T cell fate and clonality inference from single-cell transcriptomes

The reverse TRBV30 gene of mammals: a defect or superiority in evolution?

At the 3' end of the C2 gene in the mammalian TRB locus, a distinct reverse TRBV30 gene (named TRBV31 in mice) has been conserved throughout evolution. In the fully annotated TRB locus of 14 mammals (including six orders), we observed noteworthy variations in the localization and quality of the reverse V30 genes and Recombination Signal Sequences (RSSs) in the gene trees of 13 mammals. Conversely, the forward V29 genes and RSSs were generally consistent with the species tree of their corresponding species. This finding suggested that the evolution of the reverse V30 gene was not synchronous and likely played a crucial role in regulating adaptive immune responses. To further investigate this possibility, we utilized single-cell TCR sequencing (scTCR-seq) and high-throughput sequencing (HTS) to analyze TCRβ CDR3 repertoires from both central and peripheral tissues of Primates (Homo sapiens and Macaca mulatta), Rodentia (Mus musculus: BALB/c, C57BL/6, and Kunming mice), Artiodactyla (Bos taurus and Bubalus bubalis), and Chiroptera (Rhinolophus affinis and Hipposideros armige). Our investigation revealed several novel observations: (1) The reverse V30 gene exhibits classical rearrangement patterns adhering to the '12/23 rule' and the 'D-J rearrangement preceding the V-(D-J) rearrangement'. This results in the formation of rearranged V30-D2J2, V30-D1J1, and V30-D1J2. However, we also identified 'special rearrangement patterns' wherein V30-D rearrangement preceding D-J rearrangement, giving rise to rearranged V30-D2-J1 and forward Vx-D2-J. (2) Compared to the 'deletional rearrangement' (looping out) of forward V1-V29 genes, the reverse V30 gene exhibits preferential utilization with 'inversional rearrangement'. This may be attributed to the shorter distance between the V30 gene and D gene and the 'inversional rearrangement' modes. In summary, in the mammalian TRB locus, the reverse V30 gene has been uniquely preserved throughout evolution and preferentially utilized in V(D)J recombination, potentially serving a significant role in adaptive immunity. These results will pave the way for novel and specialized research into the mechanisms, efficiency, and function of V(D)J recombination in mammals.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Landscape of tumoral ecosystem for enhanced anti-PD-1 immunotherapy by gut Akkermansia muciniphila

Gut Akkermansia muciniphila (Akk) has been implicated in impacting immunotherapy or oncogenesis. This study aims to dissect the Akk-associated tumor immune ecosystem (TIME) by single-cell profiling coupled with T cell receptor (TCR) sequencing. We adopted mouse cancer models under anti-PD-1 immunotherapy, combined with oral administration of three forms of Akk, including live Akk, pasteurized Akk (Akk-past), or its membrane protein Amuc_1100 (Amuc). We show that live Akk is most effective in activation of CD8 T cells by rescuing the exhausted type into cytotoxic subpopulations. Remarkably, only live Akk activates MHC-II-pDC pathways, downregulates CXCL3 in Bgn(+)Dcn(+) cancer-associated fibroblasts (CAFs), blunts crosstalk between Bgn(+)Dcn(+) CAFs and PD-L1(+) neutrophils by a CXCL3-PD-L1 axis, and further suppresses the crosstalk between PD-L1(+) neutrophils and CD8 T cells, leading to the rescue of exhausted CD8 T cells. Together, this comprehensive picture of the tumor ecosystem provides deeper insights into immune mechanisms associated with gut Akk-dependent anti-PD-1 immunotherapy.

Proteogenomic analysis identifies neoantigens and bacterial peptides as immunotherapy targets in colorectal cancer

Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.

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.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Ultrahigh frequencies of peripherally matured LGI1- and CASPR2-reactive B cells characterize the cerebrospinal fluid in autoimmune encephalitis

