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

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Quantitative approaches for decoding the specificity of the human T cell repertoire

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.

Epigenetic regulation of HBV-specific tumor-infiltrating T cells in HBV-related HCC

BACKGROUND AND AIMS

HBV shapes the T-cell immune responses in HBV-related HCC. T cells can be recruited to the nidus, but limited T cells participate specifically in response to the HBV-related tumor microenvironment and HBV antigens. How epigenomic programs regulate T-cell compartments in virus-specific immune processes is unclear.

APPROACH AND RESULTS

We developed Ti-ATAC-seq. 2 to map the T-cell receptor repertoire, epigenomic, and transcriptomic landscape of αβ T cells at both the bulk-cell and single-cell levels in 54 patients with HCC. We deeply investigated HBV-specific T cells and HBV-related T-cell subsets that specifically responded to HBV antigens and the HBV + tumor microenvironment, respectively, characterizing their T-cell receptor clonality and specificity and performing epigenomic profiling. A shared program comprising NFKB1/2-, Proto-Oncogene, NF-KB Sub unit, NFATC2-, and NR4A1-associated unique T-cell receptor-downstream core epigenomic and transcriptomic regulome commonly regulated the differentiation of HBV-specific regulatory T-cell (Treg) cells and CD8 + exhausted T cells; this program was also selectively enriched in the HBV-related Treg-CTLA4 and CD8-exhausted T cell-thymocyte selection associated high mobility subsets and drove greater clonal expansion in HBV-related Treg-CTLA4 subset. Overall, 54% of the effector and memory HBV-specific T cells are governed by transcription factor motifs of activator protein 1, NFE2, and BACH1/2, which have been reported to be associated with prolonged patient relapse-free survival. Moreover, HBV-related tumor-infiltrating Tregs correlated with both increased viral titer and poor prognosis in patients.

CONCLUSIONS

This study provides insight into the cellular and molecular basis of the epigenomic programs that regulate the differentiation and generation of HBV-related T cells from viral infection and HBV + HCC unique immune exhaustion.

Prolonged cytopenia following CD19 CAR T cell therapy is linked with bone marrow infiltration of clonally expanded IFNγ-expressing CD8 T cells

Autologous anti-CD19 chimeric antigen receptor T cell (CAR T) therapy is highly effective in relapsed/refractory large B cell lymphoma (rrLBCL) but is associated with toxicities that delay recovery. While the biological mechanisms of cytokine release syndrome and neurotoxicity have been investigated, the pathophysiology is poorly understood for prolonged cytopenia, defined as grade ≥3 cytopenia lasting beyond 30 days after CAR T infusion. We performed single-cell RNA sequencing of bone marrow samples from healthy donors and rrLBCL patients with or without prolonged cytopenia and identified significantly increased frequencies of clonally expanded CX3CR1hi cytotoxic T cells, expressing high interferon (IFN)-γ and cytokine signaling gene sets, associated with prolonged cytopenia. In line with this, we found that hematopoietic stem cells from these patients expressed IFN-γ response signatures. IFN-γ deregulates hematopoietic stem cell self-renewal and differentiation and can be targeted with thrombopoietin agonists or IFN-γ-neutralizing antibodies, highlighting a potential mechanism-based approach for the treatment of CAR T-associated prolonged cytopenia.

Early activation of inflammatory pathways in UBA1-mutated hematopoietic stem and progenitor cells in VEXAS

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome is a pleiotropic, severe autoinflammatory disease caused by somatic mutations in the ubiquitin-like modifier activating enzyme 1 (UBA1) gene. To elucidate VEXAS pathophysiology, we performed transcriptome sequencing of single bone marrow mononuclear cells and hematopoietic stem and progenitor cells (HSPCs) from VEXAS patients. HSPCs are biased toward myeloid (granulocytic) differentiation, and against lymphoid differentiation in VEXAS. Activation of multiple inflammatory pathways (interferons and tumor necrosis factor alpha) occurs ontogenically early in primitive hematopoietic cells and particularly in the myeloid lineage in VEXAS, and inflammation is prominent in UBA1-mutated cells. Dysregulation in protein degradation likely leads to higher stress response in VEXAS HSPCs, which positively correlates with inflammation. TCR usage is restricted and there are increased cytotoxicity and IFN-γ signaling in T cells. In VEXAS syndrome, both aberrant inflammation and myeloid predominance appear intrinsic to hematopoietic stem cells mutated in UBA1.

