A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C

Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.

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A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C

10.1101/530519 ◽

2019 ◽

Author(s):

Ji Hyun Bak ◽

Min Hyeok Kim ◽

Lei Liu ◽

Changbong Hyeon

Keyword(s):

Statistical Physics ◽

Building Blocks ◽

Cell Types ◽

Unified Framework ◽

Global Pattern ◽

Chromatin Domains ◽

Chromosome Conformation ◽

Multi Scale ◽

Unified Algorithm ◽

Different Cell Types

AbstractIdentifying chromatin domains (CDs) from high-throughput chromosome conformation capture (Hi-C) data is currently a central problem in genome research. Here we present a unified algorithm, Multi-CD, which infers CDs at various genomic scales by leveraging the information from Hi-C. By integrating a model of the chromosome from polymer physics, statistical physics-based clustering analysis, and Bayesian inference, Multi-CD identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types allow us to glean the principles of chromatin organization: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. CDs obtained from Multi-CD applied to Hi-C data enable a quantitative and comparative analysis of chromosome organization in different cell types, providing glimpses into structure-function relationship in genome.

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Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes

Nature Communications ◽

10.1038/s41467-021-22666-3 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Yuanlong Liu ◽

Luca Nanni ◽

Stephanie Sungalee ◽

Marie Zufferey ◽

Daniele Tavernari ◽

...

Keyword(s):

Biological Activity ◽

Cell Lines ◽

Spatial Organization ◽

Cell Types ◽

Chromosome Conformation ◽

Algorithmic Approach ◽

Multi Scale ◽

Lineage Differentiation ◽

Data Resolution ◽

Cell Phenotypes

AbstractChromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been characterized only in a few cell types and systematic comparisons across multiple tissues and conditions are missing. Here, we present Calder, an algorithmic approach that enables the identification of multi-scale sub-compartments at variable data resolution. Calder allows to infer and compare chromatin sub-compartments and compartment domains in >100 cell lines. Our results reveal sub-compartments enriched for poised chromatin states and undergoing spatial repositioning during lineage differentiation and oncogenic transformation.

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MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

10.1101/370338 ◽

2018 ◽

Author(s):

Yusen Ye ◽

Lin Gao ◽

Shihua Zhang

Keyword(s):

Efficient Method ◽

Three Dimensional ◽

Cell Types ◽

Cell Type ◽

Range Interaction ◽

Distinct Cell Type ◽

Chromosome Conformation ◽

Topological Domains ◽

Multi Scale ◽

Cell Type Specific

AbstractThe chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. However, existing methods for domain detection were only designed based on symmetric Hi-C maps, ignoring long-range interaction structures between domains. To this end, we proposed a generic and efficient method to identify multi-scale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets. We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multi-scale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the-state-of-art methods in terms of accuracy, flexibility and efficiency.

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Hi–C interaction graph analysis reveals the impact of histone modifications in chromatin shape

Applied Network Science ◽

10.1007/s41109-021-00396-1 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Emre Sefer

Keyword(s):

Histone Modifications ◽

Spatial Organization ◽

Three Dimensional ◽

Cell Types ◽

Interaction Graph ◽

Chromosome Conformation ◽

A Genome ◽

Topologically Associated Domains ◽

Wide Scale ◽

The Impact

AbstractChromosome conformation capture experiments such as Hi–C map the three-dimensional spatial organization of genomes in a genome-wide scale. Even though Hi–C interactions are not biased towards any of the histone modifications, previous analysis has revealed denser interactions around many histone modifications. Nevertheless, simultaneous effects of these modifications in Hi–C interaction graph have not been fully characterized yet, limiting our understanding of genome shape. Here, we propose ChromatinCoverage and its extension TemporalPrizeCoverage methods to decompose Hi–C interaction graph in terms of known histone modifications. Both methods are based on set multicover with pairs, where each Hi–C interaction is tried to be covered by histone modification pairs. We find 4 histone modifications H3K4me1, H3K4me3, H3K9me3, H3K27ac to be significantly predictive of most Hi–C interactions across species, cell types and cell cycles. The proposed methods are quite effective in predicting Hi–C interactions and topologically-associated domains in one species, given it is trained on another species or cell types. Overall, our findings reveal the impact of subset of histone modifications in chromatin shape via Hi–C interaction graph.

