High-throughput and single-cell T cell receptor sequencing technologies

Immunopipe: a comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline

Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Myochromella unveiled: exploring its global distribution through a public database of amplicons

Myochromella mammosa, a novel species from southwestern China, is distinguished by its small, solitary or gregarious basidiomata. The pileus is clock-shaped, with distinct central mastoids, edge stripes, and a darker center that flattens upon maturation. The stipe is white, firm when young, and becomes hollow and slightly thickened at the base with age. We conducted a comprehensive analysis of publicly available ITS sequences from NCBI-GenBank and fungal amplicon sequencing data from NCBI-SRA to explore the global distribution and enumeration of recorded species of Myochromella. At a 98% sequence similarity threshold, eleven species were identified, including eight without formal designations. Myochromella's diversity is primarily Northern Hemisphere-centric and tends toward endemic distribution. Leveraging amplicon sequencing data sets enables precise species diversity assessments, enhancing field collection efficiency and providing novel insights into macrofungal species diversity.

Advances in T Cell-Based Cancer Immunotherapy: From Fundamental Mechanisms to Clinical Prospects

T cells and their T cell receptors (TCRs) play crucial roles in the adaptive immune system's response against pathogens and tumors. However, immunosenescence, characterized by declining T cell function and quantity with age, significantly impairs antitumor immunity. Recent years have witnessed remarkable progress in T cell-based cancer treatments, driven by a deeper understanding of T cell biology and innovative screening technologies. This review comprehensively examines T cell maturation mechanisms, T cell-mediated antitumor responses, and the implications of thymic involution on T cell diversity and cancer prognosis. We discuss recent advances in adoptive T cell therapies, including tumor-infiltrating lymphocyte (TIL) therapy, engineered T cell receptor (TCR-T) therapy, and chimeric antigen receptor T cell (CAR-T) therapy. Notably, we highlight emerging DNA-encoded library technologies in mammalian cells for high-throughput screening of TCR-antigen interactions, which are revolutionizing the discovery of novel tumor antigens and optimization of TCR affinity. The review also explores strategies to overcome challenges in the solid tumor microenvironment and emerging approaches to enhance the efficacy of T cell therapy. As our understanding of T cell biology deepens and screening technologies advances, T cell-based immunotherapies show increasing promise for delivering durable clinical benefits to a broader patient population.

T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging

Background

T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not well understood. Characterizing TCR differences between colorectal cancer (CRC) and gastric cancer (GC), as well as developing machine learning models to distinguish cancer types, metastatic status, and disease stages are crucial for guiding clinical practices.

Methods

A cohort study of 143 tumor patients (96 CRC, 47 GC) was conducted. High-throughput TCR sequencing was performed to capture TCR beta (TRB), delta (TRD), and gamma (TRG) chain data. Tissue-specific patterns in TCR repertoire features, such as V-J gene recombination, complementarity-determining region 3 (CDR3) sequences, and motif distributions, were analyzed. Multi-layer machine learning-based diagnostic models were developed by leveraging motif-based feature and deep learning-based feature extraction using ProteinBERT from the 100 most abundant CDR3 sequences per sample. These models were used to differentiate CRC from GC, distinguish between primary and metastatic CRC lesions, and predict disease stages in CRC.

Results

Tissue-specific differences in TCR repertoires were observed across CRC, GC, and between primary and metastatic lesions, as well as across disease stages in CRC. Distinct V-J gene recombination patterns were identified, with CRC showing enrichment in TRBV*-TRBJ* combinations, while GC exhibited higher levels of γδT-cell-related recombination. Primary and metastatic lesions of CRC patients displayed distinct V-J recombination preferences (e.g., TRBV7-9/TRBJ2-1 higher in metastatic; TRBV20-1/TRBJ1-2 higher in primary) and CDR3 sequence differences, with metastatic having shorter TRG CDR3 lengths (p-value = 0.019). Across CRC stages, later stages (III-IV) showed higher clonal diversity (p-value < 0.05) and stage-specific V-J patterns, alongside distinct CDR3 amino acid preferences at N-terminal (positions 1-2) and central positions (positions 5-12). Multi-dimensional machine learning models demonstrated exceptional diagnostic performance across all classification tasks. For distinguishing CRC from GC, the model achieved an accuracy of 97.9% and an area under the curve (AUC) of 0.996. For differentiating primary from metastatic CRC, the model achieved 100% accuracy with an AUC of 1.000. In predicting CRC disease stages, the model attained an accuracy of 96.9% and an AUC of 0.993. Extensive validation using simulated and publicly available datasets, confirmed the robustness and reliability of the models, demonstrating consistent performance across diverse datasets and experimental conditions.

Conclusions

Our investigation provides novel insights into TCR repertoire variations in digestive system tumors, and highlight the potential of immune repertoire features as powerful diagnostic tools for understanding cancer progression and potentially improving clinical decision-making.

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and receptor analysis, MIST (Multi-insight for T cell). MIST features three latent spaces: gene expression, TCR, and a joint latent space. Through analyses of antigen-specific T cells, and T cell datasets related to lung cancer immunotherapy and COVID19, we demonstrate MIST's interpretability and flexibility. MIST easily and accurately resolves cell function and antigen specificity by vectorizing and integrating transcriptome and TCR data of T cells. In addition, using MIST, we identified the heterogeneity of CXCL13+ subsets in lung cancer infiltrating CD8+ T cells and their association with immunotherapy, providing additional insights into the functional transition of CXCL13+ T cells related to anti-PD-1 therapy that were not reported in the original study.