Analysis of tumor infiltrating CD4+ and CD8+ CDR3 sequences reveals shared features putatively associated to the anti-tumor immune response

Introduction

Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic value in breast cancer (BC) and exert a protective function against tumor growth, indicating that it is susceptible to treatment using adoptive cell transfer of TILs or T cell receptor (TCR)-based therapies. TCR can be used to identify naturally tumor-reactive T cells, but little is known about the differences in the TCR repertoires of CD4+ and CD8+ TILs.

Methods

TCR high-throughput sequencing was performed using TILs derived from the initial cultures of 11 BC biopsies and expanded and sorted CD4+ and CD8+ TILs as well as using PBMCs from healthy donors expanded and sorted using the same methodology.

Results

Physicochemical TCR differences between T cell subsets were observed, as CD4+ TILs presented larger N(D)Nnt TRB sequences and with a higher usage of positively charged residues, although only the latest was also observed in peripheral T cells from healthy individuals. Moreover, in CD4+ TILs, a more restricted TCR repertoire with a higher abundance of similar sequences containing certain amino acid motifs was observed.

Discussion

Some differences between CD4+ and CD8+ TCRs were intrinsic to T cell subsets as can also be observed in peripheral T cells from healthy individuals, while other were only found in TILs samples and therefore may be tumor-driven. Notably, the higher similarity among CD4+ TCRs suggests a higher TCR promiscuity in this subset.

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.

TCR Sequencing in Mouse Models of Allorecognition Unveils the Features of Directly and Indirectly Activated Clonotypes

Allorecognition is known to involve a large number of lymphocytes carrying diverse T-cell receptor repertoire. Thus, one way to understand allorecognition and rejection mechanisms is via high-throughput sequencing of T-cell receptors. In this study, in order to explore and systematize the properties of the alloreactive T-cell receptor repertoire, we modeled direct and indirect allorecognition pathways using material from inbred mice in vitro and in vivo. Decoding of the obtained T-cell receptor genes using high-throughput sequencing revealed some features of the alloreactive repertoires. Thus, alloreactive T-cell receptor repertoires were characterized by specific V-gene usage patterns, changes in CDR3 loop length, and some amino acid occurrence probabilities in the CDR3 loop. Particularly pronounced changes were observed for directly alloreactive clonotypes. We also revealed a clustering of directly and indirectly alloreactive clonotypes by their ability to bind a single antigen; amino acid patterns of the CDR3 loop of alloreactive clonotypes; and the presence in alloreactive repertoires of clonotypes also associated with infectious, autoimmune, and tumor diseases. The obtained results were determined by the modeling of the simplified allorecognition reaction in inbred mice in which stimulation was performed with a single MHCII molecule. We suppose that the decomposition of the diverse alloreactive TCR repertoire observed in humans with transplants into such simple reactions will help to find alloreactive repertoire features; e.g., a dominant clonotype or V-gene usage pattern, which may be targeted to correct the entire rejection reaction in patients. In this work, we propose several technical ways for such decomposition analysis, including separate modeling of the indirect alloreaction pathway and clustering of alloreactive clonotypes according to their ability to bind a single antigen, among others.

Multi-tiered approach to detect autoimmune cross-reactivity of therapeutic T cell receptors

T cell receptor (TCR)-engineered T cell therapy using high-affinity TCRs is a promising treatment modality for cancer. Discovery of high-affinity TCRs especially against self-antigens can require approaches that circumvent central tolerance, which may increase the risk of cross-reactivity. Despite the potential for toxicity, no standardized approach to screen cross-reactivity has been established in the context of preclinical safety evaluation. Here, we describe a practical framework to prospectively detect clinically prohibitive cross-reactivity of therapeutic TCR candidates. Cross-reactivity screening consisted of multifaceted series of assays including assessment of p-MHC tetramer binding, cell line recognition, and reactivity against candidate peptide libraries. Peptide libraries were generated using conventional contact residue motif-guided search, amino acid substitution matrix-based search unguided by motif information, and combinatorial peptide library scan-guided search. We demonstrate the additive nature of a layered approach, which efficiently identifies unsafe cross-reactivity including one undetected by conventional motif-guided search. These findings have important implications for the safe development of TCR-based therapies.

