3D structures of individual mammalian genomes studied by single-cell Hi-C

TULIPs decorate the three-dimensional genome of PFA ependymoma

Posterior fossa group A (PFA) ependymoma is a lethal brain cancer diagnosed in infants and young children. The lack of driver events in the PFA linear genome led us to search its 3D genome for characteristic features. Here, we reconstructed 3D genomes from diverse childhood tumor types and uncovered a global topology in PFA that is highly reminiscent of stem and progenitor cells in a variety of human tissues. A remarkable feature exclusively present in PFA are type B ultra long-range interactions in PFAs (TULIPs), regions separated by great distances along the linear genome that interact with each other in the 3D nuclear space with surprising strength. TULIPs occur in all PFA samples and recur at predictable genomic coordinates, and their formation is induced by expression of EZHIP. The universality of TULIPs across PFA samples suggests a conservation of molecular principles that could be exploited therapeutically.

scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression

SUMMARY

Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of 23.2% in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions.

AVAILABILITY AND IMPLEMENTATION

The GitHub link: https://github.com/rsinghlab/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript.

Regulating epithelial-mesenchymal plasticity from 3D genome organization

Epithelial-mesenchymal transition (EMT) is a dynamic process enabling polarized epithelial cells to acquire mesenchymal features implicated in development and carcinoma progression. As our understanding evolves, it is clear the reversible execution of EMT arises from complex epigenomic regulation involving histone modifications and 3-dimensional (3D) genome structural changes, leading to a cascade of transcriptional events. This review summarizes current knowledge on chromatin organization in EMT, with a focus on hierarchical structures of the 3D genome and chromatin accessibility changes.

Building Haplotype-Resolved 3D Genome Maps of Chicken Skeletal Muscle

Haplotype-resolved 3D chromatin architecture related to allelic differences in avian skeletal muscle development has not been addressed so far, although chicken husbandry for meat consumption has been prevalent feature of cultures on every continent for more than thousands of years. Here, high-resolution Hi-C diploid maps (1.2-kb maximum resolution) are generated for skeletal muscle tissues in chicken across three developmental stages (embryonic day 15 to day 30 post-hatching). The sequence features governing spatial arrangement of chromosomes and characterize homolog pairing in the nucleus, are identified. Multi-scale characterization of chromatin reorganization between stages from myogenesis in the fetus to myofiber hypertrophy after hatching show concordant changes in transcriptional regulation by relevant signaling pathways. Further interrogation of parent-of-origin-specific chromatin conformation supported that genomic imprinting is absent in birds. This study also reveals promoter-enhancer interaction (PEI) differences between broiler and layer haplotypes in skeletal muscle development-related genes are related to genetic variation between breeds, however, only a minority of breed-specific variations likely contribute to phenotypic divergence in skeletal muscle potentially via allelic PEI rewiring. Beyond defining the haplotype-specific 3D chromatin architecture in chicken, this study provides a rich resource for investigating allelic regulatory divergence among chicken breeds.

Single-cell mapping of cell-type specific chromatin architecture in the central nervous system

Determining how chromatin is structured in the nucleus is critical to studying its role in gene regulation. Recent advances in the analysis of single-cell chromatin architecture have considerably improved our understanding of cell-type-specific chromosome conformation and nuclear architecture. In this review, we discuss the methods used for analysis of 3D chromatin conformation, including sequencing-based methods, imaging-based techniques, and computational approaches. We further review the application of these methods in the study of the role of chromatin topology in neural development and disorders.

Pan-evolutionary and regulatory genome architecture delineated by an integrated macro- and microsynteny approach

The forthcoming massive genome data generated by the Earth BioGenome Project will open up a new era of comparative genomics, for which genome synteny analysis provides an important framework. Profiling genome synteny represents an essential step in elucidating genome architecture, regulatory blocks/elements and their evolutionary history. Here we describe PanSyn, ( https://github.com/yhw320/PanSyn ), the most comprehensive and up-to-date genome synteny pipeline, providing step-by-step instructions and application examples to demonstrate its usage. PanSyn inherits both basic and advanced functions from existing popular tools, offering a user-friendly, highly customized approach for genome macrosynteny analysis and integrated pan-evolutionary and regulatory analysis of genome architecture, which are not yet available in public synteny software or tools. The advantages of PanSyn include: (i) advanced microsynteny analysis by functional profiling of microsynteny genes and associated regulatory elements; (ii) comprehensive macrosynteny analysis, including the inference of karyotype evolution from ancestors to extant species; and (iii) functional integration of microsynteny and macrosynteny for pan-evolutionary profiling of genome architecture and regulatory blocks, as well as integration with external functional genomics datasets from three- or four-dimensional genome and ENCODE projects. PanSyn requires basic knowledge of the Linux environment and Perl programming language and the ability to access a computer cluster, especially for large-scale genomic comparisons. Our protocol can be easily implemented by a competent graduate student or postdoc and takes several days to weeks to execute for dozens to hundreds of genomes. PanSyn provides yet the most comprehensive and powerful tool for integrated evolutionary and functional genomics.

