Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Reprogramming Müller glia to regenerate ganglion-like cells in adult mouse retina with developmental transcription factors

Many neurodegenerative diseases cause degeneration of specific types of neurons. For example, glaucoma leads to death of retinal ganglion cells, leaving other neurons intact. Neurons are not regenerated in the adult mammalian central nervous system. However, in nonmammalian vertebrates, glial cells spontaneously reprogram into neural progenitors and replace neurons after injury. We have recently developed strategies to stimulate regeneration of functional neurons in the adult mouse retina by overexpressing the proneural factor Ascl1 in Müller glia. Here, we test additional transcription factors (TFs) for their ability to direct regeneration to particular types of retinal neurons. We engineered mice to express different combinations of TFs in Müller glia, including Ascl1, Pou4f2, Islet1, and Atoh1. Using immunohistochemistry, single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and electrophysiology, we find that retinal ganglion-like cells can be regenerated in the damaged adult mouse retina in vivo with targeted overexpression of developmental retinal ganglion cell TFs.

Primate-specific transposable elements shape transcriptional networks during human development

The human genome contains more than 4.5 million inserts derived from transposable elements (TEs), the result of recurrent waves of invasion and internal propagation throughout evolution. For new TE copies to be inherited, they must become integrated in the genome of the germline or pre-implantation embryo, which requires that their source TE be expressed at these stages. Accordingly, many TEs harbor DNA binding sites for the pluripotency factors OCT4, NANOG, SOX2, and KLFs and are transiently expressed during embryonic genome activation. Here, we describe how many primate-restricted TEs have additional binding sites for lineage-specific transcription factors driving their expression during human gastrulation and later steps of fetal development. These TE integrants serve as lineage-specific enhancers fostering the transcription, amongst other targets, of KRAB-zinc finger proteins (KZFPs) of comparable evolutionary age, which in turn corral the activity of TE-embedded regulatory sequences in a similarly lineage-restricted fashion. Thus, TEs and their KZFP controllers play broad roles in shaping transcriptional networks during early human development.

High-content CRISPR screening in tumor immunology

CRISPR screening is a powerful tool that links specific genetic alterations to corresponding phenotypes, thus allowing for high-throughput identification of novel gene functions. Pooled CRISPR screens have enabled discovery of innate and adaptive immune response regulators in the setting of viral infection and cancer. Emerging methods couple pooled CRISPR screens with parallel high-content readouts at the transcriptomic, epigenetic, proteomic, and optical levels. These approaches are illuminating cancer immune evasion mechanisms as well as nominating novel targets that augment T cell activation, increase T cell infiltration into tumors, and promote enhanced T cell cytotoxicity. This review details recent methodological advances in high-content CRISPR screens and highlights the impact this technology is having on tumor immunology.

Divergent clonal differentiation trajectories of T cell exhaustion

Chronic antigen exposure during viral infection or cancer promotes an exhausted T cell (Tex) state with reduced effector function. However, whether all antigen-specific T cell clones follow the same Tex differentiation trajectory remains unclear. Here, we generate a single-cell multiomic atlas of T cell exhaustion in murine chronic viral infection that redefines Tex phenotypic diversity, including two late-stage Tex subsets with either a terminal exhaustion (Texterm) or a killer cell lectin-like receptor-expressing cytotoxic (TexKLR) phenotype. We use paired single-cell RNA and T cell receptor sequencing to uncover clonal differentiation trajectories of Texterm-biased, TexKLR-biased or divergent clones that acquire both phenotypes. We show that high T cell receptor signaling avidity correlates with Texterm, whereas low avidity correlates with effector-like TexKLR fate. Finally, we identify similar clonal differentiation trajectories in human tumor-infiltrating lymphocytes. These findings reveal clonal heterogeneity in the T cell response to chronic antigen that influences Tex fates and persistence.

