PIRD: Pan Immune Repertoire Database

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

OBJECTIVES

Recent immunologic study of the adaptive immune repertoire in the subglottic airway demonstrated high-frequency T cell clones that do not overlap between individuals. However, the anatomic distribution and antigenic target of the T cell repertoire in the proximal airway mucosa remain unresolved.

METHODS

Single-cell RNA sequencing of matched scar and unaffected mucosa from idiopathic subglottic stenosis patients (iSGS, n = 32) was performed and compared with airway mucosa from healthy controls (n = 10). T cell receptor (TCR) sequences were interrogated via similarity network analysis to explore antigenic targets using the published algorithm: Grouping of Lymphocyte Interactions by Paratope Hotspots (GLIPH2).

RESULTS

The mucosal T cell repertoire in healthy control airways consisted of highly expressed T cell clones conserved across anatomic subsites (trachea, bronchi, bronchioles, and lung). In iSGS, high-frequency clones were equally represented in both scar and adjacent non-scar tissue. Significant differences in repertoire structure between iSGS scar and unaffected mucosa was observed, driven by unique low-frequency clones. GLIPH2 results suggest low-frequency clones share targets between multiple iSGS patients.

CONCLUSION

Healthy airway mucosa has a highly conserved T cell repertoire across multiple anatomic subsites. Similarly, iSGS patients have highly expressed T cell clones present in both scar and unaffected mucosa. iSGS airway scar possesses an abundance of less highly expanded clones with predicted antigen targets shared between patients. Interrogation of these shared motifs suggests abundant adaptive immunity to viral targets in iSGS airway scar. These results provide insight into disease pathogenesis and illuminate new treatment strategies in iSGS.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:3245-3252, 2024.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning

Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.

Characterizing the T Cell Repertoire in the Proximal Airway in Health and Disease

OBJECTIVES

Recent translational scientific efforts in subglottic stenosis (SGS) support a disease model where epithelial alterations facilitate microbiome displacement, dysregulated immune activation, and localized fibrosis. Given the observed immune cell infiltrate in SGS, we sought to test the hypothesis that SGS cases possessed a low diversity (highly clonal) adaptive immune response when compared with healthy controls.

METHODS

Single cell RNA sequencing (scRNA-seq) of subglottic mucosal scar in iSGS (n = 24), iLTS (n = 8), and healthy controls (n = 7) was performed. T cell receptor (TCR) sequences were extracted, analyzed, and used to construct repertoire structure, compare diversity, interrogate overlap, and define antigenic targets using the Immunarch bioinformatics pipeline.

RESULTS

The proximal airway mucosa in health and disease are equally diverse via Hill framework quantitation (iSGS vs. iLTS vs. Control, p > 0.05). Repertoires do not significantly overlap between individuals (Morisita <0.02). Among iSGS patients, clonality of the TCR repertoire is driven by CD8+ T cells, and iSGS patients possess numerous TCRs targeting viral and intercellular pathogens. High frequency clonotypes do not map to known targets in public datasets.

CONCLUSION

SGS cases do not possess a lower diversity adaptive immune infiltrate when compared with healthy controls. Interestingly, the TCR repertoire in both health and disease contains a restricted number of high frequency clonotypes that do not significantly overlap between individuals. The target of the high frequency clonotypes in health and disease remain unresolved.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:1757-1764, 2024.

Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

BACKGROUND

In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA.

RESULTS

In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool.

CONCLUSIONS

Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.

Dynamic establishment of recipient resident memory T cell repertoire after human intestinal transplantation

BACKGROUND

Understanding formation of the human tissue resident memory T cell (TRM) repertoire requires longitudinal access to human non-lymphoid tissues.

METHODS

By applying flow cytometry and next generation sequencing to serial blood, lymphoid tissue, and gut samples from 16 intestinal transplantation (ITx) patients, we assessed the origin, distribution, and specificity of human TRMs at phenotypic and clonal levels.

FINDINGS

Donor age ≥1 year and blood T cell macrochimerism (peak level ≥4%) were associated with delayed establishment of stable recipient TRM repertoires in the transplanted ileum. T cell receptor (TCR) overlap between paired gut and blood repertoires from ITx patients was significantly greater than that in healthy controls, demonstrating increased gut-blood crosstalk after ITx. Crosstalk with the circulating pool remained high for years of follow-up. TCR sequences identifiable in pre-Tx recipient gut but not those in lymphoid tissues alone were more likely to populate post-Tx ileal allografts. Clones detected in both pre-Tx gut and lymphoid tissue had distinct transcriptional profiles from those identifiable in only one tissue. Recipient T cells were distributed widely throughout the gut, including allograft and native colon, which had substantial repertoire overlap. Both alloreactive and microbe-reactive recipient T cells persisted in transplanted ileum, contributing to the TRM repertoire.

