Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges

Genomic and T cell repertoire biomarkers associated with malignant mesothelioma survival

BACKGROUND

Malignant mesothelioma (MM) is an exceedingly rare tumor with poor prognosis due to the limited availability of effective treatment. Immunotherapy has emerged as a novel treatment approach for MM, but less than 40% of the patients benefit from it. Thus, it is necessary to identify accurate and effective biomarkers that can predict the overall survival (OS) and immunotherapy efficacy for MM.

METHODS

DNA sequencing was used to identify the genomic landscape based on the data from 86 Chinese patients. T cell receptor (TCR) sequencing was used to characterize MM TCR repertoires of 28 patients between October 2016 and April 2023.

RESULTS

Patients with TP53, NF2, or CDKN2A variants at the genomic level, as well as those exhibiting lower Shannon index (<6.637), lower evenness (<0.028), or higher clonality (≥0.194) according to baseline tumor tissue TCR indexes, demonstrated poorer OS. Furthermore, patients with TP53, CDKN2A, or CDKN2B variants and those with a lower evenness (<0.030) in baseline tumor tissue showed worse immunotherapy efficacy. The present study is the first to identify five special TCR Vβ-Jβ rearrangements associated with MM immunotherapy efficacy.

CONCLUSIONS

The present study reported the largest-scale genomic landscape and TCR repertoire of MM in Chinese patients and identified genomic and TCR biomarkers for the prognosis and immunotherapy efficacy in MM. The study results might provide new insights for prospective MM trials using specific genes, TCR indexes, and TCR clones as biomarkers and offer a reference for future antitumor drugs based on TCR-specific clones.

Identification, expression and molecular polymorphism of T-cell receptors α and β from the glacial relict Hucho bleekeri

The T-cell receptor (TCR) is a specific molecule on the surface of all T cells that mediates cellular adaptive immune responses to antigens. Hucho bleekeri is a critically endangered species and is regarded as a glacial relict that has the lowest-latitude distribution compared with any Eurasian salmonid. In the present study, two TCR genes, namely, TCR α and β, were identified and characterized in H. bleekeri. Both TCR α and TCR β have typical TCR structures, including the IgV domain, IgC domain, connecting peptide, transmembrane and cytoplasmic domains. The two TCR genes were constitutionally expressed in various tissues, with the highest expression found in the spleen for TCR α and in the trunk kidney for TCR β. Challenge of H. bleekeri with LPS or poly(I:C) resulted in significant upregulation of both TCR α and β expression in headkidney and spleen primary cells, indicating their potential roles in the immune response. Molecular polymorphism analysis of the whole ORF regions of TCR α and β in different individuals revealed high diversity of IgV domains of these two genes, especially in complementarity-determining region (CDR) 3. The ratio of nonsynonymous substitution occurred at a significantly higher frequency than synonymous substitution in the CDR of TCR α and β, demonstrating the existence of positive selection. The results obtained in the present study enhance our understanding of TCR roles in regulating immune mechanisms and provide new information for the study of TCR lineage diversity in fish.

Single-cell RNA sequencing reveals Immune Education promotes T cell survival in mice subjected to the cecal ligation and puncture sepsis model

Background

Individual T cell responses vary significantly based on the microenvironment present at the time of immune response and on prior induced T cell memory. While the cecal ligation and puncture (CLP) model is the most commonly used murine sepsis model, the contribution of diverse T cell responses has not been explored. We defined T cell subset responses to CLP using single-cell RNA sequencing and examined the effects of prior induced T cell memory (Immune Education) on these responses. We hypothesized that Immune Education prior to CLP would alter T cell responses at the single cell level at a single, early post-CLP time point.

Methods

Splenic T cells were isolated from C57BL/6 mice. Four cohorts were studied: Control, Immune-Educated, CLP, and Immune-Educated CLP. At age 8 weeks, Immune-Educated and Immune-Educated CLP mice received anti-CD3ϵ antibody; Control and CLP mice were administered an isotype control. CLP (two punctures with a 22-gauge needle) was performed at 12-13 weeks of life. Mice were sacrificed at baseline or 24-hours post-CLP. Unsupervised clustering of the transcriptome library identified six distinct T cell subsets: quiescent naïve CD4+, primed naïve CD4+, memory CD4+, naïve CD8+, activated CD8+, and CD8+ cytotoxic T cell subsets. T cell subset specific gene set enrichment analysis and Hurdle analysis for differentially expressed genes (DEGs) were performed.

