Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

Neural cell diversity in the light of single-cell transcriptomics

The development of highly parallel and affordable high-throughput single-cell transcriptomics technologies has revolutionized our understanding of brain complexity. These methods have been used to build cellular maps of the brain, its different regions, and catalog the diversity of cells in each of them during development, aging and even in disease. Now we know that cellular diversity is way beyond what was previously thought. Single-cell transcriptomics analyses have revealed that cell types previously considered homogeneous based on imaging techniques differ depending on several factors including sex, age and location within the brain. The expression profiles of these cells have also been exploited to understand which are the regulatory programs behind cellular diversity and decipher the transcriptional pathways driving them. In this review, we summarize how single-cell transcriptomics have changed our view on the cellular diversity in the human brain, and how it could impact the way we study neurodegenerative diseases. Moreover, we describe the new computational approaches that can be used to study cellular differentiation and gain insight into the functions of individual cell populations under different conditions and their alterations in disease.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

cis-Regulatory Elements in Plant Development, Adaptation, and Evolution

cis-Regulatory elements encode the genomic blueprints that ensure the proper spatiotemporal patterning of gene expression necessary for appropriate development and responses to the environment. Accumulating evidence implicates changes to gene expression as a major source of phenotypic novelty in eukaryotes, including acute phenotypes such as disease and cancer in mammals. Moreover, genetic and epigenetic variation affecting cis-regulatory sequences over longer evolutionary timescales has become a recurring theme in studies of morphological divergence and local adaptation. Here, we discuss the functions of and methods used to identify various classes of cis-regulatory elements, as well as their role in plant development and response to the environment. We highlight opportunities to exploit cis-regulatory variants underlying plant development and environmental responses for crop improvement efforts. Although a comprehensive understanding of cis-regulatory mechanisms in plants has lagged behind that in animals, we showcase several breakthrough findings that have profoundly influenced plant biology and shaped the overall understanding of transcriptional regulation in eukaryotes.

Integrated analysis of single-cell chromatin state and transcriptome identified common vulnerability despite glioblastoma heterogeneity

In 2021, the World Health Organization reclassified glioblastoma, the most common form of adult brain cancer, into isocitrate dehydrogenase (IDH)-wild-type glioblastomas and grade IV IDH mutant (G4 IDHm) astrocytomas. For both tumor types, intratumoral heterogeneity is a key contributor to therapeutic failure. To better define this heterogeneity, genome-wide chromatin accessibility and transcription profiles of clinical samples of glioblastomas and G4 IDHm astrocytomas were analyzed at single-cell resolution. These profiles afforded resolution of intratumoral genetic heterogeneity, including delineation of cell-to-cell variations in distinct cell states, focal gene amplifications, as well as extrachromosomal circular DNAs. Despite differences in IDH mutation status and significant intratumoral heterogeneity, the profiled tumor cells shared a common chromatin structure defined by open regions enriched for nuclear factor 1 transcription factors (NFIA and NFIB). Silencing of NFIA or NFIB suppressed in vitro and in vivo growths of patient-derived glioblastomas and G4 IDHm astrocytoma models. These findings suggest that despite distinct genotypes and cell states, glioblastoma/G4 astrocytoma cells share dependency on core transcriptional programs, yielding an attractive platform for addressing therapeutic challenges associated with intratumoral heterogeneity.

Small RNA transcriptome analysis using parallel single-cell small RNA sequencing

miRNA and other forms of small RNAs are known to regulate many biological processes. Single-cell small RNA sequencing can be used to profile small RNAs of individual cells; however, limitations of efficiency and scale prevent its widespread application. Here, we developed parallel single-cell small RNA sequencing (PSCSR-seq), which can overcome the limitations of existing methods and enable high-throughput small RNA expression profiling of individual cells. Analysis of PSCSR-seq data indicated that diverse cell types could be identified based on patterns of miRNA expression, and showed that miRNA content in nuclei is informative (for example, cell type marker miRNAs can be detected in isolated nuclei). PSCSR-seq is very sensitive: analysis of only 732 peripheral blood mononuclear cells (PBMCs) detected 774 miRNAs, whereas bulk small RNA analysis would require input RNA from approximately 10⁶ cells to detect as many miRNAs. We identified 42 miRNAs as markers for PBMC subpopulations. Moreover, we analyzed the miRNA profiles of 9,533 cells from lung cancer biopsies, and by dissecting cell subpopulations, we identified potentially diagnostic and therapeutic miRNAs for lung cancers. Our study demonstrates that PSCSR-seq is highly sensitive and reproducible, thus making it an advanced tool for miRNA analysis in cancer and life science research.

Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

Developments in stem cell-derived islet replacement therapy for treating type 1 diabetes

The generation of islet-like endocrine clusters from human pluripotent stem cells (hPSCs) has the potential to provide an unlimited source of insulin-producing β cells for the treatment of diabetes. In order for this cell therapy to become widely adopted, highly functional and well-characterized stem cell-derived islets (SC-islets) need to be manufactured at scale. Furthermore, successful SC-islet replacement strategies should prevent significant cell loss immediately following transplantation and avoid long-term immune rejection. This review highlights the most recent advances in the generation and characterization of highly functional SC-islets as well as strategies to ensure graft viability and safety after transplantation.

Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called 'mitochondrial single-cell assay for transposase-accessible chromatin with sequencing' (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3-4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery.

Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag

Chromatin profiling at locus resolution uncovers gene regulatory features that define cell types and developmental trajectories, but it remains challenging to map and compare different chromatin-associated proteins in the same sample. Here we describe Multiple Target Identification by Tagmentation (MulTI-Tag), an antibody barcoding approach for profiling multiple chromatin features simultaneously in single cells. We optimized MulTI-Tag to retain high sensitivity and specificity, and we demonstrate detection of up to three histone modifications in the same cell: H3K27me3, H3K4me1/2 and H3K36me3. We apply MulTI-Tag to resolve distinct cell types and developmental trajectories; to distinguish unique, coordinated patterns of active and repressive element regulatory usage associated with differentiation outcomes; and to uncover associations between histone marks. Multifactorial epigenetic profiling holds promise for comprehensively characterizing cell-specific gene regulatory landscapes in development and disease.

Matching single cells across modalities with contrastive learning and optimal transport

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning

Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq). However, the widespread application of these single-cell multiomics profiling technologies has been limited by their experimental complexity, noise in nature, and high cost. In addition, single-omics sequencing technologies have generated tremendous and high-quality single-cell datasets but have yet to be fully utilized. Here, single-cell multiomics generation (scMOG), a deep learning-based framework to generate single-cell assay for transposase-accessible chromatin (ATAC) data in silico is developed from experimentally available single-cell RNA-seq measurements and vice versa. The results demonstrate that scMOG can accurately perform cross-omics generation between RNA and ATAC, and generate paired multiomics data with biological meanings when one omics is experimentally unavailable and out of training datasets. The generated ATAC, either alone or in combination with measured RNA, exhibits equivalent or superior performance to that of the experimentally measured counterparts throughout multiple downstream analyses. scMOG is also applied to human lymphoma data, which proves to be more effective in identifying tumor samples than the experimentally measured ATAC data. Finally, the performance of scMOG is investigated in other omics such as proteomics and it still shows robust performance on surface protein generation.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

High-throughput methods for the analysis of transcription factors and chromatin modifications: Low input, single cell and spatial genomic technologies

Genome-wide analysis of transcription factors and epigenomic features is instrumental to shed light on DNA-templated regulatory processes such as transcription, cellular differentiation or to monitor cellular responses to environmental cues. Two decades of technological developments have led to a rich set of approaches progressively pushing the limits of epigenetic profiling towards single cells. More recently, disruptive technologies using innovative biochemistry came into play. Assays such as CUT&RUN, CUT&Tag and variations thereof show considerable potential to survey multiple TFs or histone modifications in parallel from a single experiment and in native conditions. These are in the path to become the dominant assays for genome-wide analysis of TFs and chromatin modifications in bulk, single-cell, and spatial genomic applications. The principles together with pros and cons are discussed.

Advances in AAV technology for delivering genetically encoded cargo to the nonhuman primate nervous system

Modern neuroscience approaches including optogenetics, calcium imaging, and other genetic manipulations have facilitated our ability to dissect specific circuits in rodent models to study their role in neurological disease. These approaches regularly use viral vectors to deliver genetic cargo (e.g., opsins) to specific tissues and genetically-engineered rodents to achieve cell-type specificity. However, the translatability of these rodent models, cross-species validation of identified targets, and translational efficacy of potential therapeutics in larger animal models like nonhuman primates remains difficult due to the lack of efficient primate viral vectors. A refined understanding of the nonhuman primate nervous system promises to deliver insights that can guide the development of treatments for neurological and neurodegenerative diseases. Here, we outline recent advances in the development of adeno-associated viral vectors for optimized use in nonhuman primates. These tools promise to help open new avenues for study in translational neuroscience and further our understanding of the primate brain.