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Systematic evaluation of chromosome conformation capture assays

10.1101/2020.12.26.424448 ◽

2020 ◽

Author(s):

Betul Akgol Oksuz ◽

Liyan Yang ◽

Sameer Abraham ◽

Sergey V. Venev ◽

Nils Krietenstein ◽

...

Keyword(s):

Spatial Organization ◽

Cell Types ◽

Chromosome Conformation Capture ◽

Chromatin Interaction ◽

Systematic Evaluation ◽

Chromosome Conformation ◽

3D Genome ◽

Experimental Parameters ◽

Genome Features ◽

Multiple Cell

AbstractChromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of cross-linkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthens the compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results for multiple cell types and cell cycle stages. We find that cell type-specific quantitative differences in chromosome folding are not detected or underestimated by some protocols. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using conventional Hi-C, Micro-C and Hi-C 3.0 for commonly used cell lines in the 4D Nucleome Project.

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Topological demarcation by HMGB2 is disrupted early upon senescence entry across cell types and induces CTCF clustering

10.1101/127522 ◽

2017 ◽

Cited By ~ 1

Author(s):

Anne Zirkel ◽

Milos Nikolic ◽

Konstantinos Sofiadis ◽

Jan-Philipp Mallm ◽

Lilija Brant ◽

...

Keyword(s):

Spatial Organization ◽

Spatial Clustering ◽

High Mobility ◽

Super Resolution ◽

Cell Types ◽

Cell Nuclei ◽

Genome Chromosome ◽

Chromosome Conformation ◽

Nuclear Factors ◽

Stochastic Events

AbstractAgeing-relevant processes, like cellular senescence, are characterized by complex, often stochastic, events giving rise to heterogeneous cell populations. We hypothesized that entry into senescence of different primary human cells can be triggered by one early molecular event affecting the spatial organization of chromosomes. To test this, we combined whole-genome chromosome conformation capture, population and single-cell transcriptomics, super-resolution imaging, and functional analyses applied on proliferating and replicatively-senescent populations from three distinct human cell types. We found a number of genes involved in DNA conformation maintenance being suppressed upon senescence across cell types. Of these, the abundant high mobility group (HMG) B1 and B2 nuclear factors are quantitatively removed from cell nuclei before typical senescence markers appear, and mark a subset of topologically-associating domain (TAD) boundaries. Their loss coincides with obvious reorganization of chromatin interactions via the dramatic spatial clustering of CTCF foci. HMGB2 knock-down recapitulates this senescence-induced CTCF clustering, while also affecting insulation at TAD boundaries. We accordingly propose that HMGB-mediated deregulation of chromosome conformation constitutes a primer for the ensuing senescent program across cell types.

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Chromatin Interaction Changes during the iPSC-NPC Model to Facilitate the Study of Biologically Significant Genes Involved in Differentiation

Genes ◽

10.3390/genes11101176 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1176

Author(s):

Won-Young Choi ◽

Ji-Hyun Hwang ◽

Jin-Young Lee ◽

Ann-Na Cho ◽

Andrew J Lee ◽

...