Human T lymphocytes at tumor sites

CD4⁺ and CD8⁺ T lymphocytes mediate most of the adaptive immune response against tumors. Naïve T lymphocytes specific for tumor antigens are primed in lymph nodes by dendritic cells. Upon activation, antigen-specific T cells proliferate and differentiate into effector cells that migrate out of peripheral blood into tumor sites in an attempt to eliminate cancer cells. After accomplishing their function, most effector T cells die in the tissue, while a small fraction of antigen-specific T cells persist as long-lived memory cells, circulating between peripheral blood and lymphoid tissues, to generate enhanced immune responses when re-encountering the same antigen. A subset of memory T cells, called resident memory T (T_RM) cells, stably resides in non-lymphoid peripheral tissues and may provide rapid immunity independently of T cells recruited from blood. Being adapted to the tissue microenvironment, T_RM cells are potentially endowed with the best features to protect against the reemergence of cancer cells. However, when tumors give clinical manifestation, it means that tumor cells have evaded immune surveillance, including that of T_RM cells. Here, we review the current knowledge as to how T_RM cells are generated during an immune response and then maintained in non-lymphoid tissues. We then focus on what is known about the role of CD4⁺ and CD8⁺ T_RM cells in antitumor immunity and their possible contribution to the efficacy of immunotherapy. Finally, we highlight some open questions in the field and discuss how new technologies may help in addressing them.

Single-cell ATAC-seq maps the comprehensive and dynamic chromatin accessibility landscape of CAR-T cell dysfunction

Chimeric antigen receptor T cells (CAR-T) therapy has achieved remarkable therapeutic success in treating a variety of hematopoietic malignancies. However, the high relapse rate and poor in vivo persistence, partially caused by CAR-T cell exhaustion, are still important barriers against CAR-T therapy. It remains largely elusive on the mechanisms of CAR-T exhaustion and how to attenuate exhaustion to achieve better therapeutic efficacy. In this study, we initially observed that CAR-T cells showed rapid differentiation and increased exhaustion after co-culture with tumor cells in vitro, and then performed single-cell ATAC-seq to depict the comprehensive and dynamic landscape of chromatin accessibility of CAR-T cells during tumor cell stimulation. Analyses of differential chromatin accessible regions and motif accessibility revealed that TFs were distinct in each cell type and reconstituted a coordinated regulatory network to drive CAR-T exhaustion. Furthermore, we performed scATAC-seq in patient-derived CAR-T cells and identified BATF and IRF4 as pivotal regulators in CAR-T cell exhaustion. Finally, knockdown of BATF or IRF4 enhanced the killing ability, inhibited exhaustion, and prolonged the persistence of CAR-T cells in vivo. Together, our study unraveled the epigenetic regulatory mechanisms of CAR-T exhaustion and provided new insights into CAR-T engineering to achieve better clinical treatment benefits.

Steroid hormone regulation of immune responses in cancer

Steroid hormones are derived from cholesterol and can be classified into sex hormones (estrogens, androgens, progesterone) that are primarily synthesized in the gonads and adrenal hormones (glucocorticoids and mineralocorticoids) that are primarily synthesized in the adrenal gland. Although, it has long been known that steroid hormones have potent effects on the immune system, recent studies have led to renewed interest in their role in regulating anti-tumor immunity. Extra-glandular cells, such as epithelial cells and immune cells, have been shown to synthesize glucocorticoids and thereby modulate immune responses in the tumor microenvironment. Additionally, new insight into the role of androgens on immune cell responses have shed light on mechanisms underpinning the observed sex bias in cancer survival outcomes. Here, we review the role of steroid hormones, specifically glucocorticoids and androgens, in regulating anti-tumor immunity and discuss how their modulation could pave the way for designing novel therapeutic strategies to improve anti-tumor immune responses.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

The role of epigenetics in T-cell lymphoma

Malignant lymphomas are a group of diseases with epigenomic abnormalities fundamental to pathogenesis and pathophysiology. They are characterized by a high frequency of abnormalities related to DNA methylation regulators (DNMT3A, TET2, IDH2, etc.) and histone modifiers (EZH2, HDAC, KMT2D/MLL2, CREBBP, EP300, etc.). These epigenomic abnormalities directly amplify malignant clones. They also originate from a hematopoietic stem cell-derived cell lineage triggered by epigenomic changes. These characteristics are linked to their high affinity for epigenomic therapies. Hematology has led disease epigenetics in the areas of basic research, clinical research, and drug discovery. However, epigenomic regulation is generally recognized as a complex system, and gaps exist between basic and clinical research. To provide an overview of the status and importance of epigenomic abnormalities in malignant lymphoma, this review first summarizes the concept and essential importance of the epigenome, then outlines the current status and future outlook of epigenomic abnormalities in malignant lymphomas.

Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC

The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs.

Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.

ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells

Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Single-cell gene regulation network inference by large-scale data integration

Single-cell ATAC-seq (scATAC-seq) has proven to be a state-of-art approach to investigating gene regulation at the single-cell level. However, existing methods cannot precisely uncover cell-type-specific binding of transcription regulators (TRs) and construct gene regulation networks (GRNs) in single-cell. ChIP-seq has been widely used to profile TR binding sites in the past decades. Here, we developed SCRIP, an integrative method to infer single-cell TR activity and targets based on the integration of scATAC-seq and a large-scale TR ChIP-seq reference. Our method showed improved performance in evaluating TR binding activity compared to the existing motif-based methods and reached a higher consistency with matched TR expressions. Besides, our method enables identifying TR target genes as well as building GRNs at the single-cell resolution based on a regulatory potential model. We demonstrate SCRIP's utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems. SCRIP is freely available at https://github.com/wanglabtongji/SCRIP.

Fundamental and practical approaches for single-cell ATAC-seq analysis

Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Single-cell methods for measuring chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) are rapidly evolving, but tools to integrate data and infer gene-regulatory relationships remain limited. Here we generate multi-omics data of resting and stimulated human blood cells and present a new computational framework for constructing gene-regulatory networks (GRNs). Specifically, we describe functional inference of gene regulation (FigR), a workflow to (1) pair scATAC-seq with scRNA-seq, (2) connect cis-regulatory elements to target genes, and (3) identify TF-gene relationships.

Modeling human extraembryonic mesoderm cells using naive pluripotent stem cells

A hallmark of primate postimplantation embryogenesis is the specification of extraembryonic mesoderm (EXM) before gastrulation, in contrast to rodents where this tissue is formed only after gastrulation. Here, we discover that naive human pluripotent stem cells (hPSCs) are competent to differentiate into EXM cells (EXMCs). EXMCs are specified by inhibition of Nodal signaling and GSK3B, are maintained by mTOR and BMP4 signaling activity, and their transcriptome and epigenome closely resemble that of human and monkey embryo EXM. EXMCs are mesenchymal, can arise from an epiblast intermediate, and are capable of self-renewal. Thus, EXMCs arising via primate-specific specification between implantation and gastrulation can be modeled in vitro. We also find that most of the rare off-target cells within human blastoids formed by triple inhibition (Kagawa et al., 2021) correspond to EXMCs. Our study impacts our ability to model and study the molecular mechanisms of early human embryogenesis and related defects.

Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer

Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework’s key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN’s ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data.

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Integrating data from multiple modalities into one analysis

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Using data geometry to regularize cycle-consistent GANs

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Quantifying uncertainty through noise augmentation

Biological experimental data are increasingly being generated along multiple different axes, with new and more complex technologies specializing in particular measurements being developed every year. Measuring a single subject or system with multiple specialized data-collecting tools creates a natural interest in integrating the results of these individual instruments to form a single unified view. The model introduced here presents a computational technique designed for this purpose. With the single-cell multi-modal GAN (scMMGAN), there is an opportunity to measure along many different omic directions and synthesize the information from each into one larger understanding of the system under study.

scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain important challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single-cell multiome datasets, including cell clustering, scATAC profile denoising, data integration across assays and transcription factor activity inference.

Natural and Experimental Rewiring of Gene Regulatory Regions

The successful development and ongoing functioning of complex organisms depend on the faithful execution of the genetic code. A critical step in this process is the correct spatial and temporal expression of genes. The highly orchestrated transcription of genes is controlled primarily by cis-regulatory elements: promoters, enhancers, and insulators. The medical importance of this key biological process can be seen by the frequency with which mutations and inherited variants that alter cis-regulatory elements lead to monogenic and complex diseases and cancer. Here, we provide an overview of the methods available to characterize and perturb gene regulatory circuits. We then highlight mechanisms through which regulatory rewiring contributes to disease, and conclude with a perspective on how our understanding of gene regulation can be used to improve human health.

Time-resolved transcriptomes reveal diverse B cell fate trajectories in the early response to Epstein-Barr virus infection

Epstein-Barr virus infection of B lymphocytes elicits diverse host responses via well-adapted transcriptional control dynamics. Consequently, this host-pathogen interaction provides a powerful system to explore fundamental processes leading to consensus fate decisions. Here, we use single-cell transcriptomics to construct a genome-wide multistate model of B cell fates upon EBV infection. Additional single-cell data from human tonsils reveal correspondence of model states to analogous in vivo phenotypes within secondary lymphoid tissue, including an EBV⁺ analog of multipotent activated precursors that can yield early memory B cells. These resources yield exquisitely detailed perspectives of the transforming cellular landscape during an oncogenic viral infection that simulates antigen-induced B cell activation and differentiation. Thus, they support investigations of state-specific EBV-host dynamics, effector B cell fates, and lymphomagenesis. To demonstrate this potential, we identify EBV infection dynamics in FCRL4⁺/TBX21⁺ atypical memory B cells that are pathogenically associated with numerous immune disorders.

