Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Genome-wide analysis of histone modifications can contribute to the identification of candidate cis-regulatory regions in the threespine stickleback fish

BACKGROUND

Cis-regulatory mutations often underlie phenotypic evolution. However, because identifying the locations of promoters and enhancers in non-coding regions is challenging, we have fewer examples of identified causative cis-regulatory mutations that underlie naturally occurring phenotypic variations than of causative amino acid-altering mutations. Because cis-regulatory elements have epigenetic marks of specific histone modifications, we can detect cis-regulatory elements by mapping and analyzing them. Here, we investigated histone modifications and chromatin accessibility with cleavage under targets and tagmentation (CUT&Tag) and assay for transposase-accessible chromatin-sequencing (ATAC-seq).

RESULTS

Using the threespine stickleback (Gasterosteus aculeatus) as a model, we confirmed that the genes for which nearby regions showed active marks, such as H3K4me1, H3K4me3, and high chromatin accessibility, were highly expressed. In contrast, the expression levels of genes for which nearby regions showed repressive marks, such as H3K27me3, were reduced, suggesting that our chromatin analysis protocols overall worked well. Genomic regions with peaks of histone modifications showed higher nucleotide diversity within and between populations. By comparing gene expression in the gills of the marine and stream ecotypes, we identified several insertions and deletions (indels) with transposable element fragments in the candidate cis-regulatory regions.

CONCLUSIONS

Thus, mapping and analyzing histone modifications can help identify cis-regulatory elements and accelerate the identification of causative mutations in the non-coding regions underlying naturally occurring phenotypic variations.

Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq

Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Single-cell sequencing advances in research on mesenchymal stem/stromal cells

Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.

Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems

Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been difficult. In this study, we use human brain and retina organoid models and present single-cell profiling of H3K27ac, H3K27me3 and H3K4me3 histone modifications from progenitor to differentiated neural fates to reconstruct the epigenomic trajectories regulating cell identity acquisition. We capture transitions from pluripotency through neuroepithelium to retinal and brain region and cell type specification. Switching of repressive and activating epigenetic modifications can precede and predict cell fate decisions at each stage, providing a temporal census of gene regulatory elements and transcription factors. Removing H3K27me3 at the neuroectoderm stage disrupts fate restriction, resulting in aberrant cell identity acquisition. Our single-cell epigenome-wide map of human neural organoid development serves as a blueprint to explore human cell fate determination.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

The neuropathobiology of multiple sclerosis

Chronic low-grade inflammation and neuronal deregulation are two components of a smoldering disease activity that drives the progression of disability in people with multiple sclerosis (MS). Although several therapies exist to dampen the acute inflammation that drives MS relapses, therapeutic options to halt chronic disability progression are a major unmet clinical need. The development of such therapies is hindered by our limited understanding of the neuron-intrinsic determinants of resilience or vulnerability to inflammation. In this Review, we provide a neuron-centric overview of recent advances in deciphering neuronal response patterns that drive the pathology of MS. We describe the inflammatory CNS environment that initiates neurotoxicity by imposing ion imbalance, excitotoxicity and oxidative stress, and by direct neuro-immune interactions, which collectively lead to mitochondrial dysfunction and epigenetic dysregulation. The neuronal demise is further amplified by breakdown of neuronal transport, accumulation of cytosolic proteins and activation of cell death pathways. Continuous neuronal damage perpetuates CNS inflammation by activating surrounding glia cells and by directly exerting toxicity on neighbouring neurons. Further, we explore strategies to overcome neuronal deregulation in MS and compile a selection of neuronal actuators shown to impact neurodegeneration in preclinical studies. We conclude by discussing the therapeutic potential of targeting such neuronal actuators in MS, including some that have already been tested in interventional clinical trials.

Inhibition of HDAC2 sensitises antitumour therapy by promoting NLRP3/GSDMD-mediated pyroptosis in colorectal cancer

BACKGROUND

Although numerous studies have indicated that activated pyroptosis can enhance the efficacy of antitumour therapy in several tumours, the precise mechanism of pyroptosis in colorectal cancer (CRC) remains unclear.

