The 4D nucleome project

Examining the dynamics of three-dimensional genome organization with multitask matrix factorization

Three-dimensional (3D) genome organization, which determines how the DNA is packaged inside the nucleus, has emerged as a key component of the gene regulation machinery. High-throughput chromosome conformation data sets, such as Hi-C, have become available across multiple conditions and time points, offering a unique opportunity to examine changes in 3D genome organization and link them to phenotypic changes in normal and disease processes. However, systematic detection of higher-order structural changes across multiple Hi-C data sets remains a major challenge. Existing computational methods either do not model higher-order structural units or cannot model dynamics across more than two conditions of interest. We address these limitations with tree-guided integrated factorization (TGIF), a generalizable multitask nonnegative matrix factorization (NMF) approach that can be applied to time series or hierarchically related biological conditions. TGIF can identify large-scale changes at the compartment or subcompartment levels, as well as local changes at boundaries of topologically associated domains (TADs). Based on benchmarking in simulated and real Hi-C data, TGIF boundaries are more accurate and reproducible across differential levels of noise and sources of technical artifacts, and are more enriched in CTCF. Application to three multisample mammalian data sets shows that TGIF can detect differential regions at compartment, subcompartment, and boundary levels that are associated with significant changes in regulatory signals and gene expression enriched in tissue-specific processes. Finally, we leverage TGIF boundaries to prioritize sequence variants for multiple phenotypes from the NHGRI GWAS catalog. Taken together, TGIF is a flexible tool to examine 3D genome organization dynamics across disease and developmental processes.

Chrombus-XMBD: a graph convolution model predicting 3D-genome from chromatin features

The 3D conformation of the chromatin is crucial for transcriptional regulation. However, current experimental techniques for detecting the 3D structure of the genome are costly and limited to the biological conditions. Here, we described "ChrombusXMBD," a graph convolution model capable of predicting chromatin interactions ab initio based on available chromatin features. Using dynamic edge convolution with multihead attention mechanism, Chrombus encodes the 2D-chromatin features into a learnable embedding space, thereby generating a genome-wide 3D-contactmap. In validation, Chrombus effectively recapitulated the topological associated domains, expression quantitative trait loci, and promoter/enhancer interactions. Especially, Chrombus outperforms existing algorithms in predicting chromatin interactions over 1-2 Mb, increasing prediction correlation by 11.8%-48.7%, and predicts long-range interactions over 2 Mb (Pearson's coefficient 0.243-0.582). Chrombus also exhibits strong generalizability across human and mouse-derived cell lines. Additionally, the parameters of Chrombus inform the biological mechanisms underlying cistrome. Our model provides a new, generalizable analytical tool for understanding the complex dynamics of chromatin interactions and the landscape of cis-regulation of gene expression.

Super RNA Pol II domains enhance minor ZGA through 3D interaction to ensure the integrity of major transcriptional waves in late-ZGA mammals

Zygotic genome activation (ZGA) occurs at distinct stages across mammals, with mice initiating ZGA at the 2-cell stage and bovines and humans activating the process in the 4- to 8-cell stages. RNA polymerase II (RNA Pol II) gradually initiates ZGA in mice, but regulation in late-ZGA species remains unclear. Here, RNA Pol II profiling in bovine embryos identified strong intergenic clusters that boost minor ZGA gene expression via chromatin interactions and are named super RNA Pol II domains (SPDs). CRISPRi perturbation of SPDs in bovine embryos decreases the expression of minor ZGA genes, whereas the knockdown of these genes disrupts major ZGA and embryogenesis. Rapid enhancement of minor ZGA genes also occurs in human embryos. Alternatively, mouse and porcine oocytes precociously express these minor ZGA genes without SPDs. Thus, SPDs appear to be an adaptation in bovine embryos, promoting minor ZGA gene expression to comparable levels as early-ZGA species, illuminating species-specific regulation of ZGA timing.

Atlas of nascent RNA transcripts reveals tissue-specific enhancer to gene linkages

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in non-coding bidirectional transcription that results in generally unstable RNA. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify tissue specific, highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as a database called DBNascent ( https://nascent.colorado.edu/ ) which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.

Tensor-FLAMINGO unravels the complexity of single-cell spatial architectures of genomes at high-resolution

The dynamic three-dimensional spatial conformations of chromosomes demonstrate complex structural variations across single cells, which plays pivotal roles in modulating single-cell specific transcription and epigenetics landscapes. The high rates of missing contacts in single-cell chromatin contact maps impose significant challenges to reconstruct high-resolution spatial chromatin configurations. We develop a data-driven algorithm, Tensor-FLAMINGO, based on a low-rank tensor completion strategy. Implemented on a diverse panel of single-cell chromatin datasets, Tensor-FLAMINGO generates 10kb- and 30kb-resolution spatial chromosomal architectures across individual cells. Tensor-FLAMINGO achieves superior accuracy in reconstructing 3D chromatin structures, recovering missing contacts, and delineating cell clusters. The unprecedented high-resolution characterization of single-cell genome folding enables expanded identification of single-cell specific long-range chromatin interactions, multi-way spatial hubs, and the mechanisms of disease-associated GWAS variants. Beyond the sparse 2D contact maps, the complete 3D chromatin conformations promote an avenue to understand the dynamics of spatially coordinated molecular processes across different cells.

High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease.