Results

T cell responses to CLP were not uniform - subsets of activated and suppressed T cells were identified. Immune Education augmented specific T cell subsets and led to genomic signatures favoring T cell survival in unoperated and CLP mice. Additionally, the combination of Immune Education and CLP effected the expression of genes related to T cell activity in ways that differed from CLP alone. Validating our finding that IL7R pathway markers were upregulated in Immune-Educated CLP mice, we found that Immune Education increased T cell surface IL7R expression in post-CLP mice.

Conclusion

Immune Education enhanced the expression of genes associated with T cell survival in unoperated and CLP mice. Induction of memory T cell compartments via Immune Education combined with CLP may increase the model's concordance to human sepsis.

PseAAC2Vec protein encoding for TCR protein sequence classification

The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.

Applications of single-cell omics for chimeric antigen receptor T cell therapy

Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.

Expanding CAR-T cell immunotherapy horizons through microfluidics

Chimeric antigen receptor (CAR)-T cell therapies have revolutionized cancer treatment, particularly in hematological malignancies. However, their application to solid tumors is limited, and they face challenges in safety, scalability, and cost. To enhance current CAR-T cell therapies, the integration of microfluidic technologies, harnessing their inherent advantages, such as reduced sample consumption, simplicity in operation, cost-effectiveness, automation, and high scalability, has emerged as a powerful solution. This review provides a comprehensive overview of the step-by-step manufacturing process of CAR-T cells, identifies existing difficulties at each production stage, and discusses the successful implementation of microfluidics and related technologies in addressing these challenges. Furthermore, this review investigates the potential of microfluidics-based methodologies in advancing cell-based therapy across various applications, including solid tumors, next-generation CAR constructs, T-cell receptors, and the development of allogeneic "off-the-shelf" CAR products.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

A mass cytometry approach to track the evolution of T cell responses during infection and immunotherapy by paired T cell receptor repertoire and T cell differentiation state analysis

T cell receptor (TCR) recognition followed by clonal expansion is a fundamental feature of adaptive immune responses. Here, we developed a mass cytometric (CyTOF) approach combining antibodies specific for different TCR Vα- and Vβ-chains with antibodies against T cell activation and differentiation proteins to identify antigen-specific expansions of T cell subsets and assess aspects of cellular function. This strategy allowed for the identification of expansions of specific Vβ and Vα chain expressing CD8+ and CD4+ T cells with varying differentiation states in response to Listeria monocytogenes, tumors, and respiratory influenza infection. Expanded Vβ chain expressing T cells could be directly linked to the recognition of specific antigens from Listeria, tumor cells, or influenza. In the setting of influenza infection, we showed that the common therapeutic approaches of intramuscular vaccination or convalescent serum transfer altered the clonal diversity and differentiation state of responding T cells. Thus, we present a new method to monitor broad changes in TCR specificity paired with T cell differentiation during adaptive immune responses.

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.

Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy

Single-cell sequencing (SCS) technology is changing our understanding of cellular components, functions, and interactions across organisms, because of its inherent advantage of avoiding noise resulting from genotypic and phenotypic heterogeneity across numerous samples. By directly and individually measuring multiple molecular characteristics of thousands to millions of single cells, SCS technology can characterize multiple cell types and uncover the mechanisms of gene regulatory networks, the dynamics of transcription, and the functional state of proteomic profiling. In this context, we conducted systematic research on SCS techniques, including the fundamental concepts, procedural steps, and applications of scDNA, scRNA, scATAC, scCITE, and scSNARE methods, focusing on the unique clinical advantages of SCS, particularly in cancer therapy. We have explored challenging but critical areas such as circulating tumor cells (CTCs), lineage tracing, tumor heterogeneity, drug resistance, and tumor immunotherapy. Despite challenges in managing and analyzing the large amounts of data that result from SCS, this technique is expected to reveal new horizons in cancer research. This review aims to emphasize the key role of SCS in cancer research and promote the application of single-cell technologies to cancer therapy.