Transcriptomic signatures in peripheral CD4+T-lymphocytes may reflect melanoma staging and immunotherapy responsiveness prior to ICI initiation

Background and purpose

Promoting adaptive immunity with ICIs has drastically improved melanoma prognosis, but not for all patients. Some cases relapse in the first few months, while others keep durable benefit, even after immunotherapy discontinuation. To identify cellular/molecular signatures in peripheral blood that could differentiate advanced from metastatic melanoma and predict dynamics for primary/secondary immune escape, we examined 100 consecutive patients with stage III/IV melanoma scheduled to start ICIs.

Materials and methods

At melanoma diagnosis, a multiparameter flow cytometric analysis and purification scheme using standard conjugated antibodies were performed for all individuals prior to ICI initiation. In each stage(III/IV) according to their RFS/PFS, we retrospectively selected the cases with the clearest clinical outcomes and focused our analysis on the extreme responders(n=7) and non-responders(n=7) to characterize the transcriptomes of circulating CD4+T-cells by bulk RNA-seq, Differential Expression Analysis(DEA)and Gene Ontology(GO)enrichment analysis. Based on our selected patient cohort, we examined for differentially expressed genes(DEGs)and key-pathways that appear preferentially activated in stage III vs. IV melanoma, and in long vs. short immunotherapy responders.

Results

Although circulating immune-cells did not numerically differ in both sets of analysis(staging and ICI responsiveness), DEA and GO data showed that patients could be clustered separately, identifying 189vs.92 DEGs in stage IV/III and 101vs.47 DEGs in early progressors/long responders. These DEGs were functionally implicated in distinct pathways. For metastatic cases: inflammatory response(logp-value=-9.2:ADGRE5/2,CYBA,GRN,HMOX1,IRF5,ITGAM), adaptive immunity(logp-value=-7.7:CD1C,CD74,CYBB,NCF2,CTSA,S100A8/9,BCL3,FCER1G), T-cell activation(logp-value=-6.3:BCL3,CD1C,CD74,FCER1G,FGL2)and lipid metabolism/catabolism(logp-value=-2.5/-2.6:ARF3,GPX1,MVD,OCRL,PCCB,CTSA,PNPLA2,NAGLU,GBA2,ABHD4); while in early-progressors to ICIs: immune effector processing(logp-value=-13.7:BCL6,FGR,HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5,NKG7,SLC11A1,TYROBP,SPON2,HAVCR2),PD-1(logp-value=-10.2:HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5)and IFN signaling(logp-value=-8.5: HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5,NCAM1,IFITM3),positive regulation of T-cell activation(logp-value=-7.7:BCL6,HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5,SASH3,HAVCR2)and CD28 co-stimulation(logp-value=-10.3:HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5), supporting an immune-mediated behavior.

Conclusions

Specific pathways and marker genes in the peripheral CD4+T-cells may predetermine melanoma staging and immunotherapy resistance.

Single-cell RNA-seq reveals disease-specific CD8+ T cell clonal expansion and a high frequency of transcriptionally distinct double-negative T cells in diabetic NOD mice

T cells primarily drive the autoimmune destruction of pancreatic beta cells in Type 1 diabetes (T1D). However, the profound yet uncharacterized diversity of the T cell populations in vivo has hindered obtaining a clear picture of the T cell changes that occur longitudinally during T1D onset. This study aimed to identify T cell clonal expansion and distinct transcriptomic signatures associated with T1D progression in Non-Obese Diabetic (NOD) mice. Here we profiled the transcriptome and T cell receptor (TCR) repertoire of T cells at single-cell resolution from longitudinally collected peripheral blood and pancreatic islets of NOD mice using single-cell RNA sequencing technology. We detected disease dependent development of infiltrating CD8 + T cells with altered cytotoxic and inflammatory effector states. In addition, we discovered a high frequency of transcriptionally distinct double negative (DN) T cells that fluctuate throughout T1D pathogenesis. This study identifies potential disease relevant TCR sequences and potential disease biomarkers that can be further characterized through future research.

General and individualized changes in T cell immunity during aging

Functional alterations with age are observed in all human systems, but the aging of the adaptive immune system displays both general changes affecting all individuals, and idiosyncratic changes that are unique to individuals. In the T cell compartment, general aging manifests in three ways: (1) the reduction of naïve T cells, (2) the accumulation of differentiated memory T cells, and (3) a reduced overall T cell receptor (TCR) repertoire. Idiosyncratic impacts of aging, such as changes in the TCR repertoires of altered memory and naïve T cells are shaped by each person's life exposures. Recent advancements in single-cell sequencing provide new information including the identification of new subpopulations of T cells, characteristics of transcriptome changes in T cells and their TCR clonotype with age, and measurement of individual cell age. Here, we focus on the changes in T cell subpopulations, transcriptomes and TCR repertoires in overall and antigen-specific T cell population with aging.

ITGA4 as a potential prognostic and immunotherapeutic biomarker in human cancer and its clinical significance in gastric cancer: an integrated analysis and validation

Background

Integrin Subunit Alpha 4 (ITGA4), a member of the integrin protein family, is involved in the progression of malignant tumors. However, its role across different cancer types is not well understood.

