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

Gene regulation by gonadal hormone receptors underlies brain sex differences

Oestradiol establishes neural sex differences in many vertebrates1-3 and modulates mood, behaviour and energy balance in adulthood4-8. In the canonical pathway, oestradiol exerts its effects through the transcription factor oestrogen receptor-α (ERα)9. Although ERα has been extensively characterized in breast cancer, the neuronal targets of ERα, and their involvement in brain sex differences, remain largely unknown. Here we generate a comprehensive map of genomic ERα-binding sites in a sexually dimorphic neural circuit that mediates social behaviours. We conclude that ERα orchestrates sexual differentiation of the mouse brain through two mechanisms: establishing two male-biased neuron types and activating a sustained male-biased gene expression program. Collectively, our findings reveal that sex differences in gene expression are defined by hormonal activation of neuronal steroid receptors. The molecular targets we identify may underlie the effects of oestradiol on brain development, behaviour and disease.

Divergent transcriptional regulation of astrocyte reactivity across disorders

Astrocytes respond to injury and disease in the central nervous system with reactive changes that influence the outcome of the disorder1-4. These changes include differentially expressed genes (DEGs) whose contextual diversity and regulation are poorly understood. Here we combined biological and informatic analyses, including RNA sequencing, protein detection, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and conditional gene deletion, to predict transcriptional regulators that differentially control more than 12,000 DEGs that are potentially associated with astrocyte reactivity across diverse central nervous system disorders in mice and humans. DEGs associated with astrocyte reactivity exhibited pronounced heterogeneity across disorders. Transcriptional regulators also exhibited disorder-specific differences, but a core group of 61 transcriptional regulators was identified as common across multiple disorders in both species. We show experimentally that DEG diversity is determined by combinatorial, context-specific interactions between transcriptional regulators. Notably, the same reactivity transcriptional regulators can regulate markedly different DEG cohorts in different disorders; changes in the access of transcriptional regulators to DNA-binding motifs differ markedly across disorders; and DEG changes can crucially require multiple reactivity transcriptional regulators. We show that, by modulating reactivity, transcriptional regulators can substantially alter disorder outcome, implicating them as therapeutic targets. We provide searchable resources of disorder-related reactive astrocyte DEGs and their predicted transcriptional regulators. Our findings show that transcriptional changes associated with astrocyte reactivity are highly heterogeneous and are customized from vast numbers of potential DEGs through context-specific combinatorial transcriptional-regulator interactions.

Chromatin accessibility analysis reveals regulatory dynamics and therapeutic relevance of Vogt-Koyanagi-Harada disease

The barrier to curing Vogt-Koyanagi-Harada disease (VKH) is thought to reside in a lack of understanding in the roles and regulations of peripheral inflammatory immune cells. Here we perform a single-cell multi-omic study of 166,149 cells in peripheral blood mononuclear cells from patients with VKH, profile the chromatin accessibility and gene expression in the same blood samples, and uncover prominent cellular heterogeneity. Immune cells in VKH blood are highly activated and pro-inflammatory. Notably, we describe an enrichment of transcription targets for nuclear factor kappa B in conventional dendritic cells (cDCs) that governed inflammation. Integrative analysis of transcriptomic and chromatin maps shows that the RELA in cDCs is related to disease complications and poor prognosis. Ligand-receptor interaction pairs also identify cDC as an important predictor that regulated multiple immune subsets. Our results reveal epigenetic and transcriptional dynamics in auto-inflammation, especially the cDC subtype that might lead to therapeutic strategies in VKH.

Single-cell chromatin profiling of the primitive gut tube reveals regulatory dynamics underlying lineage fate decisions

Development of the gastrointestinal system occurs after gut tube closure, guided by spatial and temporal control of gene expression. However, it remains unclear what forces regulate these spatiotemporal gene expression patterns. Here we perform single-cell chromatin profiling of the primitive gut tube to reveal organ-specific chromatin patterns that reflect the anatomical patterns of distinct organs. We generate a comprehensive map of epigenomic changes throughout gut development, demonstrating that dynamic chromatin accessibility patterns associate with lineage-specific transcription factor binding events to regulate organ-specific gene expression. Additionally, we show that loss of Sox2 and Cdx2, foregut and hindgut lineage-specific transcription factors, respectively, leads to fate shifts in epigenomic patterns, linking transcription factor binding, chromatin accessibility, and lineage fate decisions in gut development. Notably, abnormal expression of Sox2 in the pancreas and intestine impairs lineage fate decisions in both development and adult homeostasis. Together, our findings define the chromatin and transcriptional mechanisms of organ identity and lineage plasticity in development and adult homeostasis.