The nexus of dynamic T cell states and immune checkpoint blockade therapy in the periphery and tumor microenvironment

Immune checkpoint blockade (ICB) therapies, that is, using monoclonal antibodies to reinvigorate tumor-reactive, antigen-specific T cells from the inhibitory effects of CTLA-4, PD-1 and PD-L1 immune checkpoints, have revolutionized the therapeutic landscape of modern oncology. However, only a subset of patients can benefit from the ICB therapy. Biomarkers associated with ICB response, resistance and prognosis have been subjected to intensive research in the past decade. Early studies focused on the analysis of tumor specimens and their residing microenvironment. However, biopsies can be challenging to obtain in clinical practice, and do not reflect the dynamic changes of immunological parameters during the ICB therapy. Recent studies have investigated profiles of antigen-specific T cells derived from the peripheral compartment using multi-omics approaches. By tracking the clonotype and diversity of tumor-reactive T cell receptor repertoire, these studies collectively establish that de novo priming of antigen-specific T cells in peripheral blood occurs throughout the course of ICB, whereas preexisting T cells prior to ICB are exhausted to various degrees. Here, we review what is known about ICB-induced T cell phenotypic and functional changes in cancer patients both within the tumor microenvironment and in the peripheral compartment. A better understanding of parameters influencing the response to ICBs will provide rationales for developing novel diagnostics and combinatorial therapeutic strategies to maximize the clinical efficacies of ICB therapies.

Single-Cell RNA Sequencing of Donor-Reactive T Cells Reveals Role of Apoptosis in Donor-Specific Hyporesponsiveness of Kidney Transplant Recipients

After kidney transplantation (KT), donor-specific hyporesponsiveness (DSH) of recipient T cells develops over time. Recently, apoptosis was identified as a possible underlying mechanism. In this study, both transcriptomic profiles and complete V(D)J variable regions of TR transcripts from individual alloreactive T cells of kidney transplant recipients were determined with single-cell RNA sequencing. Alloreactive T cells were identified by CD137 expression after stimulation of peripheral blood mononuclear cells obtained from KT recipients (N = 7) prior to and 3-5 years after transplantation with cells of their donor or a third party control. The alloreactive T cells were sorted, sequenced and the transcriptome and T cell receptor profiles were analyzed using unsupervised clustering. Alloreactive T cells retain a highly polyclonal T Cell Receptor Alpha/Beta repertoire over time. Post transplantation, donor-reactive CD4+ T cells had a specific downregulation of genes involved in T cell cytokine-mediated pathways and apoptosis. The CD8+ donor-reactive T cell profile did not change significantly over time. Single-cell expression profiling shows that activated and pro-apoptotic donor-reactive CD4+ T cell clones are preferentially lost after transplantation in stable kidney transplant recipients.

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

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

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

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.

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.

Adoptive cellular immunotherapy for solid neoplasms beyond CAR-T

In recent decades, immune checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy are two milestone achievements in clinical immunotherapy. However, both show limited efficacies in most solid neoplasms, which necessitates the exploration of new immunotherapeutic modalities. The failure of CAR-T and immune checkpoint blockade in several solid neoplasms is attributed to multiple factors, including low antigenicity of tumor cells, low infiltration of effector T cells, and diverse mechanisms of immunosuppression in the tumor microenvironment. New adoptive cell therapies have been attempted for solid neoplasms, including TCR-T, CAR-natural killer cells (CAR-NK), and CAR-macrophages (CAR-M). Compared to CAR-T, these new adoptive cell therapies have certain advantages in treating solid neoplasms. In this review, we summarized the 40-year evolution of adoptive cell therapies, then focused on the advances of TCR-T, CAR-NK, and CAR-M in solid neoplasms and discussed their potential clinical applications.

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.

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

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

Multi-omic analyses in immune cell development with lessons learned from T cell development

Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but-with good antibodies-can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system.

Single-Cell RNA-Sequencing Reveals Interactions between Endometrial Stromal Cells, Epithelial Cells, and Lymphocytes during Mouse Embryo Implantation

The decidualization of endometrial stromal cells (ESCs) is an essential process facilitating embryo implantation. However, the roles of non-decidualized and decidualized ESCs in regulating the microenvironment of a receptive endometrium remain unclear. We investigated single-cell transcriptomic changes in the uterus of a CD-1 mouse model at the post-implantation stage. The implantation and inter-implantation sites of the uteruses of pregnant mice at 4.5 and 5.5 days post-coitum were dissected for single-cell RNA sequencing. We identified eight cell types: epithelial cells, stromal cells, endothelial cells, mesothelial cells, lymphocytes, myocytes, myeloids, and pericytes. The ESC transcriptome suggests that the four ESC subtypes are involved in the extracellular remodeling during implantation. The trajectory plot of ESC subtypes indicates embryo implantation that involves a differentiation pathway from undifferentiated ESCs (ESC 1) to decidualized ESCs (DEC ESCs), with distinct signaling pathways between the ESC subtypes. Furthermore, the ligand-receptor analysis suggests that ESCs communicate with epithelial cells and immune cells through nectin and ICAM signaling. Collectively, both decidualized and non-decidualized ESCs may regulate the endometrial microenvironment for optimal endometrial receptivity and immune tolerance. This study provides insights on the molecular and cellular characteristics of mouse ESCs in modulating the epithelial and lymphocyte functions during early embryo implantation.