Methods

Utilizing multi-omics data, we comprehensively evaluated ITGA4's expression, clinical relevance, diagnostic and prognostic value, functions, mutations, and methylation status, along with its impact on immunity, mismatch repair (MMR), heterogeneity, stemness, immunotherapy responsiveness, and drug resistance in pan-cancer, with partial validation in gastric cancer (GC) using transcriptomic analysis, single-cell data, western blot (WB), wound-healing assay, flow cytometry and immunohistochemistry (IHC). We further investigated its correlation with clinicopathology and serological markers on tissues from 80 GC patients.

Results

ITGA4 expression was generally low in normal tissues but varied significantly across tumor types, with higher levels in advanced stages and grades. It demonstrated diagnostic value in 20 cancer types and effectively predicted 1-, 3-, and 5-year survival rates as part of a prognostic model. ITGA4 played roles in cell adhesion, migration, immune regulation, and pathways like PI3K-Akt and TSC-mTOR. It showed alterations in 22 cancer types, with methylation at 9 sites inhibiting its expression. ITGA4 positively correlated with immune cell infiltration, immune regulatory genes, chemokines, and might reduce microsatellite instability (MSI) and tumor mutation burden (TMB) by promoting MMR gene expression. It could also predict immunotherapy efficacy and chemotherapy sensitivity. In GC, high ITGA4 expression was related to poor prognosis, promoted tumor proliferation and migration, and enhanced immune cell infiltration. ITGA4 expression was higher in GC cells and tissues than normal ones. Its downregulation inhibited GC cell migration and promoted apoptosis. Moreover, ITGA4 was correlated with N stage, pathological stage, neural and vascular invasion, serum levels of Ki-67, immune cells, CRP and CA125.

Conclusion

ITGA4 is a potential biomarker and therapeutic target to enhance cancer treatment and improve patient outcomes.

The characteristics of T-cell receptor repertoire in relation to systemic immune response of patients with ischemic stroke

T lymphocytes play a vital role in the immune-inflammatory response following a stroke. However, the specific mechanisms behind the contrasting functions of T cells in the brain and peripheral tissues after a stroke remain unclear and require further investigation. T-cell receptors (TCRs) are essential in controlling how T lymphocytes develop and become active. This study aims to gain a deeper understanding of the biological function of T lymphocytes by analyzing the TCR repertoire in patients who have experienced an acute ischemic stroke (AIS). High-throughput TCR sequencing was conducted on peripheral blood samples from 25 AIS patients and 10 healthy controls. We compared the percentage of T cells and the characteristics of the TCR repertoire, specifically focusing on the recombination of V(D)J gene fragments and the diversity of the complementarity determining region 3 (CDR3) of the Vβ gene. Additionally, this study analyzed the potential biological significance of the skewed TCR repertoire in AIS patients. In patients with AIS, the proportion of circulating lymphocytes (LY%) decreased while the systemic immune-inflammatory index (SII) increased compared to healthy controls. The average number of TCR read pairs decreased, corresponding with the presence of lymphopenia. However, the recombination of V(D)J gene fragments, the number of CDR3 clonotypes, and the diversity of CDR3 was elevated in the peripheral blood of AIS patients. Furthermore, the increased number of CDR3 amino acid or nucleotide clonotypes was negatively correlated with neurologic deficits but positively correlated with AIS patients' systemic immune condition and functional outcomes. Our findings suggest that both immunosuppression and enhanced antigen-specific T-cell response may exist in the periphery of the AIS patients. Further investigation into the mechanisms underlying these opposing changes may lead to the discovery of novel targets to reverse immunosuppression or mitigate the detrimental effects of T cells in the lesioned brain of AIS patients.

Integrative mapping of human CD8+ T cells in inflammation and cancer

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

An in-depth understanding of the role and mechanisms of T cells in immune organ aging and age-related diseases

T cells play a critical and irreplaceable role in maintaining overall health. However, their functions undergo alterations as individuals age. It is of utmost importance to comprehend the specific characteristics of T-cell aging, as this knowledge is crucial for gaining deeper insights into the pathogenesis of aging-related diseases and developing effective therapeutic strategies. In this review, we have thoroughly examined the existing studies on the characteristics of immune organ aging. Furthermore, we elucidated the changes and potential mechanisms that occur in T cells during the aging process. Additionally, we have discussed the latest research advancements pertaining to T-cell aging-related diseases. These findings provide a fresh perspective for the study of T cells in the context of aging.

T-cell Receptor Repertoire Analysis in the Context of Transarterial Chemoembolization Synergy with Systemic Therapy for Hepatocellular Carcinoma

T-cell receptor (TCR) sequencing provides a novel platform for insight into and characterization of intricate T-cell profiles, advancing the understanding of tumor immune heterogeneity. Recently, transarterial chemoembolization (TACE) combined with systemic therapy has become the recommended regimen for advanced hepatocellular carcinoma. The regulation of the immune microenvironment after TACE and its impact on tumor progression and recurrence has been a focus of research. By examining and tracking fluctuations in the TCR repertoire following combination treatment, novel perspectives on the modulation of the tumor microenvironment post-TACE and the underlying mechanisms governing tumor progression and recurrence can be gained. Clarifying the distinctive metrics and dynamic alterations of the TCR repertoire within the context of combination therapy is imperative for understanding the mechanisms of anti-tumor immunity, assessing efficacy, exploiting novel treatments, and further advancing precision oncology in the treatment of hepatocellular carcinoma. In this review, we initially summarized the fundamental characteristics of TCR repertoire and depicted immune microenvironment remodeling after TACE. Ultimately, we illustrated the prospective applications of TCR repertoires in TACE combined with systemic therapy.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data