High-dimensional investigation of the cerebrospinal fluid to explore and monitor CNS immune responses

The cerebrospinal fluid (CSF) features a unique immune cell composition and is in constant contact with the brain borders, thus permitting insights into the brain to diagnose and monitor diseases. Recently, the meninges, which are filled with CSF, were identified as a neuroimmunological interface, highlighting the potential of exploring central nervous system (CNS) immunity by studying CNS border compartments. Here, we summarize how single-cell transcriptomics of such border compartments advance our understanding of neurological diseases, the challenges that remain, and what opportunities novel multi-omic methods offer. Single-cell transcriptomics studies have detected cytotoxic CD4⁺ T cells and clonally expanded T and B cells in the CSF in the autoimmune disease multiple sclerosis; clonally expanded pathogenic CD8⁺ T cells were found in the CSF and in the brain adjacent to β-amyloid plaques of dementia patients; in patients with brain metastases, CD8⁺ T cell clonotypes were shared between the brain parenchyma and the CSF and persisted after therapy. We also outline how novel multi-omic approaches permit the simultaneous measurements of gene expression, chromatin accessibility, and protein in the same cells, which remain to be explored in the CSF. This calls for multicenter initiatives to create single-cell atlases, posing challenges in integrating patients and modalities across centers. While high-dimensional analyses of CSF cells are challenging, they hold potential for personalized medicine by better resolving heterogeneous diseases and stratifying patients.

"Demystifying non-coding GWAS variants: an overview of computational tools and methods."

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

Transcription factors TCF-1 and GATA3 are key factors for the epigenetic priming of early innate lymphoid progenitors toward distinct cell fates

The differentiation of innate lymphoid cells (ILCs) from hematopoietic stem cells needs to go through several multipotent progenitor stages. However, it remains unclear whether the fates of multipotent progenitors are predefined by epigenetic states. Here, we report the identification of distinct accessible chromatin regions in all lymphoid progenitors (ALPs), EILPs, and ILC precursors (ILCPs). Single-cell MNase-seq analyses revealed that EILPs contained distinct subpopulations epigenetically primed toward either dendritic cell lineages or ILC lineages. We found that TCF-1 and GATA3 co-bound to the lineage-defining sites for ILCs (LDS-Is), whereas PU.1 binding was enriched in the LDSs for alternative dendritic cells (LDS-As). TCF-1 and GATA3 were indispensable for the epigenetic priming of LDSs at the EILP stage. Our results suggest that the multipotency of progenitor cells is defined by the existence of a heterogeneous population of cells epigenetically primed for distinct downstream lineages, which are regulated by key transcription factors.

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

PD-1 directed immunotherapy alters Tfh and humoral immune responses to seasonal influenza vaccine

Anti-programmed death-1 (anti-PD-1) immunotherapy reinvigorates CD8 T cell responses in patients with cancer but PD-1 is also expressed by other immune cells, including follicular helper CD4 T cells (Tfh) which are involved in germinal centre responses. Little is known, however, about the effects of anti-PD-1 immunotherapy on noncancer immune responses in humans. To investigate this question, we examined the impact of anti-PD-1 immunotherapy on the Tfh-B cell axis responding to unrelated viral antigens. Following influenza vaccination, a subset of adults receiving anti-PD-1 had more robust circulating Tfh responses than adults not receiving immunotherapy. PD-1 pathway blockade resulted in transcriptional signatures of increased cellular proliferation in circulating Tfh and responding B cells compared with controls. These latter observations suggest an underlying change in the Tfh-B cell and germinal centre axis in a subset of immunotherapy patients. Together, these results demonstrate dynamic effects of anti-PD-1 therapy on influenza vaccine responses and highlight analytical vaccination as an approach that may reveal underlying immune predisposition to adverse events.

