Three-dimensional genome structures of single diploid human cells

Genomic snowflakes: how the uniqueness of DNA folding allows us to smell the chemical universe

Olfactory receptor (OR) gene choice, the stable expression of one out of >2000 OR alleles by olfactory sensory neurons, constitutes a gene regulatory process that is driven by three-dimensional nuclear architecture. Moreover, the differentiation-dependent process that culminates in monogenic and monoallelic OR transcription represents a powerful demonstration of the rich mechanistic insight that single-cell genomics and multiomics can provide toward the understanding of a biological process. At this review, we describe the latest advances in the understanding of OR gene regulation and highlight important standing questions regarding the emerging specificity of ultra-long-range genomic interaction and the contribution of transcription and noncoding RNAs.

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data

BACKGROUND

Single cell Hi-C (scHi-C) techniques make it possible to study cell-to-cell variability, but excess of zeros are makes scHi-C matrices extremely sparse and difficult for downstream analyses. The observed zeros are a combination of two events: structural zeros for which two loci never interact due to underlying biological mechanisms, or dropouts (sampling zeros) where two loci interact but not captured due to insufficient sequencing depth. Although data quality improvement approaches have been proposed, little has been done to differentiate these two types of zeros, even though such a distinction can greatly benefit downstream analysis such as clustering.

RESULTS

We propose scHiCSRS, a self-representation smoothing method that improves data quality, and a Gaussian mixture model that identifies structural zeros among observed zeros. scHiCSRS not only takes spatial dependencies of a scHi-C data matrix into account but also borrows information from similar single cells. Through an extensive set of simulation studies, we demonstrate the ability of scHiCSRS for identifying structural zeros with high sensitivity and for accurate imputation of dropout values in sampling zeros. Downstream analyses for three experimental datasets show that data improved from scHiCSRS yield more accurate clustering of cells than simply using observed data or improved data from comparison methods.

CONCLUSION

In summary, scHiCSRS provides a valuable tool for identifying structural zeros and imputing dropouts. The resulted data are improved for downstream analysis, especially for understanding cell-to-cell variation through subtype clustering.

Extensive folding variability between homologous chromosomes in mammalian cells

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single-nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, and with Polycomb occupancy. We show that histone genes are expressed with allelic imbalance in mESCs, and are involved in haplotype-specific chromatin contacts marked by H3K27me3. Conditional knockouts of Polycomb enzymatic subunits, Ezh2 or Ring1, show that one-third of ASE genes, including histone genes, is regulated through Polycomb repression. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan

Exploring the genomic basis of transcriptional programs has been a long-standing research focus. Here we report a single-cell method, ChAIR, to map chromatin accessibility, chromatin interactions and RNA expression simultaneously. After validating in cultured cells, we applied ChAIR to whole mouse brains and delineated the concerted dynamics of epigenome, three-dimensional (3D) genome and transcriptome during maturation and aging. In particular, gene-centric chromatin interactions and open chromatin states provided 3D epigenomic mechanism underlying cell-type-specific transcription and revealed spatially resolved specificity. Importantly, the composition of short-range and ultralong chromatin contacts in individual cells is remarkably correlated with transcriptional activity, open chromatin state and genome folding density. This genomic property, along with associated cellular properties, differs in neurons and non-neuronal cells across different anatomic regions throughout the lifespan, implying divergent nuclear mechano-genomic mechanisms at play in brain cells. Our results demonstrate ChAIR's robustness in revealing single-cell 3D epigenomic states of cell-type-specific transcription in complex tissues.

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.

