Single-Cell TCR and Transcriptome Analysis: An Indispensable Tool for Studying T-Cell Biology and Cancer Immunotherapy

TCR-T cell therapy: current development approaches, preclinical evaluation, and perspectives on regulatory challenges

TCR-T cell therapy represents a promising advancement in adoptive immunotherapy for cancer treatment. Despite its potential, the development and preclinical testing of TCR-T cells face significant challenges. This review provides a structured overview of the key stages in preclinical testing, including in silico, in vitro, and in vivo methods, within the context of the sequential development of novel therapies. This review aimed to systematically outline the processes for evaluating TCR-T cells at each stage: from in silico approaches used to predict target antigens, assess cross-reactivity, and minimize off-target effects, to in vitro assays designed to measure cell functionality, cytotoxicity, and activation. Additionally, the review discusses the limitations of in vivo testing in animal models, particularly in accurately reflecting the human tumor microenvironment and immune responses. Performed analysis emphasizes the importance of these preclinical stages in the safe and effective development of TCR-T cell therapies. While current models provide valuable insights, we identify critical gaps, particularly in in vivo biodistribution and toxicity assessments, and propose the need for enhanced standardization and the development of more representative models. This structured approach aims to improve the predictability and safety of TCR-T cell therapy as it advances towards clinical application.

Exploring the potential of the TCR repertoire as a tumor biomarker (Review)

T cells play an important role in adaptive immunity. Mature T cells specifically recognize antigens on major histocompatibility complex molecules through T-cell receptors (TCRs). As the TCR repertoire is highly diverse, its analysis is vital in the assessment of T cells. Advances in sequencing technology have provided convenient methods for further investigation of the TCR repertoire. In the present review, the TCR structure and the mechanisms by which TCRs function in tumor recognition are described. In addition, the potential value of the TCR repertoire in tumor diagnosis is reviewed. Furthermore, the role of the TCR repertoire in tumor immunotherapy is introduced, and the relationships between the TCR repertoire and the effects of different tumor immunotherapies are discussed. Based on the reviewed literature, it may be concluded that the TCR repertoire has the potential to serve as a biomarker for tumor prognosis. However, a wider range of cancer types and more diverse subjects require evaluation in future research to establish the TCR repertoire as a biomarker of tumor immunity.

Analysis of the sodium pump subunit ATP1A3 in glioma patients: Potential value in prognostic prediction and immunotherapy

The ATP1A3 gene is associated with the development and progression of neurological diseases. However, the pathological function and therapeutic value of ATP1A3 in glioblastoma (GBM) remains unknown. In this study, we tried to explore the correlation between the ATP1A3 gene expression and immune features in GBM samples. We found that ATP1A3 gene expression levels showed significant negative correlation with immune checkpoints such as PD-L1, CTLA-4 and IDO1. Next, ATP1A3 gene expression levels showed significant negative correlation with the anti-cancer immune cell process, the immune score and stromal score. By grouping ATP1A3 expression levels, we found that that immunomodulator-related genes and tumor-associated immune cell effector gene expression levels were associated with lower ATP1A3 expression. In addition, immunotherapy prediction pathway activity and a majority of the anti-cancer immune cell process activity levels were also showed to be correlated with lower ATP1A3 gene expression. Further, nine prognostic factors were identified by prognostic analysis, and a GBM prognostic model (risk score) was established. We applied the model to the TCGA GBM training set sample and the GSE4412 validation set sample and found that patients in the high risk score subgroup had significantly shorter survival time, demonstrating the prognostic value and prognostic efficacy of the risk score. Furthermore, ATP1A3 overexpression has also been found to sensitize cancer cells to anti-PD-1 therapy. In conclusion, we showed that ATP1A3 is a highly promising treatment target in GBM and the risk score is an independent prognostic factor for cancer and can be used to help guide the prediction of survival time in patients with GBM.

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

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

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.

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.

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.

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.

Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights

Despite recent progress in identifying the genetic drivers of acute lymphoblastic leukemia (ALL), prognosis remains poor for those individuals who experience disease recurrence. Moreover, acute leukemias of ambiguous lineage lack a biologically informed framework to guide classification and therapy. These needs have driven the adoption of multiple complementary single-cell sequencing approaches to explore key issues in the biology of these leukemias, including cell of origin, developmental hierarchy and ontogeny, and the molecular heterogeneity driving pathogenesis, progression, and therapeutic responsiveness. There are multiple single-cell techniques for profiling a specific modality, including RNA, DNA, chromatin accessibility and methylation; and an expanding range of approaches for simultaneous analysis of multiple modalities. Single-cell sequencing approaches have also enabled characterization of cell-intrinsic and -extrinsic features of ALL biology. In this review we describe these approaches and highlight the extensive heterogeneity that underpins ALL gene expression, cellular differentiation, and clonal architecture throughout disease pathogenesis and treatment resistance. In addition, we discuss the importance of the dynamic interactions that occur between leukemia cells and the nonleukemia microenvironment. We discuss potential opportunities and limitations of single-cell sequencing for the study of ALL biology and treatment responsiveness.

A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma

Background

Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8⁺ T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups.