Formamide denaturation of double-stranded DNA for fluorescence in situ hybridization (FISH) distorts nanoscale chromatin structure

As imaging techniques rapidly evolve to probe nanoscale genome organization at higher resolution, it is critical to consider how the reagents and procedures involved in sample preparation affect chromatin at the relevant length scales. Here, we investigate the effects of fluorescent labeling of DNA sequences within chromatin using the gold standard technique of three-dimensional fluorescence in situ hybridization (3D FISH). The chemical reagents involved in the 3D FISH protocol, specifically formamide, cause significant alterations to the sub-200 nm (sub-Mbp) chromatin structure. Alternatively, two labeling methods that do not rely on formamide denaturation, resolution after single-strand exonuclease resection (RASER)-FISH and clustered regularly interspaced short palindromic repeats (CRISPR)-Sirius, had minimal impact on the three-dimensional organization of chromatin. We present a polymer physics-based analysis of these protocols with guidelines for their interpretation when assessing chromatin structure using currently available techniques.

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

Genome structural dynamics: insights from Gaussian network analysis of Hi-C data

Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.

Droplet Hi-C for Fast and Scalable Profiling of Chromatin Architecture in Single Cells

Comprehensive analysis of chromatin architecture is crucial for understanding the gene regulatory programs during development and in disease pathogenesis, yet current methods often inadequately address the unique challenges presented by analysis of heterogeneous tissue samples. Here, we introduce Droplet Hi-C, which employs a commercial microfluidic device for high-throughput, single-cell chromatin conformation profiling in droplets. Using Droplet Hi-C, we mapped the chromatin architecture at single-cell resolution from the mouse cortex and analyzed gene regulatory programs in major cortical cell types. Additionally, we used this technique to detect copy number variation (CNV), structural variations (SVs) and extrachromosomal DNA (ecDNA) in cancer cells, revealing clonal dynamics and other oncogenic events during treatment. We further refined this technique to allow for joint profiling of chromatin architecture and transcriptome in single cells, facilitating a more comprehensive exploration of the links between chromatin architecture and gene expression in both normal tissues and tumors. Thus, Droplet Hi-C not only addresses critical gaps in chromatin analysis of heterogeneous tissues but also emerges as a versatile tool enhancing our understanding of gene regulation in health and disease.

Enhancer-promoter interactions are reconfigured through the formation of long-range multiway hubs as mouse ES cells exit pluripotency

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

Spatiotemporal DNA methylation dynamics shape megabase-scale methylome landscapes

DNA methylation is an essential epigenetic mechanism that regulates cellular reprogramming and development. Studies using whole-genome bisulfite sequencing have revealed distinct DNA methylome landscapes in human and mouse cells and tissues. However, the factors responsible for the differences in megabase-scale methylome patterns between cell types remain poorly understood. By analyzing publicly available 258 human and 301 mouse whole-genome bisulfite sequencing datasets, we reveal that genomic regions rich in guanine and cytosine, when located near the nuclear center, are highly susceptible to both global DNA demethylation and methylation events during embryonic and germline reprogramming. Furthermore, we found that regions that generate partially methylated domains during global DNA methylation are more likely to resist global DNA demethylation, contain high levels of adenine and thymine, and are adjacent to the nuclear lamina. The spatial properties of genomic regions, influenced by their guanine-cytosine content, are likely to affect the accessibility of molecules involved in DNA (de)methylation. These properties shape megabase-scale DNA methylation patterns and change as cells differentiate, leading to the emergence of different megabase-scale methylome patterns across cell types.

The rise of single-cell transcriptomics in yeast

The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.