Parallel recovery of chromatin accessibility and gene expression dynamics from frozen human regulatory T cells

Epigenetic features such as DNA accessibility dictate transcriptional regulation in a cell type- and cell state- specific manner, and mapping this in health vs. disease in clinically relevant material is opening the door to new mechanistic insights and new targets for therapy. Assay for Transposase Accessible Chromatin Sequencing (ATAC-seq) allows chromatin accessibility profiling from low cell input, making it tractable on rare cell populations, such as regulatory T (Treg) cells. However, little is known about the compatibility of the assay with cryopreserved rare cell populations. Here we demonstrate the robustness of an ATAC-seq protocol comparing primary Treg cells recovered from fresh or cryopreserved PBMC samples, in the steady state and in response to stimulation. We extend this method to explore the feasibility of conducting simultaneous quantitation of chromatin accessibility and transcriptome from a single aliquot of 50,000 cryopreserved Treg cells. Profiling of chromatin accessibility and gene expression in parallel within the same pool of cells controls for cellular heterogeneity and is particularly beneficial when constrained by limited input material. Overall, we observed a high correlation of accessibility patterns and transcription factor dynamics between fresh and cryopreserved samples. Furthermore, highly similar transcriptomic profiles were obtained from whole cells and from the supernatants recovered from ATAC-seq reactions. We highlight the feasibility of applying these techniques to profile the epigenomic landscape of cells recovered from cryopreservation biorepositories.

Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

CORE CONSERVED TRANSCRIPTIONAL REGULATORY NETWORKS DEFINE THE INVASIVE TROPHOBLAST CELL LINEAGE

The invasive trophoblast cell lineage in rat and human share crucial responsibilities in establishing the uterine-placental interface of the hemochorial placenta. These observations have led to the rat becoming an especially useful animal model to study hemochorial placentation. However, our understanding of similarities or differences between regulatory mechanisms governing rat and human invasive trophoblast cell populations is limited. In this study, we generated single-nucleus (sn) ATAC-seq data from gestation day (gd) 15.5 and 19.5 rat uterine-placental interface tissues and integrated the data with single-cell RNA-seq data generated at the same stages. We determined the chromatin accessibility profiles of invasive trophoblast, natural killer, macrophage, endothelial, and smooth muscle cells, and compared invasive trophoblast chromatin accessibility to extravillous trophoblast (EVT) cell accessibility. In comparing chromatin accessibility profiles between species, we found similarities in patterns of gene regulation and groups of motifs enriched in accessible regions. Finally, we identified a conserved gene regulatory network in invasive trophoblast cells. Our data, findings and analysis will facilitate future studies investigating regulatory mechanisms essential for the invasive trophoblast cell lineage.

Structural and functional properties of mSWI/SNF chromatin remodeling complexes revealed through single-cell perturbation screens

The mammalian SWI/SNF (mSWI/SNF or BAF) family of chromatin remodeling complexes play critical roles in regulating DNA accessibility and gene expression. The three final-form subcomplexes-cBAF, PBAF, and ncBAF-are distinct in biochemical componentry, chromatin targeting, and roles in disease; however, the contributions of their constituent subunits to gene expression remain incompletely defined. Here, we performed Perturb-seq-based CRISPR-Cas9 knockout screens targeting mSWI/SNF subunits individually and in select combinations, followed by single-cell RNA-seq and SHARE-seq. We uncovered complex-, module-, and subunit-specific contributions to distinct regulatory networks and defined paralog subunit relationships and shifted subcomplex functions upon perturbations. Synergistic, intra-complex genetic interactions between subunits reveal functional redundancy and modularity. Importantly, single-cell subunit perturbation signatures mapped across bulk primary human tumor expression profiles both mirror and predict cBAF loss-of-function status in cancer. Our findings highlight the utility of Perturb-seq to dissect disease-relevant gene regulatory impacts of heterogeneous, multi-component master regulatory complexes.

