High resolution single-cell chromatin 3D modeling reveals coherent chromatin aggregation with varied structures in controlling genome function stability

The genome 3D architecture is thought to be related to regulating gene expression levels in cells and can be explained by genome-wide chromatin interactions which have been explored by chromosome conformation capture based techniques, especially Hi-C. Based on single-cell Hi-C data, we developed a new method in constructing experimental consistent 3D intact genome structures for individual cells with a resolution of 10kb or higher. The modeled structures showed marked variations of 3D genome organization across different cells. However, chromosome loci marked by different proteins, such as CTCF and post-translationally modified histones, are consistently non-specifically aggregated in space. Interestingly, similar aggregations between active enhancers and active promoters were observed, especially for those separated by genomic regions of the scale of megabase or larger. Such long-range associations between active enhancers and promoters are strongly correlated with spatial aggregation of chromosome loci marked by different proteins. Through analyzing the 3D structures of intact genome, we proposed that coherent gene activation profiles among individual cells can be achieved by the consistent aggregation of protein marked loci instead of maintaining identical folded conformations.

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Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

10.1101/534800 ◽

2019 ◽

Cited By ~ 2

Author(s):

Hyeon-Jin Kim ◽

Galip Gürkan Yardımcı ◽

Giancarlo Bonora ◽

Vijay Ramani ◽

Jie Liu ◽

...

Keyword(s):

Single Cell ◽

Topic Modeling ◽

Biological Information ◽

Chromatin Interaction ◽

Cell Type ◽

3D Genome ◽

Genome Wide ◽

Significant Barrier ◽

Chromatin Structural ◽

Cell Type Specific

AbstractSingle-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate twelve different single-cell combinatorial indexed Hi-C (sciHi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 25,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sciHi-C data in the form of “chromatin topics.” We further show enrichment of particular compartment structures associated with locus pairs in these topics.

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Histone H3K9 methylation promotes formation of genome compartments inCaenorhabditis elegansvia chromosome compaction and perinuclear anchoring

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2002068117 ◽

2020 ◽

Vol 117 (21) ◽

pp. 11459-11470 ◽

Cited By ~ 1

Author(s):

Qian Bian ◽

Erika C. Anderson ◽

Qiming Yang ◽

Barbara J. Meyer

Keyword(s):

Gene Expression ◽

Nuclear Periphery ◽

Chromosome Conformation ◽

C Elegans ◽

Genome Wide ◽

Central Regions ◽

In Trans ◽

Genomic Regions ◽

In Cis ◽

Gene Expression Levels

Genomic regions preferentially associate with regions of similar transcriptional activity, partitioning genomes into active and inactive compartments within the nucleus. Here we explore mechanisms controlling genome compartment organization inCaenorhabditis elegansand investigate roles for compartments in regulating gene expression. Distal arms ofC. eleganschromosomes, which are enriched for heterochromatic histone modifications H3K9me1/me2/me3, interact with each other bothin cisandin trans,while interacting less frequently with central regions, leading to genome compartmentalization. Arms are anchored to the nuclear periphery via the nuclear envelope protein CEC-4, which binds to H3K9me. By performing genome-wide chromosome conformation capture experiments (Hi-C), we showed that eliminating H3K9me1/me2/me3 through mutations in the methyltransferase genesmet-2andset-25significantly impaired formation of inactive Arm and active Center compartments.cec-4mutations also impaired compartmentalization, but to a lesser extent. We found that H3K9me promotes compartmentalization through two distinct mechanisms: Perinuclear anchoring of chromosome arms via CEC-4 to promote theircisassociation, and an anchoring-independent mechanism that compacts individual chromosome arms. In bothmet-2 set-25andcec-4mutants, no dramatic changes in gene expression were found for genes that switched compartments or for genes that remained in their original compartment, suggesting that compartment strength does not dictate gene-expression levels. Furthermore, H3K9me, but not perinuclear anchoring, also contributes to formation of another prominent feature of chromosome organization, megabase-scale topologically associating domains on X established by the dosage compensation condensin complex. Our results demonstrate that H3K9me plays crucial roles in regulating genome organization at multiple levels.

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Multiplex-GAM: genome-wide identification of chromatin contacts yields insights not captured by Hi-C

10.1101/2020.07.31.230284 ◽

2020 ◽

Cited By ~ 1

Author(s):

Robert A. Beagrie ◽

Christoph J. Thieme ◽

Carlo Annunziatella ◽

Catherine Baugher ◽

Yingnan Zhang ◽

...