Single-cell multiplex chromatin and RNA interactions in ageing human brain

Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA1-3. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells4-7. Here we introduce the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA-chromatin associations within individual nuclei. When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an 'older' transcriptomic signature and Alzheimer's disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. In addition, female cortical cells exhibit highly heterogeneous interactions between XIST non-coding RNA and chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploration of chromatin architecture and transcription at cellular resolution in complex tissues.

Haplotype-resolved 3D chromatin architecture of the hybrid pig

In diploid mammals, allele-specific three-dimensional (3D) genome architecture may lead to imbalanced gene expression. Through ultradeep in situ Hi-C sequencing of three representative somatic tissues (liver, skeletal muscle, and brain) from hybrid pigs generated by reciprocal crosses of phenotypically and physiologically divergent Berkshire and Tibetan pigs, we uncover extensive chromatin reorganization between homologous chromosomes across multiple scales. Haplotype-based interrogation of multi-omic data revealed the tissue dependence of 3D chromatin conformation, suggesting that parent-of-origin-specific conformation may drive gene imprinting. We quantify the effects of genetic variations and histone modifications on allelic differences of long-range promoter-enhancer contacts, which likely contribute to the phenotypic differences between the parental pig breeds. We also observe the fine structure of somatically paired homologous chromosomes in the pig genome, which has a functional implication genome-wide. This work illustrates how allele-specific chromatin architecture facilitates concomitant shifts in allele-biased gene expression, as well as the possible consequential phenotypic changes in mammals.

Single-cell multiplex chromatin and RNA interactions in aging human brain

The dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA (caRNA) 1-3. Chromatin complex compositions change during cellular differentiation and aging, and are expected to be highly heterogeneous among terminally differentiated single cells 4-7. Here we introduce the Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations within individual nuclei. Applied to 14 human frontal cortex samples from elderly donors, MUSIC delineates diverse cortical cell types and states. We observed the nuclei exhibiting fewer short-range chromatin interactions are correlated with an "older" transcriptomic signature and with Alzheimer's pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci (cis eQTLs) and a promoter tends to be the cell type where these cis eQTLs specifically affect their target gene's expression. Additionally, the female cortical cells exhibit highly heterogeneous interactions between the XIST non-coding RNA and Chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploring chromatin architecture and transcription at cellular resolution in complex tissues.

Computational methods for analysing multiscale 3D genome organization

Recent progress in whole-genome mapping and imaging technologies has enabled the characterization of the spatial organization and folding of the genome in the nucleus. In parallel, advanced computational methods have been developed to leverage these mapping data to reveal multiscale three-dimensional (3D) genome features and to provide a more complete view of genome structure and its connections to genome functions such as transcription. Here, we discuss how recently developed computational tools, including machine-learning-based methods and integrative structure-modelling frameworks, have led to a systematic, multiscale delineation of the connections among different scales of 3D genome organization, genomic and epigenomic features, functional nuclear components and genome function. However, approaches that more comprehensively integrate a wide variety of genomic and imaging datasets are still needed to uncover the functional role of 3D genome structure in defining cellular phenotypes in health and disease.

Chromatin structure from high resolution microscopy: Scaling laws and microphase separation

Recent advances in experimental fluorescence microscopy allow high accuracy determination (resolution of 50 nm) of the three-dimensional physical location of multiple (up to ∼10^{2}) tagged regions of the chromosome. We investigate publicly available microscopy data for two loci of the human Chr21 obtained from multiplexed fluorescence in situ hybridization (FISH) methods for different cell lines and treatments. Inspired by polymer physics models, our analysis centers around distance distributions between different tags with the aim being to unravel the chromatin conformational arrangements. We show that for any specific genomic site, there are (at least) two different conformational arrangements of chromatin, implying coexisting distinct topologies which we refer to as phase α and phase β. These two phases show different scaling behaviors: the former is consistent with a crumpled globule, while the latter indicates a confined, but more extended conformation, such as a looped domain. The identification of these distinct phases sheds light on the coexistence of multiple chromatin topologies and provides insights into the effects of cellular context and/or treatments on chromatin structure.

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.