METHODS

Pyroptosis in CRC cells treated with antitumour agents was assessed using various techniques, including Western blotting, lactate dehydrogenase release assay and microscopy analysis. To uncover the epigenetic mechanisms that regulate NLRP3, chromatin changes and NLRP3 promoter histone modifications were assessed using Assay for Transposase-Accessible Chromatin using sequencing and RNA sequencing. Chromatin immunoprecipitation‒quantitative polymerase chain reaction was used to investigate the NLRP3 transcriptional regulatory mechanism. Additionally, xenograft and patient-derived xenograft models were constructed to validate the effects of the drug combinations.

RESULTS

As the core molecule of the inflammasome, NLRP3 expression was silenced in CRC, thereby limiting gasdermin D (GSDMD)-mediated pyroptosis. Supplementation with NLRP3 can rescue pyroptosis induced by antitumour therapy. Overexpression of HDAC2 in CRC silences NLRP3 via epigenetic regulation. Mechanistically, HDAC2 suppressed chromatin accessibility by eliminating H3K27 acetylation. HDAC2 knockout promotes H3K27ac-mediated recruitment of the BRD4-p-P65 complex to enhance NLRP3 transcription. Inhibiting HDAC2 by Santacruzamate A in combination with classic antitumour agents (5-fluorouracil or regorafenib) in CRC xenograft-bearing animals markedly activated pyroptosis and achieved a significant therapeutic effect. Clinically, HDAC2 is inversely correlated with H3K27ac/p-P65/NLRP3 and is a prognostic factor for CRC patients.

CONCLUSION

Collectively, our data revealed a crucial role for HDAC2 in inhibiting NLRP3/GSDMD-mediated pyroptosis in CRC cells and highlighted HDAC2 as a potential therapeutic target for antitumour therapy.

HIGHLIGHTS

Silencing of NLRP3 limits the GSDMD-dependent pyroptosis in colorectal cancer. HDAC2-mediated histone deacetylation leads to epigenetic silencing of NLRP3. HDAC2 suppresses the NLRP3 transcription by inhibiting the formation of H3K27ac/BRD4/p-P65 complex. Targeting HDAC2 activates pyroptosis and enhances therapeutic effect.

Leveraging zebrafish to investigate pancreatic development, regeneration, and diabetes

The zebrafish has become an outstanding model for studying organ development and tissue regeneration, which is prominently leveraged for studies of pancreatic development, insulin-producing β-cells, and diabetes. Although studied for more than two decades, many aspects remain elusive and it has only recently been possible to investigate these due to technical advances in transcriptomics, chemical-genetics, genome editing, drug screening, and in vivo imaging. Here, we review recent findings on zebrafish pancreas development, β-cell regeneration, and how zebrafish can be used to provide novel insights into gene functions, disease mechanisms, and therapeutic targets in diabetes, inspiring further use of zebrafish for the development of novel therapies for diabetes.

Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics

Ovarian cancer, a highly fatal gynecological cancer, warrants the need for understanding its heterogeneity. The disease's prevalence and impact are underscored with statistics on mortality rates. Ovarian cancer is categorized into distinct morphological groups, each with its characteristics and prognosis. Despite standard treatments, survival rates remain low due to relapses and chemoresistance. Immune system involvement is evident in ovarian cancer's progression, although the tumor employs immune evasion mechanisms. Immunotherapy, particularly immune checkpoint blockade therapy, is promising, but ovarian cancer's heterogeneity limits its efficacy. Single-cell sequencing technology could be explored as a solution to dissect the heterogeneity within tumor-associated immune cell populations and tumor microenvironments. This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research. The present review focuses on recent advancements and the challenges in applying single-cell transcriptomics to ovarian cancer.

Compensation between FOXP transcription factors maintains proper striatal function

Spiny projection neurons (SPNs) of the striatum are critical in integrating neurochemical information to coordinate motor and reward-based behavior. Mutations in the regulatory transcription factors expressed in SPNs can result in neurodevelopmental disorders (NDDs). Paralogous transcription factors Foxp1 and Foxp2, which are both expressed in the dopamine receptor 1 (D1) expressing SPNs, are known to have variants implicated in NDDs. Utilizing mice with a D1-SPN-specific loss of Foxp1, Foxp2, or both and a combination of behavior, electrophysiology, and cell-type-specific genomic analysis, loss of both genes results in impaired motor and social behavior as well as increased firing of the D1-SPNs. Differential gene expression analysis implicates genes involved in autism risk, electrophysiological properties, and neuronal development and function. Viral-mediated re-expression of Foxp1 into the double knockouts is sufficient to restore electrophysiological and behavioral deficits. These data indicate complementary roles between Foxp1 and Foxp2 in the D1-SPNs.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

