ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Protocol for bulk and single-nuclei chromatin accessibility quantification in mouse liver tissue

The accessibility of different chromatin regions to transcription factors and other DNA-binding proteins is a critical determinant of cell function. Here, we detail a modified assay for transposase-accessible chromatin sequencing (ATAC-seq) protocol which measures chromatin accessibility genome wide. We describe nuclei isolation, tagmentation, PCR amplification, and pre- and post-sequencing quality control. Our protocol is optimized for the liver, a tissue where nuclei isolation requires distinct steps. We provide two detailed vignettes: one for bulk ATAC-seq and another for single-nuclei ATAC-seq.

A scATAC-seq atlas of chromatin accessibility in axolotl brain regions

Axolotl (Ambystoma mexicanum) is an excellent model for investigating regeneration, the interaction between regenerative and developmental processes, comparative genomics, and evolution. The brain, which serves as the material basis of consciousness, learning, memory, and behavior, is the most complex and advanced organ in axolotl. The modulation of transcription factors is a crucial aspect in determining the function of diverse regions within the brain. There is, however, no comprehensive understanding of the gene regulatory network of axolotl brain regions. Here, we utilized single-cell ATAC sequencing to generate the chromatin accessibility landscapes of 81,199 cells from the olfactory bulb, telencephalon, diencephalon and mesencephalon, hypothalamus and pituitary, and the rhombencephalon. Based on these data, we identified key transcription factors specific to distinct cell types and compared cell type functions across brain regions. Our results provide a foundation for comprehensive analysis of gene regulatory programs, which are valuable for future studies of axolotl brain development, regeneration, and evolution, as well as on the mechanisms underlying cell-type diversity in vertebrate brains.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Integrated single cell analysis shows chronic alcohol drinking disrupts monocyte differentiation in the bone marrow

Chronic heavy alcohol drinking (CHD) rewires monocytes and macrophages toward heightened inflammatory states with compromised antimicrobial defenses that persist after 1-month abstinence. To determine whether these changes are mediated through alterations in the bone marrow niche, we profiled monocytes and hematopoietic stem cell progenitors (HSCPs) from CHD rhesus macaques using a combination of functional assays and single cell genomics. CHD resulted in transcriptional profiles consistent with increased activation and inflammation within bone marrow resident monocytes and macrophages. Furthermore, CHD resulted in transcriptional signatures associated with increased oxidative and cellular stress in HSCP. Differentiation of HSCP in vitro revealed skewing toward monocytes expressing "neutrophil-like" markers with greater inflammatory responses to bacterial agonists. Further analyses of HSCPs showed broad epigenetic changes that were in line with exacerbated inflammatory responses within monocytes and their progenitors. In summary, CHD alters HSCPs in the bone marrow leading to the production of monocytes poised to generate dysregulated hyper-inflammatory responses.

Lifelong restructuring of 3D genome architecture in cerebellar granule cells

The cerebellum contains most of the neurons in the human brain and exhibits distinctive modes of development and aging. In this work, by developing our single-cell three-dimensional (3D) genome assay-diploid chromosome conformation capture, or Dip-C-into population-scale (Pop-C) and virus-enriched (vDip-C) modes, we resolved the first 3D genome structures of single cerebellar cells, created life-spanning 3D genome atlases for both humans and mice, and jointly measured transcriptome and chromatin accessibility during development. We found that although the transcriptome and chromatin accessibility of cerebellar granule neurons mature in early postnatal life, 3D genome architecture gradually remodels throughout life, establishing ultra-long-range intrachromosomal contacts and specific interchromosomal contacts that are rarely seen in neurons. These results reveal unexpected evolutionarily conserved molecular processes that underlie distinctive features of neural development and aging across the mammalian life span.

Integration of spatial and single-cell data across modalities with weakly linked features

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Deciphering early human pancreas development at the single-cell level

Understanding pancreas development can provide clues for better treatments of pancreatic diseases. However, the molecular heterogeneity and developmental trajectory of the early human pancreas are poorly explored. Here, we performed large-scale single-cell RNA sequencing and single-cell assay for transposase accessible chromatin sequencing of human embryonic pancreas tissue obtained from first-trimester embryos. We unraveled the molecular heterogeneity, developmental trajectories and regulatory networks of the major cell types. The results reveal that dorsal pancreatic multipotent cells in humans exhibit different gene expression patterns than ventral multipotent cells. Pancreato-biliary progenitors that generate ventral multipotent cells in humans were identified. Notch and MAPK signals from mesenchymal cells regulate the differentiation of multipotent cells into trunk and duct cells. Notably, we identified endocrine progenitor subclusters with different differentiation potentials. Although the developmental trajectories are largely conserved between humans and mice, some distinct gene expression patterns have also been identified. Overall, we provide a comprehensive landscape of early human pancreas development to understand its lineage transitions and molecular complexity.