Conserved chromatin and repetitive patterns reveal slow genome evolution in frogs

Frogs are an ecologically diverse and phylogenetically ancient group of anuran amphibians that include important vertebrate cell and developmental model systems, notably the genus Xenopus. Here we report a high-quality reference genome sequence for the western clawed frog, Xenopus tropicalis, along with draft chromosome-scale sequences of three distantly related emerging model frog species, Eleutherodactylus coqui, Engystomops pustulosus, and Hymenochirus boettgeri. Frog chromosomes have remained remarkably stable since the Mesozoic Era, with limited Robertsonian (i.e., arm-preserving) translocations and end-to-end fusions found among the smaller chromosomes. Conservation of synteny includes conservation of centromere locations, marked by centromeric tandem repeats associated with Cenp-a binding surrounded by pericentromeric LINE/L1 elements. This work explores the structure of chromosomes across frogs, using a dense meiotic linkage map for X. tropicalis and chromatin conformation capture (Hi-C) data for all species. Abundant satellite repeats occupy the unusually long (~20 megabase) terminal regions of each chromosome that coincide with high rates of recombination. Both embryonic and differentiated cells show reproducible associations of centromeric chromatin and of telomeres, reflecting a Rabl-like configuration. Our comparative analyses reveal 13 conserved ancestral anuran chromosomes from which contemporary frog genomes were constructed.

The Nucleosome Remodelling and Deacetylation complex coordinates the transcriptional response to lineage commitment in pluripotent cells

As cells exit the pluripotent state and begin to commit to a specific lineage they must activate genes appropriate for that lineage while silencing genes associated with pluripotency and preventing activation of lineage-inappropriate genes. The Nucleosome Remodelling and Deacetylation (NuRD) complex is essential for pluripotent cells to successfully undergo lineage commitment. NuRD controls nucleosome density at regulatory sequences to facilitate transcriptional responses, and also has been shown to prevent unscheduled transcription (transcriptional noise) in undifferentiated pluripotent cells. How these activities combine to ensure cells engage a gene expression program suitable for successful lineage commitment has not been determined. Here, we show that NuRD is not required to silence all genes. Rather, it restricts expression of genes primed for activation upon exit from the pluripotent state, but maintains them in a transcriptionally permissive state in self-renewing conditions, which facilitates their subsequent activation upon exit from naïve pluripotency. We further show that NuRD coordinates gene expression changes, which acts to maintain a barrier between different stable states. Thus NuRD-mediated chromatin remodelling serves multiple functions, including reducing transcriptional noise, priming genes for activation and coordinating the transcriptional response to facilitate lineage commitment.

DiffDomain enables identification of structurally reorganized topologically associating domains

Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi-C) contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi-C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.

Probe-free optical chromatin deformation and measurement of differential mechanical properties in the nucleus

The nucleus is highly organized to facilitate coordinated gene transcription. Measuring the rheological properties of the nucleus and its sub-compartments will be crucial to understand the principles underlying nuclear organization. Here, we show that strongly localized temperature gradients (approaching 1°C/µm) can lead to substantial intra-nuclear chromatin displacements (>1 µm), while nuclear area and lamina shape remain unaffected. Using particle image velocimetry (PIV), intra-nuclear displacement fields can be calculated and converted into spatio-temporally resolved maps of various strain components. Using this approach, we show that chromatin displacements are highly reversible, indicating that elastic contributions are dominant in maintaining nuclear organization on the time scale of seconds. In genetically inverted nuclei, centrally compacted heterochromatin displays high resistance to deformation, giving a rigid, solid-like appearance. Correlating spatially resolved strain maps with fluorescent reporters in conventional interphase nuclei reveals that various nuclear compartments possess distinct mechanical identities. Surprisingly, both densely and loosely packed chromatin showed high resistance to deformation, compared to medium dense chromatin. Equally, nucleoli display particularly high resistance and strong local anchoring to heterochromatin. Our results establish how localized temperature gradients can be used to drive nuclear compartments out of mechanical equilibrium to obtain spatial maps of their material responses.

Chromatin phase separated nanoregions explored by polymer cross-linker models and reconstructed from single particle trajectories

Phase separated domains (PSDs) are ubiquitous in cell biology, representing nanoregions of high molecular concentration. PSDs appear at diverse cellular domains, such as neuronal synapses but also in eukaryotic cell nucleus, limiting the access of transcription factors and thus preventing gene expression. We develop a generalized cross-linker polymer model, to study PSDs: we show that increasing the number of cross-linkers induces a polymer condensation, preventing access of diffusing molecules. To investigate how the PSDs restrict the motion of diffusing molecules, we compute the mean residence and first escaping times. Finally, we develop a method based on mean-square-displacement of single particle trajectories to reconstruct the properties of PSDs from the continuum range of anomalous exponents. We also show here that PSD generated by polymers do not induces a long-range attracting field (potential well), in contrast with nanodomains at neuronal synapses. To conclude, PSDs can result from condensed chromatin organization, where the number of cross-linkers controls molecular access.

