T cell fate and clonality inference from single-cell transcriptomes

Understanding the complexity of retina and pluripotent stem cell derived retinal organoids with single cell RNA sequencing: current progress, remaining challenges and future prospective

Single-cell sequencing technologies have emerged as a revolutionary tool with transformative new methods to profile genetic, epigenetic, spatial, and lineage information in individual cells. Single-cell RNA sequencing (scRNA-Seq) allows researchers to collect large datasets detailing the transcriptomes of individual cells in space and time and is increasingly being applied to reveal cellular heterogeneity in retinal development, normal physiology, and disease, and provide new insights into cell-type specific markers and signaling pathways. In recent years, scRNA-Seq datasets have been generated from retinal tissue and pluripotent stem cell-derived retinal organoids. Their cross-comparison enables staging of retinal organoids, identification of specific cells in developing and adult human neural retina and provides deeper insights into cell-type sub-specification and geographical differences. In this article, we review the recent rapid progress in scRNA-Seq analyses of retina and retinal organoids, the questions that remain unanswered and the technical challenges that need to be overcome to achieve consistent results that reflect the complexity, functionality, and interactions of all retinal cell types.

Immunology in the Era of Single-Cell Technologies

Immune cells are characterized by diversity, specificity, plasticity, and adaptability-properties that enable them to contribute to homeostasis and respond specifically and dynamically to the many threats encountered by the body. Single-cell technologies, including the assessment of transcriptomics, genomics, and proteomics at the level of individual cells, are ideally suited to studying these properties of immune cells. In this review we discuss the benefits of adopting single-cell approaches in studying underappreciated qualities of immune cells and highlight examples where these technologies have been critical to advancing our understanding of the immune system in health and disease.

Exploring and analysing single cell multi-omics data with VDJView

Background

Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.

Results

We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8⁺ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.

Conclusions

VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

A Bayesian framework for high-throughput T cell receptor pairing

MOTIVATION

The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs.

RESULTS

Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications.

AVAILABILITY AND IMPLEMENTATION

The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single cell RNA-seq reveals the landscape of tumor and infiltrating immune cells in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is one of the most malignant tumors in Southern China and southeast Asia, which is characterized by a dense lymphocyte infiltration and a poor prognosis. The emergence of single-cell sequencing represents a powerful tool to resolve tumor heterogeneity and delineate the complex communication among the tumor cells with neighboring stromal and immune cells in the tumor microenvironment (TME). Here, we performed single cell RNA-seq and analyzed tumor cells together with the infiltrating immune cells from three NPC tumor tissues. In our study, the malignant cells display the intra- and inter-tumoral heterogeneity among the individual patients. Analysis of the immune cells reveal the heterogeneous composition of the distinct immune cells and the various functional states of T cells in NPC tumors. Additionally, coupled with the reconstruct of the T cell receptor (TCR) sequences from immune cells full-length single-cell sequence data, we identify the diverse T cell clonotypes and expansion distribution in individual tumors. Overall, we firstly reveal the landscape of tumor and infiltrating immune cells in nasopharyngeal cancer. These results provide deeper insights on the mechanisms of tumor clearance by immune cells in the surrounding microenvironment, which will be helpful in improving the targeted and immune therapies for NPC.

Using single-cell technologies to map the human immune system - implications for nephrology

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology.

Single-cell alternative splicing analysis reveals dominance of single transcript variant

Alternative splicing contributes to the diversity of gene products by producing multiple transcript variants from one gene. Previous studies have revealed highly variable splicing patterns in single cells, but there is still a controversy in the understanding of the simultaneous expression of multiple transcript variants. Here we show that the dominance of a single transcript variant is a common phenomenon in single cells. We analyzed several single-cell RNA sequencing datasets and observed consistent results. Our results demonstrate that single cells tend to express one major transcript variant of a gene, and the diversity of transcript variants in cell populations mainly results from the heterogeneity of splicing pattern in single cells.