Single Cell Multi-Omics of an iPSC Model of Human Sinoatrial Node Development Reveals Genetic Determinants of Heart Rate and Arrhythmia Susceptibility

Cellular heterogeneity within the sinoatrial node (SAN) is functionally important but has been difficult to model in vitro , presenting a major obstacle to studies of heart rate regulation and arrhythmias. Here we describe a scalable method to derive sinoatrial node pacemaker cardiomyocytes (PCs) from human induced pluripotent stem cells that recapitulates differentiation into distinct PC subtypes, including SAN Head, SAN Tail, transitional zone cells, and sinus venosus myocardium. Single cell (sc) RNA-sequencing, sc-ATAC-sequencing, and trajectory analyses were used to define epigenetic and transcriptomic signatures of each cell type, and to identify novel transcriptional pathways important for PC subtype differentiation. Integration of our multi-omics datasets with genome wide association studies uncovered cell type-specific regulatory elements that associated with heart rate regulation and susceptibility to atrial fibrillation. Taken together, these datasets validate a novel, robust, and realistic in vitro platform that will enable deeper mechanistic exploration of human cardiac automaticity and arrhythmia.

Single-cell multi-omics sequencing reveals the immunological disturbance underlying STAT3-V637M Hyper-IgE syndrome

Hyper-IgE syndrome (HIES) is a primary immunodeficiency characterized by, among others, the excessive production of IgE and repetitive bacterial/fungal infections. Mutations in STAT3, a transcription factor that orchestrates immune responses, may cause HIES, but the underlying mechanisms are not fully understood. Here, we used multi-omic approaches to comprehensively decipher the immune disturbance in a male HIES patient harboring STAT3-V637M. In his peripheral blood mononuclear cell (PBMC) we found significant clonal expansion of CD8 T cells (with increased CD8 subunits expression, potentially enhancing responsiveness to MHC I molecules), but not in his CD4 T cells and B cells. Although his B cells exhibited a higher potential in producing immunoglobulin, elevated SPIC binding might bias the products toward IgE isotype. Immune checkpoint inhibitors, including CTLA4, LAG3, were overexpressed in his PBMC-CD4 T cells, accompanied by reduced CD28 and IL6ST (gp130) expression. In his CD4 T cells, integrative analyses predicted upstream transcription factors (including ETV6, KLF13, and RORA) for LAG3, IL6ST, and CD28, respectively. The down-regulation of phagocytosis and nitric oxide synthesis-related genes in his PBMC-monocytes seem to be the culprit of his disseminated bacterial/fungal infection. Counterintuitively, in his PBMC we predicted increased STAT3 binding in both naïve and mature CD4 compartments, although this was not observed in most of his PBMC. In his bronchoalveolar lavage fluid (BALF), we found two macrophage subtypes with anti-bacterial properties, which were identified by CXCL8/S100A8/S100A9, or SOD2, respectively. Together, we described how the immune cell landscape was disturbed in STAT3-V637M HIES, providing a resource for further studies.

Chromatin accessibility memory of donor cells disrupts bovine somatic cell nuclear transfer blastocysts development

The post-transfer developmental capacity of bovine somatic cell nuclear transfer (SCNT) blastocysts is reduced, implying that abnormalities in gene expression regulation are present at blastocyst stage. Chromatin accessibility, as an indicator for transcriptional regulatory elements mediating gene transcription activity, has heretofore been largely unexplored in SCNT embryos, especially at blastocyst stage. In the present study, single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) of in vivo and SCNT blastocysts were conducted to segregate lineages and demonstrate the aberrant chromatin accessibility of transcription factors (TFs) related to inner cell mass (ICM) development in SCNT blastocysts. Pseudotime analysis of lineage segregation further reflected dysregulated chromatin accessibility dynamics of TFs in the ICM of SCNT blastocysts compared to their in vivo counterparts. ATAC- and ChIP-seq results of SCNT donor cells revealed that the aberrant chromatin accessibility in the ICM of SCNT blastocysts was due to the persistence of chromatin accessibility memory at corresponding loci in the donor cells, with strong enrichment of trimethylation of histone H3 at lysine 4 (H3K4me3) at these loci. Correction of the aberrant chromatin accessibility through demethylation of H3K4me3 by KDM5B diminished the expression of related genes (e.g., BCL11B) and significantly improved the ICM proliferation in SCNT blastocysts. This effect was confirmed by knocking down BCL11B in SCNT embryos to down-regulate p21 and alleviate the inhibition of ICM proliferation. These findings expand our understanding of the chromatin accessibility abnormalities in SCNT blastocysts and BCL11B may be a potential target to improve SCNT efficiency.