Unraveling the Complexity of Liver Disease One Cell at a Time

The human liver is a complex organ made up of multiple specialized cell types that carry out key physiological functions. An incomplete understanding of liver biology limits our ability to develop therapeutics to prevent chronic liver diseases, liver cancers, and death as a result of organ failure. Recently, single-cell modalities have expanded our understanding of the cellular phenotypic heterogeneity and intercellular cross-talk in liver health and disease. This review summarizes these findings and looks forward to highlighting new avenues for the application of single-cell genomics to unravel unknown pathogenic pathways and disease mechanisms for the development of new therapeutics targeting liver pathology. As these technologies mature, their integration into clinical data analysis will aid in patient stratification and in developing treatment plans for patients suffering from liver disease.

Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

Joint analysis of scATAC-seq datasets using epiConv

Background

Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information.

Results

Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable.

Conclusions

In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04858-w.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis

Background

Synovial fibroblasts (SFs) are specialized cells of the synovium that provide nutrients and lubricants for the proper function of diarthrodial joints. Recent evidence appreciates the contribution of SF heterogeneity in arthritic pathologies. However, the normal SF profiles and the molecular networks that govern the transition from homeostatic to arthritic SF heterogeneity remain poorly defined.

Methods

We applied a combined analysis of single-cell (sc) transcriptomes and epigenomes (scRNA-seq and scATAC-seq) to SFs derived from naïve and hTNFtg mice (mice that overexpress human TNF, a murine model for rheumatoid arthritis), by employing the Seurat and ArchR packages. To identify the cellular differentiation lineages, we conducted velocity and trajectory analysis by combining state-of-the-art algorithms including scVelo, Slingshot, and PAGA. We integrated the transcriptomic and epigenomic data to infer gene regulatory networks using ArchR and custom-implemented algorithms. We performed a canonical correlation analysis-based integration of murine data with publicly available datasets from SFs of rheumatoid arthritis patients and sought to identify conserved gene regulatory networks by utilizing the SCENIC algorithm in the human arthritic scRNA-seq atlas.

Results

By comparing SFs from healthy and hTNFtg mice, we revealed seven homeostatic and two disease-specific subsets of SFs. In healthy synovium, SFs function towards chondro- and osteogenesis, tissue repair, and immune surveillance. The development of arthritis leads to shrinkage of homeostatic SFs and favors the emergence of SF profiles marked by Dkk3 and Lrrc15 expression, functioning towards enhanced inflammatory responses and matrix catabolic processes. Lineage inference analysis indicated that specific Thy1+ SFs at the root of trajectories lead to the intermediate Thy1+/Dkk3+/Lrrc15+ SF states and culminate in a destructive and inflammatory Thy1− SF identity. We further uncovered epigenetically primed gene programs driving the expansion of these arthritic SFs, regulated by NFkB and new candidates, such as Runx1. Cross-species analysis of human/mouse arthritic SF data determined conserved regulatory and transcriptional networks.

Conclusions

We revealed a dynamic SF landscape from health to arthritis providing a functional genomic blueprint to understand the joint pathophysiology and highlight the fibroblast-oriented therapeutic targets for combating chronic inflammatory and destructive arthritic disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01081-3.

A reference single-cell regulomic and transcriptomic map of cynomolgus monkeys

Non-human primates are attractive laboratory animal models that accurately reflect both developmental and pathological features of humans. Here we present a compendium of cell types across multiple organs in cynomolgus monkeys (Macaca fascicularis) using both single-cell chromatin accessibility and RNA sequencing data. The integrated cell map enables in-depth dissection and comparison of molecular dynamics, cell-type compositions and cellular heterogeneity across multiple tissues and organs. Using single-cell transcriptomic data, we infer pseudotime cell trajectories and cell-cell communications to uncover key molecular signatures underlying their cellular processes. Furthermore, we identify various cell-specific cis-regulatory elements and construct organ-specific gene regulatory networks at the single-cell level. Finally, we perform comparative analyses of single-cell landscapes among mouse, monkey and human. We show that cynomolgus monkey has strikingly higher degree of similarities in terms of immune-associated gene expression patterns and cellular communications to human than mouse. Taken together, our study provides a valuable resource for non-human primate cell biology.

Non-human primates are attractive laboratory animal models that can accurately reflect some developmental and pathological features of humans. Here the authors chart a reference cell map of cynomolgus monkeys using both scATAC-seq and scRNA-seq data across multiple organs, providing insights into the molecular dynamics and cellular heterogeneity of this organism.

