. The distribution of Vβ and Jβ genes in TCR repertoires was skewed in patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer. Vβ20.1 and Vβ30 expression levels before NAC correlate with tumor response after all cycles of NAC in HER2- and HER2+ patients, respectively. Some CDR3 motifs that correlated with clinical response in either HER2+ or HER2- patients were identified. Besides, TCR repertoire evolved during NAC and the diversity of TCR repertoire decreased more after two cycles of NAC in patients with good tumor response after all cycles of NAC (P = .0061).

CONCLUSION

Our results demonstrated that TCR repertoire correlated with the characteristics of the tumor, such as the expression status of HER2. Moreover, some characteristics of TCR repertoires that correlated with clinical response were identified and they might provide useful information to tailor therapeutic regimens at the early cycle of NAC.