Single-Cell Sequencing of T cell Receptors: A Perspective on the Technological Development and Translational Application

T cells recognize peptides bound to major histocompatibility complex (MHC) class I and class II molecules at the cell surface. This recognition is accomplished by the expression of T cell receptors (TCR) which are required to be diverse and adaptable in order to accommodate the various and vast number of antigens presented on the MHCs. Thus, determining TCR repertoires of effector T cells is necessary to understand the immunological process in responding to cancer progression, infection, and autoimmune development. Furthermore, understanding the TCR repertoires will provide a solid framework to predict and test the antigen which is more critical in autoimmunity. However, it has been a technical challenge to sequence the TCRs and provide a conceptual context in correlation to the vast number of TCR repertoires in the immunological system. The exploding field of single-cell sequencing has changed how the repertoires are being investigated and analyzed. In this review, we focus on the biology of TCRs, TCR signaling and its implication in autoimmunity. We discuss important methods in bulk sequencing of many cells. Lastly, we explore the most pertinent platforms in single-cell sequencing and its application in autoimmunity.

Single-cell analysis of CD8 T lymphocyte diversity during adaptive immunity

An effective adaptive immune response to microbial infection relies on the generation of heterogeneous T lymphocyte fates and functions. CD8 T lymphocytes play a pivotal role in mediating immediate and long-term protective immune responses to intracellular pathogen infection. Systems-based analysis of the immune response to infection has begun to identify cell fate determinants and the molecular mechanisms underpinning CD8 T lymphocyte diversity at single-cell resolution. Resolving CD8 T lymphocyte heterogeneity during adaptive immunity highlights the advantages of single-cell technologies and computational approaches to better understand the ontogeny of CD8 T cellular diversity following infection. Future directions of integrating single-cell multiplex approaches capitalize on the importance of systems biology in the understanding of immune CD8 T cell differentiation and functional diversity. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Biological Mechanisms > Cell Fates.

Tracking the immune response with single-cell genomics

The immune system is composed of a diverse array of cell types, each with a specialized role in orchestrating the immune response to pathogens or cancer. Even within a single cell 'type,' individual cells can access a wide spectrum of differentiation and activation states, which reflect the physiological response of each cell to the tissue environment and immune stimuli. Thus, the cellular diversity of the immune system is inherently quite complex and understanding this complexity has greatly benefited from technologies that measure immune responses at single-cell resolution, in addition to the systems-level response as a whole. In this Commentary, we focus on recent work at the interface of immunology and single-cell genomics and highlight advances in technologies and their application to immune cells. In particular, we highlight recent single-cell genomic profiling studies of T cells, since somatic rearrangements in the T cell receptor (TCR) loci enable the tracking of clonal T cell responses through space and time. Finally, we discuss opportunities for future use of these technologies in understanding vaccination and the basis for effective vaccine-induced immunity.

Multimodal single-cell approaches shed light on T cell heterogeneity

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.

Bayesian Inference of Allelic Inclusion Rates in the Human T Cell Receptor Repertoire

A small population of αβ T cells is characterized by the expression of more than one unique T cell receptor (TCR); this outcome is the result of "allelic inclusion," that is, inclusion of both α- or β-chain alleles during V(D)J recombination. Limitations in single-cell sequencing technology, however, have precluded comprehensive enumeration of these dual receptor T cells. Here, we develop and experimentally validate a fully Bayesian inference model capable of reliably estimating the true rate of α and β TCR allelic inclusion across two different emulsion-barcoding single-cell sequencing platforms. We provide a database composed of over 51,000 previously unpublished allelic inclusion TCR sequence sets drawn from eight healthy individuals and show that allelic inclusion contributes a distinct and functionally important set of sequences to the human TCR repertoire. This database and a Python implementation of our statistical inference model are freely available at our Github repository (https://github.com/JasonACarter/Allelic_inclusion).