Intrathecal synthesis of central nervous system (CNS)-reactive autoantibodies is observed across patients with autoimmune encephalitis (AE), who show multiple residual neurobehavioral deficits and relapses despite immunotherapies. We leveraged two common forms of AE, mediated by leucine-rich glioma inactivated-1 (LGI1) and contactin-associated protein-like 2 (CASPR2) antibodies, as human models to comprehensively reconstruct and profile cerebrospinal fluid (CSF) B cell receptor (BCR) characteristics. We hypothesized that the resultant observations would both inform the observed therapeutic gap and determine the contribution of intrathecal maturation to pathogenic B cell lineages. From the CSF of three patients, 381 cognate-paired IgG BCRs were isolated by cell sorting and scRNA-seq, and 166 expressed as monoclonal antibodies (mAbs). Sixty-two percent of mAbs from singleton BCRs reacted with either LGI1 or CASPR2 and, strikingly, this rose to 100% of cells in clonal groups with ≥4 members. These autoantigen-reactivities were more concentrated within antibody-secreting cells (ASCs) versus B cells (P < 0.0001), and both these cell types were more differentiated than LGI1- and CASPR2-unreactive counterparts. Despite greater differentiation, autoantigen-reactive cells had acquired few mutations intrathecally and showed minimal variation in autoantigen affinities within clonal expansions. Also, limited CSF T cell receptor clonality was observed. In contrast, a comparison of germline-encoded BCRs versus the founder intrathecal clone revealed marked gains in both affinity and mutational distances (P = 0.004 and P < 0.0001, respectively). Taken together, in patients with LGI1 and CASPR2 antibody encephalitis, our results identify CSF as a compartment with a remarkably high frequency of clonally expanded autoantigen-reactive ASCs whose BCR maturity appears dominantly acquired outside the CNS.

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides

Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32β-major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients' skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Opportunities and tradeoffs in single-cell transcriptomic technologies

Recent technological and algorithmic advances enable single-cell transcriptomic analysis with remarkable depth and breadth. Nonetheless, a persistent challenge is the compromise between the ability to profile high numbers of cells and the achievement of full-length transcript coverage. Currently, the field is progressing and developing new and creative solutions that improve cellular throughput, gene detection sensitivity and full-length transcript capture. Furthermore, long-read sequencing approaches for single-cell transcripts are breaking frontiers that have previously blocked full transcriptome characterization. We here present a comprehensive overview of available options for single-cell transcriptome profiling, highlighting the key advantages and disadvantages of each approach.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

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

Single-cell profiling of muscle-infiltrating T cells in idiopathic inflammatory myopathies

Idiopathic inflammatory myopathies (IIM) are rare autoimmune systemic diseases characterized by muscle weakness and the presence of muscle-infiltrating T cells. IIM represent a clinical challenge due to heterogeneity of symptoms and variability of response to immunosuppressive treatment. Here, we performed in-depth single-cell sequencing on muscle-infiltrating T cells and peripheral blood memory T cells in six patients with recently diagnosed IIM. We identified tissue resident memory T-cell (TRM ) signatures including the expression of HOBIT, XCL1 and CXCR6 in the muscle biopsies of all patients with IIM. Clonally expanded T-cell clones were mainly found among cytotoxic and TRM implying their role in the disease pathogenesis. Finally, identical expanded T-cell clones persisting at follow-up in the muscle tissue of two patients suggest their involvement in disease chronicity. Our study reveals a muscle tissue resident memory T-cell signature in patients with IIM and a transcriptomic map to identify novel therapeutic targets in IIM.

scBCR-seq revealed a special and novel IG H&L V(D)J allelic inclusion rearrangement and the high proportion dual BCR expressing B cells

Since the initial report of V (D) J "allelic exclusion/inclusion" (allelic exclusion rearrangement or allelic inclusion rearrangement) and the concept of the "dual B cell receptor (BCR)" in 1961, despite ongoing discoveries, the precise proportion and source mechanism of dual BCR under physiological conditions have been puzzling immuologists. This study takes advantage of the single cell B cell receptor sequencing (scBCR-seq) technology, which can perfectly match the heavy and light chains of BCR at the level of a single B cell, and obtain the full length mRNA sequence of the complementary determining region 3 (CDR3). Through analyzing the pairing of functional IGH (immunoglobulin heavy chain) and IGL (immunoglobulin light chain) in single B cell from both human and mouse bone marrow and peripheral blood, it was observed that dual BCR B cells exhibit stable and high levels of expression. Among them, the human bone marrow and peripheral blood contain about 10% dual (or multiple) BCR B cells, while in mouse peripheral blood and bone marrow memory B cells, this proportion reaches around 20%. At the same time, we innovatively found that in each research sample of humans and mice, there are three (or more) functional rearrangements (mRNA level) of a single chain in a single B cell. By analyzing the position, direction and other compositional characteristics of the V(D)J gene family, we found that at least two (or more) of them are derived from over two (or more) specific allelic inclusion rearrangements of a single chromosome (mRNA molecular level evidence), our findings also highlighted the necessity of classified single cell sequencing data based on single, dual (or multiple) and cannot be assembled into BCR when analyzing the B cell repertoire. The results of this article provides new methods and modeling references for evaluating the proportion and source mechanisms of dual BCR B cells, as well as potential significance of allelic inclusion (exclusion escape) of V(D)J rearrangement.