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io .

Ancestry-related differences in chromatin accessibility and gene expression of APOE ε4 are associated with Alzheimer's disease risk

INTRODUCTION

European local ancestry (ELA) surrounding apolipoprotein E (APOE) ε4 confers higher risk for Alzheimer's disease (AD) compared to African local ancestry (ALA). We demonstrated significantly higher APOE ε4  expression in ELA versus ALA in AD brains from APOE ε4/ε4 carriers. Chromatin accessibility differences could contribute to these expression changes.

METHODS

We performed single nuclei assays for transposase accessible chromatin sequencing from the frontal cortex of six ALA and six ELA AD brains, homozygous for local ancestry and APOE ε4.

RESULTS

Our results showed an increased chromatin accessibility at the APOE ε4  promoter area in ELA versus ALA astrocytes. This increased accessibility in ELA astrocytes extended genome wide. Genes with increased accessibility in ELA in astrocytes were enriched for synapsis, cholesterol processing, and astrocyte reactivity.

DISCUSSION

Our results suggest that increased chromatin accessibility of APOE ε4  in ELA astrocytes contributes to the observed elevated APOE ε4  expression, corresponding to the increased AD risk in ELA versus ALA APOE ε4/ε4 carriers.

Epigenetic memory of coronavirus infection in innate immune cells and their progenitors

Inflammation can trigger lasting phenotypes in immune and non-immune cells. Whether and how human infections and associated inflammation can form innate immune memory in hematopoietic stem and progenitor cells (HSPC) has remained unclear. We found that circulating HSPC, enriched from peripheral blood, captured the diversity of bone marrow HSPC, enabling investigation of their epigenomic reprogramming following coronavirus disease 2019 (COVID-19). Alterations in innate immune phenotypes and epigenetic programs of HSPC persisted for months to 1 year following severe COVID-19 and were associated with distinct transcription factor (TF) activities, altered regulation of inflammatory programs, and durable increases in myelopoiesis. HSPC epigenomic alterations were conveyed, through differentiation, to progeny innate immune cells. Early activity of IL-6 contributed to these persistent phenotypes in human COVID-19 and a mouse coronavirus infection model. Epigenetic reprogramming of HSPC may underlie altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors.

Unravelling human hematopoietic progenitor cell diversity through association with intrinsic regulatory factors

Hematopoietic stem and progenitor cell (HSPC) transplantation is an essential therapy for hematological conditions, but finer definitions of human HSPC subsets with associated function could enable better tuning of grafts and more routine, lower-risk application. To deeply phenotype HSPCs, following a screen of 328 antigens, we quantified 41 surface proteins and functional regulators on millions of CD34+ and CD34- cells, spanning four primary human hematopoietic tissues: bone marrow, mobilized peripheral blood, cord blood, and fetal liver. We propose more granular definitions of HSPC subsets and provide new, detailed differentiation trajectories of erythroid and myeloid lineages. These aspects of our revised human hematopoietic model were validated with corresponding epigenetic analysis and in vitro clonal differentiation assays. Overall, we demonstrate the utility of using molecular regulators as surrogates for cellular identity and functional potential, providing a framework for description, prospective isolation, and cross-tissue comparison of HSPCs in humans.

Aberrant accumulation of Kras-dependent pervasive transcripts during tumor progression renders cancer cells dependent on PAF1 expression

KRAS is the most commonly mutated oncogene in human cancer, and mutant KRAS is responsible for over 90% of pancreatic ductal adenocarcinoma (PDAC), the most lethal cancer. Here, we show that RNA polymerase II-associated factor 1 complex (PAF1C) is specifically required for survival of PDAC but not normal adult pancreatic cells. We show that PAF1C maintains cancer cell genomic stability by restraining overaccumulation of enhancer RNAs (eRNAs) and promoter upstream transcripts (PROMPTs) driven by mutant Kras. Loss of PAF1C leads to cancer-specific lengthening and accumulation of pervasive transcripts on chromatin and concomitant aberrant R-loop formation and DNA damage, which, in turn, trigger cell death. We go on to demonstrate that the global transcriptional hyperactivation driven by Kras signaling during tumorigenesis underlies the specific demand for PAF1C by cancer cells. Our work provides insights into how enhancer transcription hyperactivation causes general transcription factor addiction during tumorigenesis.

