Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi

ChromMovie: A Molecular Dynamics Approach for Simultaneous Modeling of Chromatin Conformation Changes from Multiple Single-Cell Hi-C Maps

The development of 3C-based techniques for analyzing three-dimensional chromatin structure dynamics has driven significant interest in computational methods for 3D chromatin reconstruction. In particular, models based on Hi-C and its single-cell variants, such as scHi-C, have gained widespread popularity. Current approaches for reconstructing the chromatin structure from scHi-C data typically operate by processing one scHi-C map at a time, generating a corresponding 3D chromatin structure as output. Here, we introduce an alternative approach to the whole genome 3D chromatin structure reconstruction that builds upon existing methods while incorporating the broader context of dynamic cellular processes, such as the cell cycle or cell maturation. Our approach integrates scHi-C contact data with single-cell trajectory information and is based on applying simultaneous modeling of a number of cells ordered along the progression of a given cellular process. The approach is able to successfully recreate known nuclear structures while simultaneously achieving smooth, continuous changes in chromatin structure throughout the cell cycle trajectory. Although both Hi-C-based chromatin reconstruction and cellular trajectory inference are well-developed fields, little effort has been made to bridge the gap between them. To address this, we present ChromMovie, a comprehensive molecular dynamics framework for modeling 3D chromatin structure changes in the context of cellular trajectories. To our knowledge, no existing method effectively leverages both the variability of single-cell Hi-C data and explicit information from estimated cellular trajectories, such as cell cycle progression, to improve chromatin structure reconstruction.

Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE

Effective exploration and analysis tools are vital for the extraction of insights from single-cell data. However, current techniques for modeling single-cell studies performed across experimental conditions (e.g., samples) require restrictive assumptions or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that reduction and insight in single-cell exploration (RISE), an adaptation of the tensor decomposition method PARAFAC2, enables the dimensionality reduction and analysis of single-cell data across conditions. We demonstrate the benefits of RISE across distinct examples of single-cell RNA-sequencing experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus patient samples. RISE enables associations of gene variation patterns with patients or perturbations while connecting each coordinated change to single cells without requiring cell-type annotations. The theoretical grounding of RISE suggests a unified framework for many single-cell data modeling tasks while providing an intuitive dimensionality reduction approach for multi-sample single-cell studies across biological contexts. A record of this paper's transparent peer review process is included in the supplemental information.

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

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

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.

Enhancing Single-Cell and Bulk Hi-C Data Using a Generative Transformer Model

The 3D organization of chromatin in the nucleus plays a critical role in regulating gene expression and maintaining cellular functions in eukaryotic cells. High-throughput chromosome conformation capture (Hi-C) and its derivative technologies have been developed to map genome-wide chromatin interactions at the population and single-cell levels. However, insufficient sequencing depth and high noise levels in bulk Hi-C data, particularly in single-cell Hi-C (scHi-C) data, result in low-resolution contact matrices, thereby limiting diverse downstream computational analyses in identifying complex chromosomal organizations. To address these challenges, we developed a transformer-based deep learning model, HiCENT, to impute and enhance both scHi-C and Hi-C contact matrices. Validation experiments on large-scale bulk Hi-C and scHi-C datasets demonstrated that HiCENT achieves superior enhancement effects compared to five popular methods. When applied to real Hi-C data from the GM12878 cell line, HiCENT effectively enhanced 3D structural features at the scales of topologically associated domains and chromosomal loops. Furthermore, when applied to scHi-C data from five human cell lines, it significantly improved clustering performance, outperforming five widely used methods. The adaptability of HiCENT across different datasets and its capacity to improve the quality of chromatin interaction data will facilitate diverse downstream computational analyses in 3D genome research, single-cell studies and other large-scale omics investigations.

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.

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.

Nuclear remodeling during cell fate transitions

Totipotent stem cells, the earliest cells in embryonic development, can differentiate into complete embryos and extra-embryonic tissues, making them essential for understanding both development and regenerative medicine. This review examines recent advances in the dynamic remodeling of nuclear structures during the transition between totipotency and pluripotency, as well as other cell fate transition processes. Additionally, we highlight innovative experimental and computational methods that elucidate the relationship between nuclear architecture and cell fate decisions. By integrating these insights, we aim to enhance our understanding of how nuclear remodeling influences totipotency and other cell fate transitions, paving the way for future research in this critical field.

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.

Dose-dependent sensitivity of human 3D chromatin to a heart disease-linked transcription factor

Dosage-sensitive transcription factors (TFs) underlie altered gene regulation in human developmental disorders, and cell-type specific gene regulation is linked to the reorganization of 3D chromatin during cellular differentiation. Here, we show dose-dependent regulation of chromatin organization by the congenital heart disease (CHD)-linked, lineage-restricted TF TBX5 in human cardiomyocyte differentiation. Genome organization, including compartments, topologically associated domains, and chromatin loops, are sensitive to reduced TBX5 dosage in a human model of CHD, with variations in response across individual cells. Regions normally bound by TBX5 are especially sensitive, while co-occupancy with CTCF partially protects TBX5-bound TAD boundaries and loop anchors. These results highlight the importance of lineage-restricted TF dosage in cell-type specific 3D chromatin dynamics, suggesting a new mechanism for TF-dependent disease.