Unveiling Multi-Scale Architectural Features in Single-Cell Hi-C Data Using scCAFE

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, scCAFE is introduced, which is a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. This model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. This heterogeneity is then leveraged to identify a series of marker loop anchors, demontrating the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

Three-dimensional genome structures of single mammalian sperm

The three-dimensional (3D) organization of chromosomes is crucial for packaging a large mammalian genome into a confined nucleus and ensuring proper nuclear functions in somatic cells. However, the packaging of the much more condensed sperm genome is challenging to study with traditional imaging or sequencing approaches. In this study, we develop an enhanced chromosome conformation capture assay, and resolve the 3D whole-genome structures of single mammalian sperm. The reconstructed genome structures accurately delineate the species-specific nuclear morphologies for both human and mouse sperm. We discover that sperm genomes are divided into chromosomal territories and A/B compartments, similarly to somatic cells. However, neither human nor mouse sperm chromosomes contain topologically associating domains or chromatin loops. These results suggest that the fine-scale chromosomal organization of mammalian sperm fundamentally differs from that of somatic cells. The discoveries and methods established in this work will be valuable for future studies of sperm related infertility.

Uncovering Changes in 3D-Chromatin Structure and Dynamic Gene Expression During Spermatogenesis

Spermatogonial stem cells (SSCs) have the potential for self-renewal and differentiation, and normal spermatogenesis maintains a stable number of spermatogonial stem cells and spermatozoa. Spermatogenesis is accompanied by changes in the three-dimensional structure of chromatin and gene expression, but the structural differences between the stages and the higher-order chromatin dynamics have not yet been elucidated. Consequently, we conducted a high-throughput analysis of the chromatin structural organization and gene expression by using porcine spermatogonia (SPG), spermatocytes (SPY) and round spermatids (RS). We found that during spermatogenesis, SPY showed a weaker pattern of chromosomal interactions, attenuated compartmentalisation, and a reduction in the number of TADs (topological associating domains), which was restored during the subsequent period of round spermatids. These findings suggest reprogramming of higher-order chromatin structures during porcine spermatogonia differentiation. Our results reveal that chromatin structure changes during porcine spermatogenesis, along with changes in gene expression. In conclusion, our study reveals the interrelationships between higher-order chromatin structure and gene expression in spermatogonia, spermatocytes, and round spermatids, providing new insights into the understanding of spermatogenesis as well as basic theoretical data for male reproductive techniques in biological sciences.

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.

Comparing chromatin contact maps at scale: methods and insights

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, methods often disagree, and no gold standard exists for comparing pairs of maps. Here, we evaluate 25 ways to compare contact maps using Micro-C and Hi-C data from two cell types and in silico-generated contact maps. We identify similarities and differences between the methods and quantify their robustness to common sources of biological and technical variation, including losses and gains of CTCF-binding sites, changes in contact intensity or patterns, and noise. We find that global comparison methods, such as mean squared error, are suitable for initial screening; however, biologically informed methods are necessary for identifying how maps diverge and for proposing specific functional hypotheses. We provide a reference guide, codebase, and thorough evaluation for rapidly comparing chromatin contact maps at scale to enable biological insights into 3D genome organization.

Single-cell 3D genome reconstruction in the haploid setting using rigidity theory

This article considers the problem of 3-dimensional genome reconstruction for single-cell data, and the uniqueness of such reconstructions in the setting of haploid organisms. We consider multiple graph models as representations of this problem, and use techniques from graph rigidity theory to determine identifiability. Biologically, our models come from Hi-C data, microscopy data, and combinations thereof. Mathematically, we use unit ball and sphere packing models, as well as models consisting of distance and inequality constraints. In each setting, we describe and/or derive new results on realisability and uniqueness. We then propose a 3D reconstruction method based on semidefinite programming and apply it to synthetic and real data sets using our models.

Chromosome-level genome assembly of Eimeria tenella at the single-oocyst level

BACKGROUND

Eimeria are obligate protozoan parasites, and more than 1,500 species have been reported. However, Eimeria genomes lag behind many other eukaryotes since obtaining many oocysts is difficult due to a lack of sustainable in vitro culture, highly repetitive sequences, and mixed species infections. To address this challenge, we used whole-genome amplification of a single oocyst followed by long-read sequencing and obtained a chromosome-level genome of Eimeria tenella.