TCR sequencing paired with massively parallel 3' RNA-seq reveals clonotypic T cell signatures

High-throughput 3' single-cell RNA-sequencing (scRNA-seq) allows cost-effective, detailed characterization of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Here, we present a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 3'-barcoded scRNA-seq samples. This approach is compatible with common 3' scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with T cells sharing common TCRs from immunized mice and from patients with food allergy. We observed preferential phenotypes among subsets of expanded clonotypes, including type 2 helper CD4⁺ T cell (T_H2) states associated with food allergy. These results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology.

Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level

CD8⁺ T cells are important effectors of adaptive immunity against pathogens, tumors, and self antigens. Here, we asked how human cognate antigen-responsive CD8⁺ T cells and their receptors could be identified in unselected single-cell gene expression data. Single-cell RNA sequencing and qPCR of dye-labeled antigen-specific cells identified large gene sets that were congruently up- or downregulated in virus-responsive CD8⁺ T cells under different antigen presentation conditions. Combined expression of TNFRSF9, XCL1, XCL2, and CRTAM was the most distinct marker of virus-responsive cells on a single-cell level. Using transcriptomic data, we developed a machine learning-based classifier that provides sensitive and specific detection of virus-responsive CD8⁺ T cells from unselected populations. Gene response profiles of CD8⁺ T cells specific for the autoantigen islet-specific glucose-6-phosphatase catalytic subunit-related protein differed markedly from virus-specific cells. These findings provide single-cell gene expression parameters for comprehensive identification of rare antigen-responsive cells and T cell receptors.

scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions

MOTIVATION

Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq.

RESULTS

Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown 'novel' isoforms and highly computational efficiency compared to other tools.

AVAILABILITY AND IMPLEMENTATION

scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle

Motivation

The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. The vast repertoire and adaptive variation of BCR sequences due to V(D)J recombination and somatic hypermutation necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing presents the opportunity for simultaneous capture of paired BCR heavy and light chains and the transcriptomic signature.

Results

We developed VDJPuzzle, a novel bioinformatic tool that reconstructs productive, full-length B-cell receptor sequences of both heavy and light chains and extract somatic mutations on the VDJ region. VDJPuzzle successfully reconstructed BCRs from 100% (n=117) human and 96.5% (n=200) murine B cells. The reconstructed BCRs were successfully validated with single-cell Sanger sequencing.

Availability and implementation

VDJPuzzle is available at https://bitbucket.org/kirbyvisp/vdjpuzzle2.

Supplementary information

Supplementary data are available at Bioinformatics online.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer

High numbers of tissue-resident memory T (TRM) cells are associated with better clinical outcomes in cancer patients. However, the molecular characteristics that drive their efficient immune response to tumors are poorly understood. Here, single-cell and bulk transcriptomic analysis of TRM and non-TRM cells present in tumor and normal lung tissue from patients with lung cancer revealed that PD-1-expressing TRM cells in tumors were clonally expanded and enriched for transcripts linked to cell proliferation and cytotoxicity when compared with PD-1-expressing non-TRM cells. This feature was more prominent in the TRM cell subset coexpressing PD-1 and TIM-3, and it was validated by functional assays ex vivo and also reflected in their chromatin accessibility profile. This PD-1+TIM-3+ TRM cell subset was enriched in responders to PD-1 inhibitors and in tumors with a greater magnitude of CTL responses. These data highlight that not all CTLs expressing PD-1 are dysfunctional; on the contrary, TRM cells with PD-1 expression were enriched for features suggestive of superior functionality.

Targeted TCR Amplification from Single-Cell cDNA Libraries

Single-cell sequencing of TCR alleles enables determination of T cell specificity. Here we describe a sensitive protocol for targeted amplification of TCR CDR3 regions from single-cell full-length cDNA libraries. By exploiting the specificity of RNase H-dependent PCR (rhPCR), the protocol achieves amplification of TCR alleles and addition of cell barcodes in a single PCR step.