T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

Single-cell profiling reveals the trajectory of FOLR2-expressing tumor-associated macrophages to regulatory T cells in the progression of lung adenocarcinoma

An immunosuppressive microenvironment enriched with regulatory CD4+ T lymphocytes (Tregs) facilitates the progression of lung adenocarcinoma (LUAD). This study aims to investigate the cellular mechanism underlying the formation of the immunosuppressive microenvironment in LUAD. LUAD samples (n = 12) and normal lung samples (n = 3) were obtained from patients with different pathological stages of LUAD. Single-cell RNA sequencing was performed to classify cellular components and analyze the transcriptomes, including transcription factors/targets and chemokine ligands/receptors, followed by bioinformatics study such as pseudotime analysis. Myeloid cells and T cells were the most abundant cell types in tumors and normal lung tissues, while tumor-associated macrophage-folate receptor 2 (TAM-FOLR2) and CD4+ nuclear receptor subfamily 4 group A member 3 (NR4A3) exhibited sharp increases in invasive adenocarcinoma (IA). The enrichment of TAM-FOLR2 in IA might result from alveolar resident macrophage-resistin (ARM-RETN) transformation and recruitment of dendritic cells (DCs) and other TAMs, as evidenced by temporal trajectories and differential expression profiles of chemokine ligands/receptors versus those in the early stages of tumors. High expression of CCL17/19/22 was observed in IA as well as in DCs, along with the strong interaction of TAM-FOLR2 with DCs. The results of pseudotime analysis suggested that CD4+NR4A3 might potentially convert to CD4+FOXP3, further supported by the high expression of NR4A3 target genes in CD4+FOXP3 cells. This study provides a single-cell transcriptome atlas from preinvasive to invasive LUAD and reveals a potential ARM-RETN/TAM-FOLR2/DCs/CD4+NR4A3/CD4+FOXP3 trajectory in shaping the immune suppressive microenvironment along the pathogenesis of LUAD.

Heterogeneity of the tumor immune microenvironment and clinical interventions

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

scRNA-seq revealed the special TCR β & α V(D)J allelic inclusion rearrangement and the high proportion dual (or more) TCR-expressing cells

Allelic exclusion, one lymphocyte expresses one antigen receptor, is a fundamental mechanism of immunological self-tolerance and highly specific immune responses to pathogens. However, the phenomenon of V(D)J allelic inclusion (incomplete allelic exclusion or allelic escape) rearrangement and dual TCR T cells have been discovered by multiple laboratories. Despite continuous new discoveries, the proportion and underlying mechanism of dual TCR has been puzzling immunologists. In this study, we observed the presence of single T cells expressing multiple TCR chains in all samples, with the proportion of 15%, 10%, and 20% in the human thymus, human peripheral blood, and mouse lymphoid organs, respectively. The proportion of T cells possessing multiple T-cell receptors (TCR) varied significantly in different physiological states and developmental stages. By analyzing RSS category, RSS direction, and V(D)J gene position at TR locus of T cells which contain multiple TCR chains, we creatively found that one of TCR β (or TCR α) should originate from the transcription of V(D)J combination in T-cell receptor excision circle (TREC) formed after the twice successful rearrangement in the same chromosome. Moreover, human V30 (or mouse V31) gene may participate in reverse recombination and transcription to prevent allelic exclusion. In general, high proportion of T cells with multiple TCR at the transcriptome level was first made public, and we proposed a novel mechanism of secondary (or more) TCR rearrangement on a single chromosome. Our findings also indicated that the single-cell sequencing data should be classified according to the single, multiple, and abnormal TCR when analyzing the T-cell repertoire.

LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity

Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.

Human small intestine contains 2 functionally distinct regulatory T-cell subsets

BACKGROUND

Regulatory T (Treg) CD4 cells in mouse gut are mainly specific for intestinal antigens and play an important role in the suppression of immune responses against harmless dietary antigens and members of the microbiota. However, information about the phenotype and function of Treg cells in the human gut is limited.