Keyword(s):

Embryonic Stem ◽

Active Regions ◽

Detailed Comparison ◽

Contact Mapping ◽

3D Genome ◽

Topologically Associating Domains ◽

Genome Wide ◽

Wide Contact ◽

Genomic Regions ◽

Mes Cells

Summary (Abstract)Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of Genome Architecture Mapping (GAM), a ligation-free technique to map chromatin contacts genomewide. We perform a detailed comparison of contacts obtained by multiplex-GAM and Hi-C using mouse embryonic stem (mES) cells. We find that both methods detect similar topologically associating domains (TADs). However, when examining the strongest contacts detected by either method, we find that only one third of these are shared. The strongest contacts specifically found in GAM often involve “active” regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain “inactive” regions. Our work shows that active genomic regions are involved in extensive complex contacts that currently go under-estimated in genome-wide ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

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GAMtools: an automated pipeline for analysis of Genome Architecture Mapping data

10.1101/114710 ◽

2017 ◽

Cited By ~ 1

Author(s):

Robert A. Beagrie ◽

Markus Schueler

Keyword(s):

Next Generation Sequencing Data ◽

Genome Architecture ◽

Sequencing Data ◽

Cell Nuclei ◽

Automated Mapping ◽

Chromatin Interactions ◽

Genome Wide ◽

Quality Control Metrics ◽

Genomic Regions ◽

Analytical Tools

AbstractGenome Architecture Mapping (GAM) is a recently developed method for mapping chromatin interactions genome-wide. GAM is based on sequencing genomic DNA extracted from thin cryosections of cell nuclei. As a new approach, GAM datasets require specialized analytical tools and approaches. Here we present GAMtools, a pipeline for analysing GAM datasets. GAMtools covers the automated mapping of raw next-generation sequencing data generated by GAM, detection of genomic regions present in each nuclear slice, calculation of quality control metrics, generation of inferred proximity matrices, plotting of heatmaps and detection of genomic features for which chromatin interactions are enriched/depleted.

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Iteratively improving Hi-C experiments one step at a time

10.1101/287201 ◽

2018 ◽

Author(s):

Rosela Golloshi ◽

Jacob Sanders ◽

Rachel Patton McCord

Keyword(s):

Data Quality ◽

Genome Structure ◽

Small Samples ◽

List Type ◽

Chromosome Conformation ◽

3D Genome ◽

Dna Loss ◽

Measuring Interaction ◽

Genomic Regions ◽

And Control

AbstractThe 3D organization of eukaryotic chromosomes affects key processes such as gene expression, DNA replication, cell division, and response to DNA damage. The genome-wide chromosome conformation capture (Hi-C) approach can characterize the landscape of 3D genome organization by measuring interaction frequencies between all genomic regions. Hi-C protocol improvements and rapid advances in DNA sequencing power have made Hi-C useful to diverse biological systems, not only to elucidate the role of 3D genome structure in proper cellular function, but also to characterize genomic rearrangements, assemble new genomes, and consider chromatin interactions as potential biomarkers for diseases. Yet, the Hi-C protocol is still complex and subject to variations at numerous steps that can affect the resulting data. Thus, there is still a need for better understanding and control of factors that contribute to Hi-C experiment success and data quality. Here, we evaluate recently proposed Hi-C protocol modifications as well as often overlooked variables in sample preparation and examine their effects on Hi-C data quality. We examine artifacts that can occur during Hi-C library preparation, including microhomology-based artificial template copying and chimera formation that can add noise to the downstream data. Exploring the mechanisms underlying Hi-C artifacts pinpoints steps that should be further optimized in the future. To improve the utility of Hi-C in characterizing the 3D genome of specialized populations of cells or small samples of primary tissue, we identify steps prone to DNA loss which should be optimized to adapt Hi-C to lower cell numbers.Highlights3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point)Variability in Hi-C libraries can arise from early steps of cell preparationHi-C 2.0 changes to interaction capture steps also benefit 6-cutter librariesArtificial molecule fusions can arise during end repair and PCR, increasing noiseCommon causes of Hi-C DNA loss identified for future optimization

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Chromosome interactome inferred from mitosis-G1 transition

10.1101/084608 ◽

2016 ◽

Cited By ~ 1

Author(s):

Y.A. Eidelman ◽

S.V. Slanina ◽

A.V. Aleshchenko ◽

S.G. Andreev

Keyword(s):

