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.

During meiosis, the mammalian genome is organised within chromatin loops, which facilitate synapsis, crossing over and chromosome segregation, setting the stage for recombination events and the generation of genetic diversity. Chromatin looping is thought to play a major role in the establishment of cross overs during prophase I of meiosis, in diploid early primary spermatocytes. However, chromatin conformation dynamics during human meiosis are difficult to study experimentally, due to the transience of each cell division and the difficulty of obtaining stage-resolved cell populations. Here, we employed a machine learning framework trained on single cell ATAC-seq and RNA-seq data to predict CTCF-anchored looping during spermatogenesis, including cell types at different stages of meiosis.

We find dramatic changes in genome-wide looping patterns throughout meiosis: compared to pre-and-post meiotic germline cell types, loops in meiotic early primary spermatocytes are more abundant, more variable between individual cells, and more evenly spread throughout the genome. In preparation for the first meiotic division, loops also include longer stretches of DNA, encompassing more than half of the total genome. These loop structures then influence the rate of recombination initiation and resolution as cross overs. In contrast, in later mature sperm stages, we find evidence of genome compaction, with loops being confined to the telomeric ends of the chromosomes.

Overall, we find that chromatin loops do not orchestrate the gene expression dynamics seen during spermatogenesis, but loops do play important roles in recombination, influencing the positions of DNA breakage and cross over events.

Transcriptional enhancers can regulate individual or multiple genes through long-range three-dimensional (3D) genome interactions, and these interactions are commonly altered in cancer. Yet, the functional relationship between changes in 3D genome interactions associated with regulatory regions and differential gene expression appears context-dependent. In this study, we used HiChIP to capture changes in 3D genome interactions between active regulatory regions of endometrial cancer cells in response to estrogen treatment and uncovered significant differential long-range interactions strongly enriched for estrogen receptor alpha (ER, also known as ESR1)-bound sites (ERBSs). The ERBSs anchoring differential chromatin loops with either a gene's promoter or distal regions were correlated with larger transcriptional responses to estrogen compared with ERBSs not involved in differential 3D genome interactions. To functionally test this observation, CRISPR-based Enhancer-i was used to deactivate specific ERBSs, which revealed a wide range of effects on the transcriptional response to estrogen. However, these effects are only subtly and not significantly stronger for ERBSs in differential chromatin loops. In addition, we observed an enrichment of 3D genome interactions between the promoters of estrogen-upregulated genes and found that looped promoters can work together cooperatively. Overall, our work reveals that estrogen treatment causes large changes in 3D genome structure in endometrial cancer cells; however, these changes are not required for a regulatory region to contribute to an estrogen transcriptional response.

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.

Glioblastoma (GBM) is the most prevalent malignant brain tumor with poor prognosis. Although chromatin intratumoral heterogeneity is a characteristic feature of GBM, most current studies are conducted at a single tumor site. To investigate the GBM-specific 3D genome organization and its heterogeneity, we conducted Hi-C experiments in 21 GBM samples from nine patients, along with three normal brain samples. We identified genome subcompartmentalization and chromatin interactions specific to GBM, as well as extensive intertumoral and intratumoral heterogeneity at these levels. We identified copy number variants (CNVs) and structural variations (SVs) and demonstrated how they disrupted 3D genome structures. SVs could not only induce enhancer hijacking but also cause the loss of enhancers to the same gene, both of which contributed to gene dysregulation. Our findings provide insights into the GBM-specific 3D genome organization and the intratumoral heterogeneity of this organization and open avenues for understanding this devastating disease.

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.

Originating from, but independent of, linear chromosomes, extrachromosomal DNA (ecDNA) exists in a more active state of transcription and autonomous replication. It plays a crucial role in the development of malignancies and therapy resistance. Since its discovery in eukaryotic cells more than half a century ago, the biological characteristics and functions of ecDNA have remained unclear due to limitations in detection methods. However, recent advancements in research tools have transformed ecDNA research. It is believed that ecDNA exhibits greater activity in the abnormal amplification of oncogenes, thereby driving cancer progression through their overexpression. Notably, compared to linear DNA, ecDNA can also function as a genomic element with regulatory roles, including both trans- and cis-acting functions. Its critical roles in tumorigenesis, evolution, progression, and drug resistance in malignant tumors are increasingly recognized. This review provides a comprehensive summary of the evolutionary context of ecDNA and highlights significant progress in understanding its biological functions and potential applications as a therapeutic target in malignant tumors.