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.

Detection of Low-Frequency Epitope-Specific T Cells in Blood of Healthy Individuals according to an Optimized In Vitro Amplification System

Detection and amplification of epitope-specific T cells hold great promise for diagnosis and therapy of cancer patients. Currently, measurement and retrieval of epitope-specific T cells is hampered by limited availability of patients' biomaterials and lack of sensitive and easy-to-implement T cell priming and expansion. We have developed an in vitro T cell amplification system starting from healthy donor blood and tested different subsets and ratios of autologous T cells and APCs as well as the resting period between amplification cycles. We demonstrated in 10 different donors significantly enhanced frequency of T cells specific for MelanA/HLA-A2, which relied on coculturing of naive T cells and CD11c+ dendritic cells in a 1:1 ratio followed by three weekly amplification cycles using the effluent of the naive T cell sort as APCs, a 24-h rest period prior to every reamplification cycle, and IFN-γ production as a readout for epitope-specific T cells. Using this system, MelanA/HLA-A2-specific T cells were enriched by 200-fold, measuring up to 20-60% of all T cells. We extended this system to enrich NY-ESO-1/HLA-A2- and BMLF-1/HLA-A2-specific T cells, examples of a cancer germline Ag and an oncoviral Ag differing in their ability to bind to HLA-A2 and the presence of specific T cells in the naive and, in case of BMLF-1, also the Ag-experienced repertoire. Collectively, we have developed a sensitive and easy-to-implement in vitro T cell amplification method to enrich epitope-specific T cells that is expected to facilitate research and clinical utility regarding T cell diagnosis and treatments.

Rapid identification of CMV-specific TCRs via reverse TCR cloning system based on bulk TCR repertoire data

Advances in next-generation sequencing (NGS) have improved the resolution of T-cell receptor (TCR) repertoire analysis, and recent single-cell sequencing has made it possible to obtain information about TCR pairs. In our previous study, cytomegalovirus (CMV) pp65-specific T-cell response restricted by a single human leukocyte antigen (HLA) class I allotype was observed in an individual. Therefore, to effectively clone an antigen-specific TCR from these T cells, we developed a TCR cloning system that does not require a single cell level. First, we established the improved Jurkat reporter cell line, which was TCRαβ double knock-out and expressed CD8αβ molecules. Furthermore, functional TCRs were directly obtained by reverse TCR cloning using unique CDR3-specific PCR primers after bulk TCR sequencing of activation marker-positive CD8 T cells by NGS. A total of 15 TCRα and 14 TCRβ strands were successfully amplified by PCR from cDNA of 4-1BB-positive CD8 T cells restricted by HLA-A*02:01, HLA-A*02:06, HLA-B*07:02, and HLA-B*40:06. The panels with combinations of TCRα and TCRβ genes were investigated using Jurkat reporter cell line and artificial antigen-presenting cells (APCs). In two TCR pairs restricted by HLA-A*02:01, one TCR pair by HLA-A*02:06, four TCR pairs by HLA-B*07:02, and one TCR pair by HLA-B*40:06, their specificity and affinity were confirmed. The TCR pair of A*02:01/1-1 showed alloreactivity to HLA-A*02:06. The one TCR pair showed a higher response to the naturally processed antigen than that of the peptide pool. This reverse TCR cloning system will not only provide functional information to TCR repertoire analysis by NGS but also help in the development of TCR-T therapy.

A guide to systems-level immunomics

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted.

RESULTS

We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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

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

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

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

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

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

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

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

Machine Learning Approaches to TCR Repertoire Analysis

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

Single-Cell Analysis and Tracking of Antigen-Specific T Cells: Integrating Paired Chain AIRR-Seq and Transcriptome Sequencing: A Method by the AIRR Community

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) offers the possibility to access the nucleotide sequences of paired receptor chains from T-cell receptors (TCR) or B-cell receptors (BCR ). Here we describe two protocols and the downstream bioinformatic approaches that facilitate the integrated analysis of paired T-cell receptor (TR ) alpha/beta (TRA /TRB ) AIRR-seq, RNA sequencing (RNAseq), immunophenotyping, and antigen-binding information. To illustrate the methodologies with a use case, we describe how to identify, characterize, and track SARS-CoV-2-specific T cells over multiple time points following infection with the virus. The first method allows the analysis of pools of memory CD8⁺ cells, identifying expansions and contractions of clones of interest. The second method allows the study of rare or antigen-specific cells and allows studying their changes over time.