T cell receptors (TCRs) serve key roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods have been developed for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics. Yet, a comprehensive evaluation of their relative performance under different conditions remains elusive. In this study, we conducted a benchmark analysis utilizing experimental single-cell immune profiling datasets. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another SIMulator for single-cell TCR), capable of generating scTCR-seq reads containing diverse TCR-derived sequences with different sequencing depths and read lengths. Our results consistently showed that TRUST4 and MiXCR outperformed others across multiple datasets, while DeRR demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the appropriate method for reconstructing TCRs from scRNA-seq data.

Beyond Single Clones: High-Throughput Sequencing in Antibody Discovery

Antibody repertoire sequencing and display library screening are powerful approaches for antibody discovery and engineering that can connect DNA sequence with antibody function. Antibody display and screening studies have made a tremendous impact on immunology and biotechnology over the last decade, accelerated by technological advances in high-throughput DNA sequencing techniques. Indeed, bioinformatic analysis of antibody DNA library data has now taken a central role in modern antibody drug discovery, and is also critical for many ongoing studies of human immune development. Here, we describe current trends in antibody DNA library screening and analysis, and introduce a selection of protocols describing fundamental bioinformatic techniques to enable scientists to efficiently study antibody DNA libraries.

High-throughput and multimodal profiling of antigen-specific T cells with a droplet-based cell-cell interaction screening platform

Identifying antigen-specific T cells from tumor-infiltrating lymphocytes is essential for designing effective T cell immunotherapies. Traditional methods can detect antigen-specific T cells but struggle with high-throughput screening and multimodal profiling simultaneously. To address this issue, we developed DropCCI, a new strategy that transfers antigen information to co-incubated T cells for high-throughput, non-contaminated multimodal profiling. In DropCCI, droplets encapsulated DNA barcodes and antigen-loaded antigen-presenting cells (APCs), while click chemistry-modified T cells were injected into these droplets to capture free barcodes and acquire the corresponding antigen information. Following cell-cell interaction, APCs were removed via streptavidin-biotin conjugation, to prevent contamination. The resulting T cells underwent single-cell omics sequencing for comprehensive profiling of their antigen specificity, transcriptome, and genomics accurately. This click-chemistry method allowed detection of antigen-specific T cells without lysing APCs, avoiding cross-cell contamination and enabling low-noise multimodal profiling of primary T cells. With a completion time within 12 h and no requirement for complex equipment, DropCCI provides unbiased single-cell sequencing results that offer a comprehensive understanding of anti-tumor T cell responses. The concept of DropCCI holds great promise not only for advancing the field of T cell immunotherapy but also for its potential application in studying other cell-cell interactions.

Targeting cancer with precision: strategical insights into TCR-engineered T cell therapies

T cell receptor-engineered T (TCR-T) cell therapies are at the forefront of cancer immunotherapy, offering a transformative approach that significantly enhances the ability of T cells to recognize and eliminate cancer cells. This innovative method involves genetically modifying TCRs to increase their affinity for tumor-specific antigens. While these enhancements improve the ability of T cells to recognize and bind to antigens on cancer cells, rigorous assessment of specificity remains crucial to ensure safety and targeted responses. This dual focus on affinity and specificity holds significant promise for the treatment of solid tumors, enabling precise and efficient cancer cell recognition. Despite rapid advancements in TCR engineering and notable progress in TCR screening technologies, as evidenced by the growing number of specific TCRs entering clinical trials, several technical and clinical challenges remain. These challenges primarily pertain to the specificity, affinity, and safety of engineered TCRs. Moreover, the accurate identification and selection of TCRs that are both effective and safe are essential for the success of TCR-T cell therapies in cancer treatment. This review provides a comprehensive examination of the theoretical foundations of TCR therapy, explores strategies for screening specific TCRs and antigens, and highlights the ongoing challenges in this evolving therapeutic landscape.

High peripheral T cell diversity is associated with lower risk of toxicity and superior response to dual immune checkpoint inhibitor therapy in patients with metastatic NSCLC

INTRODUCTION

Despite significant successes, immune checkpoint blockade fails to achieve clinical responses in a significant proportion of patients, predictive markers for responses are imperfect and immune-related adverse events (irAEs) are unpredictable. We used T-cell receptor (TCR) sequencing to systematically analyze prospectively collected patient blood samples from a randomized clinical trial of dual immune checkpoint inhibitor therapy to evaluate changes in the T-cell repertoire and their association with response and irAEs.

METHODS

Patients with immunotherapy-naïve metastatic non-small cell lung cancer (NSCLC) were treated with ipilimumab and nivolumab according to trial protocol (LONESTAR, NCT03391869). Blood samples were systematically obtained at baseline (n=107), after 12 weeks of ipilimumab and nivolumab (n=91), and at the time of grade ≥2 irAEs (n=77). For analysis of T-cell repertoire, we performed immunoSEQ to assess the complementary determining region 3β region of the TCR involved in antigen binding.