The primitive gut tube gives rise to all major internal organs, while underlying regulatory mechanisms are unclear. Here, the authors analyze its chromatin landscape at the single-cell level and define the epigenetic regulation of lineage fate decisions and plasticity in organ development and homeostasis.

Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies (GWAS) have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements (CREs). Here, we applied single-nucleus ATAC-seq to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants. . We identified elements in smooth muscle cell (SMC) transition states (e.g. fibromyocytes) and functional variants predicted to alter SMC and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.

Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset

Recent advances in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) have allowed simultaneous epigenetic profiling over thousands of individual cells to dissect the cellular heterogeneity and elucidate regulatory mechanisms at the finest possible resolution. However, scATAC-seq is challenging to model computationally due to the ultra-high dimensionality, low signal-to-noise ratio, complex feature interactions, and high vulnerability to various confounding factors. In this study, we present Translator, an efficient transfer learning approach to capture generalizable chromatin interactions from high-quality (HQ) reference scATAC-seq data to obtain robust cell representations in low-to-moderate quality target scATAC-seq data. We applied Translator on various simulated and real scATAC-seq datasets and demonstrated that Translator could learn more biologically meaningful cell representations than other methods by incorporating information learned from the reference data, thus facilitating various downstream analyses such as clustering and motif enrichment measurements. Moreover, Translator's block-wise deep learning framework can handle nonlinear relationships with restricted connections using fewer parameters to boost computational efficiency through Graphics Processing Unit (GPU) parallelism. Finally, we have implemented Translator as a free software package available for the community to leverage large-scale, HQ reference data to study target scATAC-seq data.

Machine learning sequence prioritization for cell type-specific enhancer design

Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtypes are low yield, involving intensive transgenic strain or virus screening. Here, we present Specific Nuclear-Anchored Independent Labeling (SNAIL), an improved virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and then make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV+) neurons. Nuclear isolation using SNAIL in wild-type mice is sufficient to capture characteristic open chromatin features of PV+ neurons in the cortex, striatum, and external globus pallidus. The SNAIL framework also has high utility for multispecies cell probe engineering; expression from a mouse PV+ SNAIL enhancer sequence was enriched in PV+ neurons of the macaque cortex. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.

Doublet identification in single-cell sequencing data using scDblFinder

Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing

approaches, we developed scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.

Maladaptive innate immune training of myelopoiesis links inflammatory comorbidities

Bone marrow (BM)-mediated trained innate immunity (TII) is a state of heightened immune responsiveness of hematopoietic stem and progenitor cells (HSPC) and their myeloid progeny. We show here that maladaptive BM-mediated TII underlies inflammatory comorbidities, as exemplified by the periodontitis-arthritis axis. Experimental-periodontitis-related systemic inflammation in mice induced epigenetic rewiring of HSPC and led to sustained enhancement of production of myeloid cells with increased inflammatory preparedness. The periodontitis-induced trained phenotype was transmissible by BM transplantation to naive recipients, which exhibited increased inflammatory responsiveness and disease severity when subjected to inflammatory arthritis. IL-1 signaling in HSPC was essential for their maladaptive training by periodontitis. Therefore, maladaptive innate immune training of myelopoiesis underlies inflammatory comorbidities and may be pharmacologically targeted to treat them via a holistic approach.

Bi-order multimodal integration of single-cell data

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Derivation of totipotent-like stem cells with blastocyst-like structure forming potential

It is challenging to derive totipotent stem cells in vitro that functionally and molecularly resemble cells from totipotent embryos. Here, we report that a chemical cocktail enables the derivation of totipotent-like stem cells, designated as totipotent potential stem (TPS) cells, from 2-cell mouse embryos and extended pluripotent stem cells, and that these TPS cells can be stably maintained long term in vitro. TPS cells shared features with 2-cell mouse embryos in terms of totipotency markers, transcriptome, chromatin accessibility and DNA methylation patterns. In vivo chimera formation assays show that these cells have embryonic and extraembryonic developmental potentials at the single-cell level. Moreover, TPS cells can be induced into blastocyst-like structures resembling preimplantation mouse blastocysts. Mechanistically, inhibition of HDAC1/2 and DOT1L activity and activation of RARγ signaling are important for inducing and maintaining totipotent features of TPS cells. Our study opens up a new path toward fully capturing totipotent stem cells in vitro.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Developmental landscape of human forebrain at a single-cell level identifies early waves of oligodendrogenesis

Oligodendrogenesis in the human central nervous system has been observed mainly at the second trimester of gestation, a much later developmental stage compared to oligodendrogenesis in mice. Here, we characterize the transcriptomic neural diversity in the human forebrain at post-conception weeks (PCW) 8-10. Using single-cell RNA sequencing, we find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level through the use of single-cell ATAC-seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of 50 key genes through the use of in situ sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mice and humans and describe regulatory mechanisms involved in human OPC specification.