The counterpart congenital overgrowth syndromes Beckwith-Wiedemann Syndrome in human and large offspring syndrome in bovine involve alterations in DNA methylation, transcription, and chromatin configuration

Beckwith-Wiedemann Syndrome (BWS, OMIM #130650) is a congenital epigenetic disorder in humans which affects approximately 1 in 10,340 children. The incidence is likely an underestimation as the condition is usually recognized based on observable phenotypes at birth. BWS children have up to a 28% risk of developing tumors and currently, only 80% of patients can be corroborated molecularly (epimutations/variants). It is unknown how the subtypes of this condition are molecularly similar/dissimilar globally, therefore there is a need to deeply characterize the syndrome at the molecular level. Here we characterize the methylome, transcriptome and chromatin configuration of 18 BWS individuals together with the animal model of the condition, the bovine large offspring syndrome (LOS). Sex specific comparisons are performed for a subset of the BWS patients and LOS. Given that this epigenetic overgrowth syndrome has been characterized as a loss-of-imprinting condition, parental allele-specific comparisons were performed using the bovine animal model. In general, the differentially methylated regions (DMRs) detected in BWS and LOS showed significant enrichment for CTCF binding sites. Altered chromosome compartments in BWS and LOS were positively correlated with gene expression changes, and the promoters of differentially expressed genes showed significant enrichment for DMRs, differential topologically associating domains, and differential A/B compartments in some comparisons of BWS subtypes and LOS. We show shared regions of dysregulation between BWS and LOS, including several HOX gene clusters, and also demonstrate that altered DNA methylation differs between the clinically epigenetically identified BWS patients and those identified as having DNA variants (i.e. CDKN1C microdeletion). Lastly, we highlight additional genes and genomic regions that have the potential to serve as targets for biomarker development to improve current molecular methodologies. In summary, our results suggest that genome-wide alternation of chromosome architecture, which is partially caused by DNA methylation changes, also contribute to the development of BWS and LOS.

Reconstructing diploid 3D chromatin structures from single cell Hi-C data with a polymer-based approach

Detailed understanding of the 3D structure of chromatin is a key ingredient to investigate a variety of processes inside the cell. Since direct methods to experimentally ascertain these structures lack the desired spatial fidelity, computational inference methods based on single cell Hi-C data have gained significant interest. Here, we develop a progressive simulation protocol to iteratively improve the resolution of predicted interphase structures by maximum-likelihood association of ambiguous Hi-C contacts using lower-resolution predictions. Compared to state-of-the-art methods, our procedure is not limited to haploid cell data and allows us to reach a resolution of up to 5,000 base pairs per bead. High resolution chromatin models grant access to a multitude of structural phenomena. Exemplarily, we verify the formation of chromosome territories and holes near aggregated chromocenters as well as the inversion of the CpG content for rod photoreceptor cells.

3D models of fungal chromosomes to enhance visual integration of omics data

The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is underexploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to omics data integration. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public omics datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our results highlighted (i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, (ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and (iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration of omics data. This holistic perspective supports intuition and lays the foundation for building new concepts.

Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes

The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression. When the cell changes its identity in the cell-fate decision-making process, extensive rearrangements of chromosome structures occur accompanied by large-scale adaptations of gene expression, underscoring the importance of chromosome dynamics in shaping genome function. Over the last two decades, rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes. In parallel, these enormous data offer valuable opportunities for developing quantitative computational models. Here, we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes. Different from the underlying modeling strategies, these approaches can be classified into data-driven ('top-down') and physics-based ('bottom-up') categories. We discuss their contributions to offering valuable insights into the relationships among the structures, dynamics, and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.