BCL6-dependent TCF-1+ progenitor cells maintain effector and helper CD4+ T cell responses to persistent antigen

Soon after activation, CD4⁺ T cells are segregated into BCL6⁺ follicular helper (Tfh) and BCL6^- effector (Teff) T cells. Here, we explored how these subsets are maintained during chronic antigen stimulation using the mouse chronic LCMV infection model. Using single cell-transcriptomic and epigenomic analyses, we identified a population of PD-1⁺ TCF-1⁺ CD4⁺ T cells with memory-like features. TCR clonal tracing and adoptive transfer experiments demonstrated that these cells have self-renewal capacity and continue to give rise to both Teff and Tfh cells, thus functioning as progenitor cells. Conditional deletion experiments showed Bcl6-dependent development of these progenitors, which were essential for sustaining antigen-specific CD4⁺ T cell responses to chronic infection. An analogous CD4⁺ T cell population developed in draining lymph nodes in response to tumors. Our study reveals the heterogeneity and plasticity of CD4⁺ T cells during persistent antigen exposure and highlights their population dynamics through a stable, bipotent intermediate state.

A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs in animal models. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we developed a combinatorial index and dual PCR barcode strategy to profile the Arabidopsis thaliana root single-cell epigenome without any specialized equipment. We generated chromatin accessibility profiles for 13 576 root nuclei with an average of 12 784 unique Tn5 integrations per cell. Integration of the single-cell assay for transposase-accessible chromatin sequencing and RNA sequencing data sets enabled the identification of 24 cell clusters with unique transcription, chromatin, and cis-regulatory signatures. Comparison with single-cell data generated using the commercial microfluidic platform from 10X Genomics revealed that this low-cost combinatorial index method is capable of unbiased identification of cell-type-specific chromatin accessibility. We anticipate that, by removing cost, instrumentation, and other technical obstacles, this method will be a valuable tool for routine investigation of single-cell epigenomes and provide new insights into plant growth and development and plant interactions with the environment.

Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence

T cell exhaustion limits antitumor immunity, but the molecular determinants of this process remain poorly understood. Using a chronic stimulation assay, we performed genome-wide CRISPR-Cas9 screens to systematically discover regulators of T cell exhaustion, which identified an enrichment of epigenetic factors. In vivo CRISPR screens in murine and human tumor models demonstrated that perturbation of the INO80 and BAF chromatin remodeling complexes improved T cell persistence in tumors. In vivo Perturb-seq revealed distinct transcriptional roles of each complex and that depletion of canonical BAF complex members, including Arid1a, resulted in the maintenance of an effector program and downregulation of exhaustion-related genes in tumor-infiltrating T cells. Finally, Arid1a depletion limited the acquisition of exhaustion-associated chromatin accessibility and led to improved antitumor immunity. In summary, we provide an atlas of the genetic regulators of T cell exhaustion and demonstrate that modulation of epigenetic state can improve T cell responses in cancer immunotherapy.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

scPCOR-seq enables co-profiling of chromatin occupancy and RNAs in single cells

Cell-to-cell variation in gene expression is a widespread phenomenon, which may play important roles in cellular differentiation, function, and disease development^1–9. Chromatin is implicated in contributing to the cellular heterogeneity in gene expression^10–16. Fully understanding the mechanisms of cellular heterogeneity requires simultaneous measurement of RNA and occupancy of histone modifications and transcription factors on chromatin due to their critical roles in transcriptional regulation^17,18. We generally term the occupancy of histone modifications and transcription factors as Chromatin occupancy. Here, we report a technique, termed scPCOR-seq (single-cell Profiling of Chromatin Occupancy and RNAs Sequencing), for simultaneously profiling genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell. We demonstrated that scPCOR-seq can profile either H3K4me3 or RNAPII and RNAs in a mixture of human H1, GM12878 and 293 T cells at a single-cell resolution and either H3K4me3, RNAPII, or RNA profile can correctly separate the cells. Application of scPCOR-seq to the in vitro differentiation of the erythrocyte precursor CD36 cells from human CD34 stem or progenitor cells revealed that H3K4me3 and RNA exhibit distinct properties in clustering cells during differentiation. Overall, our work provides a promising approach to understand the relationships among different omics layers.

scPCOR-seq is a single-cell sequencing technique that enables simultaneous profiling of genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell.