Spatial multi-omics reveals cell-type-specific nuclear compartments

The mammalian nucleus is compartmentalized by diverse subnuclear structures. These subnuclear structures, marked by nuclear bodies and histone modifications, are often cell-type specific and affect gene regulation and 3D genome organization1-3. Understanding their relationships rests on identifying the molecular constituents of subnuclear structures and mapping their associations with specific genomic loci and transcriptional levels in individual cells, all in complex tissues. Here, we introduce two-layer DNA seqFISH+, which enables simultaneous mapping of 100,049 genomic loci, together with the nascent transcriptome for 17,856 genes and subnuclear structures in single cells. These data enable imaging-based chromatin profiling of diverse subnuclear markers and can capture their changes at genomic scales ranging from 100-200 kilobases to approximately 1 megabase, depending on the marker and DNA locus. By using multi-omics datasets in the adult mouse cerebellum, we showed that repressive chromatin regions are more variable by cell type than are active regions across the genome. We also discovered that RNA polymerase II-enriched foci were locally associated with long, cell-type-specific genes (bigger than 200 kilobases) in a manner distinct from that of nuclear speckles. Furthermore, our analysis revealed that cell-type-specific regions of heterochromatin marked by histone H3 trimethylated at lysine 27 (H3K27me3) and histone H4 trimethylated at lysine 20 (H4K20me3) are enriched at specific genes and gene clusters, respectively, and shape radial chromosomal positioning and inter-chromosomal interactions in neurons and glial cells. Together, our results provide a single-cell high-resolution multi-omics view of subnuclear structures, associated genomic loci and their effects on gene regulation, directly within complex tissues.

Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci

The biological functions of extragenic enhancer RNAs and their impact on disease risk remain relatively underexplored. In this work, we develop in silico models of genetically regulated expression of enhancer RNAs across 49 cell and tissue types, characterizing their degree of genetic control. Leveraging the estimated genetically regulated expression for enhancer RNAs and canonical genes in a large-scale DNA biobank (N > 70,000) and high-resolution Hi-C contact data, we train a deep learning-based model of pairwise three-dimensional chromatin contact frequency for enhancer-enhancer and enhancer-gene pairs in cerebellum and whole blood. Notably, the use of genetically regulated expression of enhancer RNAs provides substantial tissue-specific predictive power, supporting a role for these transcripts in modulating spatial chromatin organization. We identify schizophrenia-associated enhancer RNAs independent of GWAS loci using enhancer RNA-based TWAS and determine the causal effects of these enhancer RNAs using Mendelian randomization. Using enhancer RNA-based TWAS, we generate a comprehensive resource of tissue-specific enhancer associations with complex traits in the UK Biobank. Finally, we show that a substantially greater proportion (63%) of GWAS associations colocalize with causal regulatory variation when enhancer RNAs are included.

TRUHiC: A TRansformer-embedded U-2 Net to enhance Hi-C data for 3D chromatin structure characterization

High-throughput chromosome conformation capture sequencing (Hi-C) is a key technology for studying the three-dimensional (3D) structure of genomes and chromatin folding. Hi-C data reveals important patterns of genome organization such as topologically associating domains (TADs) and chromatin loops with critical roles in transcriptional regulation and disease etiology and progression. However, the relatively low resolution of existing Hi-C data often hinders robust and reliable inference of 3D structures. Hence, we propose TRUHiC, a new computational method that leverages recent state-of-the-art deep generative modeling to augment low-resolution Hi-C data for the characterization of 3D chromatin structures. Applying TRUHiC to publically available Hi-C data for human and mice, we demonstrate that the augmented data significantly improves the characterization of TADs and loops across diverse cell lines and species. We further present a pre-trained TRUHiC on human lymphoblastoid cell lines that can be adaptable and transferable to improve chromatin characterization of various cell lines, tissues, and species.

Recipes and ingredients for deep learning models of 3D genome folding

Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for predicting genome contact maps. We survey preprocessing, architecture, training, evaluation, and interpretation methods, highlighting the capabilities and limitations of different models. In each area, we highlight challenges, opportunities, and potential future directions for genome-folding models.

ChromEMT: visualizing and reconstructing chromatin ultrastructure and 3D organization in situ

Structure determines function. The discovery of the DNA double-helix structure revealed how genetic information is stored and copied. In the mammalian cell nucleus, up to two meters of DNA is compacted by histones to form nucleosome/DNA particle chains that form euchromatin and heterochromatin domains, chromosome territories and mitotic chromosomes upon cell division. A critical question is what are the structures, interactions and 3D organization of DNA as chromatin in the nucleus and how do they determine DNA replication timing, gene expression and ultimately cell fate. To visualize genomic DNA across these different length scales in the nucleus, we developed ChromEMT, a method that selectively enhances the electron density and contrast of DNA and interacting nucleosome particles, which enables nucleosome chains, chromatin domains, chromatin ultrastructure and 3D organization to be imaged and reconstructed by using multi-tilt electron microscopy tomography (EMT). ChromEMT exploits a membrane-permeable, fluorescent DNA-binding dye, DRAQ5, which upon excitation drives the photo-oxidation and precipitation of diaminobenzidine polymers on the surface of DNA/nucleosome particles that are visible in the electron microscope when stained with osmium. Here, we describe a detailed protocol for ChromEMT, including DRAQ5 staining, photo-oxidation, sample preparation and multi-tilt EMT that can be applied broadly to reconstruct genomic DNA structure and 3D interactions in cells and tissues and different kingdoms of life. The entire procedure takes ~9 days and requires expertise in electron microscopy sample sectioning and acquisition of multi-tilt EMT data sets.

Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes

Chromosome Conformation Capture (3 C) methods, including Hi-C (a high-throughput variation of 3 C), detect pairwise interactions between DNA regions, enabling the reconstruction of chromatin architecture in the nucleus. HiChIP is a modification of the Hi-C experiment that includes a chromatin immunoprecipitation (ChIP) step, allowing genome-wide identification of chromatin contacts mediated by a protein of interest. In mammalian cells, cohesin protein complex is one of the major players in the establishment of chromatin loops. We present an improved cohesin HiChIP experimental protocol. Using comprehensive bioinformatic analysis, we show that a dual chromatin fixation method compared to the standard formaldehyde-only method, results in a substantially better signal-to-noise ratio, increased ChIP efficiency and improved detection of chromatin loops and architectural stripes. Additionally, we propose an automated pipeline called nf-HiChIP ( https://github.com/SFGLab/hichip-nf-pipeline ) for processing HiChIP samples starting from raw sequencing reads data and ending with a set of significant chromatin interactions (loops), which allows efficient and timely analysis of multiple samples in parallel, without requiring additional ChIP-seq experiments. Finally, using advanced approaches for biophysical modelling and stripe calling we generate accurate loop extrusion polymer models for a region of interest and provide a detailed picture of architectural stripes, respectively.

Long-range enhancer-controlled genes are hypersensitive to regulatory factor perturbations

Cell-type-specific gene activation is regulated by enhancers, sometimes located at large genomic distances from target gene promoters. Whether distal enhancers require specific factors to orchestrate gene regulation remains unclear. Here, we used enhancer distance-controlled reporter screens to find candidate factors. We depleted them and employed activity-by-contact predictions to genome-wide classify genes based on enhancer distance. Predicted distal enhancers typically control tissue-restricted genes and often are strong enhancers. We find cohesin, but also mediator, most specifically required for long-range activation, with cohesin repressing short-range gene activation and prioritizing distal over proximal HBB genes competing for shared enhancers. Long-range controlled genes are also most sensitive to perturbations of other regulatory proteins and to BET inhibitor JQ1, this being more a consequence of their distinct enhancer features than distance. Our work predicts that lengthening of intervening sequences can help limit the expression of target genes to specialized cells with optimal trans-factor environments.

Loop Catalog: a comprehensive HiChIP database of human and mouse samples

HiChIP enables cost-effective and high-resolution profiling of chromatin loops. To leverage the increasing number of HiChIP datasets, we developed Loop Catalog (https://loopcatalog.lji.org), a web-based database featuring loop calls from 1000+ distinct human and mouse HiChIP samples from 152 studies plus 44 high-resolution Hi-C samples. We demonstrate its utility for interpreting GWAS and eQTL variants through SNP-to-gene linking, identifying enriched sequence motifs and motif pairs, and generating regulatory networks and 2D representations of chromatin structure. Our catalog spans over 4.19M unique loops, and with embedded analysis modules, constitutes an important resource for the field.

Linking DNA-packing density distribution and TAD boundary locations

DNA is heterogeneously packaged into chromatin, which is further organized into topologically associating domains (TADs) with sharp boundaries. These boundary locations are critical for genome regulation. Here, we explore how the distribution of DNA-packing density across chromatin affects the TAD boundary locations. We develop a polymer-physics-based model that utilizes DNA accessibility data to parameterize DNA-packing density along chromosomes, treating them as heteropolymers, and simulates the stochastic folding of these heteropolymers within a nucleus to yield a conformation ensemble. Such an ensemble reproduces a subset (over 60%) of TAD boundaries across the human genome, as confirmed by Hi-C data. Additionally, it reproduces the spatial distance matrices of 2-Mb genomic regions provided by FISH experiments. Furthermore, our model suggests that utilizing DNA accessibility data alone as input is sufficient to predict the emergence and disappearance of key TADs during early T cell differentiation. We show that stochastic folding of heteropolymers in a confined space can replicate both the prevalence of chromatin domain structures and the cell-to-cell variation in domain boundary positions observed in single-cell experiments. Furthermore, regions of lower DNA-packing density preferentially form domain boundaries, and this preference drives the emergence of TAD boundaries observed in ensemble-averaged Hi-C maps. The enrichment of TAD boundaries at CTCF binding sites can be attributed to the influence of CTCF binding on local DNA-packing density in our model. Collectively, our findings establish a strong link between TAD boundaries and regions of lower DNA-packing density, providing insights into the mechanisms underlying TAD formation.

Two unrelated distal genes activated by a shared enhancer benefit from localizing inside the same small topological domain

Enhancers are tissue-specific regulatory DNA elements that can activate transcription of genes over distance. Their target genes most often are located in the same contact domain-chromosomal entities formed by cohesin DNA loop extrusion and typically flanked by CTCF-bound boundaries. Enhancers shared by multiple unrelated genes are underexplored but may be more common than anticipated. Here, we analyzed the interplay between an enhancer and two distal functionally unrelated genes residing at opposite domain boundaries. The enhancer strongly activated their expression and supported their frequent interactions. Cohesin structured the domain and supported their transcription, but the genes did not rely on each other's transcription or show gene competition. Deleting either domain boundary not only extended the contact domain but led to reduced contacts within the original domain and reduction in the expression of both genes. Conversely, by isolating either gene with the enhancer in shorter domains, through insertion of new CTCF boundaries, intradomain contact frequencies increased, and the gene isolated with the enhancer was upregulated. Collectively, this shows that an enhancer can independently activate unrelated distal genes and that long-range gene regulation benefits from operating in small contact domains.