Spatial Organization ◽

Contact Map ◽

Interphase Chromosome ◽

Basic Principles ◽

Chromosome Conformation ◽

3D Genome ◽

Contact Patterns ◽

Genome Wide ◽

Genome Study ◽

Contact Data

ABSTRACTThe progress in experimental techniques aimed at 3D genome study is yet to bring about revelation of basic principles of genome folding. Chromosome conformation capture Hi-C technologies provide genome wide mapping of genomic loci interactions but spatial organization of chromosomes remains unknown. Here, we develop a polymer modeling approach to generate the ensemble of 3D chromosome conformations for mapping genetic loci contacts and the positions of megabase chromosomal domains in interphase chromosome at different time of mitosis-interphase transition. We demonstrate that (*) whole chromosome contact map (interactome) generated for mouse chromosome 18 structure and (**) contact patterns, observed soon after mitotic decondensation and remaining similar during G1, correlate well with the experimental Hi-C contact data. The results suggest that contact map formation and spatial compartmentalization of an interphase chromosome are driven by interactions between different types of domains during formation of globular chromosome state at the end of mitotis-G1 transition.

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DiffGR: Detecting differentially interacting genomic regions from Hi-C contact maps

10.1101/2020.08.29.273698 ◽

2020 ◽

Author(s):

Huiling Liu ◽

Wenxiu Ma

Keyword(s):

Large Scale ◽

Simulation Studies ◽

Cell Type ◽

Nonparametric Approach ◽

Contact Maps ◽

Chromatin Interactions ◽

Topologically Associating Domain ◽

Genome Wide ◽

Cell Type Specific ◽

Genomic Regions

AbstractRecent advances in Hi-C techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically-associating domain (TAD) are still lacking. We proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient (SCC) to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions using real Hi-C datasets. DiffGR is publicly available at https://github.com/wmalab/DiffGR.

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Nucleosome positions alone determine micro-domains in yeast chromosomes

10.1101/456202 ◽

2018 ◽

Author(s):

O. Wiese ◽

D. Marenduzzo ◽

C. A. Brackley

Keyword(s):

Length Distribution ◽

Yeast Genome ◽

Chromatin Interactions ◽

Genome Wide ◽

Simple Simulation ◽

Domain Boundaries ◽

Dynamics Simulations ◽

Genomic Regions ◽

Inactive Genes ◽

High Degree

AbstractWe use molecular dynamics simulations based on publicly available MNase-seq data for nucleosome positions to predict the 3-D structure of chromatin in the yeast genome. Our main aim is to shed light on the mechanism underlying the formation of micro-domains, chromosome regions of around 0.5-10 kbp which show enriched self-interactions, which were experimentally observed in recent MicroC experiments. We show that the sole input of nucleosome positioning data is already sufficient to determine the patterns of chromatin interactions and domain boundaries seen experimentally to a high degree of accuracy. Since the nucleosome spacing so strongly affects the larger-scale domain structure, we next examine the genome-wide linker-length distribution in more detail, finding that it is highly irregular, and varies in different genomic regions such as gene bodies, promoters, and active and inactive genes. Finally we use our simple simulation model to characterise in more detail how irregular nucleosome spacing may affect local chromatin structure.

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Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

10.1101/579573 ◽

2019 ◽

Cited By ~ 1

Author(s):

Vijay Ramani ◽

Xinxian Deng ◽

Ruolan Qiu ◽

Choli Lee ◽

Christine M Disteche ◽

...

Keyword(s):

Single Cell ◽

Genome Organization ◽

Single Cells ◽

Three Dimensional ◽

Population Based ◽

Order Structure ◽

Chromosome Conformation ◽

3D Genome ◽

A Cell ◽

Cell To Cell Variability

AbstractThe highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

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Utilizing networks for differential analysis of chromatin interactions

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972001740008x ◽

2017 ◽

Vol 15 (06) ◽

pp. 1740008 ◽

Cited By ~ 2

Author(s):

Lu Liu ◽

Jianhua Ruan

Keyword(s):

High Throughput Sequencing ◽

Signal To Noise Ratio ◽

Chromatin Interaction ◽

Differential Analysis ◽

Local Method ◽

Global Method ◽

Chromatin Interactions ◽

Normalization Strategy ◽

Genome Wide ◽

Genomic Regions

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. In this paper, we introduce two novel approaches to detect differentially interacting genomic regions between two Hi-C experiments using a network model. To make input data from multiple experiments comparable, we propose a normalization strategy guided by network topological properties. We then devise two measurements, using local and global connectivity information from the chromatin interaction networks, respectively, to assess the interaction differences between two experiments. When multiple replicates are present in experiments, our approaches provide the flexibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global method performs better on real Hi-C data. The local and global methods, regardless of pooling, are always superior to two existing methods. Furthermore, our methods work well on both unweighted and weighted networks and our normalization strategy significantly improves the performance compared with raw networks without normalization. Therefore, we believe our methods will be useful for identifying differentially interacting genomic regions.

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