Mutations from patients with IPEX ported to mice reveal different patterns of FoxP3 and Treg dysfunction

Mutations of the transcription factor FoxP3 in patients with "IPEX" (immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome) disrupt regulatory T cells (Treg), causing an array of multiorgan autoimmunity. To understand the functional impact of mutations across FoxP3 domains, without genetic and environmental confounders, six human FOXP3 missense mutations are engineered into mice. Two classes of mutations emerge from combined immunologic and genomic analyses. A mutation in the DNA-binding domain shows the same lymphoproliferation and multiorgan infiltration as complete FoxP3 knockouts but delayed by months. Tregs expressing this mutant FoxP3 are destabilized by normal Tregs in heterozygous females compared with hemizygous males. Mutations in other domains affect chromatin opening differently, involving different cofactors and provoking more specific autoimmune pathology (dermatitis, colitis, diabetes), unmasked by immunological challenges or incrossing NOD autoimmune-susceptibility alleles. This work establishes that IPEX disease heterogeneity results from the actual mutations, combined with genetic and environmental perturbations, explaining then the intra-familial variation in IPEX.

An introduction to representation learning for single-cell data analysis

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.

Single-Cell Transcriptional and Epigenetic Profiles of Male Breast Cancer Nominate Salient Cancer-Specific Enhancers

Male breast cancer represents about 1% of all breast cancer diagnoses and, although there are some similarities between male and female breast cancer, the paucity of data available on male breast cancer makes it difficult to establish targeted therapies. To date, most male breast cancers (MBCs) are treated according to protocols established for female breast cancer (FBC). Thus, defining the transcriptional and epigenetic landscape of MBC with improved resolution is critical for developing better avenues for therapeutic intervention. In this study, we present matched transcriptional (scRNA-seq) and epigenetic (scATAC-seq) profiles at single-cell resolution of two treatment naïve MBC tumors processed immediately after surgical resection. These data enable the detection of differentially expressed genes between male and female breast tumors across immune, stromal, and malignant cell types, to highlight several genes that may have therapeutic implications. Notably, MYC target genes and mTORC1 signaling genes were significantly upregulated in the malignant cells of MBC compared to the female counterparts. To understand how the regulatory landscape of MBC gives rise to these male-specific gene expression patterns, we leveraged the scATAC-seq data to systematically link changes in chromatin accessibility to changes in gene expression within each cell type. We observed cancer-specific rewiring of several salient enhancers and posit that these enhancers have a higher regulatory load than lineage-specific enhancers. We highlight two examples of previously unannotated cancer-cell-specific enhancers of ANXA2 and PRDX4 gene expression and show evidence for super-enhancer regulation of LAMB3 and CD47 in male breast cancer cells. Overall, this dataset annotates clinically relevant regulatory networks in male breast tumors, providing a useful resource that expands our current understanding of the gene expression programs that underlie the biology of MBC.

Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level

In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.

Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Decoding Aging Hallmarks at the Single-Cell Level

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

Single-Cell Multiomics

Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.

Chromatin conformational changes at human satellite II contribute to the senescence phenotype in the tumor microenvironment

Cellular senescence and senescence-associated secretory phenotype (SASP) in stromal cells within the tumor microenvironment promote cancer progression. Although cellular senescence has been shown to induce changes in the higher-order chromatin structure and abnormal transcription of repetitive elements in the genome, the functional significance of these changes is unclear. In this study, we examined the human satellite II (hSATII) loci in the pericentromere to understand these changes and their functional significance. Our results indicated that the hSATII loci decompact during senescence induction, resulting in new DNA-DNA interactions in distinct genomic regions, which we refer to as DRISR (Distinctive Regions Interacted with Satellite II in Replicative senescent Fibroblasts). Interestingly, decompaction occurs before the expression of hSATII RNA. The DRISR with altered chromatin accessibility was enriched for motifs associated with cellular senescence and inflammatory SASP genes. Moreover, DNA-fluorescence in situ hybridization analysis of the breast cancer tissues revealed hSATII decompaction in cancer and stromal cells. Furthermore, we reanalyzed the single-cell assay for transposase-accessible chromatin with sequencing data and found increased SASP-related gene expression in fibroblasts exhibiting hSATII decompaction in breast cancer tissues. These findings suggest that changes in the higher-order chromatin structure of the pericentromeric repetitive sequences during cellular senescence might directly contribute to the cellular senescence phenotype and cancer progression via inflammatory gene expression.