Coarse-grained chromatin dynamics by tracking multiple similarly labeled gene loci

The "holy grail" of chromatin research would be to follow the chromatin configuration in individual live cells over time. One way to achieve this goal would be to track the positions of multiple loci arranged along the chromatin polymer with fluorescent labels. Use of distinguishable labels would define each locus uniquely in a microscopic image but would restrict the number of loci that could be observed simultaneously, because of experimental limits to the number of distinguishable labels. Use of the same label for all loci circumvents this limitation but requires a (currently lacking) framework for how to establish each observed locus identity, i.e. to which genomic position it corresponds. Here we analyze theoretically, using simulations of Rouse-model polymers, how single-particle-tracking of multiple identically-labeled loci enables determination of loci identity. We show that the probability of correctly assigning observed loci to genomic positions converges exponentially to unity as the number of observed loci configurations increases. The convergence rate depends only weakly on the number of labeled loci, so that even large numbers of loci can be identified with high fidelity by tracking them across about 8 independent chromatin configurations. In the case of two distinct labels that alternate along the chromatin polymer, we find that the probability of the correct assignment converges faster than for same-labeled loci, requiring observation of fewer independent chromatin configurations to establish loci identities. Finally, for a modified Rouse-model polymer, that realizes a population of dynamic loops, we find that the success probability also converges to unity exponentially as the number of observed loci configurations increases, albeit slightly more slowly than for a classical Rouse model polymer. Altogether, these results establish particle tracking of multiple identically- or alternately-labeled loci over time as a feasible way to infer temporal dynamics of the coarse-grained configuration of the chromatin polymer in individual living cells.

Identification of Schizophrenia-Risk Regulatory Variant rs1399178 in the Non-coding Region With Its Impact on NRF1 Binding

AIMS

The challenges in identifying functional variants from genome-wide association studies (GWAS) and unraveling regulatory mechanisms in schizophrenia research persist, particularly in intronic regions. A non-coding regulatory variant, rs1399178, associated with schizophrenia risk, is identified and its impact on NRF1 binding is investigated.

METHODS

This study focuses on schizophrenia GWAS risk loci, using functional genomics, expression analyses and structural analysis to identify 736 schizophrenia risk single-nucleotide polymorphisms (SNPs) that disrupt transcription factor (TF) binding.

RESULTS

Among these SNPs, rs1399178 stands out as a bifunctional intergenic SNP that can switch between acting as a promoter and an enhancer, potentially influencing MLH1 and LRRFIP2 expression via expression quantitative trait loci analysis. Importantly, mutation of the G allele of rs1399178 to A significantly diminishes its binding affinity to nuclear respiratory factor 1 (NRF1). Structural analysis provides further insight into this alteration in the protein-nucleic acid complex formation.

CONCLUSION

Based on our data, a model is proposed in which rs1399178 confers schizophrenia risk by modifying NRF1 binding profiles, thereby regulating the abundance of target genes through promoter-enhancer switching. This study provides novel insights into the regulatory mechanisms of schizophrenia risk variants, highlighting the intricate nature of genetic interactions and potential therapeutic targets.

Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data

The Human Reference Atlas (HRA) for the healthy, adult body is being developed by a team of international, interdisciplinary experts across 20+ consortia. It provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions of experimental datasets and ontology-linked reference anatomical structures (AS), cell types (CT), and biomarkers (B). We introduce the HRA Knowledge Graph (KG) as central data resource for HRA v2.2, supporting cross-scale, biological queries to Resource Description Framework graphs using SPARQL. In February 2025, the HRA KG covered 71 organs with 5,800 AS, 2,268 CT, 2,531 B; it had 10,064,033 nodes, 171,250,177 edges, and a size of 125.84 GB. The HRA KG comprises 13 types of Digital Objects (DOs) using the Common Coordinate Framework Ontology to standardize core concepts and relationships across DOs. We (1) provide data and code for HRA KG construction; (2) detail HRA KG deployment by Linked Open Data principles; and (3) illustrate HRA KG usage via application programming interfaces, user interfaces, data products. A companion website is at https://cns-iu.github.io/hra-kg-supporting-information.

TF2TG: an online resource mining the potential gene targets of transcription factors in Drosophila

Sequence-specific transcription factors (TFs) are key regulators of many biological processes, controlling the expression of their target genes through binding to the cis- regulatory regions such as promoters and enhancers. Each TF has unique DNA binding site motifs, and large-scale experiments have been conducted to characterize TF-DNA binding preferences. However, no comprehensive resource currently integrates these datasets for Drosophila. To address this need, we developed TF2TG ("transcription factor to target gene"), a comprehensive resource that combines both in vitro and in vivo datasets to link transcription factors (TFs) to their target genes based on TF-DNA binding preferences along with the protein-protein interaction data, tissue-specific transcriptomic data, and chromatin accessibility data. Although the genome offers numerous potential binding sites for each TF, only a subset is actually bound in vivo, and of these, only a fraction is functionally relevant. For instance, some TFs bind to their specific sites due to synergistic interactions with other factors nearby. This integration provides users with a comprehensive list of potential candidates as well as aids users in ranking candidate genes and determining condition-specific TF binding for studying transcriptional regulation in Drosophila.

Characterization of the role of spatial proximity of DNA double-strand breaks in the formation of CRISPR-Cas9-induced large structural variations

Structural variations (SVs) play important roles in genetic diversity, evolution, and carcinogenesis and are, as such, important for human health. However, it remains unclear how spatial proximity of double-strand breaks (DSBs) affects the formation of SVs. To investigate if spatial proximity between two DSBs affects DNA repair, we used data from 3C experiments (Hi-C, ChIA-PET, and ChIP-seq) to identify highly interacting loci on six different chromosomes. The target regions correlate with the borders of megabase-sized topologically associated domains (TADs), and we used CRISPR-Cas9 nuclease and pairs of single guide RNAs (sgRNAs) against these targets to generate DSBs in both K562 cells and H9 human embryonic stem cells (hESCs). Droplet digital PCR (ddPCR) was used to quantify the resulting recombination events, and high-throughput sequencing was used to analyze the chimeric junctions created between the two DSBs. We observe a significantly higher formation frequency of deletions and inversions with DSBs in proximity compared with deletions and inversions with DSBs not in proximity in K562 cells. Additionally, our results suggest that DSB proximity may affect the ligation of chimeric deletion junctions. Taken together, spatial proximity between DSBs is a significant predictor of large-scale deletion and inversion frequency induced by CRISPR-Cas9 in K562 cells. This finding has implications for understanding SVs in the human genome and for the future application of CRISPR-Cas9 in gene editing and the modeling of rare SVs.