Rolling back human pluripotent stem cells to an eight-cell embryo-like stage

After fertilization, the quiescent zygote experiences a burst of genome activation that initiates a short-lived totipotent state. Understanding the process of totipotency in human cells would have broad applications. However, in contrast to in mice1,2, demonstration of the time of zygotic genome activation or the eight-cell (8C) stage in in vitro cultured human cells has not yet been reported, and the study of embryos is limited by ethical and practical considerations. Here we describe a transgene-free, rapid and controllable method for producing 8C-like cells (8CLCs) from human pluripotent stem cells. Single-cell analysis identified key molecular events and gene networks associated with this conversion. Loss-of-function experiments identified fundamental roles for DPPA3, a master regulator of DNA methylation in oocytes3, and TPRX1, a eutherian totipotent cell homeobox (ETCHbox) family transcription factor that is absent in mice4. DPPA3 induces DNA demethylation throughout the 8CLC conversion process, whereas TPRX1 is a key executor of 8CLC gene networks. We further demonstrate that 8CLCs can produce embryonic and extraembryonic lineages in vitro or in vivo in the form of blastoids5 and complex teratomas. Our approach provides a resource to uncover the molecular process of early human embryogenesis.

A genetic bottleneck of mitochondrial DNA during human lymphocyte development

Mitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Although the elimination of pathogenic mitochondrial DNA (mtDNA) mutations in somatic cells has been observed, the mechanisms for somatic cells to maintain proper functions despite their mtDNA mutation load are poorly understood. In this study, we analyzed somatic mtDNA mutations in more than 30,000 single human peripheral and bone marrow mononuclear cells. We observed a significant overrepresentation of homoplasmic mtDNA mutations in B, T and NK lymphocytes. Intriguingly, their overall mutational burden was lower than that in hematopoietic progenitors and myeloid cells. This characteristic mtDNA mutational landscape indicates a genetic bottleneck during lymphoid development, as confirmed with single cell datasets from multiple platforms and individuals. We further demonstrated that mtDNA replication lags behind cell proliferation in both pro-B and pre-B progenitor cells, thus likely causing the genetic bottleneck by diluting mtDNA copies per cell. Through computational simulations and approximate Bayesian computation (ABC), we recapitulated this lymphocyte-specific mutational landscape and estimated the minimal mtDNA copies as <30 in T, B, and NK lineages. Our integrative analysis revealed a novel discovery of a lymphoid-specific mtDNA genetic bottleneck, thus illuminating a potential mechanism used by highly metabolically active immune cells to limit their mtDNA mutation load.

Chromatin accessibility profiling by ATAC-seq

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.

Chemical reprogramming of human somatic cells to pluripotent stem cells

Cellular reprogramming can manipulate the identity of cells to generate the desired cell types^1-3. The use of cell intrinsic components, including oocyte cytoplasm and transcription factors, can enforce somatic cell reprogramming to pluripotent stem cells^4-7. By contrast, chemical stimulation by exposure to small molecules offers an alternative approach that can manipulate cell fate in a simple and highly controllable manner^8-10. However, human somatic cells are refractory to chemical stimulation owing to their stable epigenome^2,11,12 and reduced plasticity^13,14; it is therefore challenging to induce human pluripotent stem cells by chemical reprogramming. Here we demonstrate, by creating an intermediate plastic state, the chemical reprogramming of human somatic cells to human chemically induced pluripotent stem cells that exhibit key features of embryonic stem cells. The whole chemical reprogramming trajectory analysis delineated the induction of the intermediate plastic state at the early stage, during which chemical-induced dedifferentiation occurred, and this process was similar to the dedifferentiation process that occurs in axolotl limb regeneration. Moreover, we identified the JNK pathway as a major barrier to chemical reprogramming, the inhibition of which was indispensable for inducing cell plasticity and a regeneration-like program by suppressing pro-inflammatory pathways. Our chemical approach provides a platform for the generation and application of human pluripotent stem cells in biomedicine. This study lays foundations for developing regenerative therapeutic strategies that use well-defined chemicals to change cell fates in humans.

Cell transcriptomic atlas of the non-human primate Macaca fascicularis

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintaining specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory networks and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field.