While a rich set of putative cis-regulatory sequences involved in mouse fetal development have been annotated recently on the basis of chromatin accessibility and histone modification patterns, delineating their role in developmentally regulated gene expression continues to be challenging. To fill this gap, here we mapped chromatin contacts between gene promoters and distal sequences across the genome in seven mouse fetal tissues and across six developmental stages of the forebrain. We identified 248,620 long-range chromatin interactions centered at 14,138 protein-coding genes and characterized their tissue-to-tissue variations and developmental dynamics. Integrative analysis of the interactome with previous epigenome and transcriptome datasets from the same tissues revealed a strong correlation between the chromatin contacts and chromatin state at distal enhancers, as well as gene expression patterns at predicted target genes. We predicted target genes of 15,098 candidate enhancers and used them to annotate target genes of homologous candidate enhancers in the human genome that harbor risk variants of human diseases. We present evidence that schizophrenia and other adult disease risk variants are frequently found in fetal enhancers, providing support for the hypothesis of fetal origins of adult diseases.

The ability of cells to sense and respond to mechanical signals is essential for many biological processes that form the basis of cell identity, tissue development and maintenance. This process, known as mechanotransduction, involves crucial feedback between mechanical force and biochemical signals, including epigenomic modifications that establish transcriptional programs. These programs, in turn, reinforce the mechanical properties of the cell and its ability to withstand mechanical perturbation. The nucleus has long been hypothesized to play a key role in mechanotransduction due to its direct exposure to forces transmitted through the cytoskeleton, its role in receiving cytoplasmic signals and its central function in gene regulation. However, parsing out the specific contributions of the nucleus from those of the cell surface and cytoplasm in mechanotransduction remains a substantial challenge. In this Review, we examine the latest evidence on how the nucleus regulates mechanotransduction, both via the nuclear envelope (NE) and through epigenetic and transcriptional machinery elements within the nuclear interior. We also explore the role of nuclear mechanotransduction in establishing a mechanical memory, characterized by a mechanical, epigenetic and transcriptomic cell state that persists after mechanical stimuli cease. Finally, we discuss current challenges in the field of nuclear mechanotransduction and present technological advances that are poised to overcome them.

Three-dimensional genome organization reveals that gene regulatory elements, which are linearly distant on the genome, can spatially interact with target genes to regulate their expression. DNA fluorescence in situ hybridization (DNA-FISH) is an efficient method for studying the spatial proximity of genomic loci. In this study, we developed an optimized Tn5 transposome-based DNA-FISH method, termed Tn5-labeled DNA-FISH. This approach amplifies the target region and uses a self-assembled Tn5 transposome to simultaneously fragment the DNA into ~100 bp segments and label it with fluorescent oligonucleotides in a single step. This method enables the preparation of probes for regions as small as 4 kb and visualizes both endogenous and exogenous genomic loci at kb resolution. Tn5-labeled DNA-FISH provides a streamlined and cost-effective tool for probe generation, facilitating the investigation of chromatin spatial conformations, gene interactions, and genome architecture.

The recombinant human p53 adenovirus (Ad-p53) offers a promising approach for cancer therapy, yet its chromatin structure and effects on host chromatin organization and gene expression are not fully understood.

In this study, we employed in situ ChIA-PET to investigate the colorectal cancer cell line HCT116 with p53 knockout, comparing them to cells infected with the adenovirus-vector expressing p53. We examined alterations in chromatin interactions and gene expression following treatment with the anti-cancer drug 5-fluorouracil (5-FU).

Our results indicate that Ad-p53 forms a specific chromatin architecture within the vector and mainly interacts with repressive or inactive regions of host chromatin, without significantly affecting the expression of associated genes. Additionally, Ad-p53 does not affect topologically associating domains (TADs) or A/B compartments in the host genome.

These findings suggest that while Ad-p53 boosts p53 expression, enhancing drug sensitivity without substantially altering host HCT116 chromatin architecture.