The role of thymic tolerance in CNS autoimmune disease

The contributions of the peripheral adaptive and innate immune systems to CNS autoimmunity have been extensively studied. However, the role of thymic selection in these conditions is much less well understood. The thymus is the primary lymphoid organ for the generation of T cells; thymic mechanisms ensure that cells with an overt autoreactive specificity are eliminated before they emigrate to the periphery and control the generation of thymic regulatory T cells. Evidence from animal studies demonstrates that thymic T cell selection is important for establishing tolerance to autoantigens. However, there is a considerable knowledge gap regarding the role of thymic selection in autoimmune conditions of the human CNS. In this Review, we critically examine the current body of experimental evidence for the contribution of thymic tolerance to CNS autoimmune diseases. An understanding of why dysfunction of either thymic or peripheral tolerance mechanisms rarely leads to CNS inflammation is currently lacking. We examine the potential of de novo T cell formation and thymic selection as novel therapeutic avenues and highlight areas for future study that are likely to make these targets the focus of future treatments.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Reconstructing B-cell receptor sequences from short-read single-cell RNA sequencing with BRAPeS

BRAPeS is a software for B-cell receptor reconstruction in single cells from very short (25–30 bp) read lengths, which achieves similar success rates and accuracy as applying other methods on long reads.

RNA sequencing of single B cells provides simultaneous measurements of the cell state and its antigen specificity as determined by the B-cell receptor (BCR). However, to uncover the latter, further reconstruction of the BCR sequence is needed. We present BRAPeS (“BCR Reconstruction Algorithm for Paired-end Single cells” ), an algorithm for reconstructing BCRs from short-read paired-end single-cell RNA sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25 bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared with long reads. BRAPeS is publicly available at the following link: https://github.com/YosefLab/BRAPeS.

T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities

T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and β chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.

Single-Cell Transcriptome Analysis of T Cells

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Single T Cell Sequencing Demonstrates the Functional Role of αβ TCR Pairing in Cell Lineage and Antigen Specificity

Although structural studies of individual T cell receptors (TCRs) have revealed important roles for both the α and β chain in directing MHC and antigen recognition, repertoire-level immunogenomic analyses have historically examined the β chain alone. To determine the amount of useful information about TCR repertoire function encoded within αβ pairings, we analyzed paired TCR sequences from nearly 100,000 unique CD4+ and CD8+ T cells captured using two different high-throughput, single-cell sequencing approaches. Our results demonstrate little overlap in the healthy CD4+ and CD8+ repertoires, with shared TCR sequences possessing significantly shorter CDR3 sequences corresponding to higher generation probabilities. We further utilized tools from information theory and machine learning to show that while α and β chains are only weakly associated with lineage, αβ pairings appear to synergistically drive TCR-MHC interactions. Vαβ gene pairings were found to be the TCR feature most informative of T cell lineage, supporting the existence of germline-encoded paired αβ TCR-MHC interaction motifs. Finally, annotating our TCR pairs using a database of sequences with known antigen specificities, we demonstrate that approximately a third of the T cells possess α and β chains that each recognize different known antigens, suggesting that αβ pairing is critical for the accurate inference of repertoire functionality. Together, these findings provide biological insight into the functional implications of αβ pairing and highlight the utility of single-cell sequencing in immunogenomics.