Differential chromatin accessibility in peripheral blood mononuclear cells underlies COVID-19 disease severity prior to seroconversion

SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associate with mild or moderate symptoms are already robust and include severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity is marked by upregulation classical antiviral pathways including those regulating IRF1 and IRF7, whereas in moderate disease these classical antiviral signals diminish suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19.

Elastic dosage compensation by X-chromosome upregulation

X-chromosome inactivation and X-upregulation are the fundamental modes of chromosome-wide gene regulation that collectively achieve dosage compensation in mammals, but the regulatory link between the two remains elusive and the X-upregulation dynamics are unknown. Here, we use allele-resolved single-cell RNA-seq combined with chromatin accessibility profiling and finely dissect their separate effects on RNA levels during mouse development. Surprisingly, we uncover that X-upregulation elastically tunes expression dosage in a sex- and lineage-specific manner, and moreover along varying degrees of X-inactivation progression. Male blastomeres achieve X-upregulation upon zygotic genome activation while females experience two distinct waves of upregulation, upon imprinted and random X-inactivation; and ablation of Xist impedes female X-upregulation. Female cells carrying two active X chromosomes lack upregulation, yet their collective RNA output exceeds that of a single hyperactive allele. Importantly, this conflicts the conventional dosage compensation model in which naïve female cells are initially subject to biallelic X-upregulation followed by X-inactivation of one allele to correct the X dosage. Together, our study provides key insights to the chain of events of dosage compensation, explaining how transcript copy numbers can remain remarkably stable across developmental windows wherein severe dose imbalance would otherwise be experienced by the cell.

Single-Cell Chromatin and Gene-Regulatory Dynamics of Mouse Nephron Progenitors

BACKGROUND

We reasoned that unraveling the dynamic changes in accessibility of genomic regulatory elements and gene expression at single-cell resolution will inform the basic mechanisms of nephrogenesis.

METHODS

We performed single-cell ATAC-seq and RNA-seq both individually (Singleomes; Six2GFPcells) and jointly in the same cells (Multiomes; kidneys) to generate integrated chromatin and transcriptional maps in mouse embryonic and neonatal nephron progenitor cells (NPCs).

RESULTS

WWe demonstrate that singleomes and multiomes are comparable in assigning most cell states, identification of new cell type markers, and defining the transcription factors driving cell identity. However, multiomes are more precise in defining the progenitor population. Multiomes identified a "pioneer" bHLH/Fox motif signature in NPCs. Moreover, we identified a subset of Fox factors exhibiting high chromatin activity in podocytes. One of these Fox factors, Foxp1, is important for nephrogenesis. Key nephrogenic factors are distinguished by strong correlation between linked generegulatory elements and gene expression.

CONCLUSION

Mapping the regulatory landscape at single-cell resolution informs the regulatory hierarchy of nephrogenesis. Paired single-cell epigenomes and transcriptomes of nephron progenitors should provide a foundation to understand prenatal programming, regeneration following injury, and ex vivo nephrogenesis.

Integrated single-cell transcriptomic and epigenetic study of cell state transition and lineage commitment in embryonic mouse cerebellum

Recent studies using single-cell RNA-sequencing have revealed cellular heterogeneity in the developing mammalian cerebellum, yet the regulatory logic underlying this cellular diversity remains to be elucidated. Using integrated single-cell RNA and ATAC analyses, we resolved developmental trajectories of cerebellar progenitors and identified putative trans- and cis-elements that control cell state transition. We reverse engineered gene regulatory networks (GRNs) of each cerebellar cell type. Through in silico simulations and in vivo experiments, we validated the efficacy of GRN analyses and uncovered the molecular control of a posterior transitory zone (PTZ), a distinct progenitor zone residing immediately anterior to the morphologically defined rhombic lip (RL). We showed that perturbing cell fate specification in the PTZ and RL causes posterior cerebellar vermis hypoplasia, the most common cerebellar birth defect in humans. Our study provides a foundation for comprehensive studies of developmental programs of the mammalian cerebellum.

InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease.

Cell-specific cis-regulatory elements and mechanisms of non-coding genetic disease in human retina and retinal organoids

Cis-regulatory elements (CREs) play a critical role in the development and disease-states of all human cell types. In the retina, CREs have been implicated in several inherited disorders. To better characterize human retinal CREs, we performed single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) and single-nucleus RNA sequencing (snRNA-seq) on the developing and adult human retina and on induced pluripotent stem cell (iPSC)-derived retinal organoids. These analyses identified developmentally dynamic, cell-class-specific CREs, enriched transcription-factor-binding motifs, and putative target genes. CREs in the retina and organoids are highly correlated at the single-cell level, and this supports the use of organoids as a model for studying disease-associated CREs. As a proof of concept, we disrupted a disease-associated CRE at 5q14.3, confirming its principal target gene as the miR-9-2 primary transcript and demonstrating its role in neurogenesis and gene regulation in mature glia. This study provides a resource for characterizing human retinal CREs and showcases organoids as a model to study the function of CREs that influence development and disease.