Method

Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8⁺ T cell-associated immune genes (CDIGs) for succeeding assessment. Thereafter, through protein-protein interaction (PPI) networks analysis, univariate COX analysis, and multivariate Cox analysis, six genes (MX1, RSAD2, IRF2, GBP2, IFITM1, and OAS2) were identified to construct a CDIGPM. We detected cell proliferation of SKCM cells transfected with IRF2 siRNA. Then, we analyzed the immunologic features and the benefits of immunotherapy in CDIGPM-defined groups.

Results

The overall survival (OS) was much better in low-CDIGPM group versus high CDIGPM group in TCGA dataset and GSE65904 dataset. On the whole, the results unfolded that a low CDIGPM showed relevance to immune response-correlated pathways, high expressions of CTLA4 and PD-L1, a high infiltration rate of CD8⁺ T cells, and more benefits from immunotherapy.

Conclusion

CDIGPM is an good model to predict the prognosis, the potential immune escape from immunotherapy for SKCM, and define immunologic and molecular features.

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.

Revealing the Immune Heterogeneity between Systemic Lupus Erythematosus and Rheumatoid Arthritis Based on Multi-Omics Data Analysis

The pathogenesis of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are greatly influenced by different immune cells. Nowadays both T-cell receptor (TCR) and B-cell receptor (BCR) sequencing technology have emerged with the maturity of NGS technology. However, both SLE and RA peripheral blood TCR or BCR repertoire sequencing remains lacking because repertoire sequencing is an expensive assay and consumes valuable tissue samples. This study used computational methods TRUST4 to construct TCR repertoire and BCR repertoire from bulk RNA-seq data of both SLE and RA patients’ peripheral blood and analyzed the clonality and diversity of the immune repertoire between the two diseases. Although the functions of immune cells have been studied, the mechanism is still complicated. Differentially expressed genes in each immune cell type and cell–cell interactions between immune cell clusters have not been covered. In this work, we clustered eight immune cell subsets from original scRNA-seq data and disentangled the characteristic alterations of cell subset proportion under both SLE and RA conditions. The cell–cell communication analysis tool CellChat was also utilized to analyze the influence of MIF family and GALECTIN family cytokines, which were reported to regulate SLE and RA, respectively. Our findings correspond to previous findings that MIF increases in the serum of SLE patients. This work proved that the presence of LGALS9, PTPRC and CD44 in platelets could serve as a clinical indicator of rheumatoid arthritis. Our findings comprehensively illustrate dynamic alterations in immune cells during pathogenesis of SLE and RA. This work identified specific V genes and J genes in TCR and BCR that could be used to expand our understanding of SLE and RA. These findings provide a new insight inti the diagnosis and treatment of the two autoimmune diseases.

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Simple Summary

The TCR is the T cell antigen receptor, and it is responsible of the T cell activation, through the HLA-antigen complex recognition. Studying the TCR repertoire in patients with cancer can help to better understand the anti-tumoural responses and it has been suggested to have predictive and or/prognostic values, both for the disease and in response to treatments. The aim of this review is to summarize TCR repertoire studies performed in patients with cancer found in the literature, thoroughly analyse the different factors that can be involved in shaping the TCR repertoire, and draw the current conclusions in this field, especially focusing on whether the TCR diversity—or its opposite, the clonality—can be used as predictors or prognostic biomarkers of the disease.

Abstract

T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.

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.

T-Cell Repertoire Characterization

T-cell repertoire characterization is a methodology that enables the identification of T-cell receptor (TCR) gene sequences in a T-cell population. TCR genes are composed of modular gene segments V (D) J that undergo somatic recombination, resulting in unique and unpredictable sequences that can be utilized to identify each T-cell clone. The analysis of the TCR composition in a T-cell population can give information on the biological phenomenon such as antigen-driven expansion and heterogeneity of T-cell responses. Bulk TCR analysis can give useful information on the clonality and can help track a specific clonotype over time or in different compartments, although the information about pairing cannot be provided. Single-cell TCR sequencing, on the other hand, can provide pairing information that are necessary to reconstruct the TCR and confirm antigen specificity.Here we describe common methods to characterize T-cell repertoires based on both bulk and single-cell next-generation sequencing.

Prognostic modeling of patients with metastatic melanoma based on tumor immune microenvironment characteristics

Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs. Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT and Xcell in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 demonstrated the visible difference in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes from the nine-IRG prognostic model, and the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, the expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 were analyzed between metastatic melanoma and normal samples. Overall, a prognostic model for metastatic melanoma based on the tumor immune microenvironment characteristics was established, which left plenty of space for further studies. It could function well in helping people to understand characteristics of the immune microenvironment in metastatic melanoma.

Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective

Immunotherapy has revolutionized cancer treatment and brought new aspects into tumor immunology. Effective immunotherapy will require using the suitable target antigens, optimizing the interaction between the antigenic peptide, the APC, and the T cell, and the simultaneous inhibitor of the negative regulatory process that inhibits immunotherapeutic effects and develop resistance. Tumor heterogeneity and its microenvironment is the leading cause of resistance in patients. Recently by emerging the single-cell RNA sequencing technology and its combination with immunotherapy, now we can specifically evaluate the mechanism of tumors in the face of immunotherapy agents at the single-cell resolution by detecting the transcriptional activity of immune checkpoints, screening neoantigens with high transcription levels, identifying rare cells, and other important processes. This review focuses on scRNA-seq, particularly on its application in cancer immunotherapy.

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.