Single-cell analysis of bidirectional reprogramming between early embryonic states reveals mechanisms of differential lineage plasticities

Two distinct fates, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common progenitor cells, the inner cell mass (ICM), in mammalian embryos. To study how these sister identities are forged, we leveraged embryonic (ES) and eXtraembryonic ENdoderm (XEN) stem cells - in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses uncovered distinct rates, efficiencies and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions. While GATA4-mediated ES-to-iXEN conversion was rapid and nearly deterministic, OCT4, KLF4 and SOX2-induced XEN-to-iPS reprogramming progressed with diminished efficiency and kinetics. The dominant PrE transcriptional program, safeguarded by Gata4, and globally elevated chromatin accessibility of EPI underscored the differential plasticities of the two states. Mapping in vitro trajectories to embryos revealed reprogramming in either direction tracked along, and toggled between, EPI and PrE in vivo states without transitioning through the ICM.

Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency

The advance in single-cell RNA-sequencing (scRNA-seq) sheds light on cell-specific transcriptomic studies of cell developments, complex diseases and cancers. Nevertheless, scRNA-seq techniques suffer from 'dropout' events, and imputation tools are proposed to address the sparsity. Here, rather than imputation, we propose a tool, SMURF, to extract the low-dimensional embeddings from cells and genes utilizing matrix factorization with a mixture of Poisson-Gamma divergent as objective while preserving self-consistency. SMURF exhibits feasible cell subpopulation discovery efficacy with obtained cell embeddings on replicated in silico and eight web lab scRNA datasets with ground truth cell types. Furthermore, SMURF can reduce the cell embedding to a 1D-oval space to recover the time course of cell cycle. SMURF can also serve as an imputation tool; the in silico data assessment shows that SMURF parades the most robust gene expression recovery power with low root mean square error and high Pearson correlation. Moreover, SMURF recovers the gene distribution for the WM989 Drop-seq data. SMURF is available at https://github.com/deepomicslab/SMURF.

Lineage Commitment of Dermal Fibroblast Progenitors is Mediated by Chromatin De-repression

Dermal Fibroblast Progenitors (DFPs) differentiate into distinct fibroblast lineages during skin development. However, the mechanisms that regulate lineage commitment of naive dermal progenitors to form niches around the hair follicle, dermis, and hypodermis, are unknown. In our study, we used multimodal single-cell approaches, epigenetic assays, and allografting techniques to define a DFP state and the mechanisms that govern its differentiation potential. Our results indicate that the overall chromatin profile of DFPs is repressed by H3K27me3 and has inaccessible chromatin at lineage specific genes. Surprisingly, the repressed chromatin profile of DFPs renders them unable to reform skin in allograft assays despite their multipotent potential. Distinct fibroblast lineages, such as the dermal papilla and adipocytes contained specific chromatin profiles that were de-repressed during late embryogenesis by the H3K27-me3 demethylase, Kdm6b/Jmjd3. Tissue-specific deletion of Kdm6b/Jmjd3 resulted in ablating the adipocyte compartment and inhibiting mature dermal papilla functions in single-cell-RNA-seq, ChIPseq, and allografting assays. Altogether our studies reveal a mechanistic multimodal understanding of how DFPs differentiate into distinct fibroblast lineages, and we provide a novel multiomic search-tool within skinregeneration.org.

High-Capacity Sample Multiplexing for Single Cell Chromatin Accessibility Profiling

Single-cell chromatin accessibility has emerged as a powerful means of understanding the epigenetic landscape of diverse tissues and cell types, but profiling cells from many independent specimens is challenging and costly. Here we describe a novel approach, sciPlex-ATAC-seq, which uses unmodified DNA oligos as sample-specific nuclear labels, enabling the concurrent profiling of chromatin accessibility within single nuclei from virtually unlimited specimens or experimental conditions. We first demonstrate our method with a chemical epigenomics screen, in which we identify drug-altered distal regulatory sites predictive of compound- and dose-dependent effects on transcription. We then analyze cell type-specific chromatin changes in PBMCs from multiple donors responding to synthetic and allogeneic immune stimulation. We quantify stimulation-altered immune cell compositions and isolate the unique effects of allogeneic stimulation on chromatin accessibility specific to T-lymphocytes. Finally, we observe that impaired global chromatin decondensation often coincides with chemical inhibition of allogeneic T-cell activation.