PeakVI: A deep generative model for single-cell chromatin accessibility analysis

Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here, we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects, such as library size and region-specific biases. In addition, PeakVI provides a technique for identifying differential accessibility at a single-region resolution, which can be used for cell-type annotation as well as identification of key cis-regulatory elements. We use public datasets to demonstrate that PeakVI is scalable, stable, robust to low-quality data, and outperforms current analysis methods on a range of critical analysis tasks. PeakVI is publicly available and implemented in the scvi-tools framework.

CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells

Cleavage Under Targets and Tagmentation (CUT&Tag) is an antibody-directed transposase tethering strategy for in situ chromatin profiling in small samples and single cells. We describe a modified CUT&Tag protocol using a mixture of an antibody to the initiation form of RNA polymerase II (Pol2 Serine-5 phosphate) and an antibody to repressive Polycomb domains (H3K27me3) followed by computational signal deconvolution to produce high-resolution maps of both the active and repressive regulomes in single cells. The ability to seamlessly map active promoters, enhancers, and repressive regulatory elements using a single workflow provides a complete regulome profiling strategy suitable for high-throughput single-cell platforms.

CellWalkR: An R Package for integrating and visualizing single-cell and bulk data to resolve regulatory elements

Summary

CellWalkR is an R package that integrates single-cell open chromatin data with cell type labels and bulk epigenetic data to identify cell type-specific regulatory regions. A Graphics Processing Unit (GPU) implementation and downsampling strategies enable thousands of cells to be processed in seconds. CellWalkR’s user-friendly interface provides interactive analysis and visualization of cell labels and regulatory region mappings.

Availability and implementation

CellWalkR is freely available as an R package under a GNU GPL-2.0 License and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolutionarily conservative and non-conservative regulatory networks during primate interneuron development revealed by single-cell RNA and ATAC sequencing

The differences in size and function between primate and rodent brains, and the association of disturbed excitatory/inhibitory balance with many neurodevelopmental disorders highlight the importance to study primate ganglionic eminences (GEs) development. Here we used single-cell RNA and ATAC sequencing to characterize the emergence of cell diversity in monkey and human GEs where most striatal and cortical interneurons are generated. We identified regional and temporal diversity among progenitor cells which give rise to a variety of interneurons. These cells are specified within the primate GEs by well conserved gene regulatory networks, similar to those identified in mice. However, we detected, in human, several novel regulatory pathways or factors involved in the specification and migration of interneurons. Importantly, comparison of progenitors between our human and published mouse GE datasets led to the discovery and confirmation of outer radial glial cells in GEs in human cortex. Our findings reveal both evolutionarily conservative and nonconservative regulatory networks in primate GEs, which may contribute to their larger brain sizes and more complex neural networks compared with mouse.

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19.

Cell-Type Resolved Insights into the Cis-Regulatory Genome of NAFLD

The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing rapidly, and unmet treatment can result in the development of hepatitis, fibrosis, and liver failure. There are difficulties involved in diagnosing NAFLD early and for this reason there are challenges involved in its treatment. Furthermore, no drugs are currently approved to alleviate complications, a fact which highlights the need for further insight into disease mechanisms. NAFLD pathogenesis is associated with complex cellular changes, including hepatocyte steatosis, immune cell infiltration, endothelial dysfunction, hepatic stellate cell activation, and epithelial ductular reaction. Many of these cellular changes are controlled by dramatic changes in gene expression orchestrated by the cis-regulatory genome and associated transcription factors. Thus, to understand disease mechanisms, we need extensive insights into the gene regulatory mechanisms associated with tissue remodeling. Mapping cis-regulatory regions genome-wide is a step towards this objective and several current and emerging technologies allow detection of accessible chromatin and specific histone modifications in enriched cell populations of the liver, as well as in single cells. Here, we discuss recent insights into the cis-regulatory genome in NAFLD both at the organ-level and in specific cell populations of the liver. Moreover, we highlight emerging technologies that enable single-cell resolved analysis of the cis-regulatory genome of the liver.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Single-cell RNA-seq and chromatin accessibility profiling decipher the heterogeneity of mouse γδ T cells