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

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

Gluten induces rapid reprogramming of natural memory αβ and γδ intraepithelial T cells to induce cytotoxicity in celiac disease

Celiac disease (CD) is an autoimmune disease in which intestinal inflammation is induced by dietary gluten. The means through which gluten-specific CD4+ T cell activation culminates in intraepithelial T cell (T-IEL)-mediated intestinal damage remain unclear. Here, we performed multiplexed single-cell analysis of intestinal and gluten-induced peripheral blood T cells from patients in different CD states and healthy controls. Untreated, active, and potential CD were associated with an enrichment of activated intestinal T cell populations, including CD4+ follicular T helper (TFH) cells, regulatory T cells (Tregs), and natural CD8+ αβ and γδ T-IELs. Natural CD8+ αβ and γδ T-IELs expressing activating natural killer cell receptors (NKRs) exhibited a distinct TCR repertoire in CD and persisted in patients on a gluten-free diet without intestinal inflammation. Our data further show that NKR-expressing cytotoxic cells, which appear to mediate intestinal damage in CD, arise from a distinct NKR-expressing memory population of T-IELs. After gluten ingestion, both αβ and γδ T cell clones from this memory population of T-IELs circulated systemically along with gluten-specific CD4+ T cells and assumed a cytotoxic and activating NKR-expressing phenotype. Collectively, these findings suggest that cytotoxic T cells in CD are rapidly mobilized in parallel with gluten-specific CD4+ T cells after gluten ingestion.

NAIR: Network Analysis of Immune Repertoire

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

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.

Immunologic Characterization and T cell Receptor Repertoires of Expanded Tumor-infiltrating Lymphocytes in Patients with Renal Cell Carcinoma

The successful use of expanded tumor-infiltrating lymphocytes (TIL) in adoptive TIL therapies has been reported, but the effects of the TIL expansion, immunophenotype, function, and T cell receptor (TCR) repertoire of the infused products relative to the tumor microenvironment (TME) are not well understood. In this study, we analyzed the tumor samples (n = 58) from treatment-naïve patients with renal cell carcinoma (RCC), "pre-rapidly expanded" TILs (pre-REP TIL, n = 15) and "rapidly expanded" TILs (REP TIL, n = 25) according to a clinical-grade TIL production protocol, with single-cell RNA (scRNA)+TCRαβ-seq (TCRαβ sequencing), TCRβ-sequencing (TCRβ-seq), and flow cytometry. REP TILs encompassed a greater abundance of CD4⁺ than CD8⁺ T cells, with increased LAG-3 and low PD-1 expressions in both CD4⁺ and CD8⁺ T cell compartments compared with the pre-REP TIL and tumor T cells. The REP protocol preferentially expanded small clones of the CD4⁺ phenotype (CD4, IL7R, KLRB1) in the TME, indicating that the largest exhausted T cell clones in the tumor do not expand during the expansion protocol. In addition, by generating a catalog of RCC-associated TCR motifs from >1,000 scRNA+TCRαβ-seq and TCRβ-seq RCC, healthy and other cancer sample cohorts, we quantified the RCC-associated TCRs from the expansion protocol. Unlike the low-remaining amount of anti-viral TCRs throughout the expansion, the quantity of the RCC-associated TCRs was high in the tumors and pre-REP TILs but decreased in the REP TILs. Our results provide an in-depth understanding of the origin, phenotype, and TCR specificity of RCC TIL products, paving the way for a more rationalized production of TILs.

Significance

TILs are a heterogenous group of immune cells that recognize and attack the tumor, thus are utilized in various clinical trials. In our study, we explored the TILs in patients with kidney cancer by expanding the TILs using a clinical-grade protocol, as well as observed their characteristics and ability to recognize the tumor using in-depth experimental and computational tools.

Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer

Purpose

Although immunotherapy is the mainstay of therapy for advanced non-small cell lung cancer (NSCLC), robust biomarkers of clinical response are lacking. The heterogeneity of clinical responses together with the limited value of radiographic response assessments to timely and accurately predict therapeutic effect -especially in the setting of stable disease-call for the development of molecularly-informed real-time minimally invasive predictive biomarkers. In addition to capturing tumor regression, liquid biopsies may be informative in evaluating immune-related adverse events (irAEs).

Experimental design

We investigated longitudinal changes in circulating tumor DNA (ctDNA) in patients with metastatic NSCLC who received immunotherapy-based regimens. Using ctDNA targeted error-correction sequencing together with matched sequencing of white blood cells and tumor tissue, we tracked serial changes in cell-free tumor load (cfTL) and determined molecular response for each patient. Peripheral T-cell repertoire dynamics were serially assessed and evaluated together with plasma protein expression profiles.