Integrative Single-Cell Analysis Reveals Transcriptional and Epigenetic Regulatory Features of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) frequently features a high level of tumor heterogeneity. Elucidating the chromatin landscape of ccRCC at the single-cell level could provide a deeper understanding of the functional states and regulatory dynamics underlying the disease. Here, we performed single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on 19 ccRCC samples, and whole-exome sequencing was used to understand the heterogeneity between individuals. Single-cell transcriptome and chromatin accessibility maps of ccRCC were constructed to reveal the regulatory characteristics of different tumor cell subtypes in ccRCC. Two long noncoding RNAs (RP11-661C8.2 and CTB-164N12.1) were identified that promoted the invasion and migration of ccRCC, which was validated with in vitro experiments. Taken together, this study comprehensively characterized the gene expression and DNA regulation landscape of ccRCC, which could provide new insights into the biology and treatment of ccRCC.

SIGNIFICANCE

A comprehensive analysis of gene expression and DNA regulation in ccRCC using scATAC-seq and scRNA-seq reveals the DNA regulatory programs of ccRCC at the single-cell level.

BET inhibitors rescue anti-PD1 resistance by enhancing TCF7 accessibility in leukemia-derived terminally exhausted CD8+ T cells

Many acute myeloid leukemia (AML) patients exhibit hallmarks of immune exhaustion, such as increased myeloid-derived suppressor cells, suppressive regulatory T cells and dysfunctional T cells. Similarly, we have identified the same immune-related features, including exhausted CD8+ T cells (TEx) in a mouse model of AML. Here we show that inhibitors that target bromodomain and extra-terminal domain (BET) proteins affect tumor-intrinsic factors but also rescue T cell exhaustion and ICB resistance. Ex vivo treatment of cells from AML mice and AML patients with BET inhibitors (BETi) reversed CD8+ T cell exhaustion by restoring proliferative capacity and expansion of the more functional precursor-exhausted T cells. This reversal was enhanced by combined BETi and anti-PD1 treatment. BETi synergized with anti-PD1 in vivo, resulting in the reduction of circulating leukemia cells, enrichment of CD8+ T cells in the bone marrow, and increase in expression of Tcf7, Slamf6, and Cxcr5 in CD8+ T cells. Finally, we profiled the epigenomes of in vivo JQ1-treated AML-derived CD8+ T cells by single-cell ATAC-seq and found that JQ1 increases Tcf7 accessibility specifically in Tex cells, suggesting that BETi likely acts mechanistically by relieving repression of progenitor programs in Tex CD8+ T cells and maintaining a pool of anti-PD1 responsive CD8+ T cells.

Single-Cell Genomics for Investigating Pathogenesis of Inflammatory Diseases

Recent technical advances have enabled unbiased transcriptomic and epigenetic analysis of each cell, known as "single-cell analysis". Single-cell analysis has a variety of technical approaches to investigate the state of each cell, including mRNA levels (transcriptome), the immune repertoire (immune repertoire analysis), cell surface proteins (surface proteome analysis), chromatin accessibility (epigenome), and accordance with genome variants (eQTLs; expression quantitative trait loci). As an effective tool for investigating robust immune responses in coronavirus disease 2019 (COVID-19), many researchers performed single-cell analysis to capture the diverse, unbiased immune cell activation and differentiation. Despite challenges elucidating the complicated immune microenvironments of chronic inflammatory diseases using existing experimental methods, it is now possible to capture the simultaneous immune features of different cell types across inflamed tissues using various single-cell tools. In this review, we introduce patient-based and experimental mouse model research utilizing single-cell analyses in the field of chronic inflammatory diseases, as well as multi-organ atlas targeting immune cells.

Epigenetic Regulations in Mammalian Cells: Roles and Profiling Techniques

The genome is almost identical in all the cells of the body. However, the functions and morphologies of each cell are different, and the factors that determine them are the genes and proteins expressed in the cells. Over the past decades, studies on epigenetic information, such as DNA methylation, histone modifications, chromatin accessibility, and chromatin conformation have shown that these properties play a fundamental role in gene regulation. Furthermore, various diseases such as cancer have been found to be associated with epigenetic mechanisms. In this study, we summarized the biological properties of epigenetics and single-cell epigenomic profiling techniques, and discussed future challenges in the field of epigenetics.