Unveiling the role of chromosome structure morphology on gene function through chromosome conformation analysis

Single-cell chromosome conformations vary significantly among individual cells. We introduce a two-step dimensionality reduction method for density-based, unsupervised clustering of single-cell 3D chromosome structures from simulations or multiplexed 3D-FISH imaging. Our method clusters up to half of all structures into 5-12 prevalent conformational states per chromosome. These states are distinguished by subdivisions into chromosome territory domains, whose boundary locations influence subnuclear positions and speckle associations of certain genes and establish long-range structural variations of more than 10 Mb. Territory domain boundaries are found at few sequence locations, shared among cell types and often situated at syntenic breakpoints.

Deciphering single-cell genomic architecture: insights into cellular heterogeneity and regulatory dynamics

BACKGROUND

The genomic architecture of eukaryotes exhibits dynamic spatial and temporal changes, enabling cellular processes critical for maintaining viability and functional diversity. Recent advances in sequencing technologies have facilitated the dissection of genomic architecture and functional activity at single-cell resolution, moving beyond the averaged signals typically derived from bulk cell analyses.

MAIN BODY

The advent of single-cell genomics and epigenomics has yielded transformative insights into cellular heterogeneity, behavior, and biological complexity with unparalleled genomic resolution and reproducibility. This review summarizes recent progress in the characterization of genomic architecture at the single-cell level, emphasizing the impact of structural variation and chromatin organization on gene regulatory networks and cellular identity.

CONCLUSION

Future directions in single-cell genomics and high-resolution epigenomic methodologies are explored, focusing on emerging challenges and potential impacts on the understanding of cellular states, regulatory dynamics, and the intricate mechanisms driving cellular function and diversity. Future perspectives on the challenges and potential implications of single-cell genomics, along with high-resolution genomic and epigenomic technologies for understanding cellular states and regulatory dynamics, are also discussed.

Polymer models of chromatin organization in virally infected cells

Genome architecture is closely tied to essential biological functions, yet a complete understanding of the mechanisms governing DNA folding remains a significant challenge. Theoretical models based on polymer physics have been applied to decipher the complexity of chromatin architecture and uncover the physical processes shaping its structure. Importantly, recent findings suggest that certain viruses can alter the 3D organization of the host genome. In this review, we highlight recent advances in the development of polymer models used to study how chromatin 3D structure within a cell re-organizes following viral infection, with a particular emphasis on the SARS-CoV-2 virus, capable of altering genome organization of the host cell at different scales, including A/B compartments, TADs and gene-enhancer regulatory contacts.

UV-induced reorganization of 3D genome mediates DNA damage response

While it is well-established that UV radiation threatens genomic integrity, the precise mechanisms by which cells orchestrate DNA damage response and repair within the context of 3D genome architecture remain unclear. Here, we address this gap by investigating the UV-induced reorganization of the 3D genome and its critical role in mediating damage response. Employing temporal maps of contact matrices and transcriptional profiles, we illustrate the immediate and holistic changes in genome architecture post-irradiation, emphasizing the significance of this reconfiguration for effective DNA repair processes. We demonstrate that UV radiation triggers a comprehensive restructuring of the 3D genome organization at all levels, including loops, topologically associating domains and compartments. Through the analysis of DNA damage and excision repair maps, we uncover a correlation between genome folding, gene regulation, damage formation probability, and repair efficacy. We show that adaptive reorganization of the 3D genome is a key mediator of the damage response, providing new insights into the complex interplay of genomic structure and cellular defense mechanisms against UV-induced damage, thereby advancing our understanding of cellular resilience.

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations

MOTIVATION

Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data.

RESULTS

We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (×105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization.

AVAILABILITY AND IMPLEMENTATION

It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

MorPhiC Consortium: towards functional characterization of all human genes

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions.

ChromoGen: Diffusion model predicts single-cell chromatin conformations

Breakthroughs in high-throughput sequencing and microscopic imaging technologies have revealed that chromatin structures vary considerably between cells of the same type. However, a thorough characterization of this heterogeneity remains elusive due to the labor-intensive and time-consuming nature of these experiments. To address these challenges, we introduce ChromoGen, a generative model based on state-of-the-art artificial intelligence techniques that efficiently predicts three-dimensional, single-cell chromatin conformations de novo with both region and cell type specificity. These generated conformations accurately reproduce experimental results at both the single-cell and population levels. Moreover, ChromoGen successfully transfers to cell types excluded from the training data using just DNA sequence and widely available DNase-seq data, thus providing access to chromatin structures in myriad cell types. These achievements come at a remarkably low computational cost. Therefore, ChromoGen enables the systematic investigation of single-cell chromatin organization, its heterogeneity, and its relationship to sequencing data, all while remaining economical.