Results

Molecular response, defined as complete clearance of cfTL, was significantly associated with progression-free (log-rank p=0.0003) and overall survival (log-rank p=0.01) and was particularly informative in capturing differential survival outcomes among patients with radiographically stable disease. For patients who developed irAEs, peripheral blood T-cell repertoire reshaping, assessed by significant TCR clonotypic expansions and regressions were noted on-treatment.

Conclusions

Molecular responses assist with interpretation of heterogeneous clinical responses especially for patients with stable disease. Our complementary assessment of the tumor and immune compartments by liquid biopsies provides an approach for monitoring of clinical benefit and immune-related toxicities for patients with NSCLC receiving immunotherapy.

Statement of translational relevance

Longitudinal dynamic changes in cell-free tumor load and reshaping of the peripheral T-cell repertoire capture clinical outcomes and immune-related toxicities during immunotherapy for patients with non-small cell lung cancer.

Group A Streptococcus induces CD1a-autoreactive T cells and promotes psoriatic inflammation

Group A Streptococcus (GAS) infection is associated with multiple clinical sequelae, including different subtypes of psoriasis. Such post-streptococcal disorders have been long known but are largely unexplained. CD1a is expressed at constitutively high levels by Langerhans cells and presents lipid antigens to T cells, but the potential relevance to GAS infection has not been studied. Here, we investigated whether GAS-responsive CD1a-restricted T cells contribute to the pathogenesis of psoriasis. Healthy individuals had high frequencies of circulating and cutaneous GAS-responsive CD4+ and CD8+ T cells with rapid effector functions, including the production of interleukin-22 (IL-22). Human skin and blood single-cell CITE-seq analyses of IL-22-producing T cells showed a type 17 signature with proliferative potential, whereas IFN-γ-producing T cells displayed cytotoxic T lymphocyte characteristics. Furthermore, individuals with psoriasis had significantly higher frequencies of circulating GAS-reactive T cells, enriched for markers of activation, cytolytic potential, and tissue association. In addition to responding to GAS, subsets of expanded GAS-reactive T cell clones/lines were found to be autoreactive, which included the recognition of the self-lipid antigen lysophosphatidylcholine. CD8+ T cell clones/lines produced cytolytic mediators and lysed infected CD1a-expressing cells. Furthermore, we established cutaneous models of GAS infection in a humanized CD1a transgenic mouse model and identified enhanced and prolonged local and systemic inflammation, with resolution through a psoriasis-like phenotype. Together, these findings link GAS infection to the CD1a pathway and show that GAS infection promotes the proliferation and activation of CD1a-autoreactive T cells, with relevance to post-streptococcal disease, including the pathogenesis and treatment of psoriasis.

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.

Disrupted memory T cell expansion in HIV-exposed uninfected infants is preceded by premature skewing of T cell receptor clonality

While preventing vertical HIV transmission has been very successful, the increasing number of HIV-exposed uninfected infants (iHEU) experience an elevated risk to infections compared to HIV-unexposed and uninfected infants (iHUU). Immune developmental differences between iHEU and iHUU remains poorly understood and here we present a longitudinal multimodal analysis of infant immune ontogeny that highlights the impact of HIV/ARV exposure. Using mass cytometry, we show alterations and differences in the emergence of NK cell populations and T cell memory differentiation between iHEU and iHUU. Specific NK cells observed at birth were also predictive of acellular pertussis and rotavirus vaccine-induced IgG and IgA responses, respectively, at 3 and 9 months of life. T cell receptor Vβ clonotypic diversity was significantly and persistently lower in iHEU preceding the expansion of T cell memory. Our findings show that HIV/ARV exposure disrupts innate and adaptive immunity from birth which may underlie relative vulnerability to infections.

Quantitative annotations of T-Cell repertoire specificity

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.

Leveraging deep learning to improve vaccine design

Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.

Assessing the chronic hepatitis B adaptive immune response by profiling specific T-cell receptor repertoire

Challenges in assessing hepatitis B virus (HBV)-specific T cell immunity as an immunological biomarker still remain in chronic hepatitis B (CHB), such as the requirement of large quantities of cells. This study aims to conveniently assess HBV-specific T cells immunity in chronic HBV infected patients. We obtained T cell receptor β chains (TCRβs) from public databases and six acute hepatitis B patients to establish an HBV-specific TCRβs dataset. For some TCRs from one AHB patient, their specificities and epitopes were verified. The potential HBV-specific TCRβs from CHB patients were analyzed using GLIPH2 and established dataset. By analyzing two antiviral therapy cohorts including 42 CHB patients, we showed that individuals with better therapy response may depend more on newly emerging potential HBV-specific TCRβs. In a cross-sectional study containing 207 chronic HBV infected patients, the results exhibited that the characteristics of potential HBV-specific clusters were divergent between CHB and hepatocellular carcinoma patients. Our strategy could profile potential HBV-specific TCRβ repertoire using a small blood sample, which will complement traditional methods for assessing the HBV-specific T cell immunity.