Massively Parallel Profiling of Accessible Chromatin and Proteins with ASAP-Seq

While methods such as the Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) enable a comprehensive characterization of regulatory DNA, additional measurements are required to characterize the multifaceted nature of eukaryotic cells. Here, we delineate the ATAC with Select Antigen Profiling by sequencing (ASAP-seq) protocol, a scalable approach to quantifying proteins via oligo-tagged antibodies alongside accessible DNA in thousands of single cells. Critically, our method utilizes a custom bridge oligo that enables the utilization of a variety of oligo-conjugated antibodies, enabling the utilization and repurposing of other commercial products. The ASAP-seq method can be completed with straightforward experimental and computational modifications existing single-cell ATAC-seq workflows but yields distinct modalities underlying complex cellular states, including estimation of protein abundance on the cell surface as well as intracellular and intranuclear factors.

Genome-Wide Mapping of Active Regulatory Elements Using ATAC-seq

Active cis-regulatory elements (cREs) in eukaryotes are characterized by nucleosomal depletion and, accordingly, higher accessibility. This property has turned out to be immensely useful for identifying cREs genome-wide and tracking their dynamics across different cellular states and is the basis of numerous methods taking advantage of the preferential enzymatic cleavage/labeling of accessible DNA. ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) has emerged as the most versatile and widely adaptable method and has been widely adopted as the standard tool for mapping open chromatin regions. Here, we discuss the current optimal practices and important considerations for carrying out ATAC-seq experiments, primarily in the context of mammalian systems.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Factors and Methods for the Detection of Gene Expression Regulation

Gene-expression regulation involves multiple processes and a range of regulatory factors. In this review, we describe the key factors that regulate gene expression, including transcription factors (TFs), chromatin accessibility, histone modifications, DNA methylation, and RNA modifications. In addition, we also describe methods that can be used to detect these regulatory factors.

Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing

Here we present advancements in single-cell combinatorial indexed Assay for Transposase Accessible Chromatin (sciATAC) to measure chromatin accessibility that leverage nanowell chips to achieve atlas-scale cell throughput (>10⁵ cells) at low cost. The platform leverages the core of the sciATAC workflow where multiple indexed tagmentation reactions are performed, followed by pooling and distribution to a second set of reaction wells for polymerase chain reaction (PCR)-based indexing. In this work, we instead leverage a chip containing 5184 nanowells at the PCR stage of indexing, enabling a 52-fold improvement in scale and reduction in per-cell preparation costs. We detail three variants that balance cell throughput and depth of coverage, and apply these methods to banked mouse brain tissue, producing maps of cell types as well as neuronal subtypes that include integration with existing single-cell Assay for Transposase Accessible Chromatin (scATAC) and scRNA-seq data sets. Our optimized workflow achieves a high fraction of reads that fall within called peaks (>80%) and low cell doublet rates. The high cell coverage technique produces high unique reads per cell, while retaining high enrichment for open chromatin regions, enabling the assessment of >70,000 unique accessible loci on average for each cell profiled. When compared to current methods in the field, our technique provides similar or superior per-cell information with very low levels of cell-to-cell cross talk, and achieves this at a cost point much lower than existing assays.

Incorporating cell hierarchy to decipher the functional diversity of single cells

Cells possess functional diversity hierarchically. However, most single-cell analyses neglect the nested structures while detecting and visualizing the functional diversity. Here, we incorporate cell hierarchy to study functional diversity at subpopulation, club (i.e., sub-subpopulation), and cell layers. Accordingly, we implement a package, SEAT, to construct cell hierarchies utilizing structure entropy by minimizing the global uncertainty in cell-cell graphs. With cell hierarchies, SEAT deciphers functional diversity in 36 datasets covering scRNA, scDNA, scATAC, and scRNA-scATAC multiome. First, SEAT finds optimal cell subpopulations with high clustering accuracy. It identifies cell types or fates from omics profiles and boosts accuracy from 0.34 to 1. Second, SEAT detects insightful functional diversity among cell clubs. The hierarchy of breast cancer cells reveals that the specific tumor cell club drives AREG-EGFT signaling. We identify a dense co-accessibility network of cis-regulatory elements specified by one cell club in GM12878. Third, the cell order from the hierarchy infers periodic pseudo-time of cells, improving accuracy from 0.79 to 0.89. Moreover, we incorporate cell hierarchy layers as prior knowledge to refine nonlinear dimension reduction, enabling us to visualize hierarchical cell layouts in low-dimensional space.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research

Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. This review summarizes the current single-cell and spatial omics used in musculoskeletal disorders. Cell isolation, experimental methods, and feasible experimental designs for single-cell studies of musculoskeletal system diseases have been reviewed based on tissue characteristics. Then, the paper summarizes the latest findings of single-cell studies in musculoskeletal disorders from three aspects: bone and ossification, joint, and muscle and tendon disorders. Recent discoveries about the cell populations involved in these diseases are highlighted. Furthermore, the therapeutic responses of musculoskeletal disorders, especially single-cell changes after the treatments of implants, stem cell therapies, and drugs are described. Finally, the application potential and future development directions of single-cell and spatial omics in research on musculoskeletal diseases are discussed.

RefTM: reference-guided topic modeling of single-cell chromatin accessibility data

Single-cell analysis is a valuable approach for dissecting the cellular heterogeneity, and single-cell chromatin accessibility sequencing (scCAS) can profile the epigenetic landscapes for thousands of individual cells. It is challenging to analyze scCAS data, because of its high dimensionality and a higher degree of sparsity compared with scRNA-seq data. Topic modeling in single-cell data analysis can lead to robust identification of the cell types and it can provide insight into the regulatory mechanisms. Reference-guided approach may facilitate the analysis of scCAS data by utilizing the information in existing datasets. We present RefTM (Reference-guided Topic Modeling of single-cell chromatin accessibility data), which not only utilizes the information in existing bulk chromatin accessibility and annotated scCAS data, but also takes advantage of topic models for single-cell data analysis. RefTM simultaneously models: (1) the shared biological variation among reference data and the target scCAS data; (2) the unique biological variation in scCAS data; (3) other variations from known covariates in scCAS data.

Epigenetic remodeling of the immune landscape in cancer: therapeutic hurdles and opportunities

The tumor immune microenvironment represents a sophisticated ecosystem where various immune cell subtypes communicate with cancer cells and stromal cells. The dynamic cellular composition and functional characteristics of the immune landscape along the trajectory of cancer development greatly impact the therapeutic efficacy and clinical outcome in patients receiving systemic antitumor therapy. Mounting evidence has suggested that epigenetic mechanisms are the underpinning of many aspects of antitumor immunity and facilitate immune state transitions during differentiation, activation, inhibition, or dysfunction. Thus, targeting epigenetic modifiers to remodel the immune microenvironment holds great potential as an integral part of anticancer regimens. In this review, we summarize the epigenetic profiles and key epigenetic modifiers in individual immune cell types that define the functional coordinates of tumor permissive and non-permissive immune landscapes. We discuss the immunomodulatory roles of current and prospective epigenetic therapeutic agents, which may open new opportunities in enhancing cancer immunotherapy or overcoming existing therapeutic challenges in the management of cancer.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

A universal sequencing read interpreter

Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. These experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads to extract their encoded information for downstream biological analysis has been developed. Here, we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that decodes data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of another structure of choice. We demonstrated that INTERSTELLAR successfully extracted information from a range of short- and long-read sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of sequencing-based experiments and sharing of data analysis pipelines.

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss

BACKGROUND

Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss.

RESULTS

By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research.

CONCLUSIONS

MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Current and future perspectives of single-cell multi-omics technologies in cardiovascular research

Single-cell technology has become an indispensable tool in cardiovascular research since its first introduction in 2009. Here, we highlight the recent remarkable progress in using single-cell technology to study transcriptomic and epigenetic heterogeneity in cardiac disease and development. We then introduce the key concepts in single-cell multi-omics modalities that apply to cardiovascular research. Lastly, we discuss some of the trending concepts in single-cell technology that are expected to propel cardiovascular research to the next phase of single-cell research.