Development of Cancer Immunotherapies

Cancer immunotherapy, or the utilization of components of the immune system to target and eliminate cancer, has become a highly active area of research in the past several decades and a common treatment strategy for several cancer types. The concept of harnessing the immune system for this purpose originated over 100 years ago when a physician by the name of William Coley successfully treated several of his cancer patients with a combination of live and attenuated bacteria, later known as "Coley's Toxins", after observing a subset of prior patients enter remission following their diagnosis with the common bacterial infection, erysipelas. However, it was not until late in the twentieth century that cancer immunotherapies were developed for widespread use, thereby transforming the treatment landscape of numerous cancer types. Pivotal studies elucidating molecular and cellular functions of immune cells, such as the discovery of IL-2 and production of monoclonal antibodies, fostered the development of novel techniques for studying the immune system and ultimately the development and approval of several cancer immunotherapies by the United States Food and Drug Association in the 1980s and 1990s, including the tuberculosis vaccine-Bacillus Calmette-Guérin, IL-2, and the CD20-targeting monoclonal antibody. Approval of the first therapeutic cancer vaccine, Sipuleucel-T, for the treatment of metastatic castration-resistant prostate cancer and the groundbreaking success and approval of immune checkpoint inhibitors and chimeric antigen receptor T cell therapy in the last decade, have driven an explosion of interest in and pursuit of novel cancer immunotherapy strategies. A broad range of modalities ranging from antibodies to adoptive T cell therapies is under investigation for the generalized treatment of a broad spectrum of cancers as well as personalized medicine. This chapter will focus on the recent advances, current strategies, and future outlook of immunotherapy development for the treatment of cancer.

BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data

MOTIVATION

Single-cell sequencing methods provide previously impossible resolution into the transcriptome of individual cells. Cell hashing reduces single-cell sequencing costs by increasing capacity on droplet-based platforms. Cell hashing methods rely on demultiplexing algorithms to accurately classify droplets; however, assumptions underlying these algorithms limit accuracy of demultiplexing, ultimately impacting the quality of single-cell sequencing analyses.

RESULTS

We present Bimodal Flexible Fitting (BFF) demultiplexing algorithms BFFcluster and BFFraw, a novel class of algorithms that rely on the single inviolable assumption that barcode count distributions are bimodal. We integrated these and other algorithms into cellhashR, a new R package that provides integrated QC and a single command to execute and compare multiple demultiplexing algorithms. We demonstrate that BFFcluster demultiplexing is both tunable and insensitive to issues with poorly behaved data that can confound other algorithms. Using two well-characterized reference datasets, we demonstrate that demultiplexing with BFF algorithms is accurate and consistent for both well-behaved and poorly behaved input data.

AVAILABILITY AND IMPLEMENTATION

cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Activation pathways that drive CD4+ T cells to break tolerance in autoimmune diseases. Immunol Rev 2022;307:161-190. [PMID: 35142369 PMCID: PMC9255211 DOI: 10.1111/imr.13071]

Autoimmune diseases are characterized by dysfunctional immune systems that misrecognize self as non-self and cause tissue destruction. Several cell types have been implicated in triggering and sustaining disease. Due to a strong association of major histocompatibility complex II (MHC-II) proteins with various autoimmune diseases, CD4+ T lymphocytes have been thoroughly investigated for their roles in dictating disease course. CD4+ T cell activation is a coordinated process that requires three distinct signals: Signal 1, which is mediated by antigen recognition on MHC-II molecules; Signal 2, which boosts signal 1 in a costimulatory manner; and Signal 3, which helps to differentiate the activated cells into functionally relevant subsets. These signals are disrupted during autoimmunity and prompt CD4+ T cells to break tolerance. Herein, we review our current understanding of how each of the three signals plays a role in three different autoimmune diseases and highlight the genetic polymorphisms that predispose individuals to autoimmunity. We also discuss the drawbacks of existing therapies and how they can be addressed to achieve lasting tolerance in patients.

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes

Background

T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed.

Methods

We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined.

Results

Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients.