RESULTS

The assembled genome was 52.13 Mb long, encompassing 15 chromosomes and 46.94% repeat sequences. In total, 7,296 protein-coding genes were predicted, exhibiting high completeness, with 92.00% single-copy BUSCO genes. To the best of our knowledge, this is the first chromosome-level assembly of E. tenella using a combination of single-oocyst whole-genome amplification and long-read sequencing. Comparative genomic and transcriptome analyses confirmed evolutionary relationship and supported estimates of divergence time of apicomplexan parasites and identified AP2 and Myb gene families that may play indispensable roles in regulating the growth and development of E. tenella.

CONCLUSION

This high-quality genome assembly and the established sequencing strategy provide valuable community resources for comparative genomic and evolutionary analyses of the Eimeria clade. Additionally, our study also provides a valuable resource for exploring the roles of AP2 and Myb transcription factor genes in regulating the development of Eimeria parasites.

Reconstruction of diploid higher-order human 3D genome interactions from noisy Pore-C data using Dip3D

Differential high-order chromatin interactions between homologous chromosomes affect many biological processes. Traditional chromatin conformation capture genome analysis methods mainly identify two-way interactions and cannot provide comprehensive haplotype information, especially for low-heterozygosity organisms such as human. Here, we present a pipeline of methods to delineate diploid high-order chromatin interactions from noisy Pore-C outputs. We trained a previously published single-nucleotide variant (SNV)-calling deep learning model, Clair3, on Pore-C data to achieve superior SNV calling, applied a filtering strategy to tag reads for haplotypes and established a haplotype imputation strategy for high-order concatemers. Learning the haplotype characteristics of high-order concatemers from high-heterozygosity mouse allowed us to devise a progressive haplotype imputation strategy, which improved the haplotype-informative Pore-C contact rate 14.1-fold to 76% in the HG001 cell line. Overall, the diploid three-dimensional (3D) genome interactions we derived using Dip3D surpassed conventional methods in noise reduction and contact distribution uniformity, with better haplotype-informative contact density and genomic coverage rates. Dip3D identified previously unresolved haplotype high-order interactions, in addition to an understanding of their relationship with allele-specific expression, such as in X-chromosome inactivation. These results lead us to conclude that Dip3D is a robust pipeline for the high-quality reconstruction of diploid high-order 3D genome interactions.

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Single-Cell Hi-C Technologies and Computational Data Analysis

Single-cell chromatin conformation capture (scHi-C) techniques have evolved to provide significant insights into the structural organization and regulatory mechanisms in individual cells. Although many scHi-C protocols have been developed, they often involve intricate procedures and the resulting data are sparse, leading to computational challenges for systematic data analysis and limited applicability. This review provides a comprehensive overview, quantitative evaluation of thirteen protocols and practical guidance on computational topics. It is first assessed the efficiency of these protocols based on the total number of contacts recovered per cell and the cis/trans ratio. It is then provided systematic considerations for scHi-C quality control and data imputation. Additionally, the capabilities and implementations of various analysis methods, covering cell clustering, A/B compartment calling, topologically associating domain (TAD) calling, loop calling, 3D reconstruction, scHi-C data simulation and differential interaction analysis is summarized. It is further highlighted key computational challenges associated with the specific complexities of scHi-C data and propose potential solutions.

Cell type-specific 3D-genome organization and transcription regulation in the brain

3D organization of the genome plays a critical role in regulating gene expression. How 3D-genome organization differs among different cell types and relates to cell type-dependent transcriptional regulation remains unclear. Here, we used genome-scale DNA and RNA imaging to investigate 3D-genome organization in transcriptionally distinct cell types in the mouse cerebral cortex. We uncovered a wide spectrum of differences in the nuclear architecture and 3D-genome organization among different cell types, ranging from the size of the cell nucleus to higher-order chromosome structures and radial positioning of chromatin loci within the nucleus. These cell type-dependent variations in nuclear architecture and chromatin organization exhibit strong correlations with both the total transcriptional activity of the cell and transcriptional regulation of cell type-specific marker genes. Moreover, we found that the methylated DNA binding protein MeCP2 promotes active-inactive chromatin segregation and regulates transcription in a nuclear radial position-dependent manner that is highly correlated with its function in modulating active-inactive chromatin compartmentalization.