RESULTS

A total of 250 samples from 119 patients were analyzed. Patients who had a response to therapy exhibited greater T-cell diversity at baseline. Interestingly, patients with irAEs demonstrated lower T-cell richness at the time of toxicity compared with those without irAEs.

CONCLUSION

Our study highlights the potential impact of peripheral blood T-cell repertoire on clinical response and toxicities from the combination of ipilimumab and nivolumab in patients with metastatic NSCLC. These findings suggest that analysis of peripheral blood T-cell repertoire may help to guide patient selection for treatment with ipilimumab and nivolumab.

TRIAL REGISTRATION NUMBER

NCT03391869.

Impact of T cell characteristics on CAR-T cell therapy in hematological malignancies

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment paradigms for hematological malignancies. However, more than half of these patients cannot achieve sustainable tumor control, partially due to the inadequate potency of CAR-T cells in eradicating tumor cells. T cells are crucial components of the anti-tumor immune response, and multiple intrinsic T-cell features significantly influence the outcomes of CAR-T cell therapy. Herein, we review progressing research on T-cell characteristics that impact the effectiveness of CAR-T cells, including T-cell exhaustion, memory subsets, senescence, regulatory T-cells, the CD4+ to CD8+ T-cell ratio, metabolism, and the T-cell receptor repertoire. With comprehensive insight into the biological processes underlying successful CAR-T cell therapy, we will further refine the applications of these novel therapeutic modalities, and enhance their efficacy and safety for patients.

Decoding the mosaic of inflammatory bowel disease: Illuminating insights with single-cell RNA technology

Inflammatory bowel diseases (IBD), comprising ulcerative colitis (UC) and Crohn's disease (CD), are complex chronic inflammatory intestinal conditions with a multifaceted pathology, influenced by immune dysregulation and genetic susceptibility. The challenges in understanding IBD mechanisms and implementing precision medicine include deciphering the contributions of individual immune and non-immune cell populations, pinpointing specific dysregulated genes and pathways, developing predictive models for treatment response, and advancing molecular technologies. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to address these challenges, offering comprehensive transcriptome profiles of various cell types at the individual cell level in IBD patients, overcoming limitations of bulk RNA sequencing. Additionally, single-cell proteomics analysis, T-cell receptor repertoire analysis, and epigenetic profiling provide a comprehensive view of IBD pathogenesis and personalized therapy. This review summarizes significant advancements in single-cell sequencing technologies for enhancing our understanding of IBD, covering pathogenesis, diagnosis, treatment, and prognosis. Furthermore, we discuss the challenges that persist in the context of IBD research, including the need for longitudinal studies, integration of multiple single-cell and spatial transcriptomics technologies, and the potential of microbial single-cell RNA-seq to shed light on the role of the gut microbiome in IBD.

Investigating TCR-pMHC interactions for TCRs without identified epitopes by constructing a computational pipeline

The molecular mechanisms underlying epitope recognition by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a computational pipeline that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms for TCRs without identified epitopes. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope-unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919-927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790-1798), for a TCR expressed by 204 T cells (TCR-204). By molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. We hope that this strategy could facilitate the discovery and optimization of TCR-based therapeutics.

Identifying T-cell clubs by embracing the local harmony between TCR and gene expressions

T cell receptors (TCR) and gene expression provide two complementary and essential aspects in T cell understanding, yet their diversity presents challenges in integrative analysis. We introduce TCRclub, a novel method integrating single-cell RNA sequencing data and single-cell TCR sequencing data using local harmony to identify functionally similar T cell groups, termed 'clubs'. We applied TCRclub to 298,106 T cells across seven datasets encompassing various diseases. First, TCRclub outperforms the state-of-the-art methods in clustering T cells on a dataset with over 400 verified peptide-major histocompatibility complex categories. Second, TCRclub reveals a transition from activated to exhausted T cells in cholangiocarcinoma patients. Third, TCRclub discovered the pathways that could intervene in response to anti-PD-1 therapy for patients with basal cell carcinoma by analyzing the pre-treatment and post-treatment samples. Furthermore, TCRclub unveiled different T-cell responses and gene patterns at different severity levels in patients with COVID-19. Hence, TCRclub aids in developing more effective immunotherapeutic strategies for cancer and infectious diseases.

Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection: PULSE

Laboratory automation technologies have revolutionized biomedical research. However, the availability of automation solutions at the single-cell level remains scarce, primarily owing to the inherent challenges of handling cells with such small dimensions in a precise, biocompatible manner. Here, we present a single-cell-level laboratory automation solution that configures various experiments onto standardized, microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology, known as PULSE. PULSE enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable, scalable, and biocompatible manner. Unlike pipetting robots, PULSE enables researchers to conduct biological experiments using single cells as anchoring points (e.g., 1 cell vs. 1000 cells per "tube"), achieving higher resolution and potentially more relevant data for modeling and downstream analyses. We demonstrate the ability of PULSE to perform biofabrication, precision gating, and deterministic array barcoding via preallocated droplet-addressable primers. Single cells can be gently printed at a speed range of 5-20 cell⋅s-1 with an accuracy of 90.5-97.7%, which can then adhere to the substrate and grow for up to 72 h while preserving cell integrity. In the deterministic barcoding experiment, 95.6% barcoding accuracy and 2.7% barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single cells. Our PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level, offering researchers a powerful tool in biomedical research.