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.

Antigen-specificity measurements are the key to understanding T cell responses

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8+ T cell responses post SARS-CoV-2 infection

T cells are a critical component of the response to SARS-CoV-2, but their kinetics after infection and vaccination are insufficiently understood. Using "spheromer" peptide-MHC multimer reagents, we analyzed healthy subjects receiving two doses of the Pfizer/BioNTech BNT162b2 vaccine. Vaccination resulted in robust spike-specific T cell responses for the dominant CD4+ (HLA-DRB1∗15:01/S191) and CD8+ (HLA-A∗02/S691) T cell epitopes. Antigen-specific CD4+ and CD8+ T cell responses were asynchronous, with the peak CD4+ T cell responses occurring 1 week post the second vaccination (boost), whereas CD8+ T cells peaked 2 weeks later. These peripheral T cell responses were elevated compared with COVID-19 patients. We also found that previous SARS-CoV-2 infection resulted in decreased CD8+ T cell activation and expansion, suggesting that previous infection can influence the T cell response to vaccination.

Identification and tracking of HTLV-1-infected T cell clones in virus-associated neurologic disease

Human T lymphotropic virus type 1-assoicated (HTLV-1-associated) myelopathy/tropical spastic paraparesis (HAM/TSP) is a neuroinflammatory disease caused by the persistent proliferation of HTLV-1-infected T cells. Here, we performed a T cell receptor (TCR) repertoire analysis focused on HTLV-1-infected cells to identify and track the infected T cell clones that are preserved in patients with HAM/TSP and migrate to the CNS. TCRβ repertoire analysis revealed higher clonal expansion in HTLV-1-infected cells compared with noninfected cells from patients with HAM/TSP and asymptomatic carriers (ACs). TCR clonality in HTLV-1-infected cells was similar in patients with HAM/TSP and ACs. Longitudinal analysis showed that the TCR repertoire signature in HTLV-1-infected cells remained stable, and highly expanded infected clones were preserved within each patient with HAM/TSP over years. Expanded HTLV-1-infected clones revealed different distributions between cerebrospinal fluid (CSF) and peripheral blood and were enriched in the CSF of patients with HAM/TSP. Cluster analysis showed similarity in TCRβ sequences in HTLV-1-infected cells, suggesting that they proliferate after common antigen stimulation. Our results indicate that exploring TCR repertoires of HTLV-1-infected cells can elucidate individual clonal dynamics and identify potential pathogenic clones expanded in the CNS.

Multimodal immunogenomic biomarker analysis of tumors from pediatric patients enrolled to a phase 1-2 study of single-agent atezolizumab

We report herein an exploratory biomarker analysis of refractory tumors collected from pediatric patients before atezolizumab therapy (iMATRIX-atezolizumab, NCT02541604 ). Elevated levels of CD8⁺ T cells and PD-L1 were associated with progression-free survival and a diverse baseline infiltrating T-cell receptor repertoire was prognostic. Differential gene expression analysis revealed elevated expression of CALCA (preprocalcitonin) and CCDC183 (highly expressed in testes) in patients who experienced clinical activity, suggesting that tumor neoantigens from these genes may contribute to immune response. In patients who experienced partial response or stable disease, elevated Igα2 expression correlated with T- and B-cell infiltration, suggesting that tertiary lymphoid structures existed in these patients' tumors. Consensus gene co-expression network analysis identified core cellular pathways that may play a role in antitumor immunity. Our study uncovers features associated with response to immune-checkpoint inhibition in pediatric patients with cancer and provides biological and translational insights to guide prospective biomarker profiling in future clinical trials.

Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium

Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.