Transcriptional enhancers can regulate individual or multiple genes through long-range three-dimensional (3D) genome interactions, and these interactions are commonly altered in cancer. Yet, the functional relationship between changes in 3D genome interactions associated with regulatory regions and differential gene expression appears context-dependent. In this study, we used HiChIP to capture changes in 3D genome interactions between active regulatory regions of endometrial cancer cells in response to estrogen treatment and uncovered significant differential long-range interactions strongly enriched for estrogen receptor α (ER) bound sites (ERBS). The ERBS anchoring differential chromatin loops with either a gene's promoter or distal regions were correlated with larger transcriptional responses to estrogen compared to ERBS not involved in differential 3D genome interactions. To functionally test this observation, CRISPR-based Enhancer-i was used to deactivate specific ERBS, which revealed a wide range of effects on the transcriptional response to estrogen. However, these effects are only subtly and not significantly stronger for ERBS in differential chromatin loops. In addition, we observed an enrichment of 3D genome interactions between the promoters of estrogen upregulated genes and found that looped promoters can work together cooperatively. Overall, our work reveals that estrogen treatment causes large changes in 3D genome structure in endometrial cancer cells; however, these changes are not required for a regulatory region to contribute to an estrogen transcriptional response.

The human genome is tightly packed into the 3D environment of the cell nucleus. Rapidly evolving and sophisticated methods of mapping 3D genome architecture have shed light on fundamental principles of genome organization and gene regulation. The genome is physically organized on different scales, from individual genes to entire chromosomes. Nuclear landmarks such as the nuclear envelope and nucleoli have important roles in compartmentalizing the genome within the nucleus. Genome activity (for example, gene transcription) is also functionally partitioned within this 3D organization. Rather than being static, the 3D organization of the genome is tightly regulated over various time scales. These dynamic changes in genome structure over time represent the fourth dimension of the genome. Innovative methods have been used to map the dynamic regulation of genome structure during important cellular processes including organism development, responses to stimuli, cell division and senescence. Furthermore, disruptions to the 4D genome have been linked to various diseases, including of the kidney. As tools and approaches to studying the 4D genome become more readily available, future studies that apply these methods to study kidney biology will provide insights into kidney function in health and disease.

Single-cell three-dimensional (3D) genome techniques have advanced our understanding of cell-type-specific chromatin structures in complex tissues, yet current methodologies are limited in cell throughput. Here we introduce a high-throughput single-cell Hi-C (dscHi-C) approach and its transcriptome co-assay (dscHi-C-multiome) using droplet microfluidics. Using dscHi-C, we investigate chromatin structural changes during mouse brain aging by profiling 32,777 single cells across three developmental stages (3 months, 12 months, and 23 months), yielding a median of 78,220 unique contacts. Our results show that genes with significant structural changes are enriched in pathways related to metabolic process and morphology change in neurons, and innate immune response in glial cells, highlighting the role of 3D genome organization in physiological brain aging. Furthermore, our multi-omics joint assay, dscHi-C-multiome, enables precise cell type identification in the adult mouse brain and uncovers the intricate relationship between genome architecture and gene expression. Collectively, we developed the sensitive, high-throughput dscHi-C and its multi-omics derivative, dscHi-C-multiome, demonstrating their potential for large-scale cell atlas studies in development and disease.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.

Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses.

The spatial organization of the genome plays a critical role in regulating gene expression, cellular differentiation, and genome stability. This review provides an in-depth examination of the methodologies, computational tools, and frameworks developed to map the three-dimensional (3D) architecture of the genome, focusing on both ligation-based and ligation-free techniques. We also explore the limitations of these methods, including biases introduced by restriction enzyme digestion and ligation inefficiencies, and compare them to more recent ligation-free approaches such as Genome Architecture Mapping (GAM) and Split-Pool Recognition of Interactions by Tag Extension (SPRITE). These techniques offer unique insights into higher-order chromatin structures by bypassing ligation steps, thus enabling the capture of complex multi-way interactions that are often challenging to resolve with traditional methods. Furthermore, we discuss the integration of chromatin interaction data with other genomic layers through multimodal approaches, including recent advances in single-cell technologies like sci-HiC and scSPRITE, which help unravel the heterogeneity of chromatin architecture in development and disease.

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.