The future of rapid and automated single-cell data analysis using reference mapping

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Liver sinusoidal endothelial cells contribute to portal hypertension through collagen type IV-driven sinusoidal remodeling

Portal hypertension (PHTN) is a severe complication of liver cirrhosis and is associated with intrahepatic sinusoidal remodeling induced by sinusoidal resistance and angiogenesis. Collagen type IV (COL4), a major component of basement membrane, forms in liver sinusoids upon chronic liver injury. However, the role, cellular source, and expression regulation of COL4 in liver diseases are unknown. Here, we examined how COL4 is produced and how it regulates sinusoidal remodeling in fibrosis and PHTN. Human cirrhotic liver sample RNA sequencing showed increased COL4 expression, which was further verified via immunofluorescence staining. Single-cell RNA sequencing identified liver sinusoidal endothelial cells (LSECs) as the predominant source of COL4 upregulation in mouse fibrotic liver. In addition, COL4 was upregulated in a TNF-α/NF-κB-dependent manner through an epigenetic mechanism in LSECs in vitro. Indeed, by utilizing a CRISPRi-dCas9-KRAB epigenome-editing approach, epigenetic repression of the enhancer-promoter interaction showed silencing of COL4 gene expression. LSEC-specific COL4 gene mutation or repression in vivo abrogated sinusoidal resistance and angiogenesis, which thereby alleviated sinusoidal remodeling and PHTN. Our findings reveal that LSECs promote sinusoidal remodeling and PHTN during liver fibrosis through COL4 deposition.

Kernel-based testing for single-cell differential analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Scalable integration of multiomic single-cell data using generative adversarial networks

MOTIVATION

Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable.

RESULTS

We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

AVAILABILITY AND IMPLEMENTATION

Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

Chromatin accessibility during human first-trimester neurodevelopment

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

Human ASXL1-Mutant Hematopoiesis Is Driven by a Truncated Protein Associated with Aberrant Deubiquitination of H2AK119

Mutations in additional sex combs like 1 (ASXL1) confer poor prognosis both in myeloid malignancies and in premalignant clonal hematopoiesis (CH). However, the mechanisms by which these mutations contribute to disease initiation remain unresolved, and mutation-specific targeting has remained elusive. To address this, we developed a human disease model that recapitulates the disease trajectory from ASXL1-mutant CH to lethal myeloid malignancy. We demonstrate that mutations in ASXL1 lead to the expression of a functional, truncated protein and determine that truncated ASXL1 leads to global redistribution of the repressive chromatin mark H2AK119Ub, increased transposase-accessible chromatin, and activation of both myeloid and stem cell gene-expression programs. Finally, we demonstrate that H2AK119Ub levels are tied to truncated ASXL1 expression levels and leverage this observation to demonstrate that inhibition of the PRC1 complex might be an ASXL1-mutant-specific therapeutic vulnerability in both premalignant CH and myeloid malignancy.

SIGNIFICANCE

Mutant ASXL1 is a common driver of CH and myeloid malignancy. Using primary human HSPCs, we determine that truncated ASXL1 leads to redistribution of H2AK119Ub and may affect therapeutic vulnerability to PRC1 inhibition.

Epigenetic control of microglial immune responses

Microglia, the major population of brain-resident macrophages, are now recognized as a heterogeneous population comprising several cell subtypes with different (so far mostly supposed) functions in health and disease. A number of studies have performed molecular characterization of these different microglial activation states over the last years making use of "omics" technologies, that is transcriptomics, proteomics and, less frequently, epigenomics profiling. These approaches offer the possibility to identify disease mechanisms, discover novel diagnostic biomarkers, and develop new therapeutic strategies. Here, we focus on epigenetic profiling as a means to understand microglial immune responses beyond what other omics methods can offer, that is, revealing past and present molecular responses, gene regulatory networks and potential future response trajectories, and defining cell subtype-specific disease relevance through mapping non-coding genetic variants. We review the current knowledge in the field regarding epigenetic regulation of microglial identity and function, provide an exemplary analysis that demonstrates the advantages of performing joint transcriptomic and epigenomic profiling of single microglial cells and discuss how comprehensive epigenetic analyses may enhance our understanding of microglial pathophysiology.