Single-cell characterization of anti-LAG-3 and anti-PD-1 combination treatment in patients with melanoma

BackgroundRelatlimab plus nivolumab (anti-lymphocyte-activation gene 3 plus anti-programmed death 1 [anti-LAG-3+anti-PD-1]) has been approved by the FDA as a first-line therapy for stage III/IV melanoma, but its detailed effect on the immune system is unknown.MethodsWe evaluated blood samples from 40 immunotherapy-naive or prior immunotherapy-refractory patients with metastatic melanoma treated with anti-LAG-3+anti-PD-1 in a phase I trial using single-cell RNA and T cell receptor sequencing (scRNA+TCRαβ-Seq) combined with other multiomics profiling.ResultsThe highest LAG3 expression was noted in NK cells, Tregs, and CD8+ T cells, and these cell populations underwent the most significant changes during the treatment. Adaptive NK cells were enriched in responders and underwent profound transcriptomic changes during the therapy, resulting in an active phenotype. LAG3+ Tregs expanded, but based on the transcriptome profile, became metabolically silent during the treatment. Last, higher baseline TCR clonality was observed in responding patients, and their expanding CD8+ T cell clones gained a more cytotoxic and NK-like phenotype.ConclusionAnti-LAG-3+anti-PD-1 therapy has profound effects on NK cells and Tregs in addition to CD8+ T cells.Trial registrationClinicalTrials.gov (NCT01968109)FundingCancer Foundation Finland, Sigrid Juselius Foundation, Signe and Ane Gyllenberg Foundation, Relander Foundation, State funding for university-level health research in Finland, a Helsinki Institute of Life Sciences Fellow grant, Academy of Finland (grant numbers 314442, 311081, 335432, and 335436), and an investigator-initiated research grant from BMS.

T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

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.

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

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

Single-cell T cell receptor sequencing of paired human atherosclerotic plaques and blood reveals autoimmune-like features of expanded effector T cells

Atherosclerosis is a lipid-driven chronic inflammatory disease; however, whether it can be classified as an autoimmune disease remains unclear. In this study, we applied single-cell T cell receptor seqencing (scTCR-seq) on human carotid artery plaques and matched peripheral blood mononuclear cell samples to assess the extent of TCR clonality and antigen-specific activation within the various T cell subsets. We observed the highest degree of plaque-specific clonal expansion in effector CD4+ T cells, and these clonally expanded T cells expressed genes such as CD69, FOS and FOSB, indicative of recent TCR engagement, suggesting antigen-specific stimulation. CellChat analysis suggested multiple potential interactions of these effector CD4+ T cells with foam cells. Finally, we integrated a published scTCR-seq dataset of the autoimmune disease psoriatic arthritis, and we report various commonalities between the two diseases. In conclusion, our data suggest that atherosclerosis has an autoimmune compondent driven by autoreactive CD4+ T cells.

Measures of epitope binding degeneracy from T cell receptor repertoires

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

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

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

Structure-based prediction of T cell receptor:peptide-MHC interactions

The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on other cells. The specificity of TCR:peptide-MHC interactions, thus, underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models of TCR:peptide-MHC specificity remain out of reach; two key barriers are the diversity of TCR recognition modes and the paucity of training data. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling as a potential avenue for prediction of TCR epitope specificity. We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR:peptide-MHC interactions that can be used to discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains to be done for these predictions to have widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to generalizable prediction of TCR:peptide-MHC interaction specificity.

Immune environment and antigen specificity of the T cell receptor repertoire of malignant ascites in ovarian cancer

We evaluated the association of disease outcome with T cell immune-related characteristics and T cell receptor (TCR) repertoire in malignant ascites from patients with high-grade epithelial ovarian cancer. Ascitic fluid samples were collected from 47 high-grade epithelial ovarian cancer patients and analyzed using flow cytometry and TCR sequencing to characterize the complementarity determining region 3 TCR β-chain. TCR functions were analyzed using the McPAS-TCR and VDJ databases. TCR clustering was implemented using Grouping of Lymphocyte Interactions by Paratope Hotspots software. Patients with poor prognosis had ascites characterized by an increased ratio of CD8+ T cells to regulatory T cells, which correlated with an increased productive frequency of the top 100 clones and decreased productive entropy. TCRs enriched in patients with an excellent or good prognosis were more likely to recognize cancer antigens and contained more TCR reads predicted to recognize epithelial ovarian cancer antigens. In addition, a TCR motif that is predicted to bind the TP53 neoantigen was identified, and this motif was enriched in patients with an excellent or good prognosis. Ascitic fluid in high-grade epithelial ovarian cancer patients with an excellent or good prognosis is enriched with TCRs that may recognize ovarian cancer-specific neoantigens, including mutated TP53 and TEAD1. These results suggest that an effective antigen-specific immune response in ascites is vital for a good outcome in high-grade epithelial ovarian cancer.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Immune repertoire profiling for disease pathobiology

Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, lifestyle diseases, neurodegenerative diseases, and cancers. Recently, immune repertoire sequencing has attracted much attention because it helps obtain global profiles of the immune receptor sequences of infiltrating T and B cells in specimens. Immune repertoire sequencing not only helps deepen our understanding of the molecular mechanisms of immune-related pathology but also assists in discovering novel therapeutic modalities for diseases, thereby shedding colorful light on otherwise tiny monotonous cells when observed under a microscope. In this review article, we introduce and detail the background and methodology of immune repertoire sequencing and summarize recent scientific achievements in association with human diseases. Future perspectives on this genetic technique in the field of histopathological research will also be discussed.

T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection

Antigen-specific, MHC-restricted αβ T cells are necessary for protective immunity against Mycobacterium tuberculosis, but the ability to broadly study these responses has been limited. In the present study, we used single-cell and bulk T cell receptor (TCR) sequencing and the GLIPH2 algorithm to analyze M. tuberculosis-specific sequences in two longitudinal cohorts, comprising 166 individuals with M. tuberculosis infection who progressed to either tuberculosis (n = 48) or controlled infection (n = 118). We found 24 T cell groups with similar TCR-β sequences, predicted by GLIPH2 to have common TCR specificities, which were associated with control of infection (n = 17), and others that were associated with progression to disease (n = 7). Using a genome-wide M. tuberculosis antigen screen, we identified peptides targeted by T cell similarity groups enriched either in controllers or in progressors. We propose that antigens recognized by T cell similarity groups associated with control of infection can be considered as high-priority targets for future vaccine development.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Progress in kidney transplantation: The role for systems immunology

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

T cell repertoire profiling in allografts and native tissues in recipients with COVID-19 after solid organ transplantation: Insight into T cell-mediated allograft protection from viral infection

Introduction

The effects of the SARS-CoV-2 virus on the body, and why the effects are more severe in certain patients, remain incompletely understood. One population of special interest is transplant recipients because of their immunosuppressed state. Understanding the pathophysiology of graft dysfunction in transplant patients with the COVID-19 viral syndrome is important for prognosticating the risk to the graft as well as understanding how best to prevent and, if necessary, treat graft injury in these patients.

Methods

We analyzed multiple types of solid organ transplant recipients (liver, kidney, heart or lung) at our institution who died from SARS-CoV-2 and underwent autopsy (n = 6) or whose grafts were biopsied during active SARS-CoV-2 infection (n = 8). Their serum inflammatory markers were examined together with the histological appearance, viral load, and TCR repertoire of their graft tissue and, for autopsy patients, several native tissues.

Results

Histology and clinical lab results revealed a systemic inflammatory pattern that included elevated inflammatory markers and diffuse tissue damage regardless of graft rejection. Virus was detected throughout all tissues, although most abundant in lungs. The TCR repertoire was broadly similar throughout the tissues of each individual, with greater sharing of dominant clones associated with more rapid disease course. There was no difference in viral load or clonal distribution of overall, COVID-associated, or putative SARS-CoV-2-specific TCRs between allograft and native tissue. We further demonstrated that SARSCoV-2-specific TCR sequences in transplant patients lack a donor HLArestricted pattern, regardless of distribution in allograft or native tissues,suggesting that recognition of viral antigens on infiltrating recipient cells can effectively trigger host T cell anti-viral responses in both the host and graft.

Discussion

Our findings suggest a systemic immune response to the SARS-CoV-2 virus in solid organ transplant patients that is not associated with rejection and consistent with a largely destructive effect of recipient HLA-restricted T cell clones that affects donor and native organs similarly.

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer

Tumor behavior is intricately dependent on the oncogenic properties of cancer cells and their multi-cellular interactions. To understand these dependencies within the wider microenvironment, we studied over 270,000 single-cell transcriptomes and 100 microdissected whole exomes from 12 patients with kidney tumors, prior to validation using spatial transcriptomics. Tissues were sampled from multiple regions of the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. We find that the tissue-type location of CD8+ T cell clonotypes largely defines their exhaustion state with intra-tumoral spatial heterogeneity that is not well explained by somatic heterogeneity. De novo mutation calling from single-cell RNA-sequencing data allows us to broadly infer the clonality of stromal cells and lineage-trace myeloid cell development. We report six conserved meta-programs that distinguish tumor cell function, and find an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages, offering a potential therapeutic target.