Many breast cancer therapeutics target the PI3K/AKT/mTOR oncogenic pathway. Development of resistance to the therapeutics targeting this pathway is a frequent occurrence. Therapeutics targeting p70S6K1, a downstream member of this pathway, have recently gained importance due to its critical role in all types of breast cancer and its status as a prognostic marker. We have developed a new class of p70S6K1 inhibitors that show growth inhibition of MCF7 breast cancer cells.

A series of 6-amido-4-aminoisoindolyn-1,3-dione compounds was developed against p70S6K1 using docking, computational modeling tools, and synthesis of the designed compounds. The p70S6K1 inhibition potency of the compounds was investigated in an initial high-throughput screening followed by IC50 determination for the most active ones. The best compounds were subjected to proliferation assays on MCF7 breast cancer cells. The targeting of p70S6K1 by the compounds was confirmed by studying the phosphorylation status of downstream protein rpS6.

In this study, we have identified a new class of compounds as p70S6K1 inhibitors that function as growth inhibitors of MCF7 breast cancer cells. The structural features imparting p70S6K1 inhibition potency to the compounds have been mapped. Our studies indicate that substitutions on the phenacetyl group residing in the cleft A of the protein do not contribute to the inhibition potency. Three compounds (5b, 5d, and 5f) have been identified to have sub-micromolar inhibition potency for p70S6K1. These compounds also exhibited growth inhibition of MCF7 cells by 40%-60% in the presence of estradiol.

The enhancer-promoter looping model, in which enhancers activate their target genes via physical contact, has long dominated the field of gene regulation. However, the ubiquity of this model has been questioned due to evidence of alternative mechanisms and the lack of its systematic validation, primarily owing to the absence of suitable experimental techniques. In this study, we present a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. By applying MChIP-C to study H3K4me3 promoter-centered interactions in K562 cells, we found that it had greatly improved resolution and sensitivity compared to restriction endonuclease-based C-methods. This allowed us to identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions. Finally, leveraging data from published CRISPRi screens, we found that most functionally verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.

Progesterone receptors (PR) can regulate transcription by RNA Polymerase III (Pol III), which transcribes small non-coding RNAs, including all transfer RNAs (tRNAs). We have previously demonstrated that PR is associated with the Pol III complex at tRNA genes and that progestins downregulate tRNA transcripts in breast tumor models. To further elucidate the mechanism of PR-mediated regulation of Pol III, we studied the interplay between PR, the Pol III repressor Maf1, and TFIIIB, a core transcription component. ChIP-seq was performed for PR, the Pol III subunit POLR3A, the TFIIIB component Brf1, and Maf1 in breast cancer cells with or without progestin treatment. Upon progestin exposure, PR localized to approximately half of POLR3A-occupied tRNA genes, with Maf1 co-recruited to many of these PR-POLR3A sites. While progestin treatment did not significantly alter the number of tRNA genes occupied by Pol III or Brf1, Brf1 occupancy was stabilized, as indicated by increased peak amplitudes. Analysis of nascent tRNA transcription revealed a specific progestin-induced downregulation of approximately one-third of highly expressed tRNA genes. This repression was significantly reduced by Maf1 knockdown, indicating that Maf1 is necessary for PR-mediated tRNA transcription downregulation. Overall, these findings demonstrate a ligand-dependent PR-mediated repression of tRNA transcription through Maf1.

Chromatin organization is important for gene transcription in pig genome. However, its three-dimensional (3D) structure and dynamics are much less investigated than those in human. Here, we applied the long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) method to map the whole-genome chromatin interactions mediated by CCCTC-binding factor (CTCF) and RNA polymerase II (RNAPII) in porcine macrophage cells before and after polyinosinic-polycytidylic acid [Poly(I:C)] induction. Our results reveal that Poly(I:C) induction impacts the 3D genome organization in the 3D4/21 cells at the fine-scale chromatin loop level rather than at the large-scale domain level. Furthermore, our findings underscore the pivotal role of CTCF-anchored chromatin interactions in reshaping chromatin architecture during immune responses. Knockout of the CTCF-binding locus further confirms that the CTCF-anchored enhancers are associated with the activation of immune genes via long-range interactions. Notably, the ChIA-PET data also support the spatial relationship between single nucleotide polymorphisms (SNPs) and related gene transcription in 3D genome aspect. Our findings in this study provide new clues and potential targets to explore key elements related to diseases in pigs and are also likely to shed light on elucidating chromatin organization and dynamics underlying the process of mammalian infectious diseases.