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.

Visualization of chromosomal reorganization induced by heterologous fusions in the mammalian nucleus

Chromosomes are spatially organized and functionally folded into a specific macro-structure in the nucleus. Recently, we and others created haploid cells with chromosome fusions. However, there is still lack of an effective strategy for precisely investigating how the genome copes with fusions. Here, we developed a down-sampling method to convert the populational Hi-C dataset into single cell-like Khimaira Matrix (K-matrix). K-matrix preserves not only the most prominent functional genomic features but also cell-to-cell variations. K-matrix-originated genome 3D models display spatial approach of fused chromosomes and minor global structure alterations. Combined with a layered positional decomposition analysis, our models indicate slight re-adjustment of chromosome distributions accordingly with an increasing tendency following more fusions involved. Nevertheless, the radial distribution of the A/B compartment is not affected dramatically. By contrast, natural populations harboring Rb fusions display significant alterations of chromosome radial location. Overall, K-matrix-originated models enable visualization of chromosomal reorganization with high resolution.

Dynamic changes in 3D chromatin structure during male gametogenesis in Arabidopsis thaliana

BACKGROUND

Chromatin higher-order structure plays an important role in genome stability maintenance and gene transcriptional regulation; however, the dynamics of the three-dimensional (3D) chromatin in male gametophytes during the two rounds of mitosis remains elusive.

RESULTS

Here, we use the optimized single-nucleus and low-input Hi-C methods to investigate changes in 3D chromatin structure in four types of male gametophyte nucleus at different stages. The reconstructed genome structures show that microspore nuclei develop towards two different directions. Although the 3D chromatin organization in generative nuclei is similar to that in microspore nuclei, vegetative nuclei lose chromosome territories, display dispersed centromeres, and switched A/B compartments, which are associated with vegetative specific gene expression. Additionally, we find that there is an active transcriptional center in sperm nuclei, emphasizing the transcription in Arabidopsis sperm is not completely inhibited despite the chromosomes being condensed.

CONCLUSIONS

Our data suggest that the special 3D structures of vegetative and sperm nuclei contribute to cell type-specific expression patterns.

Generalization of the sci-L3 method to achieve high-throughput linear amplification for replication template strand sequencing, genome conformation capture, and the joint profiling of RNA and chromatin accessibility

Single-cell combinatorial indexing (sci) methods have addressed major limitations of throughput and cost for many single-cell modalities. With the incorporation of linear amplification and three-level barcoding in our suite of methods called sci-L3, we further addressed the limitations of uniformity in single-cell genome amplification. Here, we build on the generalizability of sci-L3 by extending it to template strand sequencing (sci-L3-Strand-seq), genome conformation capture (sci-L3-Hi-C), and the joint profiling of RNA and chromatin accessibility (sci-L3-RNA/ATAC). We demonstrate the ease of adapting sci-L3 to these new modalities by only requiring a single-step modification of the original protocol. As a proof of principle, we show our ability to detect sister chromatid exchanges, genome compartmentalization, and cell state-specific features in thousands of single cells. We anticipate sci-L3 to be compatible with additional modalities, including DNA methylation (sci-MET) and chromatin-associated factors (CUT&Tag), and ultimately enable a multi-omics readout of them.

A guide to studying 3D genome structure and dynamics in the kidney

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.

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.