Comparing T cell receptor repertoires using optimal transport

The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

T cells are potent at eliminating pathogens and playing a crucial role in the adaptive immune response. T cell receptor (TCR) convergence describes T cells that share identical TCRs with the same amino acid sequences but have different DNA sequences due to codon degeneracy. We conducted a systematic investigation of TCR convergence using single-cell immune profiling and bulk TCRβ-sequence (TCR-seq) data obtained from both mouse and human samples and uncovered a strong link between antigen-specificity and convergence. This association was stronger than T cell expansion, a putative indicator of antigen-specific T cells. By using flow-sorted tetramer+ single T cell data, we discovered that convergent T cells were enriched for a neoantigen-specific CD8+ effector phenotype in the tumor microenvironment. Moreover, TCR convergence demonstrated better prediction accuracy for immunotherapy response than the existing TCR repertoire indexes. In conclusion, convergent T cells are likely to be antigen-specific and might be a novel prognostic biomarker for anti-cancer immunotherapy.

A Microfluidic Approach for Probing Heterogeneity in Cytotoxic T-Cells by Cell Pairing in Hydrogel Droplets

Cytotoxic T-cells (CTLs) exhibit strong effector functions to leverage antigen-specific anti-tumoral and anti-viral immunity. When naïve CTLs are activated by antigen-presenting cells (APCs) they display various levels of functional heterogeneity. To investigate this, we developed a single-cell droplet microfluidics platform that allows for deciphering single CTL activation profiles by multi-parameter analysis. We identified and correlated functional heterogeneity based on secretion profiles of IFNγ, TNFα, IL-2, and CD69 and CD25 surface marker expression levels. Furthermore, we strengthened our approach by incorporating low-melting agarose to encapsulate pairs of single CTLs and artificial APCs in hydrogel droplets, thereby preserving spatial information over cell pairs. This approach provides a robust tool for high-throughput and single-cell analysis of CTLs compatible with flow cytometry for subsequent analysis and sorting. The ability to score CTL quality, combined with various potential downstream analyses, could pave the way for the selection of potent CTLs for cell-based therapeutic strategies.

The role of APTX4870 peptide in reducing cellular inflammatory responses by inhibiting Mycobacterium tuberculosis-derived mycolic acid-induced cytotoxicity

Tuberculosis is a serious zoonotic disease caused by Mycobacterium tuberculosis (M.tb) and the M.tb complex. Mycolic acid is an extracellular carbohydrate polymer produced, secreted, and accumulated outside the cells of various Mycobacterium tuberculosis strains. Mycolic acid produced by Mycobacterium plays an important role in infection. However, there have been few reports on drugs that inhibit mycolic acid-induced cytotoxicity. The purpose of this study was to investigate the role of the panned peptide in Mycobacterium-derived mycolic acid (M.tb-MA)-induced cell injury. The heptapeptide (APTX4870) was isolated from various phage libraries using phage display (Ph.D-7, Ph.D-12, and Ph.D-C7C). The efficacy of APTX4870 against mycolic acid was demonstrated by evaluating clinical samples and conducting in vitro and Vivo. APTX4870 inhibited apoptosis, increased autophagy to decrease inflammation, and reduced M.tb-MA-induced lung damage. These findings suggest that this heptapeptide, which selectively targets M.tb-MA, might be exploited as a potential novel M.tb therapeutic treatment.

Exploration of shared features of B cell receptor and T cell receptor repertoires reveals distinct clonotype clusters

Although B cells and T cells are integral players of the adaptive immune system and act in co-dependent ways to orchestrate immune responses, existing methods to study the immune repertoire have largely focused on separate analyses of B cell receptor (BCR) and T cell receptor (TCR) repertoires. Based on our hypothesis that the shared history of immune exposures and the shared cellular machinery for recombination result in similarities between BCR and TCR repertoires in an individual, we examine any commonalities and interrelationships between BCR and TCR repertoires. We find that the BCR and TCR repertoires have covarying clonal architecture and diversity, and that the pattern of correlations appears to be altered in immune-mediated diseases. Furthermore, hierarchical clustering of public B and T cell clonotypes in both health and disease based on correlation of clonal proportion revealed distinct clusters of B and T cell clonotypes that exhibit increased sequence similarity, share motifs, and have distinct amino acid characteristics. Our findings point to common principles governing memory formation, recombination, and clonal expansion to antigens in B and T cells within an individual. A significant proportion of public BCR and TCR repertoire can be clustered into nonoverlapping and correlated clusters, suggesting a novel way of grouping B and T cell clonotypes.