Common and divergent gene regulatory networks control injury-induced and developmental neurogenesis in zebrafish retina

Following acute retinal damage, zebrafish possess the ability to regenerate all neuronal subtypes. This regeneration requires Müller glia (MG) to reprogram and divide asymmetrically to produce a multipotent Müller glia-derived neuronal progenitor cell (MGPC). This raises three key questions. First, does loss of different retinal cell subtypes induce unique MG regeneration responses? Second, do MG reprogram to a developmental retinal progenitor cell state? And finally, to what extent does regeneration recapitulate retinal development? We examined these questions by performing single-nuclear and single-cell RNA-Seq and ATAC-Seq in both developing and regenerating retinas. While MG reprogram to a state similar to late-stage retinal progenitors in developing retinas, there are transcriptional differences between reprogrammed MG/MGPCs and late progenitors, as well as reprogrammed MG in outer and inner retinal damage models. Validation of candidate genes confirmed that loss of different subtypes induces differences in transcription factor gene expression and regeneration outcomes. This work identifies major differences between gene regulatory networks activated following the selective loss of different subtypes of retina neurons, as well as between retinal regeneration and development.

Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state

A balance between self-renewal and differentiation is critical for the regenerative capacity of tissue-resident stem cells. In skeletal muscle, successful regeneration requires the orchestrated activation, proliferation, and differentiation of muscle satellite cells (MuSCs) that are normally quiescent. A subset of MuSCs undergoes self-renewal to replenish the stem cell pool, but the features that identify and define self-renewing MuSCs remain to be elucidated. Here, through single-cell chromatin accessibility analysis, we reveal the self-renewal versus differentiation trajectories of MuSCs over the course of regeneration in vivo. We identify Betaglycan as a unique marker of self-renewing MuSCs that can be purified and efficiently contributes to regeneration after transplantation. We also show that SMAD4 and downstream genes are genetically required for self-renewal in vivo by restricting differentiation. Our study unveils the identity and mechanisms of self-renewing MuSCs, while providing a key resource for comprehensive analysis of muscle regeneration.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

MOTIVATION

Single-cell chromatin accessibility sequencing (scCAS) technology provides an epigenomic perspective to characterize gene regulatory mechanisms at single-cell resolution. With an increasing number of computational methods proposed for analyzing scCAS data, a powerful simulation framework is desirable for evaluation and validation of these methods. However, existing simulators generate synthetic data by sampling reads from real data or mimicking existing cell states, which is inadequate to provide credible ground-truth labels for method evaluation.

RESULTS

We present simCAS, an embedding-based simulator, for generating high-fidelity scCAS data from both cell- and peak-wise embeddings. We demonstrate simCAS outperforms existing simulators in resembling real data and show that simCAS can generate cells of different states with user-defined cell populations and differentiation trajectories. Additionally, simCAS can simulate data from different batches and encode user-specified interactions of chromatin regions in the synthetic data, which provides ground-truth labels more than cell states. We systematically demonstrate that simCAS facilitates the benchmarking of four core tasks in downstream analysis: cell clustering, trajectory inference, data integration, and cis-regulatory interaction inference. We anticipate simCAS will be a reliable and flexible simulator for evaluating the ongoing computational methods applied on scCAS data.

AVAILABILITY AND IMPLEMENTATION

simCAS is freely available at https://github.com/Chen-Li-17/simCAS.

Solid-phase capture and profiling of open chromatin by spatial ATAC

Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.

Fibrolamellar carcinomas-growth arrested by paracrine signals complexed with synthesized 3-O sulfated heparan sulfate oligosaccharides

Fibrolamellar carcinomas (FLCs), lethal tumors occurring in children to young adults, have genetic signatures implicating derivation from biliary tree stem cell (BTSC) subpopulations, co-hepato/pancreatic stem cells, involved in hepatic and pancreatic regeneration. FLCs and BTSCs express pluripotency genes, endodermal transcription factors, and stem cell surface, cytoplasmic and proliferation biomarkers. The FLC-PDX model, FLC-TD-2010, is driven ex vivo to express pancreatic acinar traits, hypothesized responsible for this model's propensity for enzymatic degradation of cultures. A stable ex vivo model of FLC-TD-2010 was achieved using organoids in serum-free Kubota's Medium (KM) supplemented with 0.1% hyaluronans (KM/HA). Heparins (10 ng/ml) caused slow expansion of organoids with doubling times of ∼7-9 days. Spheroids, organoids depleted of mesenchymal cells, survived indefinitely in KM/HA in a state of growth arrest for more than 2 months. Expansion was restored with FLCs co-cultured with mesenchymal cell precursors in a ratio of 3:7, implicating paracrine signaling. Signals identified included FGFs, VEGFs, EGFs, Wnts, and others, produced by associated stellate and endothelial cell precursors. Fifty-three, unique heparan sulfate (HS) oligosaccharides were synthesized, assessed for formation of high affinity complexes with paracrine signals, and each complex screened for biological activity(ies) on organoids. Ten distinct HS-oligosaccharides, all 10-12 mers or larger, and in specific paracrine signal complexes elicited particular biological responses. Of note, complexes of paracrine signals and 3-O sulfated HS-oligosaccharides elicited slowed growth, and with Wnt3a, elicited growth arrest of organoids for months. If future efforts are used to prepare HS-oligosaccharides resistant to breakdown in vivo, then [paracrine signal-HS-oligosaccharide] complexes are potential therapeutic agents for clinical treatments of FLCs, an exciting prospect for a deadly disease.