A Joint Analysis of RNA-DNA and DNA-DNA Interactomes Reveals Their Strong Association

At the moment, many non-coding RNAs that perform a variety of functions in the regulation of chromatin processes are known. An increasing number of protocols allow researchers to study RNA-DNA interactions and shed light on new aspects of the RNA-chromatin interactome. The Hi-C protocol, which enables the study of chromatin's three-dimensional organization, has already led to numerous discoveries in the field of genome 3D organization. We conducted a comprehensive joint analysis of the RNA-DNA interactome and chromatin structure across different human and mouse cell lines. We show that these two phenomena are closely related in many respects, with the nature of this relationship being both tissue specific and conserved across humans and mice.

An integrative TAD catalog in lymphoblastoid cell lines discloses the functional impact of deletions and insertions in human genomes

The human genome is packaged within a three-dimensional (3D) nucleus and organized into structural units known as compartments, topologically associating domains (TADs), and loops. TAD boundaries, separating adjacent TADs, have been found to be well conserved across mammalian species and more evolutionarily constrained than TADs themselves. Recent studies show that structural variants (SVs) can modify 3D genomes through the disruption of TADs, which play an essential role in insulating genes from outside regulatory elements' aberrant regulation. However, how SV affects the 3D genome structure and their association among different aspects of gene regulation and candidate cis-regulatory elements (cCREs) have rarely been studied systematically. Here, we assess the impact of SVs intersecting with TAD boundaries by developing an integrative Hi-C analysis pipeline, which enables the generation of an in-depth catalog of TADs and TAD boundaries in human lymphoblastoid cell lines (LCLs) to fill the gap of limited resources. Our catalog contains 18,865 TADs, including 4596 sub-TADs, with 185 SVs (TAD-SVs) that alter chromatin architecture. By leveraging the ENCODE registry of cCREs in humans, we determine that 34 of 185 TAD-SVs intersect with cCREs and observe significant enrichment of TAD-SVs within cCREs. This study provides a database of TADs and TAD-SVs in the human genome that will facilitate future investigations of the impact of SVs on chromatin structure and gene regulation in health and disease.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Acute multi-level response to defective de novo chromatin assembly in S-phase

Long-term perturbation of de novo chromatin assembly during DNA replication has profound effects on epigenome maintenance and cell fate. The early mechanistic origin of these defects is unknown. Here, we combine acute degradation of chromatin assembly factor 1 (CAF-1), a key player in de novo chromatin assembly, with single-cell genomics, quantitative proteomics, and live microscopy to uncover these initiating mechanisms in human cells. CAF-1 loss immediately slows down DNA replication speed and renders nascent DNA hyper-accessible. A rapid cellular response, distinct from canonical DNA damage signaling, is triggered and lowers histone mRNAs. In turn, histone variants' usage and their modifications are altered, limiting transcriptional fidelity and delaying chromatin maturation within a single S-phase. This multi-level response induces a p53-dependent cell-cycle arrest after mitosis. Our work reveals the immediate consequences of defective de novo chromatin assembly during DNA replication, indicating how at later times the epigenome and cell fate can be altered.

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Over the past decade, there has been substantial growth in both the quantity and complexity of available biomedical data. In order to more efficiently harness this extensive data and alleviate challenges associated with integration of multi-omics data, we developed Petagraph, a biomedical knowledge graph that encompasses over 32 million nodes and 118 million relationships. Petagraph leverages more than 180 ontologies and standards in the Unified Biomedical Knowledge Graph (UBKG) to embed millions of quantitative genomics data points. Petagraph provides a cohesive data environment that enables users to efficiently analyze, annotate, and discern relationships within and across complex multi-omics datasets supported by UBKG's annotation scaffold. We demonstrate how queries on Petagraph can generate meaningful results across various research contexts and use cases.

A mini-review of single-cell Hi-C embedding methods

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.

Transcribing RNA polymerases: Dynamics of twin supercoiled domains

Gene transcription by an RNA polymerase (RNAP) enzyme requires that double-stranded DNA be locally and transiently opened, which results in an increase of DNA supercoiling downstream of the RNAP and a decrease of supercoiling upstream of it. When the DNA is initially torsionally relaxed and the RNAP experiences sufficiently large rotational drag, these variations lead to positively supercoiled plectonemes ahead of the RNAPs and negatively supercoiled ones behind it, a feature known as "twin supercoiled domain" (TSD). This work aims at deciphering into some more detail the torsional dynamics of circular DNA molecules being transcribed by RNAP enzymes. To this end, we performed Brownian dynamics simulations with a specially designed coarse-grained model. Depending on the superhelical density of the DNA molecule and the ratio of RNAP's twist injection rate and rotational relaxation speed, simulations reveal a rich panel of behaviors, which sometimes differ markedly from the crude TSD picture. In particular, for sufficiently slow rotational relaxation speed, positively supercoiled plectonemes never form ahead of an RNAP that transcribes a DNA molecule with physiological negative supercoiling. Rather, negatively supercoiled plectonemes form almost periodically at the upstream side of the RNAP and grow up to a certain length before detaching from the RNAP and destabilizing rapidly. The extent to which topological barriers hinder the dynamics of TSDs is also discussed.

The extracellular vesicle transcriptome provides tissue-specific functional genomic annotation relevant to disease susceptibility in obesity

We characterized circulating extracellular vesicles (EVs) in obese and lean humans, identifying transcriptional cargo differentially expressed in obesity. Since circulating EVs may have broad origin, we compared this obesity EV transcriptome to expression from human visceral adipose tissue derived EVs from freshly collected and cultured biopsies from the same obese individuals. Using a comprehensive set of adipose-specific epigenomic and chromatin conformation assays, we found that the differentially expressed transcripts from the EVs were those regulated in adipose by BMI-associated SNPs from a large-scale GWAS. Using a phenome-wide association study of the regulatory SNPs for the EV-derived transcripts, we identified a substantial enrichment for inflammatory phenotypes, including type 2 diabetes. Collectively, these findings represent the convergence of the GWAS (genetics), epigenomics (transcript regulation), and EV (liquid biopsy) fields, enabling powerful future genomic studies of complex diseases.