The distinct characteristics of γδ T cells determine their vital roles in the formation of local immune responses and contribute to tissue homeostasis. However, the heterogeneity of γδ T cells across tissues remains unclear. By combining transcriptional and chromatin analyses with a truly unbiased fashion, we constructed a single-cell transcriptome and chromatin accessibility landscape of mouse γδ T cells in the lymph, spleen, and thymus. We also revealed the heterogeneity of γδ T1 and γδ T17 cells across these tissues and inferred their potential regulatory mechanisms. In the thymus, we reconstructed the developmental trajectory and gained further insights into the signature genes from the mature stage, intermediate stage, and immature stage of γδ T cells on the basis of single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing data. Notably, a novel Gzma⁺ γδ T cell subset was identified with immature properties and only localized to the thymus. Finally, NR1D1, a circadian transcription factor (TF), was validated as a key and negative regulator of γδ T17 cell differentiation by performing a combined analysis of TF motif enrichment, regulon enrichment, and Nr1d1 knockout mice. In summary, our data represent a comprehensive mapping on the transcriptome and chromatin accessibility dynamics of mouse γδ T cells, providing a valuable resource and reference for future studies on γδ T cells.

Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, elucidating gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach: a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase its benefits for detailed cell-type characterization on individual real and simulated datasets as well as for integrating multiple complex datasets.

8C-like cells capture the human zygotic genome activation program in vitro

The activation of the embryonic genome marks the first major wave of transcription in the developing organism. Zygotic genome activation (ZGA) in mouse 2-cell embryos and 8-cell embryos in humans is crucial for development. Here, we report the discovery of human 8-cell-like cells (8CLCs) among naive embryonic stem cells, which transcriptionally resemble the 8-cell human embryo. They express ZGA markers, including ZSCAN4 and LEUTX, and transposable elements, such as HERVL and MLT2A1. 8CLCs show reduced SOX2 levels and can be identified using TPRX1 and H3.Y marker proteins in vitro. Overexpression of the transcription factor DUX4 and spliceosome inhibition increase human ZGA-like transcription. Excitingly, the 8CLC markers TPRX1 and H3.Y are also expressed in ZGA-stage 8-cell human embryos and may thus be relevant in vivo. 8CLCs provide a unique opportunity to characterize human ZGA-like transcription and might provide critical insights into early events in embryogenesis in humans.

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ZGA genes and transposable elements are expressed in 8CLCs but not in naive stem cells

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DUX4 overexpression and spliceosome inhibition induce ZGA-like transcription

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8CLC marker proteins TPRX1 and H3.Y are expressed in 8-cell human embryos

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8CLCs can be used to study human ZGA-like programs in vitro

Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level

Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.

Simultaneous cellular and molecular phenotyping of embryonic mutants using single-cell regulatory trajectories

Developmental progression and cellular diversity are largely driven by transcription factors (TFs); yet, characterizing their loss-of-function phenotypes remains challenging and often disconnected from their underlying molecular mechanisms. Here, we combine single-cell regulatory genomics with loss-of-function mutants to jointly assess both cellular and molecular phenotypes. Performing sci-ATAC-seq at eight overlapping time points during Drosophila mesoderm development could reconstruct the developmental trajectories of all major muscle types and reveal the TFs and enhancers involved. To systematically assess mutant phenotypes, we developed a single-nucleus genotyping strategy to process embryo pools of mixed genotypes. Applying this to four TF mutants could identify and quantify their characterized phenotypes de novo and discover new ones, while simultaneously revealing their regulatory input and mode of action. Our approach is a general framework to dissect the functional input of TFs in a systematic, unbiased manner, identifying both cellular and molecular phenotypes at a scale and resolution that has not been feasible before.

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scATAC time course constructs regulatory trajectories of Drosophila muscle lineages

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Combining a wild-type trajectory with mutants identifies and quantifies phenotypes de novo

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Digital nuclear genotyping enables the processing of pooled embryos of mixed genotypes

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This framework simultaneously uncovers cellular and molecular phenotypes in embryos

Multimodal profiling of the transcriptional regulatory landscape of the developing mouse cortex identifies Neurog2 as a key epigenome remodeler

How multiple epigenetic layers and transcription factors (TFs) interact to facilitate brain development is largely unknown. Here, to systematically map the regulatory landscape of neural differentiation in the mouse neocortex, we profiled gene expression and chromatin accessibility in single cells and integrated these data with measurements of enhancer activity, DNA methylation and three-dimensional genome architecture in purified cell populations. This allowed us to identify thousands of new enhancers, their predicted target genes and the temporal relationships between enhancer activation, epigenome remodeling and gene expression. We characterize specific neuronal transcription factors associated with extensive and frequently coordinated changes across multiple epigenetic modalities. In addition, we functionally demonstrate a new role for Neurog2 in directly mediating enhancer activity, DNA demethylation, increasing chromatin accessibility and facilitating chromatin looping in vivo. Our work provides a global view of the gene regulatory logic of lineage specification in the cerebral cortex.