OBJECTIVE

We performed a detailed characterization of Foxp3+ CD4 Treg cells in human normal small intestine (SI) as well as from transplanted duodenum and celiac disease lesions.

METHODS

Treg cells and conventional CD4 T cells derived from SI were subjected to extensive immunophenotyping and their suppressive activity and ability to produce cytokines assessed.

RESULTS

SI Foxp3+ CD4 T cells were CD45RA-CD127-CTLA-4+ and suppressed proliferation of autologous T cells. Approximately 60% of Treg cells expressed the transcription factor Helios. When stimulated, Helios-negative Treg cells produced IL-17, IFN-γ, and IL-10, whereas Helios-positive Treg cells produced very low levels of these cytokines. By sampling mucosal tissue from transplanted human duodenum, we demonstrated that donor Helios-negative Treg cells persisted for at least 1 year after transplantation. In normal SI, Foxp3+ Treg cells constituted only 2% of all CD4 T cells, while in active celiac disease, both Helios-negative and Helios-positive subsets expanded 5- to 10-fold.

CONCLUSION

The SI contains 2 subsets of Treg cells with different phenotypes and functional capacities. Both subsets are scarce in healthy gut but increase dramatically in active celiac disease.

A current and future perspective on T cell receptor repertoire profiling

T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Anti-tumor memory CD4 and CD8 T-cells quantified by bulk T-cell receptor (TCR) clonal analysis

Simple, reliable methods to detect anti-tumor memory T-cells are necessary to develop a clinical tumor vaccination program. A mouse model of curative viral onco-immunotherapy found that peritoneal tumor challenge following cure identified an oligoclonal anti-tumor memory CD4 and CD8 T-cell response. Clonotypes differed among the challenged animals but were congruent in blood, spleen and peritoneal cells (PC) of the same animal. Adoptive transfer demonstrated that the high-frequency responding T-cells were tumor specific. Tetramer analysis confirmed that clonotype frequency determined by T-cell receptor (TCR)- chain (TRB) analysis closely approximated cell clone frequency. The mean frequency of resting anti-tumor memory CD4 T-cells in unchallenged spleen was 0.028% and of memory CD8 T-cells was 0.11% which was not high enough to distinguish them from background. Stimulation produced a mean ~10-fold increase in splenic and 100-fold increase in peritoneal anti-tumor memory T-cell clonotypes. This methodology can be developed to use blood and tissue sampling to rapidly quantify the effectiveness of a tumor vaccine or any vaccine generating therapeutic T-cells.

TCR sequencing: applications in immuno-oncology research

•T-cell receptor (TCR) interaction with major histocompatibility complex-antigen complexes leads to antitumour responses.•TCR sequencing analysis allows characterisation of T cells that recognise tumour neoantigens.•T-cell clonal revival and clonal replacement potentially underpin immunotherapy responses.

Early expression of CD94 and loss of CD96 on CD8+ T cells after allogeneic stem cell tranplantation is predictive of subsequent relapse and survival

Allogeneic stem cell transplantation is used widely in the treatment of hematopoietic malignancy. However, relapse of malignant disease is the primary cause of treatment failure and reflects loss of immunological graft-versus-leukemia effect. We studied the transcriptional and phenotypic profile of CD8+ T cells in the first month following transplantation and related this to risk of subsequent relapse. Single cell transcriptional profiling identified five discrete CD8+ T-cell clusters. High levels of T-cell activation and acquisition of a regulatory transcriptome were apparent in patients who went on to suffer disease relapse. A relapse-associated gene signature of 47 genes was then assessed in a confirmation cohort of 34 patients. High expression of the inhibitory receptor CD94/NKG2A on CD8+ T cells within the first month was associated with 4.8 fold increased risk of relapse and 2.7 fold reduction in survival. Furthermore, reduced expression of the activatory molecule CD96 was associated with 2.2 fold increased risk of relapse and 1.9 fold reduction in survival. This work identifies CD94 and CD96 as potential targets for CD8-directed immunotherapy in the very early phase following allogeneic transplantation with the potential to reduce long term relapse rates and improve patient survival.