Droplet-based high-throughput 3D genome structure mapping of single cells with simultaneous transcriptomics

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.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Genome-wide single-cell and single-molecule footprinting of transcription factors with deaminase

Decades of research have established that mammalian transcription factors (TFs) bind to each gene's regulatory regions and cooperatively control tissue specificity, timing, and intensity of gene transcription. Mapping the combination of TF binding sites genome wide is critically important for understanding functional genomics. Here, we report a technique to measure TFs' binding sites on the human genome with a near single-base resolution by footprinting with deaminase (FOODIE) on a single-molecule and single-cell basis. Single-molecule sequencing reads after enzymatic deamination allow detection of the TF binding fraction on a particular footprint and the binding cooperativity of any two adjacent TFs, which can be either positive or negative. As a newcomer of single-cell genomics, single-cell FOODIE enables the detection of cell-type-specific TF footprints in a pure cell population in a heterogeneous tissue, such as the brain. We found that genes carrying out a certain biological function together in a housing-keeping correlated gene module (CGM) or a tissues-specific CGM are coordinated by shared TFs in the gene's promoters and enhancers, respectively. Scalable and cost-effective, FOODIE allows us to create an open FOODIE database for cell lines, with applicability to human tissues and clinical samples.

Mapping the 3D genome architecture

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.

Uncovering the lung cancer mechanisms through the chromosome structural ensemble characteristics and nucleation seeds

Lung cancer is one of the most common cancers in humans. However, there is still a need to understand the underlying mechanisms of a normal cell developing into a cancer cell. Here, we develop the chromosome dynamic structural model and quantify the important characteristics of the chromosome structural ensemble of the normal lung cell and the lung cancer A549 cell. Our results demonstrate the essential relationship among the chromosome ensemble, the epigenetic marks, and the gene expressions, which suggests the linkage between chromosome structure and function. The analysis reveals that the lung cancer cell may have a higher level of relative ensemble fluctuation (micro CFI) and a higher degree of phase separation between the two compartments than the normal lung cell. In addition, the significant conformational "switching off" events (from compartment A to B) are more than the significant conformational "switching on" events during the lung cancerization. We identify "nucleation seeds" or hot spots in chromosomes, which initiate the transitions and determine the mechanisms. The hot spots and interaction network results reveal that the lung cancerization process (from normal lung to A549) and the reversion process have different mechanisms. These investigations have revealed the cell fate determination mechanism of the lung cancer process, which will be helpful for the further prevention and control of cancers.

Multiple Displacement Amplification Facilitates SMRT Sequencing of Microscopic Animals and the Genome of the Gastrotrich Lepidodermella squamata (Dujardin 1841)

Obtaining adequate DNA for long-read genome sequencing remains a roadblock to producing contiguous genomes from small-bodied organisms, hindering understanding of phylogenetic relationships and genome evolution. Multiple displacement amplification leverages Phi29 DNA polymerase to produce micrograms of DNA from picograms of input. However, multiple displacement amplification's inherent biases in amplification related to guanine and cytosine (GC) content, repeat content and chimera production are a problem for long-read genome assembly, which has been little investigated. We explored the utility of multiple displacement amplification for generating template DNA for High Fidelity (HiFi) sequencing directly from living cells of Caenorhabditis elegans (Nematoda) and Lepidodermella squamata (Gastrotricha) containing one order of magnitude less DNA than required for the PacBio Ultra-Low DNA Input Workflow. High Fidelity sequencing of libraries prepared from multiple displacement amplification products resulted in highly contiguous and complete genomes for both C. elegans (102 Mbp assembly; 336 contigs; N50 = 868 kbp; L50 = 39; BUSCO_nematoda_nucleotide: S:96.1%, D:2.8%) and L. squamata (122 Mbp assembly; 157 contigs; N50 = 3.9 Mbp; L50 = 13; BUSCO_metazoa_nucleotide: S:80.8%, D:2.8%). Coverage uniformity for reads from multiple displacement amplification DNA (Gini Index: 0.14, normalized mean across all 100 kbp blocks: 0.49) and reads from pooled nematode DNA (Gini Index: 0.16, normalized mean across all 100 kbp blocks: 0.49) proved similar. Using this approach, we sequenced the genome of the microscopic invertebrate L. squamata (Gastrotricha), the first of its phylum. Using the newly sequenced genome, we infer Gastrotricha's long-debated phylogenetic position as the sister taxon of Platyhelminthes and conduct a comparative analysis of the Hox cluster.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