Shared lung and joint T cell repertoire in early rheumatoid arthritis driven by cigarette smoking

OBJECTIVES

Smoking has been associated with an increased risk of developing rheumatoid arthritis (RA) in individuals carrying shared epitope (SE) HLA-DRB1 alleles. Yet, little is known about the regional and systemic T cell dynamics of smoking and a potential link to T cell infiltration in inflamed synovia. In this study, we, therefore, sought to study T cell features in lung and inflamed joints in smoking versus non-smoking patients.

METHODS

We set up a framework to monitor T cells in paired bronchoalveolar lavage fluid, blood and inflamed synovium tissue samples from 17 new-onset treatment naïve anticitrullinated protein antibody+RA patients. T cell receptor (TCR) repertoire of index-sorted tissue residing in T cells was determined by single-cell TCR sequencing coupled with deep immunophenotyping.

RESULTS

A significant enrichment of CD4+ and CD8+ T cells was seen in synovial samples from smoking versus non-smoking patients, along with an increase in expanded T cell clonotypes. This was particularly pronounced among SE+smokers, suggestive of a synergic gene-smoke effect. Strikingly, identical TCR clonalities were present in matched lung and joint samples of RA smokers, the majority being also detectable in circulation. This was mirrored by an increased clustering of lung and synovium TCRs across patients, suggesting a shared specificity by conserved motifs. The lung-joint shared T cell clonotypes showed a restricted TCR gene usage and exhibited a particular 4-1BB+CD57 hi effector profile within the inflamed synovium.

CONCLUSION

The data indicate a profound interplay between a strong MHC predisposition, smoking and induction of autoimmunity by shaping the TCR repertoire.

TCellR2Vec: efficient feature selection for TCR sequences for cancer classification

Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient's immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T-cells have receptors that recognize and bind to cancer cells. Sequencing these T-cell receptors allows to provide insights into their immune response, but extracting useful information is challenging. In this study, we propose a new computational method, TCellR2Vec, to select key features from T-cell receptor sequences for classifying different cancer types. We extracted features like amino acid composition, charge, and diversity measures and combined them with other sequence embedding techniques. For our experiments, we used a dataset of over 50,000 T-cell receptor sequences from five cancer types, which showed that TCellR2Vec improved classification accuracy and efficiency over baseline methods. These results demonstrate TCellR2Vec's ability to capture informative aspects of complex T-cell receptor sequences. By improving computational analysis of the immune response, TCellR2Vec could aid the development of personalized immunotherapies tailored to each patient's T-cells. This has important implications for creating more effective cancer treatments based on the individual's immune system.

Immune repertoire profiling in myasthenia gravis

Myasthenia gravis (MG) is the most frequent immune-mediated neurological disorder, characterized by fluctuating muscle weakness. Specific recognition of self-antigens by T-cell receptors (TCRs) and B-cell receptors (BCRs), coupled with T-B cell interactions, activates B cells to produce autoantibodies, which are critical for the initiation and perpetuation of MG. The immune repertoire comprises all functionally diverse T and B cells at a specific time point in an individual, reflecting the essence of immune selectivity. By sequencing the nucleotide sequences of TCRs and BCRs, it is possible to track individual T- and B-cell clones. This review delves into the generation of autoreactive TCRs and BCRs in MG and comprehensively examines the applications of immune repertoire sequencing in understanding disease pathogenesis, developing diagnostic and prognostic markers and informing targeted therapies. We also discuss the current limitations and future potential of this approach.

Single-cell sequencing: Current applications in various tuberculosis specimen types

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Single-cell sequencing reveals immune features of treatment response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Neoadjuvant immunochemotherapy (nICT) has dramatically changed the treatment landscape of operable esophageal squamous cell carcinoma (ESCC), but factors influencing tumor response to nICT are not well understood. Here, using single-cell RNA sequencing paired with T cell receptor sequencing, we profile tissues from ESCC patients accepting nICT treatment and characterize the tumor microenvironment context. CXCL13+CD8+ Tex cells, a subset of exhausted CD8+ T cells, are revealed to highly infiltrate in pre-treatment tumors and show prominent progenitor exhaustion phenotype in post-treatment samples from responders. We validate CXCL13+CD8+ Tex cells as a predictor of improved response to nICT and reveal CXCL13 to potentiate anti-PD-1 efficacy in vivo. Post-treatment tumors from non-responders are enriched for CXCL13+CD8+ Tex cells with notably remarkable exhaustion phenotype and TNFRSF4+CD4+ Tregs with activated immunosuppressive function and a significant clone expansion. Several critical markers for therapeutic resistance are also identified, including LRRC15+ fibroblasts and SPP1+ macrophages, which may recruit Tregs to form an immunosuppressive landscape. Overall, our findings unravel immune features of distinct therapeutic response to nICT treatment, providing a rationale for optimizing individualized neoadjuvant strategy in ESCC.

DEPICT-seq: Single-Cell Transcriptomic Analysis of Rare Cell Subsets Isolated via Nucleic Acid Cytometry

The ability to dive deep into specific rare cell populations is critical for understanding tissue physiology and pathology across various biological domains. As single-cell RNA-seq flourishes, many newly discovered cell subtypes are defined by their transcriptomic markers. However, our ability to retrieve and analyze cells based on their nucleic acid markers remains underdeveloped. Here, we present Double Emulsion PCR-Initiated Cell sorting and Transcriptomic Sequencing (DEPICT-seq), a high-throughput droplet nucleic acid cytometry method that integrates batch cell fixation for cellular information preservation, double emulsion digital PCR-based cell sorting to target nucleic acid markers of interest, and in-depth full-length transcriptomic analyses at single-cell resolution. We utilize DEPICT-seq to isolate and characterize T cell receptor (TCR)-engineered T cells within a mixed population and also demonstrate a variation of the workflow by incorporating an RNase H-dependent PCR step to enrich full-length TCR sequences for paired single-cell TCR sequencing and transcriptomic profiling.