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.

scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data

Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation. In this study, we propose four new feature sets based on the contact matrix with clear interpretability and biological significance. Furthermore, we develop a novel deep learning framework named scHiClassifier based on multi-head self-attention encoder, 1D convolution and feature fusion, which integrates information from these four feature sets to predict cell types accurately. Through comprehensive comparison experiments with benchmark frameworks on six datasets, we demonstrate the superior classification performance and the universality of the scHiClassifier framework. We further assess the robustness of scHiClassifier through data perturbation experiments and data dropout experiments. Moreover, we demonstrate that using all feature sets in the scHiClassifier framework yields optimal performance, supported by comparisons of different feature set combinations. The effectiveness and the superiority of the multiple feature set extraction are proven by comparison with four unsupervised dimensionality reduction methods. Additionally, we analyze the importance of different feature sets and chromosomes using the "SHapley Additive exPlanations" method. Furthermore, the accuracy and reliability of the scHiClassifier framework in cell classification for single-cell Hi-C data are supported through enrichment analysis. The source code of scHiClassifier is freely available at https://github.com/HaoWuLab-Bioinformatics/scHiClassifier.

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.

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.

scGHOST: identifying single-cell 3D genome subcompartments

Single-cell Hi-C (scHi-C) technologies allow for probing of genome-wide cell-to-cell variability in three-dimensional (3D) genome organization from individual cells. Computational methods have been developed to reveal single-cell 3D genome features based on scHi-C, including A/B compartments, topologically associating domains and chromatin loops. However, no method exists for annotating single-cell subcompartments, which is important for understanding chromosome spatial localization in single cells. Here we present scGHOST, a single-cell subcompartment annotation method using graph embedding with constrained random walk sampling. Applications of scGHOST to scHi-C data and contact maps derived from single-cell 3D genome imaging demonstrate reliable identification of single-cell subcompartments, offering insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from complex tissues, scGHOST identifies cell-type-specific or allele-specific subcompartments linked to gene transcription across various cell types and developmental stages, suggesting functional implications of single-cell subcompartments. scGHOST is an effective method for annotating single-cell 3D genome subcompartments in a broad range of biological contexts.

Computational methods for analysing multiscale 3D genome organization

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

DiffDomain enables identification of structurally reorganized topologically associating domains

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

ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization

The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.

[Advances in methods and applications of single-cell Hi-C data analysis]

Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.

Single-cell DNA methylation and 3D genome architecture in the human brain

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

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

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

Lifelong restructuring of 3D genome architecture in cerebellar granule cells

The cerebellum contains most of the neurons in the human brain and exhibits distinctive modes of development and aging. In this work, by developing our single-cell three-dimensional (3D) genome assay-diploid chromosome conformation capture, or Dip-C-into population-scale (Pop-C) and virus-enriched (vDip-C) modes, we resolved the first 3D genome structures of single cerebellar cells, created life-spanning 3D genome atlases for both humans and mice, and jointly measured transcriptome and chromatin accessibility during development. We found that although the transcriptome and chromatin accessibility of cerebellar granule neurons mature in early postnatal life, 3D genome architecture gradually remodels throughout life, establishing ultra-long-range intrachromosomal contacts and specific interchromosomal contacts that are rarely seen in neurons. These results reveal unexpected evolutionarily conserved molecular processes that underlie distinctive features of neural development and aging across the mammalian life span.

Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function.

Interpreting non-coding disease-associated human variants using single-cell epigenomics

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

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

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

Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq

Much progress has been made recently in single-cell chromosome conformation capture technologies. However, a method that allows simultaneous profiling of chromatin architecture and gene expression has not been reported. Here, we developed an assay named "Hi-C and RNA-seq employed simultaneously" (HiRES) and performed it on thousands of single cells from developing mouse embryos. Single-cell three-dimensional genome structures, despite being heavily determined by the cell cycle and developmental stages, gradually diverged in a cell type-specific manner as development progressed. By comparing the pseudotemporal dynamics of chromatin interactions with gene expression, we found a widespread chromatin rewiring that occurred before transcription activation. Our results demonstrate that the establishment of specific chromatin interactions is tightly related to transcriptional control and cell functions during lineage specification.

scGHOST: Identifying single-cell 3D genome subcompartments

New single-cell Hi-C (scHi-C) technologies enable probing of the genome-wide cell-to-cell variability in 3D genome organization from individual cells. Several computational methods have been developed to reveal single-cell 3D genome features based on scHi-C data, including A/B compartments, topologically-associating domains, and chromatin loops. However, no scHi-C analysis method currently exists for annotating single-cell subcompartments, which are crucial for providing a more refined view of large-scale chromosome spatial localization in single cells. Here, we present scGhost, a single-cell subcompartment annotation method based on graph embedding with constrained random walk sampling. Applications of scGhost to scHi-C data and single-cell 3D genome imaging data demonstrate the reliable identification of single-cell subcompartments and offer new insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from the human prefrontal cortex, scGhost identifies cell type-specific subcompartments that are strongly connected to cell type-specific gene expression, suggesting the functional implications of single-cell subcompartments. Overall, scGhost is an effective new method for single-cell 3D genome subcompartment annotation based on scHi-C data for a broad range of biological contexts.