Inter-chromosomal contacts demarcate genome topology along a spatial gradient

Non-homologous chromosomal contacts (NHCCs) between different chromosomes participate considerably in gene and genome regulation. Due to analytical challenges, NHCCs are currently considered as singular, stochastic events, and their extent and fundamental principles across cell types remain controversial. We develop a supervised and unsupervised learning algorithm, termed Signature, to call NHCCs in Hi-C datasets to advance our understanding of genome topology. Signature reveals 40,282 NHCCs and their properties across 62 Hi-C datasets of 53 diploid human cell types. Genomic regions of NHCCs are gene-dense, highly expressed, and harbor genes for cell-specific and sex-specific functions. Extensive inter-telomeric and inter-centromeric clustering occurs across cell types [Rabl's configuration] and 61 NHCCs are consistently found at the nuclear speckles. These constitutive 'anchor loci' facilitate an axis of genome activity whilst cell-type-specific NHCCs act in discrete hubs. Our results suggest that non-random chromosome positioning is supported by constitutive NHCCs that shape genome topology along an off-centered spatial gradient of genome activity.

PU.1 eviction at lymphocyte-specific chromatin domains mediates glucocorticoid response in acute lymphoblastic leukemia

The epigenetic landscape plays a critical role in cancer progression, yet its therapeutic potential remains underexplored. Glucocorticoids are essential components of treatments for lymphoid cancers, but resistance, driven in part by epigenetic changes at glucocorticoid-response elements, poses a major challenge to effective therapies. Here we show that glucocorticoid treatment induces distinct patterns of chromosomal organization in glucocorticoid-sensitive and resistant acute lymphoblastic leukemia xenograft models. These glucocorticoid-response elements are primed by the pioneer transcription factor PU.1, which interacts with the glucocorticoid receptor. Eviction of PU.1 promotes receptor binding, increasing the expression of genes involved in apoptosis and facilitating a stronger therapeutic response. Treatment with a PU.1 inhibitor enhances glucocorticoid sensitivity, demonstrating the clinical potential of targeting this pathway. This study uncovers a mechanism involving PU.1 and the glucocorticoid receptor, linking transcription factor activity with drug response, and suggesting potential therapeutic strategies for overcoming resistance.

An integrated view of the structure and function of the human 4D nucleome

The dynamic three-dimensional (3D) organization of the human genome (the "4D Nucleome") is closely linked to genome function. Here, we integrate a wide variety of genomic data generated by the 4D Nucleome Project to provide a detailed view of human 3D genome organization in widely used embryonic stem cells (H1-hESCs) and immortalized fibroblasts (HFFc6). We provide extensive benchmarking of 3D genome mapping assays and integrate these diverse datasets to annotate spatial genomic features across scales. The data reveal a rich complexity of chromatin domains and their sub-nuclear positions, and over one hundred thousand structural loops and promoter-enhancer interactions. We developed 3D models of population-based and individual cell-to-cell variation in genome structure, establishing connections between chromosome folding, nuclear organization, chromatin looping, gene transcription, and DNA replication. We demonstrate the use of computational methods to predict genome folding from DNA sequence, uncovering potential effects of genetic variants on genome structure and function. Together, this comprehensive analysis contributes insights into human genome organization and enhances our understanding of connections between the regulation of genome function and 3D genome organization in general.

Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention

Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine structures, and applies deep learning only to the third type of signals, such that enhancement of the first two is unaffected by the patches. For the deep learning part, SHARP uses both local and global attention mechanisms to capture multi-scale contextual information. We compare SHARP with state-of-the-art methods extensively, including application to data from new samples and another species, and show that SHARP has superior performance in terms of resolution enhancement accuracy, avoiding creation of artificial structures, identifying significant interactions, and enrichment in chromatin states.

Impact of Chromatin Organization and Epigenetics on CRISPR-Cas and TALEN Genome Editing

DNA lies at the heart of the central dogma of life. Altering DNA can modify the flow of information in fundamental cellular processes such as transcription and translation. The ability to precisely manipulate DNA has led to remarkable advances in treating incurable human genetic ailments and has changed the landscape of biological research. Genome editors such as CRISPR-Cas nucleases and TALENs have become ubiquitous tools in basic and applied biological research and have been translated to the clinic to treat patients. The specificity and modularity of these genome editors have made it possible to efficiently engineer genomic DNA; however, underlying principles governing editing outcomes in eukaryotes are still being uncovered. Editing efficiency can vary from cell type to cell type for the same DNA target sequence, necessitating de novo design and validation efforts. Chromatin structure and epigenetic modifications have been shown to affect the activity of genome editors because of the role they play in hierarchical organization of the underlying DNA. Understanding the nuclear search mechanism of genome editors and their molecular interactions with higher order chromatin will lead to improved models for predicting precise genome editing outcomes. Insights from such studies will unlock the entire genome to be engineered for the creation of novel therapies to treat critical illnesses.

Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns

Understanding variation in chromatin contact patterns across diverse humans is critical for interpreting noncoding variants and their effects on gene expression and phenotypes. However, experimental determination of chromatin contact patterns across large samples is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the variation in 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We apply this approach to thousands of human genomes from the 1000 Genomes Project and the inferred hominin ancestral genome. While patterns of 3D contact divergence genome wide are qualitatively similar to patterns of sequence divergence, we find substantial differences in 3D divergence and sequence divergence in local 1 megabase genomic windows. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, for 31% of genomic windows, a single individual has a rare divergent 3D contact map pattern. Using in silico mutagenesis, we find that most single nucleotide sequence changes do not result in changes to 3D chromatin contacts. However, in windows with substantial 3D divergence just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals variable contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.