Modified Dendritic cell-based T-cell expansion protocol and single-cell multi-omics allow for the selection of the most expanded and in vitro-effective clonotype via profiling of thousands of MAGE-A3-specific T-cells

Introduction

Adoptive cell therapy using TCR-engineered T-cells is one of the most effective strategies against tumor cells. The TCR T-cell approach has been well tested against a variety of blood neoplasms but is yet to be deeply tested against solid tumors. Among solid tumors, cancer-testis antigens are the most prominent targets for tumor-specific therapy, as they are usually found on cells that lie behind blood-tissue barriers.

Methods

We have employed a novel efficient protocol for MAGE-A3-specific T-cell clonal expansion, performed single-cell multi-omic analysis of the expanded T-cells via BD Rhapsody, engineered a selected T-cell receptor into a lentiviral construct, and tested it in an in vitro LDH-cytotoxicity test.

Results and discussion

We have observed a 191-fold increase in the MAGE-A3-specific T-cell abundance, obtained a dominant T-cell receptor via single-cell multi-omic BD Rhapsody data analysis in the TCRscape bioinformatics tool, and observed potent cytotoxicity of the dominant-clonotype transduced TCR T-cells against a MAGE-A3-positive tumor. We have demonstrated the efficiency of our T-cell enrichment protocol in obtaining potent anti-tumor T-cells and their T-cell receptors, especially when paired with the modern single-cell analysis methods.

Systems immunology approaches to study T cells in health and disease

T cells are dynamically regulated immune cells that are implicated in a variety of diseases ranging from infection, cancer and autoimmunity. Recent advancements in sequencing methods have provided valuable insights in the transcriptional and epigenetic regulation of T cells in various disease settings. In this review, we identify the key sequencing-based methods that have been applied to understand the transcriptomic and epigenomic regulation of T cells in diseases.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

The T-cell repertoire of Spanish patients with COVID-19 as a strategy to link T-cell characteristics to the severity of the disease

BACKGROUND

The architecture and dynamics of T cell populations are critical in orchestrating the immune response to SARS-CoV-2. In our study, we used T Cell Receptor sequencing (TCRseq) to investigate TCR repertoires in 173 post-infection COVID-19 patients.

METHODS

The cohort included 98 mild and 75 severe cases with a median age of 53. We amplified and sequenced the TCR β chain Complementary Determining Region 3 (CDR3b) and performed bioinformatic analyses to assess repertoire diversity, clonality, and V/J allelic usage between age, sex and severity groups. CDR3b amino acid sequence inference was performed by clustering structural motifs and filtering validated reactive CDR3b to COVID-19.

RESULTS

Our results revealed a pronounced decrease in diversity and an increase in clonal expansion in the TCR repertoires of severe COVID-19 patients younger than 55 years old. These results reflect the observed trends in patients older than 55 years old (both mild and severe). In addition, we identified a significant reduction in the usage of key V alleles (TRBV14, TRBV19, TRBV15 and TRBV6-4) associated with disease severity. Notably, severe patients under 55 years old had allelic patterns that resemble those over 55 years old, accompanied by a skewed frequency of COVID-19-related motifs.

CONCLUSIONS

Present results suggest that severe patients younger than 55 may have a compromised TCR repertoire contributing to a worse disease outcome.

A mass cytometry method pairing T cell receptor and differentiation state analysis

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

Posttransplant complications: molecular mechanisms and therapeutic interventions

Posttransplantation complications pose a major challenge to the long-term survival and quality of life of organ transplant recipients. These complications encompass immune-mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft-versus-host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.

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.

Human TCR repertoire in cancer

BACKGROUND

T cells, the "superstar" of the immune system, play a crucial role in antitumor immunity. T-cell receptors (TCR) are crucial molecules that enable T cells to identify antigens and start immunological responses. The body has evolved a unique method for rearrangement, resulting in a vast diversity of TCR repertoires. A healthy TCR repertoire is essential for the particular identification of antigens by T cells.

METHODS

In this article, we systematically summarized the TCR creation mechanisms and analysis methodologies, particularly focusing on the application of next-generation sequencing (NGS) technology. We explore the TCR repertoire in health and cancer, and discuss the implications of TCR repertoire analysis in understanding carcinogenesis, cancer progression, and treatment.

RESULTS

The TCR repertoire analysis has enormous potential for monitoring the emergence and progression of malignancies, as well as assessing therapy response and prognosis. The application of NGS has dramatically accelerated our comprehension of TCR diversity and its role in cancer immunity.

CONCLUSIONS

To substantiate the significance of TCR repertoires as biomarkers, more thorough and exhaustive research should be conducted. The TCR repertoire analysis, enabled by advanced sequencing technologies, is poised to become a crucial tool in the future of cancer diagnosis, monitoring, and therapy evaluation.

Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway

Skin cutaneous melanoma (SKCM) is a highly fatal form of skin cancer that develops from the malignant transformation of epidermal melanocytes. There is substantial evidence linking autophagy to cancer etiology and immunotherapy efficacy. This study aimed to conduct a comprehensive analysis of autophagy-related genes (ARGs) using TCGA datasets and further explore the potential function of critical ARGs in SKCM progression. We performed comprehensive bioinformatics analysis uses the TCGA dataset. RT-PCR was applied to examine the expression of CAPNS1 in SKCM cells. Lost-of-function experiments were performed to detect the expression of the related proteins. In this search, we screed 70 differentially expressed autophagy-related genes (DE-ARGs), including 33 up-DE-ARGs and 37 down-DE-ARGs. Enrichment assays revealed that these 70 DE-ARGs may exert influence on critical cellular processes such as autophagy, protein kinase activity, and signaling pathways, impacting cell growth, differentiation, survival, and tumor development. Then, we further explore the prognostic value of 70 DE-ARGs and confirmed 18 survival-related DE-ARGs in SKCM patients. Nearly all the 18 DE-ARGs' methylation was negatively correlated with their corresponding expression in SKCM. The 12 survival-related DE-ARGs were used to develop a unique predictive model that effectively classified SKCM patients into high- and low-risk groups with regard to overall survival. Furthermore, tumor environment analysis indicated that the risk score was associated with several immune cells. Among the 12 survival-related DE-ARGs, our attention focused on CAPNS1 which was highly expressed in SKCM patients and predicted a poor prognosis. In addition, we confirmed that knockdown of CAPNS1 distinctly suppressed the proliferation, metastasis and EMT of SKCM cells, and promoted autophagy via regulating Notch signaling pathway. Overall, this study enhances our understanding of the intricate molecular landscape of SKCM progression and presents promising avenues for future research and clinical applications.

TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data

MOTIVATION

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data.

RESULTS

We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/ha01994/TSpred.

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

TCR clustering by contrastive learning on antigen specificity

Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pretrained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.

Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data

Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.

Mitochondrial genetics through the lens of single-cell multi-omics

Mitochondria carry their own genetic information encoding for a subset of protein-coding genes and translational machinery essential for cellular respiration and metabolism. Despite its small size, the mitochondrial genome, its natural genetic variation and molecular phenotypes have been challenging to study using bulk sequencing approaches, due to its variation in cellular copy number, non-Mendelian modes of inheritance and propensity for mutations. Here we highlight emerging strategies designed to capture mitochondrial genetic variation across individual cells for lineage tracing and studying mitochondrial genetics in primary human cells and clinical specimens. We review recent advances surrounding single-cell mitochondrial genome sequencing and its integration with functional genomic readouts, including leveraging somatic mitochondrial DNA mutations as clonal markers that can resolve cellular population dynamics in complex human tissues. Finally, we discuss how single-cell whole mitochondrial genome sequencing approaches can be utilized to investigate mitochondrial genetics and its contribution to cellular heterogeneity and disease.

[Advancements in Single-cell RNA Sequencing Technology in the Study of the Tumor Microenvironment in Lung Cancer]

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

A pathologically expanded, clonal lineage of IL-21-producing CD4+ T cells drives inflammatory neuropathy

Inflammatory neuropathies, which include chronic inflammatory demyelinating polyneuropathy (CIDP) and Guillain Barré syndrome (GBS), result from autoimmune destruction of the PNS and are characterized by progressive weakness and sensory loss. CD4+ T cells play a key role in the autoimmune destruction of the PNS. Yet, key properties of pathogenic CD4+ T cells remain incompletely understood. Here, we used paired single-cell RNA-Seq (scRNA-Seq) and single-cell T cell receptor-sequencing (scTCR-Seq) of peripheral nerves from an inflammatory neuropathy mouse model to identify IL-21-expressing CD4+ T cells that were clonally expanded and multifunctional. These IL-21-expressing CD4+ T cells consisted of 2 transcriptionally distinct expanded cell populations, which expressed genes associated with T follicular helper (Tfh) and T peripheral helper (Tph) cell subsets. Remarkably, TCR clonotypes were shared between these 2 IL-21-expressing cell populations, suggesting a common lineage differentiation pathway. Finally, we demonstrated that IL-21 receptor-KO (IL-21R-KO) mice were protected from neuropathy development and had decreased immune infiltration into peripheral nerves. IL-21 signaling upregulated CXCR6, a chemokine receptor that promotes CD4+ T cell localization in peripheral nerves. Together, these findings point to IL-21 signaling, Tfh/Tph differentiation, and CXCR6-mediated cellular localization as potential therapeutic targets in inflammatory neuropathies.

The complexities of T-cell dysfunction in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by profound alterations and defects in the T-cell compartment. This observation has gained renewed interest as T-cell treatment strategies, which are successfully applied in more aggressive B-cell malignancies, have yielded disappointing results in CLL. Despite ongoing efforts to understand and address the observed T-cell defects, the exact mechanisms and nature underlying this dysfunction remain largely unknown. In this review, we examine the supporting signals from T cells to CLL cells in the lymph node niche, summarize key findings on T-cell functional defects, delve into potential underlying causes, and explore novel strategies for reversing these deficiencies. Our goal is to identify strategies aimed at resolving CLL-induced T-cell dysfunction which, in the future, will enhance the efficacy of autologous T-cell-based therapies for CLL patients.