RNase H-dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis

RNase H-dependent PCR-enabled T-cell receptor sequencing (rhTCRseq) can be used to determine paired alpha/beta T-cell receptor (TCR) clonotypes in single cells or perform alpha and beta TCR repertoire analysis in bulk RNA samples. With the enhanced specificity of RNase H-dependent PCR (rhPCR), it achieves TCR-specific amplification and addition of dual-index barcodes in a single PCR step. For single cells, the protocol includes sorting of single cells into plates, generation of cDNA libraries, a TCR-specific amplification step, a second PCR on pooled sample to generate a sequencing library, and sequencing. In the bulk method, sorting and cDNA library steps are replaced with a reverse-transcriptase (RT) reaction that adds a unique molecular identifier (UMI) to each cDNA molecule to improve the accuracy of repertoire-frequency measurements. Compared to other methods for TCR sequencing, rhTCRseq has a streamlined workflow and the ability to analyze single cells in 384-well plates. Compared to TCR reconstruction from single-cell transcriptome sequencing data, it improves the success rate for obtaining paired alpha/beta information and ensures recovery of complete complementarity-determining region 3 (CDR3) sequences, a prerequisite for cloning/expression of discovered TCRs. Although it has lower throughput than droplet-based methods, rhTCRseq is well-suited to analysis of small sorted populations, especially when analysis of 96 or 384 single cells is sufficient to identify predominant T-cell clones. For single cells, sorting typically requires 2-4 h and can be performed days, or even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol takes ~3 d.

Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients

T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we have generated a RNA sequencing dataset of 11,138 T cells isolated from peripheral blood, adjacent normal and tumour tissues of 12 colorectal cancer (CRC) patients, including 4 with microsatellite instability (MSI). The dataset contained an expression profile of 10,805 T cells, as well as the full-length T cell receptor (TCR) sequences of 9,878 cells after quality control. To facilitate data mining of our T cell dataset, we developed a web-based application to deliver systematic interrogations and customizable functionalities ( http://crctcell.cancer-pku.cn/ ). Functioning with our dataset, the web tool enables the characterization of TILs based on both transcriptome and assembled TCR sequences at the single cell level, which will help unleash the potential value of our CRC T cell data resource.

The national blueprint for pregnancy/birth longitudinal cohorts to study factor VIII immunogenicity: NHLBI State of the Science (SOS) Workshop on factor VIII inhibitors

INTRODUCTION

Patients with haemophilia can develop inhibitors to exogenous coagulation factors. Some patients are tolerant to factor, while those who develop inhibitors do so early in life. Genetics and environmental factors are known to contribute to inhibitor risk. However, it is not yet possible to predict inhibitor formation or treatment responsiveness in individuals. We hypothesize that factors in the antenatal/neonatal period inform inhibitor risk development.

AIM

To consider the design of longitudinal studies beginning in the antenatal/neonatal period and the use of new technologies to better understand haemophilia inhibitors.

METHODS

A working group was formed for the NHLBI State of the Science Workshop: Factor VIII Inhibitors: Generating a National Blueprint for Future Research to solicit input from the US haemophilia community and international collaborators to consider design of pregnancy/birth longitudinal cohorts that leverage -omics, existing phenotypic data, and in silico modelling to study inhibitors.

RESULTS

An antenatal/neonatal longitudinal cohort should begin with enrolment of pregnant genetic carriers of haemophilia and span the at-risk period for inhibitor development in the child. Data and samples from the mother, placenta, neonate and young child can be obtained that are amenable to existing assays, genomics and other -omics studies. Data can inform in silico prediction and mathematical models.

CONCLUSION

A longitudinal study beginning before birth offers the unique opportunity to study factors that influence inhibitor development prior to exposure. Advances in -omics and computational biology can study complex phenotypes in this rare disease. This study could be accomplished through interdisciplinary efforts and patient community engagement.

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

High-throughput single-cell RNA sequencing is a powerful technique but only generates short reads from one end of a cDNA template, limiting the reconstruction of highly diverse sequences such as antigen receptors. To overcome this limitation, we combined targeted capture and long-read sequencing of T-cell-receptor (TCR) and B-cell-receptor (BCR) mRNA transcripts with short-read transcriptome profiling of barcoded single-cell libraries generated by droplet-based partitioning. We show that Repertoire and Gene Expression by Sequencing (RAGE-Seq) can generate accurate full-length antigen receptor sequences at nucleotide resolution, infer B-cell clonal evolution and identify alternatively spliced BCR transcripts. We apply RAGE-Seq to 7138 cells sampled from the primary tumor and draining lymph node of a breast cancer patient to track transcriptome profiles of expanded lymphocyte clones across tissues. Our results demonstrate that RAGE-Seq is a powerful method for tracking the clonal evolution from large numbers of lymphocytes applicable to the study of immunity, autoimmunity and cancer.