By profiling multiple epigenetic layers and enhancer activity in vivo, the authors show a widespread remodeling of the regulatory landscape during mouse cortical development and identify Neurog2 as a key transcription factor driving this process.

Establish an assessment model to characterized metastasis ability Single-cell multiomics reveals heterogeneous cell states linked to metastatic potential in liver cancer cell lines

Hepatocellular carcinoma (HCC) is the most common liver cancer with a high rate of metastasis. However, the molecular mechanisms that drive metastasis remain unclear. We combined single-cell transcriptomic, proteomic, and chromatin accessibility data to investigate how heterogeneous phenotypes contribute to metastatic potential in five HCC cell lines. We confirmed that the prevalence of a mesenchymal state and levels of cell proliferation are linked to the metastatic potential. We also identified a rare hypoxic subtype that has a higher capacity for glycolysis and exhibits dormant, invasive, and malignant characteristics. This subtype has increased metastatic potential. We further identified a robust 14-gene panel representing this hypoxia signature and this hypoxia signature could serve as a prognostic index. Our data provide a valuable data resource, facilitate a deeper understanding of metastatic mechanisms, and may help diagnosis of metastatic potential in individual patients, thus supporting personalized medicine.

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Provide a high-resolution single-cell triple-omics data of five liver cancer cell lines

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Identify a robust 14-gene set representing hypoxia signature

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The hypoxia signature is associated with prognosis

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Establish an assessment model to characterized metastasis ability

MUON: multimodal omics analysis framework

Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analyses, from data preprocessing to flexible multi-omics alignment.

Recent advances on single-cell sequencing technologies

Single-cell omics sequencing was first achieved for transcriptome in 2009, which was followed by fast development of technologies for profiling genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications and so on, in an individual cell. In this review we mainly focus on the recent progresses in four topics in single cell omics field- single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics, and third-generation sequencing platform-based single cell omics sequencing. We also discuss the potential applications and future directions of these single cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.

A molecular atlas of innate immunity to adjuvanted and live attenuated vaccines, in mice

Adjuvants hold great potential in enhancing vaccine efficacy, making the understanding and improving of adjuvants critical goals in vaccinology. The TLR7/8 agonist, 3M-052, induces long-lived humoral immunity in non-human primates and is currently being evaluated in human clinical trials. However, the innate mechanisms of 3M-052 have not been fully characterized. Here, we perform flow cytometry, single cell RNA-seq and ATAC-seq to profile the kinetics, transcriptomics and epigenomics of innate immune cells in murine draining lymph nodes following 3M-052-Alum/Ovalbumin immunization. We find that 3M-052-Alum/OVA induces a robust antiviral and interferon gene program, similar to the yellow fever vaccine, which is known to confer long-lasting protection. Activation of myeloid cells in dLNs persists through day 28 and single cell analysis reveals putative TF-gene regulatory programs in distinct myeloid cells and heterogeneity of monocytes. This study provides a comprehensive characterization of the transcriptomics and epigenomics of innate populations in the dLNs after vaccination.

Adjuvants provide additional impetus for the immune response to vaccination regimens, however their modes of activity and impact on particular compartments of the immune response are currently not well understood. Here the authors perform high resolution assessment of the immune response to a well-established vaccination model and show innate immune transcriptomic and epigenomic alterations of innate cells in the lymph nodes following vaccination.

Integration of single-cell transcriptomes and chromatin landscapes reveals regulatory programs driving pharyngeal organ development

Maldevelopment of the pharyngeal endoderm, an embryonic tissue critical for patterning of the pharyngeal region and ensuing organogenesis, ultimately contributes to several classes of human developmental syndromes and disorders. Such syndromes are characterized by a spectrum of phenotypes that currently cannot be fully explained by known mutations or genetic variants due to gaps in characterization of critical drivers of normal and dysfunctional development. Despite the disease-relevance of pharyngeal endoderm, we still lack a comprehensive and integrative view of the molecular basis and gene regulatory networks driving pharyngeal endoderm development. To close this gap, we apply transcriptomic and chromatin accessibility single-cell sequencing technologies to generate a multi-omic developmental resource spanning pharyngeal endoderm patterning to the emergence of organ-specific epithelia in the developing mouse embryo. We identify cell-type specific gene regulation, distill GRN models that define developing organ domains, and characterize the role of an immunodeficiency-associated forkhead box transcription factor.