Quantifying the large-scale chromosome structural dynamics during the mitosis-to-G1 phase transition of cell cycle

Cell cycle is known to be regulated by the underlying gene network. Chromosomes, which serve as the scaffold for gene expressions, undergo significant structural reorganizations during mitosis. Understanding the mechanism of the cell cycle from the chromosome structural perspective remains a grand challenge. In this study, we applied an integrated theoretical approach to investigate large-scale chromosome structural dynamics during the mitosis-to-G1 phase transition. We observed that the chromosome structural expansion and adaptation of the structural asphericity do not occur synchronously and attributed this behaviour to the unique unloading sequence of the two types of condensins. Furthermore, we observed that the coherent motions between the chromosomal loci are primarily enhanced within the topologically associating domains (TADs) as cells progress to the G1 phase, suggesting that TADs can be considered as both structural and dynamical units for organizing the three-dimensional chromosome. Our analysis also reveals that the quantified pathways of chromosome structural reorganization during the mitosis-to-G1 phase transition exhibit high stochasticity at the single-cell level and show nonlinear behaviours in changing TADs and contacts formed at the long-range regions. Our findings offer valuable insights into large-scale chromosome structural dynamics after mitosis.

Live-cell three-dimensional single-molecule tracking reveals modulation of enhancer dynamics by NuRD

To understand how the nucleosome remodeling and deacetylase (NuRD) complex regulates enhancers and enhancer-promoter interactions, we have developed an approach to segment and extract key biophysical parameters from live-cell three-dimensional single-molecule trajectories. Unexpectedly, this has revealed that NuRD binds to chromatin for minutes, decompacts chromatin structure and increases enhancer dynamics. We also uncovered a rare fast-diffusing state of enhancers and found that NuRD restricts the time spent in this state. Hi-C and Cut&Run experiments revealed that NuRD modulates enhancer-promoter interactions in active chromatin, allowing them to contact each other over longer distances. Furthermore, NuRD leads to a marked redistribution of CTCF and, in particular, cohesin. We propose that NuRD promotes a decondensed chromatin environment, where enhancers and promoters can contact each other over longer distances, and where the resetting of enhancer-promoter interactions brought about by the fast decondensed chromatin motions is reduced, leading to more stable, long-lived enhancer-promoter relationships.

A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level

Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi-C loop calling by adapting the training and inferencing strategies of graph-based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single-cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi-connected hubs and their underlying mechanisms.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

c-Jun as a one-way valve at the naive to primed interface

BACKGROUND

c-Jun is a proto-oncogene functioning as a transcription factor to activate gene expression under many physiological and pathological conditions, particularly in somatic cells. However, its role in early embryonic development remains unknown.

RESULTS

Here, we show that c-Jun acts as a one-way valve to preserve the primed state and impair reversion to the naïve state. c-Jun is induced during the naive to primed transition, and it works to stabilize the chromatin structure and inhibit the reverse transition. Loss of c-Jun has surprisingly little effect on the naïve to primed transition, and no phenotypic effect on primed cells, however, in primed cells the loss of c-Jun leads to a failure to correctly close naïve-specific enhancers. When the primed cells are induced to reprogram to a naïve state, these enhancers are more rapidly activated when c-Jun is lost or impaired, and the conversion is more efficient.

CONCLUSIONS

The results of this study indicate that c-Jun can function as a chromatin stabilizer in primed EpiSCs, to maintain the epigenetic cell type state and act as a one-way valve for cell fate conversions.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We found the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

Technological advances in probing 4D genome organization

The last two decades of work on chromosome conformation in eukaryotic nuclei have revealed a complex and highly regulated hierarchy of architectural features, from self-associating domains and compartmental interactions to locus-specific loops. Recent findings have shown that these structures are dynamic and heterogeneous, with emerging insights into the factors that shape them and implications for the control of transcription and other nuclear processes. Here, we review the latest advances in the DNA sequencing- and microscopy-based technologies for probing these features in space and time (4D) and discuss how they have been combined with complementary approaches such as genetic perturbations, protein and RNA measurements, and modeling to gain mechanistic insights about genome regulation across space and time.

scNanoHi-C: a single-cell long-read concatemer sequencing method to reveal high-order chromatin structures within individual cells

The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within an individual cell. To address this, herein, we developed scNanoHi-C, which applies Nanopore long-read sequencing to explore genome-wide proximal high-order chromatin contacts within individual cells. We show that scNanoHi-C can reliably and effectively profile 3D chromatin structures and distinguish structure subtypes among individual cells. This method could also be used to detect genomic variations, including copy-number variations and structural variations, as well as to scaffold the de novo assembly of single-cell genomes. Notably, our results suggest that extensive high-order chromatin structures exist in active chromatin regions across the genome, and multiway interactions between enhancers and their target promoters were systematically identified within individual cells. Altogether, scNanoHi-C offers new opportunities to investigate high-order 3D genome structures at the single-cell level.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Application of single-cell multi-omics approaches in horticulture research