Conclusion

The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

Rapid identification of tumor-reactive T-cell receptors by RNA preamplification-based single-cell sequencing

T-cell receptor (TCR)-transduced T (TCR-T) cell therapy has shown promising efficacy in the clinical treatment of malignant cancers. However, the populations covered by reported TCRs are still limited. Tumor infiltrating lymphocytes (TILs) are natural reservoirs of tumor-reactive T cells and TCRs. Approaches are required for the fast and cost-effective identification of tumor-reactive TCRs from TILs. The widely employed TCR identification approaches by the clonal expansion of TILs involve a TCR singularization process for the direct pairing of TCR Vα and the Vβ chain. However, the clonal expansion of T cells is well known to require extensive time and effort due to the involvement of T cell cultures. Several single-cell multiplexing PCR methods followed by Sanger sequencing have been developed, representing a cost-effective and fast approach for single-cell TCR identification. In this study, an RNA-based preamplification step was included in the single-cell TCR sequencing, which can reduce the multiplexing PCR amplification to one round. Moreover, the cDNA product of RNA preamplification is derived from the whole genome mRNA, instead of TCR mRNA only by multiplexing primers-based DNA preamplification, which is valuable for many other analyses (e.g., phenotypic analysis) of the tumor-reactive T cells that can be correlated with the identified TCRs. The feasibility for both single α chain and dual α chain TILs of this approach highlights its potential value as a rapid and cost-effective sequencing strategy for the development of TCR-T therapies for solid cancers.

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

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

A Specific CD44lo CD25lo Subpopulation of Regulatory T Cells Inhibits Anti-Leukemic Immune Response and Promotes the Progression in a Mouse Model of Chronic Lymphocytic Leukemia

Regulatory T cells (Tregs) are capable of inhibiting the proliferation, activation and function of T cells and play an important role in impeding the immune response to cancer. In chronic lymphocytic leukemia (CLL) a dysfunctional immune response and elevated percentage of effector-like phenotype Tregs have been described. In this study, using the Eµ-TCL1 mouse model of CLL, we evaluated the changes in the Tregs phenotype and their expansion at different stages of leukemia progression. Importantly, we show that Tregs depletion in DEREG mice triggered the expansion of new anti-leukemic cytotoxic T cell clones leading to leukemia eradication. In TCL1 leukemia-bearing mice we identified and characterized a specific Tregs subpopulation, the phenotype of which suggests its role in the formation of an immunosuppressive microenvironment, supportive for leukemia survival and proliferation. This observation was also confirmed by the gene expression profile analysis of these TCL1-specific Tregs. The obtained data on Tregs are consistent with those described so far, however, above all show that the changes in the Tregs phenotype described in CLL result from the formation of a specific, described in this study Tregs subpopulation. In addition, functional tests revealed the ability of Tregs to inhibit T cells that recognize model antigens expressed by leukemic cells. Moreover, inhibition of Tregs with a MALT1 inhibitor provided a therapeutic benefit, both as monotherapy and also when combined with an immune checkpoint inhibitor. Altogether, activation of Tregs appears to be crucial for CLL progression.

Identification and Tracking of Alloreactive T Cell Clones in Rhesus Macaques Through the RM-scTCR-Seq Platform

T cell receptor (TCR) clonotype tracking is a powerful tool for interrogating T cell mediated immune processes. New methods to pair a single cell's transcriptional program with its TCR identity allow monitoring of T cell clonotype-specific transcriptional dynamics. While these technologies have been available for human and mouse T cells studies, they have not been developed for Rhesus Macaques (RM), a critical translational organism for autoimmune diseases, vaccine development and transplantation. We describe a new pipeline, 'RM-scTCR-Seq', which, for the first time, enables RM specific single cell TCR amplification, reconstruction and pairing of RM TCR's with their transcriptional profiles. We apply this method to a RM model of GVHD, and identify and track in vitro detected alloreactive clonotypes in GVHD target organs and explore their GVHD driven cytotoxic T cell signature. This novel, state-of-the-art platform fundamentally advances the utility of RM to study protective and pathogenic T cell responses.

Single-cell transcriptomics in human skin research: available technologies, technical considerations, and disease applications

Single‐cell technologies have revolutionized research in the last decade, including for skin biology. Single‐cell RNA sequencing has emerged as a powerful tool allowing the dissection of human disease pathophysiology at unprecedented resolution by assessing cell‐to‐cell variation, facilitating identification of rare cell populations and elucidating cellular heterogeneity. In dermatology, this technology has been widely applied to inflammatory skin disorders, fibrotic skin diseases, wound healing complications and cutaneous neoplasms. Here, we discuss the available technologies and technical considerations of single‐cell RNA sequencing and describe its applications to a broad spectrum of dermatological diseases.