Application of ATAC-seq in tumor-specific T cell exhaustion

Researches show that chronic viral infection and persistent antigen and/or inflammatory signal exposure in cancer causes the functional status of T cells to be altered, mainly by major changes in the epigenetic and metabolic environment, which then leads to T cell exhaustion. The discovery of the immune checkpoint pathway is an important milestone in understanding and reversing T cell exhaustion. Antibodies targeting these pathways have shown superior ability to reverse T cell exhaustion. However, there are still some limitations in immune checkpoint blocking therapy, such as the short-term nature of therapeutic effects and high individual heterogeneity. Assay for transposase-accessible chromatin with sequencing(ATAC-seq) is a method used to analyze the accessibility of whole-genome chromatin. It uses hyperactive Tn5 transposase to assess chromatin accessibility. Recently, a growing number of studies have reported that ATAC-seq can be used to characterize the dynamic changes of epigenetics in the process of T cell exhaustion. It has been determined that immune checkpoint blocking can only temporarily restore the function of exhausted T cells because of an irreversible change in the epigenetics of exhausted T cells. In this study, we review the latest developments, which provide a clearer molecular understanding of T cell exhaustion, reveal potential new therapeutic targets for persistent viral infection and cancer, and provide new insights for designing effective immunotherapy for treating cancer and chronic infection.

Linking Expression of Cell-Surface Receptors with Transcription Factors by Computational Analysis of Paired Single-Cell Proteomes and Transcriptomes

Complex signaling and transcriptional programs control the development and physiology of specialized cell types. Genetic perturbations in these programs cause human cancers to arise from a diverse set of specialized cell types and developmental states. Understanding these complex systems and their potential to drive cancer is critical for the development of immunotherapies and druggable targets. Pioneering single-cell multi-omics technologies that analyze transcriptional states have been coupled with the expression of cell-surface receptors. This chapter describes SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network), a computational framework, to link transcription factors with cell-surface protein expression. SPaRTAN uses CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) data and cis-regulatory sites to model the effect of interactions between transcription factors and cell-surface receptors on gene expression. We demonstrate the pipeline for SPaRTAN using CITE-seq data from peripheral blood mononuclear cells.

Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data

We propose Destin2, a novel statistical and computational method for cross-modality dimension reduction, clustering, and trajectory reconstruction for single-cell ATAC-seq data. The framework integrates cellular-level epigenomic profiles from peak accessibility, motif deviation score, and pseudo-gene activity and learns a shared manifold using the multimodal input, followed by clustering and/or trajectory inference. We apply Destin2 to real scATAC-seq datasets with both discretized cell types and transient cell states and carry out benchmarking studies against existing methods based on unimodal analyses. Using cell-type labels transferred with high confidence from unmatched single-cell RNA sequencing data, we adopt four performance assessment metrics and demonstrate how Destin2 corroborates and improves upon existing methods. Using single-cell RNA and ATAC multiomic data, we further exemplify how Destin2's cross-modality integrative analyses preserve true cell-cell similarities using the matched cell pairs as ground truths. Destin2 is compiled as a freely available R package available at https://github.com/yuchaojiang/Destin2.

Optimized single-nucleus transcriptional profiling by combinatorial indexing

Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2-3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a 'Tiny-Sci' protocol for experiments in which input material is very limited.

Standardization of Single-Cell RNA-Sequencing Analysis Workflow to Study Drosophila Ovary

Developments in single-cell technology have considerably changed the way we study biology. Significant efforts have been made over the last few years to build comprehensive cell-type-specific transcriptomic atlases for a wide range of tissues in several model organisms in order to discover cell-type-specific markers and drivers of gene expression. One such tissue is the ovary of the fruit-fly Drosophila melanogaster, which is a popular model system with wide-ranging applications in the study of both development and disease. Three independent studies have recently produced comprehensive maps of cell-type-specific gene expression that describe both spatiotemporal regulation of the process of oogenesis and unique transcriptomic profiles of different cell types that constitute the ovary. In this chapter, we outlined the wet-lab protocol that was followed in our recent study for sample preparation and reanalyze the resultant dataset to discuss the benchmarks in data analysis, which are fundamental to comprehensive curation of the single-cell dataset representing the fly ovary.

Single-Cell Joint Profiling of Open Chromatin and Transcriptome by Paired-Seq

Simultaneous detection of chromatin accessibility and transcription from the same cells promises to greatly facilitate the dissection of cell-type-specific gene regulatory programs in complex tissues. Paired-seq enables joint analysis of open chromatin and nuclear transcriptome from up to a million cells in parallel. It achieves ultra-high-throughput single-cell multiomics with the use of a combinatorial barcoding strategy involving sequential ligation of multiplexed DNA barcodes to chromatin DNA fragments and reverse transcription products, followed by high-throughput DNA sequencing of the resulting DNA libraries and deconvolution of single-cell multiomic maps based on cell-specific barcodes.

Characterizing cis-regulatory elements using single-cell epigenomics

Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.