Trained immunity as a possible newcomer in autoinflammatory and autoimmune diseases pathophysiology

Autoimmune disorders have been well characterized over the years and many pathways-but not all of them-have been found to explain their pathophysiology. Autoinflammatory disorders, on the other hand, are still hiding most of their molecular and cellular mechanisms. During the past few years, a newcomer has challenged the idea that only adaptive immunity could display memory response. Trained immunity is defined by innate immune responses that are faster and stronger to a second stimulus than to the first one, being the same or not. In response to the trained immunity inducer, and through metabolic and epigenetic changes of hematopoietic stem and progenitor cells in the bone marrow that are transmitted to their cellular progeny (peripheral trained immunity), or directly of tissue-resident cells (local innate immunity), innate cells responsiveness and functions upon stimulation are improved in the long-term. Innate immunity can be beneficial, but it could also be detrimental when maladaptive. Here, we discuss how trained immunity could contribute to the physiopathology of autoimmune and autoinflammatory diseases.

Single-cell transcriptomes and T cell receptors of vaccine-expanded apolipoprotein B-specific T cells

Atherosclerotic cardiovascular diseases are the major cause of death worldwide. CD4 T cells responding to Apolipoprotein B (ApoB), the core protein of most lipoproteins, have been identified as critical disease modulators. In healthy individuals, ApoB-reactive (ApoB+) CD4 T cells are mostly regulatory T cells (Tregs), which exert anti-inflammatory effects. Yet, they may obtain pro-inflammatory features and thus become proatherogenic. Evidence from animal studies suggests that vaccination against certain major histocompatibility complex (MHC) II-binding ApoB peptides induces an expansion of ApoB+ Tregs and thus confers atheroprotection. To date, in-depth phenotyping of vaccine-expanded ApoB+ T cells has not yet been performed. To this end, we vaccinated C57BL/6J mice with the ApoB-peptide P6 (ApoB978-993 TGAYSNASSTESASY) and performed single-cell RNA sequencing of tetramer-sorted P6+ T cells. P6+ cells were clonally expanded (one major, two minor clones) and formed a transcriptional cluster distinct from clusters mainly containing non-expanded P6+ and P6- cells. Transcriptomic profiling revealed that most expanded P6+ cells had a strong Treg signature and highly expressed genes mediating suppressive functions. Yet, some expanded P6+ cells only had a residual Treg signature and expressed genes related to T helper 1 (TH1) cells, which are proatherogenic. Modeling the T cell receptor (TCR) and P6:MHC-II interaction showed that only three amino acid residues in the α and β chain contact the P6 peptide in the MHC-II groove and thus determine the specificity of this TCR to P6. Our data begin to reveal the vaccination-induced response to an ApoB epitope.

Transcriptional re-programming of insulin B-chain epitope-specific T-follicular helper cells into anti-diabetogenic T-regulatory type-1 cells

Systemic delivery of nanoparticles (NPs) coated with mono-specific autoimmune disease-relevant peptide-major histocompatibility complex class II (pMHCII) molecules can resolve organ inflammation in various disease models in a disease-specific manner without impairing normal immunity. These compounds invariably trigger the formation and systemic expansion of cognate pMHCII-specific T-regulatory type 1 (TR1) cells. By focusing on type 1 diabetes (T1D)-relevant pMHCII-NP types that display an epitope from the insulin B-chain bound to the same MHCII molecule (IA^g7) on three different registers, we show that pMHCII-NP-induced TR1 cells invariably co-exist with cognate T-Follicular Helper (TFH)-like cells of quasi-identical clonotypic composition and are oligoclonal, yet transcriptionally homogeneous. Furthermore, these three different TR1 specificities have similar diabetes reversal properties in vivo despite being uniquely reactive against the peptide MHCII-binding register displayed on the NPs. Thus, pMHCII-NP treatment using nanomedicines displaying different epitope specificities results in the simultaneous differentiation of multiple antigen-specific TFH-like cell clones into TR1-like cells that inherit the fine antigenic specificity of their precursors while acquiring a defined transcriptional immunoregulatory program.