A single-cell map of dynamic chromatin landscapes of immune cells in renal cell carcinoma

A complete chart of the chromatin regulatory elements of immune cells in patients with cancer and their dynamic behavior is necessary to understand the developmental fates and guide therapeutic strategies. Here, we map the single-cell chromatin landscape of immune cells from blood, normal tumor-adjacent kidney tissue and malignant tissue from patients with early-stage clear cell renal cell carcinoma (ccRCC). We catalog the T cell states dictated by tissue-specific and developmental-stage-specific chromatin accessibility patterns, infer key chromatin regulators and observe rewiring of regulatory networks in the progression to dysfunction in CD8⁺ T cells. Unexpectedly, among the transcription factors orchestrating the path to dysfunction, NF-κB is associated with a pro-apoptotic program in late stages of dysfunction in tumor-infiltrating CD8⁺ T cells. Importantly, this epigenomic profiling stratified ccRCC patients based on a NF-κB-driven pro-apoptotic signature. This study provides a rich resource for understanding the functional states and regulatory dynamics of immune cells in ccRCC.

Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

Ranking reprogramming factors for cell differentiation

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Background

Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.

Results

Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.

Conclusions

We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-022-00456-5.

scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously

It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data. The pre-defined gene activity matrix is often of low quality and does not reflect the dataset-specific relationship between the two data modalities. We propose scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. Specifically, the design of scDART allows it to preserve cell trajectories in continuous cell populations and can be applied to trajectory inference on integrated data.

Cochlear Development; New Tools and Approaches

The sensory epithelium of the mammalian cochlea, the organ of Corti, is comprised of at least seven unique cell types including two functionally distinct types of mechanosensory hair cells. All of the cell types within the organ of Corti are believed to develop from a population of precursor cells referred to as prosensory cells. Results from previous studies have begun to identify the developmental processes, lineage restrictions and signaling networks that mediate the specification of many of these cell types, however, the small size of the organ and the limited number of each cell type has hampered progress. Recent technical advances, in particular relating to the ability to capture and characterize gene expression at the single cell level, have opened new avenues for understanding cellular specification in the organ of Corti. This review will cover our current understanding of cellular specification in the cochlea, discuss the most commonly used methods for single cell RNA sequencing and describe how results from a recent study using single cell sequencing provided new insights regarding cellular specification.

Single-Cell RNA and ATAC Sequencing Reveal Hemodialysis-Related Immune Dysregulation of Circulating Immune Cell Subpopulations

Background

An increased risk of infection, malignancy, and cardiovascular diseases in maintenance hemodialysis patients is associated with hemodialysis-related immunity disturbances. Although defects in T-lymphocyte-dependent immune responses and preactivation of antigen-presenting cells have been documented in hemodialysis patients, the effects of long-term hemodialysis on the transcriptional program and chromosomal accessibility of circulating immune cell subpopulations remain poorly defined.

Methods

We integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to characterize the transcriptome profiles of peripheral mononuclear cells (PBMCs) from healthy controls and maintenance hemodialysis patients. Validation of differentially expressed genes in CD4+ T cells and monocytes were performed by magnetic bead separation and quantitative real-time PCR.

Results

We identified 16 and 15 PBMC subgroups in scRNA-seq and scATAC-seq datasets, respectively. Hemodialysis significantly suppressed the expression levels of T cell receptor (TCR) genes in CD4+ T cell subsets (e.g., TRAV4, CD45, CD3G, CD3D, CD3E) and major histocompatibility complex II (MHC-II) pathway-related genes in monocytes (HLA-DRB1, HLA-DQA2, HLA-DQA1, HLA-DPB1). Downstream pathways of TCR signaling, including PI3K-Akt-mTOR, MAPK, TNF, and NF-κB pathways, were also inhibited in CD4+ T cell subpopulations during the hemodialysis procedure. Hemodialysis altered cellular communication patterns between PBMC subgroups, particularly TGF-TGFBR, HVEM-BTLA, and IL16-CD4 signalings between CD4+ T cells and monocytes. Additionally, we found that hemodialysis inhibited the expression of AP-1 family transcription factors (JUN, JUND, FOS, FOSB) by interfering with the chromatin accessibility profile.

Conclusions

Our study provides a valuable framework for future investigations of hemodialysis-related immune dysregulation and identifies potential therapeutic targets for reconstituting the circulating immune system in maintenance hemodialysis patients.