Cell heterogeneity shapes the morphology and function of various tissues and organs in multicellular organisms. Elucidation of the differences among cells and the mechanism of intercellular regulation is essential for an in-depth understanding of the developmental process. In recent years, the rapid development of high-throughput single-cell transcriptome sequencing technologies has influenced the study of plant developmental biology. Additionally, the accuracy and sensitivity of tools used to study the epigenome and metabolome have significantly increased, thus enabling multi-omics analysis at single-cell resolution. Here, we summarize the currently available single-cell multi-omics approaches and their recent applications in plant research, review the single-cell based studies in fruit, vegetable, and ornamental crops, and discuss the potential of such approaches in future horticulture research.

SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data

Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.

insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology

A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells' spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences.

Simultaneous profiling of chromatin architecture and transcription in single cells

The three-dimensional structure of chromatin plays a crucial role in development and disease, both of which are associated with transcriptional changes. However, given the heterogeneity in single-cell chromatin architecture and transcription, the regulatory relationship between the three-dimensional chromatin structure and gene expression is difficult to explain based on bulk cell populations. Here we develop a single-cell, multimodal, omics method allowing the simultaneous detection of chromatin architecture and messenger RNA expression by sequencing (single-cell transcriptome sequencing (scCARE-seq)). Applying scCARE-seq to examine chromatin architecture and transcription from 2i to serum single mouse embryonic stem cells, we observe improved separation of cell clusters compared with single-cell chromatin conformation capture. In addition, after defining the cell-cycle phase of each cell through chromatin architecture extracted by scCARE-seq, we find that periodic changes in chromatin architecture occur in parallel with transcription during the cell cycle. These findings highlight the potential of scCARE-seq to facilitate comprehensive analyses that may boost our understanding of chromatin architecture and transcription in the same single cell.

Genome control by SMC complexes

Many cellular processes require large-scale rearrangements of chromatin structure. Structural maintenance of chromosomes (SMC) protein complexes are molecular machines that can provide structure to chromatin. These complexes can connect DNA elements in cis, walk along DNA, build and processively enlarge DNA loops and connect DNA molecules in trans to hold together the sister chromatids. These DNA-shaping abilities place SMC complexes at the heart of many DNA-based processes, including chromosome segregation in mitosis, transcription control and DNA replication, repair and recombination. In this Review, we discuss the latest insights into how SMC complexes such as cohesin, condensin and the SMC5-SMC6 complex shape DNA to direct these fundamental chromosomal processes. We also consider how SMC complexes, by building chromatin loops, can counteract the natural tendency of alike chromatin regions to cluster. SMC complexes thus control nuclear organization by participating in a molecular tug of war that determines the architecture of our genome.

Three-dimensional genome structure and function

Linear DNA undergoes a series of compression and folding events, forming various three-dimensional (3D) structural units in mammalian cells, including chromosomal territory, compartment, topologically associating domain, and chromatin loop. These structures play crucial roles in regulating gene expression, cell differentiation, and disease progression. Deciphering the principles underlying 3D genome folding and the molecular mechanisms governing cell fate determination remains a challenge. With advancements in high-throughput sequencing and imaging techniques, the hierarchical organization and functional roles of higher-order chromatin structures have been gradually illuminated. This review systematically discussed the structural hierarchy of the 3D genome, the effects and mechanisms of cis-regulatory elements interaction in the 3D genome for regulating spatiotemporally specific gene expression, the roles and mechanisms of dynamic changes in 3D chromatin conformation during embryonic development, and the pathological mechanisms of diseases such as congenital developmental abnormalities and cancer, which are attributed to alterations in 3D genome organization and aberrations in key structural proteins. Finally, prospects were made for the research about 3D genome structure, function, and genetic intervention, and the roles in disease development, prevention, and treatment, which may offer some clues for precise diagnosis and treatment of related diseases.