Technological advancements in deciphering RNA-RNA interactions

RNA-RNA interactions (RRIs) can dictate RNA molecules to form intricate higher-order structures and bind their RNA substrates in diverse biological processes. To elucidate the function, binding specificity, and regulatory mechanisms of various RNA molecules, especially the vast repertoire of non-coding RNAs, advanced technologies and methods that globally map RRIs are extremely valuable. In the past decades, many state-of-the-art technologies have been developed for this purpose. This review focuses on those high-throughput technologies for the global mapping of RRIs. We summarize the key concepts and the pros and cons of different technologies. In addition, we highlight the novel biological insights uncovered by these RRI mapping methods and discuss the future challenges for appreciating the crucial roles of RRIs in gene regulation across bacteria, viruses, archaea, and mammals.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

Long Terminal Repeats of Gammaretroviruses Retain Stable Expression after Integration Retargeting

Retroviruses integrate into the genomes of infected host cells to form proviruses, a genetic platform for stable viral gene expression. Epigenetic silencing can, however, hamper proviral transcriptional activity. As gammaretroviruses (γRVs) preferentially integrate into active promoter and enhancer sites, the high transcriptional activity of γRVs can be attributed to this integration preference. In addition, long terminal repeats (LTRs) of some γRVs were shown to act as potent promoters by themselves. Here, we investigate the capacity of different γRV LTRs to drive stable expression within a non-preferred epigenomic environment in the context of diverse retroviral vectors. We demonstrate that different γRV LTRs are either rapidly silenced or remain active for long periods of time with a predominantly active proviral population under normal and retargeted integration. As an alternative to the established γRV systems, the feline leukemia virus and koala retrovirus LTRs are able to drive stable, albeit intensity-diverse, transgene expression. Overall, we show that despite the occurrence of rapid silencing events, most γRV LTRs can drive stable expression outside of their preferred chromatin landscape after retrovirus integrations.

Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations

Here, we employ polymer physics models of chromatin to investigate the 3D folding of a 2 Mb wide genomic region encompassing the human LTN1 gene, a crucial DNA locus involved in key cellular functions. Through extensive Molecular Dynamics simulations, we reconstruct in silico the ensemble of single-molecule LTN1 3D structures, which we benchmark against recent in situ Hi-C 2.0 data. The model-derived single molecules are then used to predict structural folding features at the single-cell level, providing testable predictions for super-resolution microscopy experiments.

HiCMC: High-Efficiency Contact Matrix Compressor

BACKGROUND

Chromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data.

RESULTS

By modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms the state-of-the-art method CMC by approximately 8% and the other state-of-the-art methods cooler, LZMA, and bzip2 by over 50% across multiple cell lines and contact matrix resolutions. In addition, HiCMC integrates domain-specific information into the compressed bitstreams that it generates, and this information can be used to speed up downstream analyses.

CONCLUSION

HiCMC is a novel compression approach that utilizes intrinsic properties of contact matrix, such as compartments and domains. It allows for a better compression in comparison to the state-of-the-art methods. HiCMC is available at https://github.com/sXperfect/hicmc .

Harmonizing the Generation and Pre-publication Stewardship of FAIR bioimage data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured bioimage data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable bioimage data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing bioimage data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). Here, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse bioimage data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made generating community standard practices for imaging Quality Control (QC) and metadata (Faklaris et al., 2022; Hammer et al., 2021; Huisman et al., 2021; Microscopy Australia, 2016; Montero Llopis et al., 2021; Rigano et al., 2021; Sarkans et al., 2021). We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Chromatin context-dependent effects of epigenetic drugs on CRISPR-Cas9 editing

The efficiency and outcome of CRISPR/Cas9 editing depends on the chromatin state at the cut site. It has been shown that changing the chromatin state can influence both the efficiency and repair outcome, and epigenetic drugs have been used to improve Cas9 editing. However, because the target proteins of these drugs are not homogeneously distributed across the genome, the efficacy of these drugs may be expected to vary from locus to locus. Here, we systematically analyzed this chromatin context-dependency for 160 epigenetic drugs. We used a human cell line with 19 stably integrated reporters to induce a double-stranded break in different chromatin environments. We then measured Cas9 editing efficiency and repair pathway usage by sequencing the mutational signatures. We identified 58 drugs that modulate Cas9 editing efficiency and/or repair outcome dependent on the local chromatin environment. For example, we find a subset of histone deacetylase inhibitors that improve Cas9 editing efficiency throughout all types of heterochromatin (e.g. PCI-24781), while others were only effective in euchromatin and H3K27me3-marked regions (e.g. apicidin). In summary, this study reveals that most epigenetic drugs alter CRISPR editing in a chromatin-dependent manner, and provides a resource to improve Cas9 editing more selectively at the desired location.

ARGV: 3D genome structure exploration using augmented reality

Over the past two decades, scientists have increasingly realized the importance of the three-dimensional (3D) genome organization in regulating cellular activity. Hi-C and related experiments yield 2D contact matrices that can be used to infer 3D models of chromosome structure. Visualizing and analyzing genomes in 3D space remains challenging. Here, we present ARGV, an augmented reality 3D Genome Viewer. ARGV contains more than 350 pre-computed and annotated genome structures inferred from Hi-C and imaging data. It offers interactive and collaborative visualization of genomes in 3D space, using standard mobile phones or tablets. A user study comparing ARGV to existing tools demonstrates its benefits.