MultiVI: deep generative model for the integration of multimodal data

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org .

Interpreting non-coding disease-associated human variants using single-cell epigenomics

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases

Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

AtlasXplore: a web platform for visualizing and sharing spatial epigenome data

MOTIVATION

In recent years, a growing number of spatial epigenome datasets have been generated, presenting rich opportunities for studying the regulation mechanisms in solid tissue sections. However, visual exploration of these datasets requires extensive computational processing of raw data, presenting a challenge for researchers without advanced computational skills to fully explore and analyze such datasets.

RESULTS

Here, we introduce AtlasXplore, a web-based platform that enables scientists to interactively navigate a growing collection of spatial epigenome data using an expanding set of tools.

AVAILABILITY AND IMPLEMENTATION

https://web.atlasxomics.com.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.

The Impact of Genomic Variation on Function (IGVF) Consortium

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.

MOF-mediated histone H4 Lysine 16 acetylation governs mitochondrial and ciliary functions by controlling gene promoters

Histone H4 lysine 16 acetylation (H4K16ac), governed by the histone acetyltransferase MOF, orchestrates gene expression regulation and chromatin interaction. However, the roles of MOF and H4K16ac in controlling cellular function and regulating mammalian tissue development remain unclear. Here we show that conditional deletion of Mof in the skin, but not Kansl1, causes severe defects in the self-renewal of basal epithelial progenitors, epidermal differentiation, and hair follicle growth, resulting in barrier defects and perinatal lethality. MOF-regulated genes are highly enriched for essential functions in the mitochondria and cilia. Genetic deletion of Uqcrq, an essential subunit for the electron transport chain (ETC) Complex III, in the skin, recapitulates the defects in epidermal differentiation and hair follicle growth observed in MOF knockout mouse. Together, this study reveals the requirement of MOF-mediated epigenetic mechanism for regulating mitochondrial and ciliary gene expression and underscores the important function of the MOF/ETC axis for mammalian skin development.

Single-cell chromatin accessibility profiling of acute myeloid leukemia reveals heterogeneous lineage composition upon therapy-resistance

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by high rate of therapy resistance. Since the cell of origin can impact response to therapy, it is crucial to understand the lineage composition of AML cells at time of therapy resistance. Here we leverage single-cell chromatin accessibility profiling of 22 AML bone marrow aspirates from eight patients at time of therapy resistance and following subsequent therapy to characterize their lineage landscape. Our findings reveal a complex lineage architecture of therapy-resistant AML cells that are primed for stem and progenitor lineages and spanning quiescent, activated and late stem cell/progenitor states. Remarkably, therapy-resistant AML cells are also composed of cells primed for differentiated myeloid, erythroid and even lymphoid lineages. The heterogeneous lineage composition persists following subsequent therapy, with early progenitor-driven features marking unfavorable prognosis in The Cancer Genome Atlas AML cohort. Pseudotime analysis further confirms the vast degree of heterogeneity driven by the dynamic changes in chromatin accessibility. Our findings suggest that therapy-resistant AML cells are characterized not only by stem and progenitor states, but also by a continuum of differentiated cellular lineages. The heterogeneity in lineages likely contributes to their therapy resistance by harboring different degrees of lineage-specific susceptibilities to therapy.

Pan-cancer survey of lncRNA rewiring and functional alternation in tumor-infiltrating T cell by scLNC

Long non-coding RNAs (lncRNAs) have been reported to involve in diverse biological processes, including tumor immunity. Since lncRNAs are expressed with high cell-type specificity, investigation of lncRNAs at the single-cell level will unveil the cell-type-specific functions of lncRNAs. However, at the single-cell level, a systematic pan-cancer analysis of lncRNA functions in tumor immune microenvironments (TIMEs) remains lacking. Here, we performed pan-cancer single-cell profiling of lncRNA functions in TIMEs and developed a tool, scLNC, tailored for lncRNA functional characterization at the single-cell level. scLNC enabled the comparison of lncRNA function from the levels of lncRNA-mRNA pairs, lncRNA regulatory unit activity and unit function in a cell-type-specific manner. Applying scLNC, our analysis depicted the cross-tumor and tumor-specific lncRNA regulatory profiles in the T cell subtypes and revealed the new regulatory units that lncRNAs established in tumor-infiltrating T cells, particularly in the tumor-enriched T cells. We further characterized the activity and functional alternations of lncRNAs through their regulatory units. Overall, our findings suggested that lncRNAs played an important role in the regulation of cytokine production, cell activation and migration in tumor-enriched T cells and further in immunotherapy.