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

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

Bayesian methods in integrative structure modeling

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells

The organization of mammalian genomes within the nucleus features a complex, multiscale three-dimensional (3D) architecture. The functional significance of these 3D genome features, however, remains largely elusive due to limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we report GAGE-seq, a highly scalable, robust single-cell co-assay that simultaneously measures 3D genome structure and transcriptome within the same cell. Employing GAGE-seq on mouse brain cortex and human bone marrow CD34+ cells, we comprehensively characterized the intricate relationships between 3D genome and gene expression. We found that these multiscale 3D genome features collectively inform cell type-specific gene expressions, hence contributing to defining cell identity at the single-cell level. Integration of GAGE-seq data with spatial transcriptomic data revealed in situ variations of the 3D genome in mouse cortex. Moreover, our observations of lineage commitment in normal human hematopoiesis unveiled notable discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level that is more nuanced than previously appreciated. Together, GAGE-seq provides a powerful, cost-effective approach for interrogating genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

Hi-TrAC detects active sub-TADs and reveals internal organizations of super-enhancers

The spatial folding of eukaryotic genome plays a key role in genome function. We report here that our recently developed method, Hi-TrAC, which specializes in detecting chromatin loops among accessible genomic regions, can detect active sub-TADs with a median size of 100 kb, most of which harbor one or two cell specifically expressed genes and regulatory elements such as super-enhancers organized into nested interaction domains. These active sub-TADs are characterized by highly enriched histone mark H3K4me1 and chromatin-binding proteins, including Cohesin complex. Deletion of selected sub-TAD boundaries have different impacts, such as decreased chromatin interaction and gene expression within the sub-TADs or compromised insulation between the sub-TADs, depending on the specific chromatin environment. We show that knocking down core subunit of the Cohesin complex using shRNAs in human cells or decreasing the H3K4me1 modification by deleting the H3K4 methyltransferase Mll4 gene in mouse Th17 cells disrupted the sub-TADs structure. Our data also suggest that super-enhancers exist as an equilibrium globule structure, while inaccessible chromatin regions exist as a fractal globule structure. In summary, Hi-TrAC serves as a highly sensitive and inexpensive approach to study dynamic changes of active sub-TADs, providing more explicit insights into delicate genome structures and functions.

Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome

Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the pairwise motion of distal chromosomal elements such as enhancers and promoters is essential and necessitates dynamic fluidity. Here, we used a live-imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output while systematically varying the genomic separation between these two DNA loci. Our analysis reveals the coexistence of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation leading to long-ranged correlations. Thus, encounter times of DNA loci are much less dependent on genomic distance than predicted by existing polymer models, with potential consequences for eukaryotic gene expression.

Recent advances in chromosome capture techniques unraveling 3D genome architecture in germ cells, health, and disease

In eukaryotes, the genome does not emerge in a specific shape but rather as a hierarchial bundle within the nucleus. This multifaceted genome organization consists of multiresolution cellular structures, such as chromosome territories, compartments, and topologically associating domains, which are frequently defined by architecture, design proteins including CTCF and cohesin, and chromatin loops. This review briefly discusses the advances in understanding the basic rules of control, chromatin folding, and functional areas in early embryogenesis. With the use of chromosome capture techniques, the latest advancements in technologies for visualizing chromatin interactions come close to revealing 3D genome formation frameworks with incredible detail throughout all genomic levels, including at single-cell resolution. The possibility of detecting variations in chromatin architecture might open up new opportunities for disease diagnosis and prevention, infertility treatments, therapeutic approaches, desired exploration, and many other application scenarios.

Histone exchange sensors reveal variant specific dynamics in mouse embryonic stem cells

Eviction of histones from nucleosomes and their exchange with newly synthesized or alternative variants is a central epigenetic determinant. Here, we define the genome-wide occupancy and exchange pattern of canonical and non-canonical histone variants in mouse embryonic stem cells by genetically encoded exchange sensors. While exchange of all measured variants scales with transcription, we describe variant-specific associations with transcription elongation and Polycomb binding. We found considerable exchange of H3.1 and H2B variants in heterochromatin and repeat elements, contrasting the occupancy and little exchange of H3.3 in these regions. This unexpected association between H3.3 occupancy and exchange of canonical variants is also evident in active promoters and enhancers, and further validated by reduced H3.1 dynamics following depletion of H3.3-specific chaperone, HIRA. Finally, analyzing transgenic mice harboring H3.1 or H3.3 sensors demonstrates the vast potential of this system for studying histone exchange and its impact on gene expression regulation in vivo.