Single cell RNA sequencing generates short reads from one end of a template, providing incomplete transcript coverage and limiting identification of diverse sequences such as antigen receptors. Here the authors combine long read nanopore sequencing with short read profiling of barcoded libraries to generate full-length antigen receptor sequences.

Primary EBV Infection Induces an Acute Wave of Activated Antigen-Specific Cytotoxic CD4+ T Cells

Primary EBV infection drives highly cytotoxic virus-specific CD4⁺ T cell responses.

EBV-specific memory CD4⁺ T cells are polyfunctional but lack cytotoxic activity.

Acute EBV-specific CD4-CTLs differ transcriptionally from classical memory CD4-CTLs.

CD4⁺ T cells are essential for immune protection against viruses, yet their multiple roles remain ill-defined at the single-cell level in humans. Using HLA class II tetramers, we studied the functional properties and clonotypic architecture of EBV-specific CD4⁺ T cells in patients with infectious mononucleosis, a symptomatic manifestation of primary EBV infection, and in long-term healthy carriers of EBV. We found that primary infection elicited oligoclonal expansions of T_H1-like EBV-specific CD4⁺ T cells armed with cytotoxic proteins that responded immediately ex vivo to challenge with EBV-infected B cells. Importantly, these acutely generated cytotoxic CD4⁺ T cells were highly activated and transcriptionally distinct from classically described cytotoxic CD4⁺ memory T cells that accumulate during other persistent viral infections, including CMV and HIV. In contrast, EBV-specific memory CD4⁺ T cells displayed increased cytokine polyfunctionality but lacked cytotoxic activity. These findings suggested an important effector role for acutely generated cytotoxic CD4⁺ T cells that could potentially be harnessed to improve the efficacy of vaccines against EBV.

Single-cell analysis reveals T cell infiltration in old neurogenic niches

The mammalian brain contains neurogenic niches comprising neural stem cells (NSCs) and other cell types. Neurogenic niches become less functional with age, but how they change during aging remains unclear. Here we perform single cell RNA-sequencing of young and old neurogenic niches in mice. Analysis of 14,685 single cell transcriptomes reveals a decrease in activated NSCs, changes in endothelial cells and microglia, and infiltration of T cells in old neurogenic niches. Surprisingly, T cells in old brains are clonally expanded and generally distinct from those in old blood, suggesting they may experience specific antigens. T cells from old brains express interferon γ, and the subset of NSCs with a high interferon response shows decreased proliferation in vivo. Interestingly, T cells can inhibit NSC proliferation in co-cultures and in vivo, in part by secreting interferon. Our study reveals an interaction between T cells and NSCs in old brains, opening potential avenues to counter age-related decline in brain function.

Application of single-cell RNA sequencing methodologies in understanding haematopoiesis and immunology

The blood and immune system are characterised by utmost diversity in its cellular components. This heterogeneity can solely be resolved with the application of single-cell technologies that enable precise examination of cell-to-cell variation. Single-cell transcriptomics is continuously pushing forward our understanding of processes driving haematopoiesis and immune responses in physiological settings as well as in disease. Remarkably, in the last five years, a number of studies involving single-cell RNA sequencing (scRNA-seq) allowed the discovery of new immune cell types and revealed that haematopoiesis is a continuous rather than a stepwise process, thus challenging the classical haematopoietic lineage tree model. This review summarises the most recent studies which applied scRNA-seq to answer outstanding questions in the fields of haematology and immunology and discusses the present challenges and future directions.