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

INKILN is a Novel Long Noncoding RNA Promoting Vascular Smooth Muscle Inflammation via Scaffolding MKL1 and USP10

BACKGROUND

Activation of vascular smooth muscle cell (VSMC) inflammation is vital to initiate vascular disease. The role of human-specific long noncoding RNAs in VSMC inflammation is poorly understood.

METHODS

Bulk RNA sequencing in differentiated human VSMCs revealed a novel human-specific long noncoding RNA called inflammatory MKL1 (megakaryoblastic leukemia 1) interacting long noncoding RNA (INKILN). INKILN expression was assessed in multiple in vitro and ex vivo models of VSMC phenotypic modulation as well as human atherosclerosis and abdominal aortic aneurysm. The transcriptional regulation of INKILN was verified through luciferase reporter and chromatin immunoprecipitation assays. Loss-of-function and gain-of-function studies and multiple RNA-protein and protein-protein interaction assays were used to uncover a mechanistic role of INKILN in the VSMC proinflammatory gene program. Bacterial artificial chromosome transgenic mice were used to study INKILN expression and function in ligation injury-induced neointimal formation.

RESULTS

INKILN expression is downregulated in contractile VSMCs and induced in human atherosclerosis and abdominal aortic aneurysm. INKILN is transcriptionally activated by the p65 pathway, partially through a predicted NF-κB (nuclear factor kappa B) site within its proximal promoter. INKILN activates proinflammatory gene expression in cultured human VSMCs and ex vivo cultured vessels. INKILN physically interacts with and stabilizes MKL1, a key activator of VSMC inflammation through the p65/NF-κB pathway. INKILN depletion blocks interleukin-1β-induced nuclear localization of both p65 and MKL1. Knockdown of INKILN abolishes the physical interaction between p65 and MKL1 and the luciferase activity of an NF-κB reporter. Furthermore, INKILN knockdown enhances MKL1 ubiquitination through reduced physical interaction with the deubiquitinating enzyme USP10 (ubiquitin-specific peptidase 10). INKILN is induced in injured carotid arteries and exacerbates ligation injury-induced neointimal formation in bacterial artificial chromosome transgenic mice.

CONCLUSIONS

These findings elucidate an important pathway of VSMC inflammation involving an INKILN/MKL1/USP10 regulatory axis. Human bacterial artificial chromosome transgenic mice offer a novel and physiologically relevant approach for investigating human-specific long noncoding RNAs under vascular disease conditions.

Single-cell analysis reveals transcriptomic and epigenomic impacts on the maternal-fetal interface following SARS-CoV-2 infection

During pregnancy the maternal-fetal interface plays vital roles in fetal development. Its disruption is frequently found in pregnancy complications. Recent studies show increased incidences of adverse pregnancy outcomes in patients with COVID-19; however, the mechanism remains unclear. Here we analysed the molecular impacts of SARS-CoV-2 infection on the maternal-fetal interface. Generating bulk and single-nucleus transcriptomic and epigenomic profiles from patients with COVID-19 and control samples, we discovered aberrant immune activation and angiogenesis patterns in distinct cells from patients. Surprisingly, retrotransposons were also dysregulated in specific cell types. Notably, reduced enhancer activities of LTR8B elements were functionally linked to the downregulation of pregnancy-specific glycoprotein genes in syncytiotrophoblasts. Our findings revealed that SARS-CoV-2 infection induced substantial changes to the epigenome and transcriptome at the maternal-fetal interface, which may be associated with pregnancy complications.

Drivers of heterogeneity in synovial fibroblasts in rheumatoid arthritis

Inflammation of non-barrier immunologically quiescent tissues is associated with a massive influx of blood-borne innate and adaptive immune cells. Cues from the latter are likely to alter and expand activated states of the resident cells. However, local communications between immigrant and resident cell types in human inflammatory disease remain poorly understood. Here, we explored drivers of fibroblast-like synoviocyte (FLS) heterogeneity in inflamed joints of patients with rheumatoid arthritis using paired single-cell RNA and ATAC sequencing, multiplexed imaging and spatial transcriptomics along with in vitro modeling of cell-extrinsic factor signaling. These analyses suggest that local exposures to myeloid and T cell-derived cytokines, TNF, IFN-γ, IL-1β or lack thereof, drive four distinct FLS states some of which closely resemble fibroblast states in other disease-affected tissues including skin and colon. Our results highlight a role for concurrent, spatially distributed cytokine signaling within the inflamed synovium.