Advancements and future directions in single-cell Hi-C based 3D chromatin modeling

Single-cell Hi-C data provides valuable insights into the three-dimensional organization of chromatin within individual cells, yet modeling this data poses significant challenges due to its inherent sparsity and variability. This review comprehensively explores the predominant approaches to reconstructing 3D chromatin structures from single-cell Hi-C data, positioning these methods within the broader contexts of single-cell Hi-C research and bulk Hi-C data modeling. We categorize the modeling strategies based on their objective functions, which are framed in terms of force fields, potentials, cost functions, or likelihood probabilities. Despite their diverse methodologies, these approaches exhibit deep underlying similarities. We further dissect the basic components of these models, such as attractive restraint forces and repulsive forces, and discuss additional terms like fluid viscosity and variation penalties. The review also critically evaluates the current state of model validation, highlighting the inconsistencies across various studies and emphasizing the need for a comprehensive validation framework. We detail common validation techniques, including the comparison of distance matrices and the assessment of contact violations. We argue that the future of single-cell Hi-C modeling lies in integrating multiple data modalities and incorporating cell cycle trajectory information. Such integration could significantly advance our understanding of chromatin conformation dynamics during cell cycle progression and cell differentiation. We also foresee the continued growth of optimization-based and molecular dynamics approaches, supported by general molecular dynamics toolkits.

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.

The chromosome folding problem and how cells solve it

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.

Epigenetic dynamics of partially methylated domains in human placenta and trophoblast stem cells

BACKGROUND

The placenta is essential for nutrient exchange and hormone production between the mother and the developing fetus and serves as an invaluable model for epigenetic research. Most epigenetic studies of the human placenta have used whole placentas from term pregnancies and have identified the presence of partially methylated domains (PMDs). However, the origin of these domains, which are typically absent in most somatic cells, remains unclear in the placental context.

RESULTS

Using whole-genome bisulfite sequencing and analysis of histone H3 modifications, we generated epigenetic profiles of human cytotrophoblasts during the first trimester and at term, as well as human trophoblast stem cells. Our study focused specifically on PMDs. We found that genomic regions likely to form PMDs are resistant to global DNA demethylation during trophectoderm reprogramming, and that PMDs arise through a slow methylation process within condensed chromatin near the nuclear lamina. In addition, we found significant differences in histone H3 modifications between PMDs in cytotrophoblasts and trophoblast stem cells.

CONCLUSIONS

Our findings suggest that spatiotemporal genomic features shape megabase-scale DNA methylation patterns, including PMDs, in the human placenta and highlight distinct differences in PMDs between human cytotrophoblasts and trophoblast stem cells. These findings advance our understanding of placental biology and provide a basis for further research into human development and related diseases.

ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues

Current methods for analyzing chromatin architecture are not readily scalable to heterogeneous tissues. Here we introduce Droplet Hi-C, which uses a commercial microfluidic device for high-throughput, single-cell chromatin conformation profiling in droplets. Using Droplet Hi-C, we mapped the chromatin architecture of the mouse cortex and analyzed gene regulatory programs in major cortical cell types. In addition, we used this technique to detect copy number variations, structural variations and extrachromosomal DNA in human glioblastoma, colorectal and blood cancer cells, revealing clonal dynamics and other oncogenic events during treatment. We refined the technique to allow joint profiling of chromatin architecture and transcriptome in single cells, facilitating exploration of the links between chromatin architecture and gene expression in both normal tissues and tumors. Thus, Droplet Hi-C both addresses critical gaps in chromatin analysis of heterogeneous tissues and enhances understanding of gene regulation.