Single-Cell-Based High-Throughput Ig and TCR Repertoire Sequencing Analysis in Rhesus Macaques

Recent advancements in microfluidics and high-throughput sequencing technologies have enabled recovery of paired H and L chains of Igs and VDJ and VJ chains of TCRs from thousands of single cells simultaneously in humans and mice. Despite rhesus macaques being one of the most well-studied model organisms for the human adaptive immune response, high-throughput single-cell immune repertoire sequencing assays are not yet available due to the complexity of these polyclonal receptors. We used custom primers that capture all known rhesus macaque Ig and TCR isotypes and chains that are fully compatible with a commercial solution for single-cell immune repertoire profiling. Using these rhesus-specific assays, we sequenced Ig and TCR repertoires in >60,000 cells from cryopreserved rhesus PBMCs, splenocytes, and FACS-sorted B and T cells. We were able to recover every Ig isotype and TCR chain, measure clonal expansion in proliferating T cells, and pair Ig and TCR repertoires with gene expression profiles of the same single cells. Our results establish the ability to perform high-throughput immune repertoire analysis in rhesus macaques at the single-cell level.

Analysis of T-Cell Receptor Repertoire in Transplantation: Fingerprint of T Cell-mediated Alloresponse

T cells play a key role in determining allograft function by mediating allogeneic immune responses to cause rejection, and recent work pointed their role in mediating tolerance in transplantation. The unique T-cell receptor (TCR) expressed on the surface of each T cell determines the antigen specificity of the cell and can be the specific fingerprint for identifying and monitoring. Next-generation sequencing (NGS) techniques provide powerful tools for deep and high-throughput TCR profiling, and facilitate to depict the entire T cell repertoire profile and trace antigen-specific T cells in circulation and local tissues. Tailing T cell transcriptomes and TCR sequences at the single cell level provides a full landscape of alloreactive T-cell clones development and biofunction in alloresponse. Here, we review the recent advances in TCR sequencing techniques and computational tools, as well as the recent discovery in overall TCR profile and antigen-specific T cells tracking in transplantation. We further discuss the challenges and potential of using TCR sequencing-based assays to profile alloreactive TCR repertoire as the fingerprint for immune monitoring and prediction of rejection and tolerance.

OUP accepted manuscript

The study and manipulation of T cell receptors (TCRs) is central to multiple fields across basic and translational immunology research. Produced by V(D)J recombination, TCRs are often only recorded in the literature and data repositories as a combination of their V and J gene symbols, plus their hypervariable CDR3 amino acid sequence. However, numerous applications require full-length coding nucleotide sequences. Here we present Stitchr, a software tool developed to specifically address this limitation. Given minimal V/J/CDR3 information, Stitchr produces complete coding sequences representing a fully spliced TCR cDNA. Due to its modular design, Stitchr can be used for TCR engineering using either published germline or novel/modified variable and constant region sequences. Sequences produced by Stitchr were validated by synthesizing and transducing TCR sequences into Jurkat cells, recapitulating the expected antigen specificity of the parental TCR. Using a companion script, Thimble, we demonstrate that Stitchr can process a million TCRs in under ten minutes using a standard desktop personal computer. By systematizing the production and modification of TCR sequences, we propose that Stitchr will increase the speed, repeatability, and reproducibility of TCR research. Stitchr is available on GitHub.

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.

Neoadjuvant PD-1 inhibitor combines with chemotherapy versus neoadjuvant chemotherapy in resectable squamous cell carcinoma of the lung

BACKGROUND

A single-agent of anti programmed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) therapy has been explored for resectable lung cancer before surgery. However, the effectiveness and safety of neoadjuvant programmed cell death 1 (PD-1) blockade combined with chemotherapy have not been published.

METHODS

Twenty-one consecutive patients with potentially resectable squamous cell carcinoma of the lung who received neoadjuvant therapy followed by surgery in Beijing Cancer Hospital were included in this study. Eight patients received two cycles of neoadjuvant platinum-based doublet chemotherapy combined with anti-programmed cell death 1 (anti-PD-1) therapy, while 13 patients received two cycles of neoadjuvant platinum-based doublet chemotherapy only. Chest computed tomography was repeated before neoadjuvant treatment and surgery. Adverse events were monitored. The major pathological response (MPR) rate was determined after surgery. Selected specimens were sent for immunohistochemical and multiplex immunofluorescence analyses, and T-cell receptor DNA sequencing.

RESULTS

Compared with neoadjuvant chemotherapy alone, the combination of PD-1 blockade and chemotherapy increased the pathological complete response rate (37.5% vs. 7.69%) and MPR rate (50% vs. 38.46%). The pathological and radiological evaluations are not consistent. No unknown adverse effects were reported for all the patients. More tumor infiltrating lymphocytes were observed in patients who received PD-1 blockade. No unknown pathological features associated with PD-1 blockade were found. Immune suppression in the peritumoral spaces around the residual tumor cells was observed. The amino acid sequences of the T-cell receptors are not significantly shared among the patients.