CD8+ T Cells Trigger Auricular Dermatitis and Blepharitis in Mice after Zika Virus Infection in the Absence of CD4+ T Cells

Zika virus (ZIKV) became a public health concern when it re-emerged in 2015 owing to its ability to cause congenital deformities in the fetus and neurological complications in adults. Despite extensive data on protection, the interplay of protective and pathogenic adaptive immune responses toward ZIKV infection remains poorly understood. In this study, using a T-cell‒deficient mouse model that retains persistent ZIKV viral titers in the blood and organs, we show that the adoptive transfer of CD8+ T cells led to a significant reduction in viral load. This mouse model reveals that ZIKV can induce grossly visible auricular dermatitis and blepharitis, mediated by ZIKV-specific CD8+ T cells. Single-cell RNA sequencing of these causative CD8+ T cells from the ears shows an overactivated and elevated cytotoxic signature in mice with severe symptoms. Our results strongly suggest a role for CD8+ T-cell‒associated pathologies after ZIKV infection in CD4+ T-cell‒immunodeficient patients.

T-cell repertoire diversity: friend or foe for protective antitumor response?

Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.

Upcoming progress of transcriptomics studies on plants: An overview

Transcriptome sequencing or RNA-Sequencing is a high-resolution, sensitive and high-throughput next-generation sequencing (NGS) approach used to study non-model plants and other organisms. In other words, it is an assembly of RNA transcripts from individual or whole samples of functional and developmental stages. RNA-Seq is a significant technique for identifying gene predictions and mining functional analysis that improves gene ontology understanding mechanisms of biological processes, molecular functions, and cellular components, but there is limited information available on this topic. Transcriptomics research on different types of plants can assist researchers to understand functional genes in better ways and regulatory processes to improve breeding selection and cultivation practices. In recent years, several advancements in RNA-Seq technology have been made for the characterization of the transcriptomes of distinct cell types in biological tissues in an efficient manner. RNA-Seq technologies are briefly introduced and examined in terms of their scientific applications. In a nutshell, it introduces all transcriptome sequencing and analysis techniques, as well as their applications in plant biology research. This review will focus on numerous existing and forthcoming strategies for improving transcriptome sequencing technologies for functional gene mining in various plants using RNA- Seq technology, based on the principles, development, and applications.

The Value of Single-cell Technologies in Solid Organ Transplantation Studies

Single-cell technologies open up new opportunities to explore the behavior of cells at the individual level. For solid organ transplantation, single-cell technologies can provide in-depth insights into the underlying mechanisms of the immunological processes involved in alloimmune responses after transplantation by investigating the role of individual cells in tolerance and rejection. Here, we review the value of single-cell technologies, including cytometry by time-of-flight and single-cell RNA sequencing, in the context of solid organ transplantation research. Various applications of single-cell technologies are addressed, such as the characterization and identification of immune cell subsets involved in rejection or tolerance. In addition, we explore the opportunities for analyzing specific alloreactive T- or B-cell clones by linking phenotype data to T- or B-cell receptor data, and for distinguishing donor- from recipient-derived immune cells. Moreover, we discuss the use of single-cell technologies in biomarker identification and risk stratification, as well as the remaining challenges. Together, this review highlights that single-cell approaches contribute to a better understanding of underlying immunological mechanisms of rejection and tolerance, thereby potentially accelerating the development of new or improved therapies to avoid allograft rejection.

The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes

Blocking PD-1/PD-L1 signaling transforms cancer therapy and is assumed to unleash exhausted tumor-reactive CD8⁺ T cells in the tumor microenvironment (TME). However, recent studies have also indicated that the systemic tumor-reactive CD8⁺ T cells may respond to PD-1/PD-L1 immunotherapy. These discrepancies highlight the importance of further defining tumor-specific CD8⁺ T cell responders to PD-1/PD-L1 blockade. Here, using multiple preclinical tumor models, we revealed that a subset of tumor-specific CD8⁺ cells in the tumor draining lymph nodes (TdLNs) was not functionally exhausted but exhibited canonical memory characteristics. TdLN-derived tumor-specific memory (T_TSM) cells established memory-associated epigenetic program early during tumorigenesis. More importantly, TdLN-T_TSM cells exhibited superior anti-tumor therapeutic efficacy after adoptive transfer and were characterized as bona fide responders to PD-1/PD-L1 blockade. These findings highlight that TdLN-T_TSM cells could be harnessed to potentiate anti-tumor immunotherapy.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Motivation

Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression.