INTERPRETATION

Our studies reveal human intestinal TRM repertoire establishment from the circulation, preferentially involving lymphoid tissue counterparts of recipient intestinal T cell clones, including TRMs. We have described the temporal and spatial dynamics of this active crosstalk between the circulating pool and the intestinal TRM pool.

FUNDING

This study was funded by the National Institute of Allergy and Infectious Diseases (NIAID) P01 grant AI106697.

Exploratory analysis of the cervix tumoral HPV antigen-specific T-cell repertoire during chemoradiation and after brachytherapy

BACKGROUND

Chemoradiation (CRT) may modulate the immune milieu as an in-situ vaccine. Rapid dose delivery of brachytherapy has unclear impact on T-cell repertoires. HPV-associated cancers express viral oncoproteins E6/E7, which enable tracking antigen/tumor-specific immunity during CRT.

METHODS

Thirteen cervical cancer patients on a multi-institutional prospective protocol from 1/2020-1/2023 underwent standard-of-care CRT with pulsed-dose-rate brachytherapy boost (2 fractions). Cervix swabs at various timepoints underwent multiplex DNA deep sequencing of the TCR-β/CDR3 region with immunoSEQ. Separately, HPV-responsive T-cell clones were also expanded ex vivo. Statistical analysis was via Mann-Whitney-U.

RESULTS

TCR productive clonality, templates, frequency, or rearrangements increased post-brachytherapy in 8 patients. Seven patients had E6/E7-responsive evolution over CRT with increased productive templates (ranges: 1.2-50.2 fold-increase from baseline), frequency (1.2-1.7), rearrangements (1.2-40.2), and clonality (1.2-15.4). Five patients had HPV-responsive clonal expansion post-brachytherapy, without changes in HPV non-responsive clones. Epitope mapping revealed VDJ rearrangements targeting cervical cancer-associated antigens in 5 patients. The only two patients with disease recurrence lacked response in all metrics. A lack of global TCR remodeling correlated with worse recurrence-free survival, p = 0.04.

CONCLUSION

CRT and brachytherapy alters the cervical cancer microenvironment to facilitate the expansion of specific T-cell populations, which may contribute to treatment efficacy.

The clinical applications of immunosequencing

Technological advances in high-throughput sequencing have opened the door for the interrogation of adaptive immune responses at unprecedented scale. It is now possible to determine the sequences of antibodies or T-cell receptors produced by individual B and T cells in a sample. This capability, termed immunosequencing, has transformed the study of both infectious and non-infectious diseases by allowing the tracking of dynamic changes in B and T cell clonal populations over time. This has improved our understanding of the pathology of cancers, autoimmune diseases, and infectious diseases. However, to date there has been only limited clinical adoption of the technology. Advances over the last decade and on the horizon that reduce costs and improve interpretability could enable widespread clinical use. Many clinical applications have been proposed and, while most are still undergoing research and development, some methods relying on immunosequencing data have been implemented, the most widespread of which is the detection of measurable residual disease. Here, we review the diagnostic, prognostic, and therapeutic applications of immunosequencing for both infectious and non-infectious diseases.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method

Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.

Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer

Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.

Virus-specific TRM cells of both donor and recipient origin reside in human kidney transplants

Tissue-resident lymphocytes (TRLs) are critical for local protection against viral pathogens in peripheral tissue. However, it is unclear if TRLs perform a similar role in transplanted organs under chronic immunosuppressed conditions. In this study, we aimed to characterize the TRL compartment in human kidney transplant nephrectomies and examine its potential role in antiviral immunity. The TRL compartment of kidney transplants contained diverse innate, innate-like, and adaptive TRL populations expressing the canonical residency markers CD69, CD103, and CD49a. Chimerism of donor and recipient cells was present in 43% of kidney transplants and occurred in all TRL subpopulations. Paired single-cell transcriptome and T cell receptor (TCR) sequencing showed that donor and recipient tissue-resident memory T (TRM) cells exhibit striking similarities in their transcriptomic profiles and share numerous TCR clonotypes predicted to target viral pathogens. Virus dextramer staining further confirmed that CD8 TRM cells of both donor and recipient origin express TCRs with specificities against common viruses, including CMV, EBV, BK polyomavirus, and influenza A. Overall, the study results demonstrate that a diverse population of TRLs resides in kidney transplants and offer compelling evidence that TRM cells of both donor and recipient origin reside within this TRL population and may contribute to local protection against viral pathogens.

Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans

SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.

Immunophenotypic correlates of sustained MRD negativity in patients with multiple myeloma

The role of the immune microenvironment in maintaining disease remission in patients with multiple myeloma (MM) is not well understood. In this study, we comprehensively profile the immune system in patients with newly diagnosed MM receiving continuous lenalidomide maintenance therapy with the aim of discovering correlates of long-term treatment response. Leveraging single-cell RNA sequencing and T cell receptor β sequencing of the peripheral blood and CyTOF mass cytometry of the bone marrow, we longitudinally characterize the immune landscape in 23 patients before and one year after lenalidomide exposure. We compare patients achieving sustained minimal residual disease (MRD) negativity to patients who never achieved or were unable to maintain MRD negativity. We observe that the composition of the immune microenvironment in both the blood and the marrow varied substantially according to both MRD negative status and history of autologous stem cell transplant, supporting the hypothesis that the immune microenvironment influences the depth and duration of treatment response.

Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders

BACKGROUND AND OBJECTIVES

Neuromyelitis optica spectrum disorders (NMOSD) is widely recognized as a CNS demyelinating disease associated with AQP4-IgG (T cell-dependent antibody), and its trigger is still unclear. In addition, although the treatment of NMOSD currently can rely on traditional immunosuppressive and modulating agents, effective methods to predict the efficacy of these therapeutics are lacking.

METHODS

In this study, high-throughput T-cell receptor (TCR) sequencing was performed on peripheral blood from 151 pretreatment patients with AQP4-IgG⁺ NMOSD and 151 healthy individuals. We compared the TCR repertoire of those with NMOSD with that of healthy individuals and identified TCR clones that were significantly enriched in NMOSD. In addition, we treated 28 patients with AQP4-IgG⁺ NMOSD with immunosuppressants and followed up for 6 months to compare changes in NMOSD-specific TCRs (NMOSD-TCRs) before and after treatment. Moreover, we analyzed transcriptome and single-cell B-cell receptor (BCR) data from public databases and performed T-cell activation experiments using antigenic epitopes of cytomegalovirus (CMV) to further explore the triggers of AQP4-IgG⁺ NMOSD.

RESULTS

Compared with healthy controls, patients with AQP4-IgG⁺ NMOSD had significantly reduced diversity and shorter CDR3 length of TCRβ repertoire. Furthermore, we identified 597 NMOSD-TCRs with a high sequence similarity that have the potential to be used in the diagnosis and prognosis of NMOSD. The characterization of NMOSD-TCRs and pathology-associated clonotype annotation indicated that the occurrence of AQP4-IgG⁺ NMOSD may be associated with CMV infection, which was further corroborated by transcriptome and single-cell BCR analysis results from public databases and T-cell activation experiments.

DISCUSSION

Our findings suggest that the occurrence of AQP4-IgG⁺ NMOSD may be associated with CMV infection. In conclusion, our study provides new clues to uncover the causative factors of AQP4-IgG⁺ NMOSD and provides a theoretical foundation for treating and monitoring the disease.

Can we predict T cell specificity with digital biology and machine learning?

Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures

Background

The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.

Methods

In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.

Results

We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.

Conclusions

Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

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.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

UcTCRdb: An unconventional T cell receptor sequence database with online analysis functions

Unlike conventional major histocompatibility complex (MHC) class I and II molecules reactive T cells, the unconventional T cell subpopulations recognize various non-polymorphic antigen-presenting molecules and are typically characterized by simplified patterns of T cell receptors (TCRs), rapid effector responses and ‘public’ antigen specificities. Dissecting the recognition patterns of the non-MHC antigens by unconventional TCRs can help us further our understanding of the unconventional T cell immunity. The small size and irregularities of the released unconventional TCR sequences are far from high-quality to support systemic analysis of unconventional TCR repertoire. Here we present UcTCRdb, a database that contains 669,900 unconventional TCRs collected from 34 corresponding studies in humans, mice, and cattle. In UcTCRdb, users can interactively browse TCR features of different unconventional T cell subsets in different species, search and download sequences under different conditions. Additionally, basic and advanced online TCR analysis tools have been integrated into the database, which will facilitate the study of unconventional TCR patterns for users with different backgrounds. UcTCRdb is freely available at http://uctcrdb.cn/.