Long or Post COVID-19 is a condition of collected symptoms persisted after recovery from COVID-19. Host genetic factors play a crucial role in developing Long COVID-19, and GWAS studies identified several SNPs/genes in various ethnic populations. In African-American population two SNPS, rs10999901 (C>T, p = 3.6E-08, OR = 1.39, MAF-0,27, GRCH38, chr10:71584799 bp) and rs1868001 (G>A, p = 6.7E-09, OR = 1.40, MAF-0.46, GRCH38, chr10:71587815 bp) and in Hispanic population, rs3759084 (A>C, p = 9.7E-09, OR = 1.56, MAF-0.17, chr12: 81,110,156 bp) are strongly associated with Long COVID-19. All these three SNPs reside in noncoding regions implying their regulatory function in the genome. In silico dissection suggests that rs10999901 and rs1868001 physically interact with the CDH23 and C10orf105 genes. Both SNPs act as distant enhancers and bind with several transcription factors (TFs). Further, rs10999901 SNP is a CpG that is methylated in CD4++ T cells and monocytes and loses its methylation due to transition from C>T. rs3759084 is located in the promoter (- 687 bp) of MYF5, acts as a distant enhancer, and physically interacts with PTPRQ. These results offer plausible explanations for their association and provide the basis for experiments to dissect the development of symptoms of Long COVID-19.

Regulation of steroid hormone receptors (SHRs) on transcriptional reprogramming is crucial for breast cancer progression. SHRs, including estrogen receptor (ER), androgen receptor (AR), progesterone receptor (PR), and glucocorticoid receptor (GR) play key roles in remodeling the transcriptome of breast cancer cells. However, the molecular mechanisms by which SHRs regulate chromatin landscape in enhancer regions and transcription factor interactions remain largely unknown. In this review, we summarized the regulatory effects of 3 types of SHRs (AR, PR, and GR) on gene transcription through chromatin interactions and enhancer reprogramming. Specifically, AR and PR exhibit bi-directional regulatory effects (both inhibitory and promoting) on ER-mediated gene transcription, while GR modulates the transcription of pro-proliferation genes in ER-positive breast cancer cells. In addition, we have presented four enhancer reprogramming mechanisms (transcription factor cooperation, pioneer factor binding, dynamic assisted loading, and tethering) and the multiple enhancer-promoter contact models. Based on these mechanisms and models, this review proposes that the combination of multiple therapy strategies such as agonists/antagonists of SHRs plus endocrine therapy and the adoption of the latest sequencing technologies are expected to improve the efficacy of ER positive breast cancer treatment.

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.

Human genome projects in the 1990s identified about 20,000 protein-coding sequences. We are now in the RNA revolution, propelled by the realization that genes determine phenotype beyond the foundational central molecular biology dogma, stating that inherited linear pieces of DNA are transcribed to RNAs and translated into proteins. Crucially, over 95% of the genome, initially considered junk DNA between protein-coding genes, encodes essential, functionally diverse non-protein-coding RNAs, raising the gene count by at least one order of magnitude. Most inherited phenotype-determining changes in DNA are in regulatory areas that control RNA and regulatory sequences. RNAs can directly or indirectly determine phenotypes by regulating protein and RNA function, transferring information within and between organisms, and generating DNA. RNAs also exhibit high structural, functional, and biomolecular interaction plasticity and are modified via editing, methylation, glycosylation, and other mechanisms, which bestow them with diverse intra- and extracellular functions without altering the underlying DNA. RNA is, therefore, currently considered the primary determinant of cellular to populational functional diversity, disease-linked and biomolecular structural variations, and cell function regulation. As demonstrated by RNA-based coronavirus vaccines' success, RNA technology is transforming medicine, agriculture, and industry, as did the advent of recombinant DNA technology in the 1980s.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

Every cell must solve the problem of how to fold its genome. We describe how the folded state of chromosomes is the result of the combined activity of multiple conserved mechanisms. Homotypic affinity-driven interactions lead to spatial partitioning of active and inactive loci. Molecular motors fold chromosomes through loop extrusion. Topological features such as supercoiling and entanglements contribute to chromosome folding and its dynamics, and tethering loci to sub-nuclear structures adds additional constraints. Dramatically diverse chromosome conformations observed throughout the cell cycle and across the tree of life can be explained through differential regulation and implementation of these basic mechanisms. We propose that the first functions of chromosome folding are to mediate genome replication, compaction, and segregation and that mechanisms of folding have subsequently been co-opted for other roles, including long-range gene regulation, in different conditions, cell types, and species.

Identifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility across metacells, and a CTCF motif track as inputs and employs a lightweight architecture to train on standard GPUs. Trained on paired scATAC-seq and Hi-C data in human samples, ChromaFold accurately predicts the 3D contact map and peak-level interactions across diverse human and mouse test cell types. Compared to leading contact map prediction models that use ATAC-seq and CTCF ChIP-seq, ChromaFold achieves state-of-the-art performance using only scATAC-seq. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations.

Neurodegenerative disorders are major health concerns globally, especially in aging societies. The exploration of brain epigenomes, which consist of multiple forms of DNA methylation and covalent histone modifications, offers new and unanticipated perspective into the mechanisms of aging and neurodegenerative diseases. Initially, chromatin defects in the brain were thought to be static abnormalities from early development associated with rare genetic syndromes. However, it is now evident that mutations and the dysregulation of the epigenetic machinery extend across a broader spectrum, encompassing adult-onset neurodegenerative diseases. Hence, it is crucial to develop methodologies that can enhance epigenetic research. Several approaches have been created to investigate alterations in epigenetics on a spectrum of scales-ranging from low to high-with a particular focus on detecting DNA methylation and histone modifications. This article explores the burgeoning realm of neuroepigenetics, emphasizing its role in enhancing our mechanistic comprehension of neurodegenerative disorders and elucidating the predominant techniques employed for detecting modifications in the epigenome. Additionally, we ponder the potential influence of these advancements on shaping future therapeutic approaches.

The human brain contains multiple cell types that are spatially organized into functionally distinct regions. The proper development of the brain requires complex gene regulation mechanisms in both neurons and the non-neuronal cell types that support neuronal function. Studies across the last decade have discovered that the 3D nuclear organization of the genome is instrumental in the regulation of gene expression in the diverse cell types of the brain. In this review, we describe the fundamental biochemical mechanisms that regulate the 3D genome, and comprehensively describe in vitro and ex vivo studies on mouse and human brain development that have characterized the roles of the 3D genome in gene regulation. We highlight the significance of the 3D genome in linking distal enhancers to their target promoters, which provides insights on the etiology of psychiatric and neurological disorders, as the genetic variants associated with these disorders are primarily located in noncoding regulatory regions. We also describe the molecular mechanisms that regulate chromatin folding and gene expression in neurons. Furthermore, we describe studies with an evolutionary perspective, which have investigated features that are conserved from mice to human, as well as human gained 3D chromatin features. Although most of the insights on disease and molecular mechanisms have been obtained from bulk 3C based experiments, we also highlight other approaches that have been developed recently, such as single cell 3C approaches, as well as non-3C based approaches. In our future perspectives, we highlight the gaps in our current knowledge and emphasize the need for 3D genome engineering and live cell imaging approaches to elucidate mechanisms and temporal dynamics of chromatin interactions, respectively.

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.

Enhancers are central for the regulation of metazoan transcription but have proven difficult to study, primarily due to a myriad of interdependent variables shaping their activity. Consequently, synthetic biology has emerged as the main approach for dissecting mechanisms of enhancer function. We start by reviewing simple but highly parallel reporter assays, which have been successful in quantifying the complexity of the activator/coactivator mechanisms at enhancers. We then describe studies that examine how enhancers function in the genomic context and in combination with other enhancers, revealing that they activate genes through a variety of different mechanisms, working together as a system. Here, we primarily focus on synthetic reporter genes that can quantify the dynamics of enhancer biology through time. We end by considering the consequences of having many genes and enhancers within a 'local environment', which we believe leads to correlated gene expression and likely reports on the general principles of enhancer biology.

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.