Exhaustive identification of genome-wide binding events of transcriptional regulators

Genome-wide binding assays aspire to map the complete binding pattern of gene regulators. Common practice relies on replication-duplicates or triplicates-and high stringency statistics to favor false negatives over false positives. Here we show that duplicates and triplicates of CUT&RUN are not sufficient to discover the entire activity of transcriptional regulators. We introduce ICEBERG (Increased Capture of Enrichment By Exhaustive Replicate aGgregation), a pipeline that harnesses large numbers of CUT&RUN replicates to discover the full set of binding events and chart the line between false positives and false negatives. We employed ICEBERG to map the full set of H3K4me3-marked regions, the targets of the co-factor β-catenin, and those of the transcription factor TBX3, in human colorectal cancer cells. The ICEBERG datasets allow benchmarking of individual replicates, comparing the performance of peak calling and replication approaches, and expose the arbitrary nature of strategies to identify reproducible peaks. Instead of a static view of genomic targets, ICEBERG establishes a spectrum of detection probabilities across the genome for a given factor, underlying the intrinsic dynamicity of its mechanism of action, and permitting to distinguish frequent from rare regulation events. Finally, ICEBERG discovered instances, undetectable with other approaches, that underlie novel mechanisms of colorectal cancer progression.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma

Hepatoblastomas (HB) display heterogeneous cellular phenotypes that influence the clinical outcome, but the underlying mechanisms are poorly understood. Here, we use a single-cell multiomic strategy to unravel the molecular determinants of this plasticity. We identify a continuum of HB cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activations underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation states. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.

CUT&Tag Applied to Zebrafish Adult Tail Fins Reveals a Return of Embryonic H3K4me3 Patterns During Regeneration

Regenerative potential is governed by a complex process of transcriptional reprogramming, involving chromatin reorganization and dynamics in transcription factor binding patterns throughout the genome. The degree to which chromatin and epigenetic changes contribute to this process remains partially understood. Here we provide a modified CUT&Tag protocol suitable for improved characterization and interrogation of epigenetic changes during adult fin regeneration in zebrafish. Our protocol generates data that recapitulates results from previously published ChIP-Seq methods, requires far fewer cells as input, and significantly improves signal to noise ratios. We deliver high-resolution enrichment maps for H3K4me3 of uninjured and regenerating fin tissues. During regeneration, we find that H3K4me3 levels increase over gene promoters which become transcriptionally active and genes which lose H3K4me3 become silenced. Interestingly, these epigenetic reprogramming events recapitulate the H3K4me3 patterns observed in developing fin folds of 24-hour old zebrafish embryos. Our results indicate that changes in genomic H3K4me3 patterns during fin regeneration occur in a manner consistent with reactivation of developmental programs, demonstrating CUT&Tag to be an effective tool for profiling chromatin landscapes in regenerating tissues.

Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3

Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility

We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Nano-CUT&Tag for multimodal chromatin profiling at single-cell resolution

The ability to comprehensively analyze the chromatin state with single-cell resolution is crucial for understanding gene regulatory principles in heterogenous tissues or during development. Recently, we developed a nanobody-based single-cell CUT&Tag (nano-CT) protocol to simultaneously profile three epigenetic modalities-two histone marks and open chromatin state-from the same single cell. Nano-CT implements a new set of secondary nanobody-Tn5 fusion proteins to direct barcoded tagmentation by Tn5 transposase to genomic targets labeled by primary antibodies raised in different species. Such nanobody-Tn5 fusion proteins are currently not commercially available, and their in-house production and purification can be completed in 3-4 d by following our detailed protocol. The single-cell indexing in nano-CT is performed on a commercially available platform, making it widely accessible to the community. In comparison to other multimodal methods, nano-CT stands out in data complexity, low sample requirements and the flexibility to choose two of the three modalities. In addition, nano-CT works efficiently with fresh brain samples, generating multimodal epigenomic profiles for thousands of brain cells at single-cell resolution. The nano-CT protocol can be completed in just 3 d by users with basic skills in standard molecular biology and bioinformatics, although previous experience with single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is beneficial for more in-depth data analysis. As a multimodal assay, nano-CT holds immense potential to reveal interactions of various chromatin modalities, to explore epigenetic heterogeneity and to increase our understanding of the role and interplay that chromatin dynamics has in cellular development.