Single-Cell High-Throughput Technologies in Cerebrospinal Fluid Research and Diagnostics

High-throughput single-cell technologies have recently emerged as essential tools in biomedical research with great potential for clinical pathology when studying liquid and solid biopsies. We provide an update on current single-cell methods in cerebrospinal fluid research and diagnostics, focusing on high-throughput cell-type specific proteomic and genomic technologies. Proteomic methods comprising flow cytometry and mass cytometry as well as genomic approaches including immune cell repertoire and single-cell transcriptomic studies are critically reviewed and future directions discussed.

Human Dendritic Cell Subsets, Ontogeny, and Impact on HIV Infection

Dendritic cells (DCs) play important roles in orchestrating host immunity against invading pathogens, representing one of the first responders to infection by mucosal invaders. From their discovery by Ralph Steinman in the 1970s followed shortly after with descriptions of their in vivo diversity and distribution by Derek Hart, we are still continuing to progressively elucidate the spectrum of DCs present in various anatomical compartments. With the power of high-dimensional approaches such as single-cell sequencing and multiparameter cytometry, recent studies have shed new light on the identities and functions of DC subtypes. Notable examples include the reclassification of plasmacytoid DCs as purely interferon-producing cells and re-evaluation of intestinal conventional DCs and macrophages as derived from monocyte precursors. Collectively, these observations have changed how we view these cells not only in steady-state immunity but also during disease and infection. In this review, we will discuss the current landscape of DCs and their ontogeny, and how this influences our understanding of their roles during HIV infection.

Inferring population dynamics from single-cell RNA-sequencing time series data

Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.

Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics

Lineage tracing provides key insights into the fate of individual cells in complex organisms. Although effective genetic labeling approaches are available in model systems, in humans, most approaches require detection of nuclear somatic mutations, which have high error rates, limited scale, and do not capture cell state information. Here, we show that somatic mutations in mtDNA can be tracked by single-cell RNA or assay for transposase accessible chromatin (ATAC) sequencing. We leverage somatic mtDNA mutations as natural genetic barcodes and demonstrate their utility as highly accurate clonal markers to infer cellular relationships. We track native human cells both in vitro and in vivo and relate clonal dynamics to gene expression and chromatin accessibility. Our approach should allow clonal tracking at a 1,000-fold greater scale than with nuclear genome sequencing, with simultaneous information on cell state, opening the way to chart cellular dynamics in human health and disease.

Deciphering Human Tumor Biology by Single-Cell Expression Profiling

Human tumors are complex ecosystems where diverse cancer and noncancer cells interact to determine tumor biology and response to therapies. Genomic and transcriptomic methods have traditionally profiled these intricate ecosystems as bulk samples, thereby masking individual cellular programs and the variability among them. Recent advances in single-cell profiling have paved the way for studying tumors at the resolution of individual cells, providing a compelling strategy to bridge gaps in our understanding of human tumors. Here, we review methodologies for single-cell expression profiling of tumors and the initial studies deploying them in clinical contexts. We highlight how these studies uncover new biology and provide insights into drug resistance, stem cell programs, metastasis, and tumor classifications. We also discuss areas of technology development in single-cell genomics that provide new tools to address key questions in cancer biology. These emerging studies and technologies have the potential to revolutionize our understanding and management of human malignancies.

Genesis of the αβ T-cell receptor

The T-cell (TCR) repertoire relies on the diversity of receptors composed of two chains, called α and β, to recognize pathogens. Using results of high throughput sequencing and computational chain-pairing experiments of human TCR repertoires, we quantitively characterize the αβ generation process. We estimate the probabilities of a rescue recombination of the β chain on the second chromosome upon failure or success on the first chromosome. Unlike β chains, α chains recombine simultaneously on both chromosomes, resulting in correlated statistics of the two genes which we predict using a mechanistic model. We find that ∼35% of cells express both α chains. Altogether, our statistical analysis gives a complete quantitative mechanistic picture that results in the observed correlations in the generative process. We learn that the probability to generate any TCRαβ is lower than 10(-12) and estimate the generation diversity and sharing properties of the αβ TCR repertoire.