Results

We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

Availability and implementation

The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

New insights on the role of human leukocyte antigen complex in primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a rare autoimmune cholangiopathy. Genetic studies have shown that the strongest statistical association with PBC has been mapped in the human leukocyte antigen (HLA) locus, a highly polymorphic area that mostly contribute to the genetic variance of the disease. Furthermore, PBC presents high variability throughout different population groups, which may explain the different geoepidemiology of the disease. A major role in defining HLA genetic contribution has been given by genome-wide association studies (GWAS) studies; more recently, new technologies have been developed to allow a deeper understanding. The study of the altered peptides transcribed by genetic alterations also allowed the development of novel therapeutic strategies in the context of immunotolerance. This review summarizes what is known about the immunogenetics of PBC with a focus on the HLA locus, the different distribution of HLA alleles worldwide, and how HLA modifications are associated with the pathogenesis of PBC. Novel therapeutic strategies are also outlined.

Adenoviral-based vaccine promotes neoantigen-specific CD8+ T cell stemness and tumor rejection

Upon chronic antigen exposure, CD8⁺ T cells become exhausted, acquiring a dysfunctional state correlated with the inability to control infection or tumor progression. In contrast, stem-like CD8⁺ T progenitors maintain the ability to promote and sustain effective immunity. Adenovirus (Ad)-vectored vaccines encoding tumor neoantigens have been shown to eradicate large tumors when combined with anti-programmed cell death protein 1 (αPD-1) in murine models; however, the mechanisms and translational potential have not yet been elucidated. Here, we show that gorilla Ad vaccine targeting tumor neoepitopes enhances responses to αPD-1 therapy by improving immunogenicity and antitumor efficacy. Single-cell RNA sequencing demonstrated that the combination of Ad vaccine and αPD-1 increased the number of murine polyfunctional neoantigen-specific CD8⁺ T cells over αPD-1 monotherapy, with an accumulation of Tcf1⁺ stem-like progenitors in draining lymph nodes and effector CD8⁺ T cells in tumors. Combined T cell receptor (TCR) sequencing analysis highlighted a broader spectrum of neoantigen-specific CD8⁺ T cells upon vaccination compared to αPD-1 monotherapy. The translational relevance of these data is supported by results obtained in the first 12 patients with metastatic deficient mismatch repair (dMMR) tumors vaccinated with an Ad vaccine encoding shared neoantigens. Expansion and diversification of TCRs were observed in post-treatment biopsies of patients with clinical response, as well as an increase in tumor-infiltrating T cells with an effector memory signature. These findings indicate a promising mechanism to overcome resistance to PD-1 blockade by promoting immunogenicity and broadening the spectrum and magnitude of neoantigen-specific T cells infiltrating tumors.

Microenvironmental Landscape of Human Melanoma Brain Metastases in Response to Immune Checkpoint Inhibition

Melanoma-derived brain metastases (MBM) represent an unmet clinical need because central nervous system progression is frequently an end stage of the disease. Immune checkpoint inhibitors (ICI) provide a clinical opportunity against MBM; however, the MBM tumor microenvironment (TME) has not been fully elucidated in the context of ICI. To dissect unique elements of the MBM TME and correlates of MBM response to ICI, we collected 32 fresh MBM and performed single-cell RNA sequencing of the MBM TME and T-cell receptor clonotyping on T cells from MBM and matched blood and extracranial lesions. We observed myeloid phenotypic heterogeneity in the MBM TME, most notably multiple distinct neutrophil states, including an IL8-expressing population that correlated with malignant cell epithelial-to-mesenchymal transition. In addition, we observed significant relationships between intracranial T-cell phenotypes and the distribution of T-cell clonotypes intracranially and peripherally. We found that the phenotype, clonotype, and overall number of MBM-infiltrating T cells were associated with response to ICI, suggesting that ICI-responsive MBMs interact with peripheral blood in a manner similar to extracranial lesions. These data identify unique features of the MBM TME that may represent potential targets to improve clinical outcomes for patients with MBM.

B-Cell Receptor Repertoire Sequencing: Deeper Digging into the Mechanisms and Clinical Aspects of Immune-mediated Diseases

B cells play an essential role in adaptive immunity and are intimately correlated with pleiotropic immune-mediated diseases. Each B cell occupies a unique B cell receptor (BCR), and all BCRs throughout our body form “BCR repertoire.” With the development of sequencing technology and coupled bioinformatics, accumulating evidence indicates that BCR repertoire largely varies under physiological and pathological conditions. Therefore, comprehensive grasp of BCR repertoire will provide new insights into the pathogenesis of immune-mediated diseases and help exploit efficient diagnostic and treatment strategies. In this review, we start with an overview of BCR repertoire and related sequencing technologies and summarize their current applications in immune-mediated diseases. We also underscore the challenges of this emerging field and propose promising future directions in advancing BCR repertoire exploration.