STEAP3 promotes colon cancer cell proliferation and migration via regulating histone acetylation

Colorectal cancer (CRC) is the third most prevalent diagnosed cancer in men and second most prevalent cancer in women. H3K27ac alterations are more commonly than gene mutations in colorectal cancer. Most colorectal cancer genes have significant H3K27ac changes, which leads to an over-expression disorder in gene transcription. Over-expression of STEAP3 is involved in a variety of tumors, participating in the regulation of cancer cell proliferation and migration. The purpose of this work is to investigate the role of STEAP3 in the regulation of histone modification (H3K27ac) expression in colon cancer. Bioinformatic ChIP-seq, ChIP-qPCR and ATAC-seq were used to analyze the histone modification properties and gene accessibility of STEAP3. Western blot and qRT-PCR were used to evaluate relative protein and gene expression, respectively. CRISPR/Cas9 technology was used to knockout STEAP3 on colon cancer cells to analyze the effect of ATF3 on STEAP3. STEAP3 was over-expressed in colon cancer and associated with higher metastases and more invasive and worse stage of colon cancer. ChIP-seq and ChIP-qPCR analyses revealed significant enrichment of H3K27ac in the STEAP3 gene. In addition, knocking down STEAP3 significantly inhibits colon cancer cell proliferation and migration and down-regulates H3K27ac expression. ChIP-seq found that ATF3 is enriched in the STEAP3 gene and CRISPR/Cas9 technology used for the deletion of the ATF3 binding site suppresses the expression of STEAP3. Over-expression of STEAP3 promotes colon cancer cell proliferation and migration. Mechanical studies have indicated that H3K27ac and ATF3 are significantly enriched in the STEAP3 gene and regulate the over-expression of STEAP3.

From cellular to fear memory: An epigenetic toolbox to remember

Throughout development, the neuronal epigenome is highly sensitive to external stimuli, yet capable of safeguarding cellular memory for a lifetime. In the adult brain, memories of fearful experiences are rapidly instantiated, yet can last for decades, but the mechanisms underlying such longevity remain unknown. Here, we showcase how fear memory formation and storage - traditionally thought to exclusively affect synapse-based events - elicit profound and enduring changes to the chromatin, proposing epigenetic regulation as a plausible molecular template for mnemonic processes. By comparing these to mechanisms occurring in development and differentiation, we notice that an epigenetic machinery similar to that preserving cellular memories might be employed by brain cells so as to form, store, and retrieve behavioral memories.

Redefining Mucosal Inflammation with Spatial Genomics

The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.

An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Histone Modifications and Their Contributions to Hypertension

Essential hypertension, a multifaceted disorder, is a worldwide health problem. A complex network of genetic, epigenetic, physiological, and environmental components regulates blood pressure (BP), and any dysregulation of this network may result in hypertension. Growing evidence suggests a role for epigenetic factors in BP regulation. Any alterations in the expression or functions of these epigenetic regulators may dysregulate various determinants of BP, thereby promoting the development of hypertension. Histone posttranslational modifications are critical epigenetic regulators that have been implicated in hypertension. Several studies have demonstrated a clear association between the increased expression of some histone-modifying enzymes, especially HDACs (histone deacetylases), and hypertension. In addition, treatment with HDAC inhibitors lowers BP in hypertensive animal models, providing an excellent opportunity to design new drugs to treat hypertension. In this review, we discuss the potential contribution of different histone modifications to the regulation of BP.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon's information entropy, and the set of normalizer regions, which we named the 'Greenlist', was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.

Single-cell joint profiling of multiple epigenetic proteins and gene transcription

Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Single Cell Sequencing Technology and Its Application in Alzheimer's Disease

Alzheimer's disease (AD) involves degeneration of cells in the brain. Due to insidious onset and slow progression, AD is often not diagnosed until it gets progressed to a more severe stage. The diagnosis and treatment of AD has been a challenge. In recent years, high-throughput sequencing technologies have exhibited advantages in exploring the pathogenesis of diseases. However, the types of cells of the central nervous system are complex and traditional bulk sequencing cannot reflect their heterogeneity. Single-cell sequencing technology enables study at the individual cell level and has an irreplaceable advantage in the study of complex diseases. In recent years, this field has expanded rapidly and several types of single-cell sequencing technologies have emerged, including transcriptomics, epigenomics, genomics and proteomics. This review article provides an overview of these single-cell sequencing technologies and their application in AD.