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency

The interaction of T-cell receptors with peptide-major histocompatibility complex molecules (TCR-pMHC) plays a crucial role in adaptive immune responses. Currently there are various models aiming at predicting TCR-pMHC binding, while a standard dataset and procedure to compare the performance of these approaches is still missing. In this work we provide a general method for data collection, preprocessing, splitting and generation of negative examples, as well as comprehensive datasets to compare TCR-pMHC prediction models. We collected, harmonized, and merged all the major publicly available TCR-pMHC binding data and compared the performance of five state-of-the-art deep learning models (TITAN, NetTCR-2.0, ERGO, DLpTCR and ImRex) using this data. Our performance evaluation focuses on two scenarios: 1) different splitting methods for generating training and testing data to assess model generalization and 2) different data versions that vary in size and peptide imbalance to assess model robustness. Our results indicate that the five contemporary models do not generalize to peptides that have not been in the training set. We can also show that model performance is strongly dependent on the data balance and size, which indicates a relatively low model robustness. These results suggest that TCR-pMHC binding prediction remains highly challenging and requires further high quality data and novel algorithmic approaches.

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions

T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Profiling the peripheral blood T cell receptor repertoires of gastric cancer patients

Cancer driven by somatic mutations may express neoantigens that can trigger T-cell immune responses. Since T-cell receptor (TCR) repertoires play critical roles in anti-tumor immune responses for oncology, next-generation sequencing (NGS) was used to profile the hypervariable complementarity-determining region 3 (CDR3) of the TCR-beta chain in peripheral blood samples from 68 gastric cancer patients and 49 healthy controls. We found that most hyper-expanded CDR3 are individual-specific, and the gene usage of TRBV3-1 is more frequent in the tumor group regardless of tumor stage than in the healthy control group. We identified 374 hyper-expanded tumor-specific CDR3, which may play a vital role in anti-tumor immune responses. The patients with stage IV gastric cancer have higher EBV-specific CDR3 abundance than the control. In conclusion, analysis of the peripheral blood TCR repertoires may provide the biomarker for gastric cancer prognosis and guide future immunotherapy.

Combination anti-HIV antibodies provide sustained virological suppression

Antiretroviral therapy is highly effective in suppressing human immunodeficiency virus (HIV)1. However, eradication of the virus in individuals with HIV has not been possible to date2. Given that HIV suppression requires life-long antiretroviral therapy, predominantly on a daily basis, there is a need to develop clinically effective alternatives that use long-acting antiviral agents to inhibit viral replication3. Here we report the results of a two-component clinical trial involving the passive transfer of two HIV-specific broadly neutralizing monoclonal antibodies, 3BNC117 and 10-1074. The first component was a randomized, double-blind, placebo-controlled trial that enrolled participants who initiated antiretroviral therapy during the acute/early phase of HIV infection. The second component was an open-label single-arm trial that enrolled individuals with viraemic control who were naive to antiretroviral therapy. Up to 8 infusions of 3BNC117 and 10-1074, administered over a period of 24 weeks, were well tolerated without any serious adverse events related to the infusions. Compared with the placebo, the combination broadly neutralizing monoclonal antibodies maintained complete suppression of plasma viraemia (for up to 43 weeks) after analytical treatment interruption, provided that no antibody-resistant HIV was detected at the baseline in the study participants. Similarly, potent HIV suppression was seen in the antiretroviral-therapy-naive study participants with viraemia carrying sensitive virus at the baseline. Our data demonstrate that combination therapy with broadly neutralizing monoclonal antibodies can provide long-term virological suppression without antiretroviral therapy in individuals with HIV, and our experience offers guidance for future clinical trials involving next-generation antibodies with long half-lives.

Data Sharing and Reuse: A Method by the AIRR Community

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.

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.

Follicular T cells are clonally and transcriptionally distinct in B cell-driven mouse autoimmune disease

Pathogenic autoantibodies contribute to tissue damage and clinical decline in autoimmune disease. Follicular T cells are central regulators of germinal centers, although their contribution to autoantibody-mediated disease remains unclear. Here we perform single cell RNA and T cell receptor (TCR) sequencing of follicular T cells in a mouse model of autoantibody-mediated disease, allowing for analyses of paired transcriptomes and unbiased TCRαβ repertoires at single cell resolution. A minority of clonotypes are preferentially shared amongst autoimmune follicular T cells and clonotypic expansion is associated with differential gene signatures in autoimmune disease. Antigen prediction using algorithmic and machine learning approaches indicates convergence towards shared specificities between non-autoimmune and autoimmune follicular T cells. However, differential autoimmune transcriptional signatures are preserved even amongst follicular T cells with shared predicted specificities. These results demonstrate that follicular T cells are phenotypically distinct in B cell-driven autoimmune disease, providing potential therapeutic targets to modulate autoantibody development.

RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database

The antibody repertoire is a critical component of the adaptive immune system and is believed to reflect an individual’s immune history and current immune status. Delineating the antibody repertoire has advanced our understanding of humoral immunity, facilitated antibody discovery, and showed great potential for improving the diagnosis and treatment of disease. However, no tool to date has effectively integrated big Rep-seq data and prior knowledge of functional antibodies to elucidate the remarkably diverse antibody repertoire. We developed a Rep-seq dataset Analysis Platform with an Integrated antibody Database (RAPID; https://rapid.zzhlab.org/), a free and web-based tool that allows researchers to process and analyse Rep-seq datasets. RAPID consolidates 521 WHO-recognized therapeutic antibodies, 88,059 antigen- or disease-specific antibodies, and 306 million clones extracted from 2,449 human IGH Rep-seq datasets generated from individuals with 29 different health conditions. RAPID also integrates a standardized Rep-seq dataset analysis pipeline to enable users to upload and analyse their datasets. In the process, users can also select set of existing repertoires for comparison. RAPID automatically annotates clones based on integrated therapeutic and known antibodies, and users can easily query antibodies or repertoires based on sequence or optional keywords. With its powerful analysis functions and rich set of antibody and antibody repertoire information, RAPID will benefit researchers in adaptive immune studies.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Characteristics of T-Cell Receptor Repertoire and Correlation With EGFR Mutations in All Stages of Lung Cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, and its occurrence is related to the accumulation of gene mutations and immune escape of the tumor. Sequencing of the T-cell receptor (TCR) repertoire can reveal the immunosurveillance status of the tumor microenvironment, which is related to tumor escape and immunotherapy. This study aimed to determine the characteristics and clinical significance of the TCR repertoire in lung cancer. To comprehensively profile the TCR repertoire, results from high-throughput sequencing of samples from 93 Chinese patients with lung cancer were analyzed. We found that the TCR clonality of tissues was related to smoking, with higher clonality in patients who had quit smoking for less than 1 year. As expected, TCR clonality was correlated with stages: patients with stage IV disease showed higher clonality than others. The correlation between TCR repertoire and epidermal growth factor receptor (EGFR) status was also investigated. Patients with EGFR non-L858R mutations showed higher clonality and a lower Shannon index than other groups, including patients with EGFR L858R mutation and wild-type EGFR. Furthermore, we analyzed the TCR similarity metrics—that is, the TCR shared between postoperative peripheral blood and tissue of patients with non-distant metastasis of lung cancer. A similar trend was found, in which patients with EGFR L858R mutations had lower overlap index (OLI) and Morisita index (MOI) scores. Moreover, the OLI showed a positive correlation with several clinical characteristics, including the tumor mutational burden of tissues and the maximum somatic allele frequency of blood; OLI showed a negative correlation with the ratio of CD4+CD28+ in CD4+ cells and the ratio of CD8+CD28+ in CD8+ cells. In conclusion, TCR clonality and TCR similarity metrics correlated with clinical characteristics of patients with lung cancer. Differences in TCR clonality, Shannon index, and OLI across EGFR subtypes provide information to improve understanding about varied responses to immunotherapy in patients with different EGFR mutations.

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

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

TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function

T cells and the T-cell receptor (TCR) repertoire play pivotal roles in immune response and immunotherapy. TCR sequencing (TCR-Seq) technology has enabled accurate profiling TCR repertoire and currently a large number of TCR-Seq data are available in public. Based on the urgent need to effectively re-use these data, we developed TCRdb, a comprehensive human TCR sequences database, by a uniform pipeline to characterize TCR sequences on TCR-Seq data. TCRdb contains more than 277 million highly reliable TCR sequences from over 8265 TCR-Seq samples across hundreds of tissues/clinical conditions/cell types. The unique features of TCRdb include: (i) comprehensive and reliable sequences for TCR repertoire in different samples generated by a strict and uniform pipeline of TCRdb; (ii) powerful search function, allowing users to identify their interested TCR sequences in different conditions; (iii) categorized sample metadata, enabling comparison of TCRs in different sample types; (iv) interactive data visualization charts, describing the TCR repertoire in TCR diversity, length distribution and V-J gene utilization. The TCRdb database is freely available at http://bioinfo.life.hust.edu.cn/TCRdb/ and will be a useful resource in the research and application community of T cell immunology.

How repertoire data are changing antibody science

Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.