CONCLUSIONS

The combination of neoadjuvant chemotherapy and PD-1 blockade is safe and feasible, and might indicate an increased MPR and pathological complete response rate. More investigations are needed for the best combination of the neoadjuvant therapy.

Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes

Human islet antigen reactive CD4⁺ memory T cells (IAR T cells) play a key role in the pathogenesis of autoimmune type 1 diabetes (T1D). Using single-cell RNA sequencing (scRNA-Seq) to identify T cell receptors (TCRs) in IAR T cells, we have identified a class of TCRs that share TCRα chains between individuals (“public” chains). We isolated IAR T cells from blood of healthy, new-onset T1D and established T1D donors using multiplexed CD154 enrichment and identified paired TCRαβ sequences from 2767 individual cells. More than a quarter of cells shared TCR junctions between 2 or more cells (“expanded”), and 29/47 (~62%) of expanded TCRs tested showed specificity for islet antigen epitopes. Public TCRs sharing TCRα junctions were most prominent in new-onset T1D. Public TCR sequences were more germline like than expanded unique, or “private,” TCRs, and had shorter junction sequences, suggestive of fewer random nucleotide insertions. Public TCRα junctions were often paired with mismatched TCRβ junctions in TCRs; remarkably, a subset of these TCRs exhibited cross-reactivity toward distinct islet antigen peptides. Our findings demonstrate a prevalent population of IAR T cells with diverse specificities determined by TCRs with restricted TCRα junctions and germline-constrained antigen recognition properties. Since these “innate-like” TCRs differ from previously described immunodominant TCRβ chains in autoimmunity, they have implications for fundamental studies of disease mechanisms. Self-reactive restricted TCRα chains and their associated epitopes should be considered in fundamental and translational investigations of TCRs in T1D.

An efficient single-cell based method for linking human T cell phenotype to T cell receptor sequence and specificity

Single-cell antigen-receptor gene amplification and sequencing platforms have been used to characterize T cell receptor (TCR) repertoires but typically fail to generate paired full-length gene products for direct expression cloning and do not enable linking this data to cell phenotype information. To overcome these limitations, we established a high-throughput platform for the quantitative and qualitative analysis of human TCR repertoires that provides insights into the clonal and functional composition of human CD4⁺ and CD8⁺ αβ T cells at the molecular and cellular level. The strategy is a powerful tool to qualitatively assess differences between antigen receptors of phenotypically defined αβ T cell subsets, e.g. in immune responses to cancer, vaccination, or infection, and in autoimmune diseases.

The Value of Flow Cytometry Clonality in Large Granular Lymphocyte Leukemia

Simple Summary

Large granular lymphocyte (LGL) leukemia, a lymphoproliferative disease, is characterized by an increased frequency of large-sized lymphocytes with typical expression of T-cell receptor (TCR) αβ, CD3, CD8, CD16, CD45RA, and CD57, and with the expansion of one to three subfamilies of the TCR variable β chain reflecting gene rearrangements. Molecular analysis remains the gold standard for confirmation of TCR clonality; however, flow cytometry is time and labor saving, and can be associated with simultaneous investigation of other surface markers. Moreover, Vβ usage by flow cytometry can be employed for monitoring clonal kinetics during treatment and follow-up of LGL leukemia patients.

Abstract

Large granular lymphocyte (LGL) leukemia is a lymphoproliferative disorder of mature T or NK cells frequently associated with autoimmune disorders and other hematological conditions, such as myelodysplastic syndromes. Immunophenotype of LGL cells is similar to that of effector memory CD8⁺ T cells with T-cell receptor (TCR) clonality defined by molecular and/or flow cytometric analysis. Vβ usage by flow cytometry can identify clonal TCR rearrangements at the protein level, and is fast, sensitive, and almost always available in every Hematology Center. Moreover, Vβ usage can be associated with immunophenotypic characterization of LGL clone in a multiparametric staining, and clonal kinetics can be easily monitored during treatment and follow-up. Finally, Vβ usage by flow cytometry might identify LGL clones silently underlying other hematological conditions, and routine characterization of Vβ skewing might identify recurrent TCR rearrangements that might trigger aberrant immune responses during hematological or autoimmune conditions.

SwarmTCR: a computational approach to predict the specificity of T cell receptors

Background

With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance.

Results

We compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable.

Conclusions

Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04335-w.