Combinatorial single-cell profiling of major chromatin types with MAbID

Gene expression programs result from the collective activity of numerous regulatory factors. Studying their cooperative mode of action is imperative to understand gene regulation, but simultaneously measuring these factors within one sample has been challenging. Here we introduce Multiplexing Antibodies by barcode Identification (MAbID), a method for combinatorial genomic profiling of histone modifications and chromatin-binding proteins. MAbID employs antibody-DNA conjugates to integrate barcodes at the genomic location of the epitope, enabling combined incubation of multiple antibodies to reveal the distributions of many epigenetic markers simultaneously. We used MAbID to profile major chromatin types and multiplexed measurements without loss of individual data quality. Moreover, we obtained joint measurements of six epitopes in single cells of mouse bone marrow and during mouse in vitro differentiation, capturing associated changes in multifactorial chromatin states. Thus, MAbID holds the potential to gain unique insights into the interplay between gene regulatory mechanisms, especially for low-input samples and in single cells.

Scalable single-cell profiling of chromatin modifications with sciCUT&Tag

Cleavage under targets and tagmentation (CUT&Tag) is an antibody-directed in situ chromatin profiling strategy that is rapidly replacing immune precipitation-based methods, such as chromatin immunoprecipitation-sequencing. The efficiency of the method enables chromatin profiling in single cells but is limited by the numbers of cells that can be profiled. Here, we describe a combinatorial barcoding strategy for CUT&Tag that harnesses a nanowell dispenser for simple, high-resolution, high-throughput, single-cell chromatin profiling. In this single-cell combinatorial indexing CUT&Tag (sciCUT&Tag) protocol, lightly cross-linked nuclei are bound to magnetic beads and incubated with primary and secondary antibodies in bulk and then arrayed in a 96-well plate for a first round of cellular indexing by antibody-directed Tn5 tagmentation. The sample is then repooled, mixed and arrayed across 5,184 nanowells at a density of 12-24 nuclei per well for a second round of cellular indexing during PCR amplification of the sequencing-ready library. This protocol can be completed in 1.5 days by a research technician, and we illustrate the optimized protocol by profiling histone modifications associated with developmental gene repression (H3K27me3) as well as transcriptional activation (H3K4me1-2-3) in human peripheral blood mononuclear cells and use single-nucleotide polymorphisms to facilitate collision removal. We have also used sciCUT&Tag for simultaneous profiling of multiple chromatin epitopes in single cells. The reduced cost, improved resolution and scalability of sciCUT&Tag make it an attractive platform to profile chromatin features in single cells.

Epigenomic analysis of the myometrium during late implantation revealed regulatory elements in genes related to the cellular zinc homeostasis pathway in pigs

The myometrium, composed of the inner circular muscle (CM) and outer longitudinal muscle (LM), is crucial in establishing and maintaining early pregnancy. However, the molecular mechanisms involved are not well understood. In this study, we identified the transcriptomic features of the CM and LM collected from the mesometrial (M) and anti-mesometrial (AM) sides of the pig uterus on day 18 of pregnancy during the placentation initiation phase. Some genes in the cellular zinc ion level regulatory pathways (MT-1A, MT-1D, MT-2B, SLC30A2, and SLC39A2) were spatially and highly enriched in uterine CM at the mesometrial side. In addition, the histone modification profiles of H3K27ac and H3K4me3 in uterine CM and LM collected from the mesometrial side were characterized. Genomic regions associated with the expression of genes regulating the cellular zinc ion level were detected. Moreover, six highly linked variants in the H3K27ac-enriched region of the pig SLC30A2 gene were identified and found to be significantly associated with the total number born at the second parity (P < 0.05). In conclusion, the genes in the pathways of cellular zinc homeostasis and their regulatory elements identified have implications for pig reproduction trait improvement and warrant further investigations.

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.