Keyword(s):

Gene Regulation ◽

Cell Fate ◽

Spatial Organization ◽

Spinal Cord Injuries ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Interaction ◽

Patient Specific ◽

Differentiation Process ◽

Chromosome Conformation

Given the difficulties of obtaining diseased cells, differentiation of neurons from patient-specific human induced pluripotent stem cells (iPSCs) with neural progenitor cells (NPCs) as intermediate precursors is of great interest. While cellular and transcriptomic changes during the differentiation process have been tracked, little attention has been given to examining spatial re-organization, which has been revealed to control gene regulation in various cells. To address the regulatory mechanism by 3D chromatin structure during neuronal differentiation, we examined the changes that take place during differentiation process using two cell types that are highly valued in the study of neurodegenerative disease - iPSCs and NPCs. In our study, we used Hi-C, a derivative of chromosome conformation capture that enables unbiased, genome-wide analysis of interaction frequencies in chromatin. We showed that while topologically associated domains remained mostly the same during differentiation, the presence of differential interacting regions in both cell types suggested that spatial organization affects gene regulation of both pluripotency maintenance and neuroectodermal differentiation. Moreover, closer analysis of promoter–promoter pairs suggested that cell fate specification is under the control of cis-regulatory elements. Our results are thus a resourceful addition in benchmarking differentiation protocols and also provide a greater appreciation of NPCs, the common precursors from which required neurons for applications in neurodegenerative diseases such as Parkinson’s disease, Alzheimer’s disease, schizophrenia and spinal cord injuries are utilized.

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Street context of various demographic groups in their daily mobility

Applied Network Science ◽

10.1007/s41109-021-00382-7 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Ariel Salgado ◽

Weixin Li ◽

Fahad Alhasoun ◽

Inés Caridi ◽

Marta Gonzalez

Keyword(s):

Geographical Information Systems ◽

Building Blocks ◽

Geographical Information ◽

Unified Framework ◽

Demographic Groups ◽

Daily Mobility ◽

Urban Science ◽

Science Framework ◽

The City ◽

Racial Ethnic

AbstractWe present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

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Advantages of using graph databases to explore chromatin conformation capture experiments

BMC Bioinformatics ◽

10.1186/s12859-020-03937-0 ◽

2021 ◽

Vol 22 (S2) ◽

Author(s):

Daniele D’Agostino ◽

Pietro Liò ◽

Marco Aldinucci ◽

Ivan Merelli

Keyword(s):

Web Application ◽

High Throughput Sequencing ◽

Cell Types ◽

Graph Database ◽

Graph Databases ◽

Sources Of Information ◽

Chromosome Conformation ◽

Wide Scale ◽

User Friendly ◽

Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

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A Comprehensive Review: Sphingolipid Metabolism and Implications of Disruption in Sphingolipid Homeostasis

International Journal of Molecular Sciences ◽

10.3390/ijms22115793 ◽

2021 ◽

Vol 22 (11) ◽

pp. 5793

Author(s):

Brianna M. Quinville ◽

Natalie M. Deschenes ◽

Alex E. Ryckman ◽

Jagdeep S. Walia

Keyword(s):

Nervous System ◽

Large Scale ◽

System Development ◽

Building Blocks ◽

Cell Types ◽

Nervous System Development ◽

Sphingolipid Metabolism ◽

Lysosomal Storage ◽

Bioactive Lipids ◽

Comprehensive Review

Sphingolipids are a specialized group of lipids essential to the composition of the plasma membrane of many cell types; however, they are primarily localized within the nervous system. The amphipathic properties of sphingolipids enable their participation in a variety of intricate metabolic pathways. Sphingoid bases are the building blocks for all sphingolipid derivatives, comprising a complex class of lipids. The biosynthesis and catabolism of these lipids play an integral role in small- and large-scale body functions, including participation in membrane domains and signalling; cell proliferation, death, migration, and invasiveness; inflammation; and central nervous system development. Recently, sphingolipids have become the focus of several fields of research in the medical and biological sciences, as these bioactive lipids have been identified as potent signalling and messenger molecules. Sphingolipids are now being exploited as therapeutic targets for several pathologies. Here we present a comprehensive review of the structure and metabolism of sphingolipids and their many functional roles within the cell. In addition, we highlight the role of sphingolipids in several pathologies, including inflammatory disease, cystic fibrosis, cancer, Alzheimer’s and Parkinson’s disease, and lysosomal storage disorders.

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