Neoantigens in precision cancer immunotherapy: from identification to clinical applications

Immunotherapies targeting cancer neoantigens are safe, effective, and precise. Neoantigens can be identified mainly by genomic techniques such as next-generation sequencing and high-throughput single-cell sequencing; proteomic techniques such as mass spectrometry; and bioinformatics tools based on high-throughput sequencing data, mass spectrometry data, and biological databases. Neoantigen-related therapies are widely used in clinical practice and include neoantigen vaccines, neoantigen-specific CD8+ and CD4+ T cells, and neoantigen-pulsed dendritic cells. In addition, neoantigens can be used as biomarkers to assess immunotherapy response, resistance, and prognosis. Therapies based on neoantigens are an important and promising branch of cancer immunotherapy. Unremitting efforts are needed to unravel the comprehensive role of neoantigens in anti-tumor immunity and to extend their clinical application. This review aimed to summarize the progress in neoantigen research and to discuss its opportunities and challenges in precision cancer immunotherapy.

Epigenetic Memories in Hematopoietic Stem and Progenitor Cells

The recent development of next-generation sequencing (NGS) technologies has contributed to research into various biological processes. These novel NGS technologies have revealed the involvement of epigenetic memories in trained immunity, which are responses to transient stimulation and result in better responses to secondary challenges. Not only innate system cells, such as macrophages, monocytes, and natural killer cells, but also bone marrow hematopoietic stem cells (HSCs) have been found to gain memories upon transient stimulation, leading to the enhancement of responses to secondary challenges. Various stimuli, including microbial infection, can induce the epigenetic reprogramming of innate immune cells and HSCs, which can result in an augmented response to secondary stimulation. In this review, we introduce novel NGS technologies and their application to unraveling epigenetic memories that are key in trained immunity and summarize the recent findings in trained immunity. We also discuss our most recent finding regarding epigenetic memory in aged HSCs, which may be associated with the exposure of HSCs to aging-related stresses.

Peyer's patch TH17 cells are dispensable for gut IgA responses to oral immunization

T helper 17 (T_H17) cells located at the Peyer's patch (PP) inductive site and at the lamina propria effector site of the intestinal immune system are responsive to both pathogenic and commensal bacteria. Their plasticity to convert into follicular helper T (T_FH) cells has been proposed to be central to gut immunoglobulin A (IgA) responses. Here, we used an IL-17A fate reporter mouse and an MHC-II tetramer to analyze antigen-specific CD4⁺ T cell subsets and isolate them for single-cell RNA sequencing after oral immunization with cholera toxin and ovalbumin. We found a T_FH-dominated response with only rare antigen-specific T_H17 cells (<8%) in the PP. A clonotypic analysis provided little support that clonotypes were shared between T_FH and T_H17 cells, arguing against T_H17 plasticity as a major contributor to T_FH differentiation. Two mouse models of T_H17 deficiency confirmed that gut IgA responses to oral immunization do not require T_H17 cells, with CD4^CreRorc^fl/fl mice exhibiting normal germinal centers in PP and unperturbed total IgA production in the intestine.

An introduction to spatial transcriptomics for biomedical research

Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.

Antibody repertoire sequencing analysis

High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.

WAT3R: Recovery of T Cell Receptor Variable Regions From 3' Single-Cell RNA-Sequencing

SUMMARY

Diversity of the T cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and β chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells (PBMCs) from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T cell types, indicating an association between T cell clonotypes and differentiation states.

AVAILABILITY AND IMPLEMENTATION

The Workflow for Association of T cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r), and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress

Current single-cell RNA sequencing (scRNA-seq) methods with high cellular throughputs sacrifice full-transcript coverage and often sensitivity. Here we describe Smart-seq3xpress, which miniaturizes and streamlines the Smart-seq3 protocol to substantially reduce reagent use and increase cellular throughput. Smart-seq3xpress analysis of peripheral blood mononuclear cells resulted in a granular atlas complete with common and rare cell types. Compared with droplet-based single-cell RNA sequencing that sequences RNA ends, the additional full-transcript coverage revealed cell-type-associated isoform variation.

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

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