Genomic correlates of response to immune checkpoint blockade

Despite impressive durable responses, immune checkpoint inhibitors do not provide a long-term benefit to the majority of patients with cancer. Understanding genomic correlates of response and resistance to checkpoint blockade may enhance benefits for patients with cancer by elucidating biomarkers for patient stratification and resistance mechanisms for therapeutic targeting. Here we review emerging genomic markers of checkpoint blockade response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways. Compelling evidence also points to a role for T cell functionality, checkpoint regulators, chromatin modifiers, and copy-number alterations in mediating selective response to immune checkpoint blockade. Ultimately, efforts to contextualize genomic correlates of response into the larger understanding of tumor immune biology will build a foundation for the development of novel biomarkers and therapies to overcome resistance to checkpoint blockade.

Revolutionizing immunology with single-cell RNA sequencing

The immune system is composed of a complex hierarchy of cell types that protect the organism against disease and maintain homeostasis. Identifying heterogeneity of immune cells is the key to understanding the immune system. Advanced single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing our ability to study immunology. By measuring transcriptomes at the single-cell level, scRNA-seq enables identification of cellular heterogeneity in far greater detail than conventional methods. In this review, we introduce the existing scRNA-seq technologies and present their strengths and weaknesses. We also discuss potential applications and future innovations of scRNA-seq in immunology.

Casting a wider net: Immunosurveillance by nonclassical MHC molecules

Most studies of T lymphocytes focus on recognition of classical major histocompatibility complex (MHC) class I or II molecules presenting oligopeptides, yet there are numerous variations and exceptions of biological significance based on recognition of a wide variety of nonclassical MHC molecules. These include αβ and γδ T cells that recognize different class Ib molecules (CD1, MR-1, HLA-E, G, F, et al.) that are nearly monomorphic within a given species. Collectively, these T cells can be considered “unconventional,” in part because they recognize lipids, metabolites, and modified peptides. Unlike classical MHC-specific cells, unconventional T cells generally exhibit limited T-cell antigen receptor (TCR) repertoires and often produce innate immune cell-like rapid effector responses. Exploiting this system in new generation vaccines for human immunodeficiency virus (HIV), tuberculosis (TB), other infectious agents, and cancer was the focus of a recent workshop, “Immune Surveillance by Non-classical MHC Molecules: Improving Diversity for Antigens,” sponsored by the National Institute of Allergy and Infectious Diseases. Here, we summarize salient points presented regarding the basic immunobiology of unconventional T cells, recent advances in methodologies to measure unconventional T-cell activity in diseases, and approaches to harness their considerable clinical potential.

Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation

Non-lymphoid tissues (NLTs) harbor a pool of adaptive immune cells with largely unexplored phenotype and development. We used single-cell RNA-seq to characterize 35,000 CD4+ regulatory (Treg) and memory (Tmem) T cells in mouse skin and colon, their respective draining lymph nodes (LNs) and spleen. In these tissues, we identified Treg cell subpopulations with distinct degrees of NLT phenotype. Subpopulation pseudotime ordering and gene kinetics were consistent in recruitment to skin and colon, yet the initial NLT-priming in LNs and the final stages of NLT functional adaptation reflected tissue-specific differences. Predicted kinetics were recapitulated using an in vivo melanoma-induction model, validating key regulators and receptors. Finally, we profiled human blood and NLT Treg and Tmem cells, and identified cross-mammalian conserved tissue signatures. In summary, we describe the relationship between Treg cell heterogeneity and recruitment to NLTs through the combined use of computational prediction and in vivo validation.