Spatially resolved multiomics of human cardiac niches

The function of a cell is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here we combine single-cell and spatial transcriptomics data to discover cellular niches within eight regions of the human heart. We map cells to microanatomical locations and integrate knowledge-based and unsupervised structural annotations. We also profile the cells of the human cardiac conduction system1. The results revealed their distinctive repertoire of ion channels, G-protein-coupled receptors (GPCRs) and regulatory networks, and implicated FOXP2 in the pacemaker phenotype. We show that the sinoatrial node is compartmentalized, with a core of pacemaker cells, fibroblasts and glial cells supporting glutamatergic signalling. Using a custom CellPhoneDB.org module, we identify trans-synaptic pacemaker cell interactions with glia. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions to provide mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches that may contribute to infection defence. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be applied to other tissues and organs.

Single-cell analyses reveal cannabidiol rewires tumor microenvironment via inhibiting alternative activation of macrophage and synergizes with anti-PD-1 in colon cancer

Colorectal tumors often create an immunosuppressive microenvironment that prevents them from responding to immunotherapy. Cannabidiol (CBD) is a non-psychoactive natural active ingredient from the cannabis plant that has various pharmacological effects, including neuroprotective, antiemetic, anti-inflammatory, and antineoplastic activities. This study aimed to elucidate the specific anticancer mechanism of CBD by single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) technologies. Here, we report that CBD inhibits colorectal cancer progression by modulating the suppressive tumor microenvironment (TME). Our single-cell transcriptome and ATAC sequencing results showed that CBD suppressed M2-like macrophages and promoted M1-like macrophages in tumors both in strength and quantity. Furthermore, CBD significantly enhanced the interaction between M1-like macrophages and tumor cells and restored the intrinsic anti-tumor properties of macrophages, thereby preventing tumor progression. Mechanistically, CBD altered the metabolic pattern of macrophages and related anti-tumor signaling pathways. We found that CBD inhibited the alternative activation of macrophages and shifted the metabolic process from oxidative phosphorylation and fatty acid oxidation to glycolysis by inhibiting the phosphatidylinositol 3-kinase-protein kinase B signaling pathway and related downstream target genes. Furthermore, CBD-mediated macrophage plasticity enhanced the response to anti-programmed cell death protein-1 (PD-1) immunotherapy in xenografted mice. Taken together, we provide new insights into the anti-tumor effects of CBD.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Clonal hematopoiesis is associated with protection from Alzheimer's disease

Clonal hematopoiesis of indeterminate potential (CHIP) is a premalignant expansion of mutated hematopoietic stem cells. As CHIP-associated mutations are known to alter the development and function of myeloid cells, we hypothesized that CHIP may also be associated with the risk of Alzheimer's disease (AD), a disease in which brain-resident myeloid cells are thought to have a major role. To perform association tests between CHIP and AD dementia, we analyzed blood DNA sequencing data from 1,362 individuals with AD and 4,368 individuals without AD. Individuals with CHIP had a lower risk of AD dementia (meta-analysis odds ratio (OR) = 0.64, P = 3.8 × 10-5), and Mendelian randomization analyses supported a potential causal association. We observed that the same mutations found in blood were also detected in microglia-enriched fraction of the brain in seven of eight CHIP carriers. Single-nucleus chromatin accessibility profiling of brain-derived nuclei in six CHIP carriers revealed that the mutated cells comprised a large proportion of the microglial pool in the samples examined. While additional studies are required to validate the mechanistic findings, these results suggest that CHIP may have a role in attenuating the risk of AD.

An atlas of rabbit development as a model for single-cell comparative genomics

Traditionally, the mouse has been the favoured vertebrate model for biomedical research, due to its experimental and genetic tractability. However, non-rodent embryological studies highlight that many aspects of early mouse development, such as its egg-cylinder gastrulation and method of implantation, diverge from other mammals, thus complicating inferences about human development. Like the human embryo, rabbits develop as a flat-bilaminar disc. Here we constructed a morphological and molecular atlas of rabbit development. We report transcriptional and chromatin accessibility profiles for over 180,000 single cells and high-resolution histology sections from embryos spanning gastrulation, implantation, amniogenesis and early organogenesis. Using a neighbourhood comparison pipeline, we compare the transcriptional landscape of rabbit and mouse at the scale of the entire organism. We characterize the gene regulatory programmes underlying trophoblast differentiation and identify signalling interactions involving the yolk sac mesothelium during haematopoiesis. We demonstrate how the combination of both rabbit and mouse atlases can be leveraged to extract new biological insights from sparse macaque and human data. The datasets and computational pipelines reported here set a framework for a broader cross-species approach to decipher early mammalian development, and are readily adaptable to deploy single-cell comparative genomics more broadly across biomedical research.