Variant to function mapping at single-cell resolution through network propagation.. [PMID: 35118467 PMCID: PMC8811900 DOI: 10.1101/2022.01.23.477426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

With burgeoning human disease genetic associations and single-cell genomic atlases covering a range of tissues, there are unprecedented opportunities to systematically gain insights into the mechanisms of disease-causal variation. However, sparsity and noise, particularly in the context of single-cell epigenomic data, hamper the identification of disease- or trait-relevant cell types, states, and trajectories. To overcome these challenges, we have developed the SCAVENGE method, which maps causal variants to their relevant cellular context at single-cell resolution by employing the strategy of network propagation. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation including enrichment of blood traits at distinct stages of human hematopoiesis, defining monocyte subsets that increase the risk for severe coronavirus disease 2019 (COVID-19), and identifying intermediate lymphocyte developmental states that are critical for predisposition to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution, but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.

FoxP3 associates with enhancer-promoter loops to regulate Treg-specific gene expression

Gene expression programs are specified by higher-order chromatin structure and enhancer-promoter loops (EPLs). T regulatory cell (T_reg) identity is dominantly specified by the transcription factor (TF) FoxP3, whose mechanism of action is unclear. We applied chromatin conformation capture with immunoprecipitation (HiChIP) in T_reg and closely related conventional CD4⁺ T cells (T_conv). EPLs identified by H3K27Ac HiChIP showed a range of connection intensity, with some superconnected genes. TF-specific HiChIP showed that FoxP3 interacts with EPLs at a large number of genes, including some not differentially expressed in T_reg versus T_conv, but enriched at the core T_reg signature loci that it up-regulates. FoxP3 association correlated with heightened H3K27Ac looping, as ascertained by analysis of FoxP3-deficient T_reg-like cells. There was marked asymmetry in the loci where FoxP3 associated at the enhancer- or the promoter-side of EPLs, with enrichment for different transcriptional cofactors. FoxP3 EPL intensity distinguished gene clusters identified by single-cell ATAC-seq as covarying between individual T_regs, supporting a direct transactivation model for FoxP3 in determining T_reg identity.

Single-cell-resolved dynamics of chromatin architecture delineate cell and regulatory states in zebrafish embryos

DNA accessibility of cis-regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many genes regulating embryonic development have been identified, the underlying CRE dynamics controlling their expression remain largely uncharacterized. To address this, we produced a multimodal resource and genomic regulatory map for the zebrafish community, which integrates single-cell combinatorial indexing assay for transposase-accessible chromatin with high-throughput sequencing (sci-ATAC-seq) with bulk histone PTMs and Hi-C data to achieve a genome-wide classification of the regulatory architecture determining transcriptional activity in the 24-h post-fertilization (hpf) embryo. We characterized the genome-wide chromatin architecture at bulk and single-cell resolution, applying sci-ATAC-seq on whole 24-hpf stage zebrafish embryos, generating accessibility profiles for ∼23,000 single nuclei. We developed a genome segmentation method, ScregSeg (single-cell regulatory landscape segmentation), for defining regulatory programs, and candidate CREs, specific to one or more cell types. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization and identified new regulatory principles between chromatin modalities prevalent during zebrafish development. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos identified novel cellular roles for this hematovascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work defines regulatory architecture and principles in the zebrafish embryo and establishes a resource of cell-type-specific genome-wide regulatory annotations and candidate CREs, providing a valuable open resource for genomics, developmental, molecular, and computational biology.

Single-cell chromatin accessibility and lipid profiling reveals SCD1-dependent metabolic shift in adipocytes induced by bariatric surgery

Obesity promotes type 2 diabetes and cardiometabolic pathologies. Vertical sleeve gastrectomy (VSG) is used to treat obesity resulting in long-term weight loss and health improvements that precede weight loss; however, the mechanisms underlying the immediate benefits remain incompletely understood. Because adipose plays a crucial role in energy homeostasis and utilization, we hypothesized that VSG exerts its influences, in part, by modulating adipose functional states. We applied single-cell ATAC sequencing and lipid profiling to inguinal and epididymal adipose depots from mice that received sham surgery or VSG. We observed depot-specific cellular composition and chromatin accessibility patterns that were altered by VSG. Specifically, accessibility at Scd1, a fatty acid desaturase, was substantially reduced after VSG in mature adipocytes of inguinal but not epididymal depots. This was accompanied by reduced accumulation of SCD1-produced unsaturated fatty acids. Given these findings and reports that reductions in Scd1 attenuate obesity and insulin resistance our results suggest VSG exerts its beneficial effects through an inguinal depot-specific reduction of SCD1 activity.

Benchmarking atlas-level data integration in single-cell genomics

Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.