Chromatin interaction maps of human arterioles reveal new mechanisms for the genetic regulation of blood pressure

Arterioles are small blood vessels located just upstream of capillaries in nearly all tissues. The constriction and dilation of arterioles regulate tissue perfusion and are primary determinants of systemic blood pressure (BP). Abnormalities in arterioles are central to the development of major diseases such as hypertension, stroke, and microvascular complications of diabetes. Despite the broad and essential role of arterioles in physiology and disease, current knowledge of the functional genomics of arterioles is largely absent, partly because it is challenging to obtain and analyze human arteriole samples. Here, we report extensive maps of chromatin interactions, single-cell expression, and other molecular features in human arterioles and uncover new mechanisms linking human genetic variants to gene expression in vascular cells and the development of hypertension. Compared to large arteries, arterioles exhibited a higher proportion of pericytes which were strongly associated with BP traits. BP-associated single nucleotide polymorphisms (SNPs) were enriched in chromatin interaction regions in arterioles, particularly through enhancer SNP-promoter interactions, which were further linked to gene expression specificity across tissue components and cell types. Using genomic editing in animal models and human induced pluripotent stem cells, we discovered novel mechanisms linking BP-associated noncoding SNP rs1882961 to gene expression through long-range chromatin contacts and revealed remarkable effects of a 4-bp noncoding genomic segment on hypertension in vivo. We anticipate that our rich data and findings will advance the study of the numerous diseases involving arterioles. Moreover, our approach of integrating chromatin interaction mapping in trait-relevant tissues with SNP analysis and in vivo and in vitro genome editing can be applied broadly to bridge the critical gap between genetic discoveries and physiological understanding.

Emerging methods and applications in 3D genomics

Since the advent of Hi-C in 2009, a plethora of high-throughput sequencing methods have emerged to profile the three-dimensional (3D) organization of eukaryotic genomes, igniting the era of 3D genomics. In recent years, the genomic resolution achievable by these approaches has dramatically increased and several single-cell versions of Hi-C have been developed. Moreover, a new repertoire of tools not based on proximity ligation of digested chromatin has emerged, enabling the investigation of the higher-order organization of chromatin in the nucleus. In this review, we summarize the expanding portfolio of 3D genomic technologies, highlighting recent developments and applications from the past three years. Lastly, we present an outlook of where this technology-driven field might be headed.

Deep neural network models for cell type prediction based on single-cell Hi-C data

BACKGROUND

Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a computational method is urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets.

RESULTS

We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, the ARI and NMI values of SCANN increase by 14% and 11% over those of scHiCluster respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN increase by 63% and 88% over those of scHiCluster respectively. These findings show that SCANN is highly accurate in predicting the type of independent cell samples using single-cell Hi-C data.

CONCLUSIONS

SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types.

Tracing the evolution of single-cell cancer 3D genomes: an atlas for cancer gene discovery

Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding heterogeneity, compaction, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish histologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed compartment-associated genes are more homogeneously regulated, but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.

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

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

SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model

Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.

Transposase-assisted tagmentation: an economical and scalable strategy for single-worm whole-genome sequencing

AlphaMissense identifies 23 million human missense variants as likely pathogenic, but only 0.1% have been clinically classified. To experimentally validate these predictions, chemical mutagenesis presents a rapid, cost-effective method to produce billions of mutations in model organisms. However, the prohibitive costs and limitations in the throughput of whole-genome sequencing (WGS) technologies, crucial for variant identification, constrain its widespread application. Here, we introduce a Tn5 transposase-assisted tagmentation technique for conducting WGS in Caenorhabditis elegans, Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii. This method, demands merely 20 min of hands-on time for a single-worm or single-cell clones and incurs a cost below 10 US dollars. It effectively pinpoints causal mutations in mutants defective in cilia or neurotransmitter secretion and in mutants synthetically sterile with a variant analogous to the B-Raf Proto-oncogene, Serine/Threonine Kinase (BRAF) V600E mutation. Integrated with chemical mutagenesis, our approach can generate and identify missense variants economically and efficiently, facilitating experimental investigations of missense variants in diverse species.

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

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

Regulating epithelial-mesenchymal plasticity from 3D genome organization

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

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

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

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

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