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

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.

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

2021 ◽  
Vol 17 (3) ◽  
pp. e1008834
Author(s):  
Ji Hyun Bak ◽  
Min Hyeok Kim ◽  
Lei Liu ◽  
Changbong Hyeon
Keyword(s):  
Statistical Physics ◽  
Spatial Organization ◽  
Building Blocks ◽  
Cell Types ◽  
Specific Pattern ◽  
Unified Framework ◽  
Global Pattern ◽  
Chromatin Domains ◽  
Chromosome Conformation ◽  
Multi Scale

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.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

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.

scholarly journals The organisation and functions of local Ca2+ signals

2001 ◽  
Vol 114 (12) ◽  
pp. 2213-2222 ◽  
Author(s):  
Martin D. Bootman ◽  
Peter Lipp ◽  
Michael J. Berridge
Keyword(s):  
Cell Proliferation ◽  
Gene Transcription ◽  
Cell Biology ◽  
Building Blocks ◽  
Cell Types ◽  
Diverse Range ◽  
Cellular Processes ◽  
Different Cell Types ◽  
Intracellular Messenger ◽  
Sensory Mechanisms

Calcium (Ca2+) is a ubiquitous intracellular messenger, controlling a diverse range of cellular processes, such as gene transcription, muscle contraction and cell proliferation. The ability of a simple ion such as Ca2+ to play a pivotal role in cell biology results from the facility that cells have to shape Ca2+ signals in space, time and amplitude. To generate and interpret the variety of observed Ca2+ signals, different cell types employ components selected from a Ca2+ signalling ‘toolkit’, which comprises an array of homeostatic and sensory mechanisms. By mixing and matching components from the toolkit, cells can obtain Ca2+ signals that suit their physiology. Recent studies have demonstrated the importance of local Ca2+ signals in defining the specificity of the interaction of Ca2+ with its targets. Furthermore, local Ca2+ signals are the triggers and building blocks for larger global signals that propagate throughout cells.

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

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.

scholarly journals Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

Neuronal Diversity In The Retina

e-Neuroforum ◽  
2017 ◽  
Vol 23 (2) ◽  
Author(s):  
Philipp Berens ◽  
Thomas Euler
Keyword(s):  
Building Blocks ◽  
Cell Types ◽  
Relevant Information ◽  
Future Research ◽  
Neuronal Diversity ◽  
Form Complex ◽  
Current State ◽  
Complex Circuits ◽  
Different Cell Types ◽  
The Brain

AbstractThe retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. We review the current state of this endeavour and highlight possible directions for future research.

scholarly journals Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation

2019 ◽  
Vol 48 (3) ◽  
pp. 1131-1145
Author(s):  
She Zhang ◽  
Fangyuan Chen ◽  
Ivet Bahar
Keyword(s):  
Cell Differentiation ◽  
Cell Lines ◽  
Spatial Dynamics ◽  
Cell Types ◽  
Global Scale ◽  
Elastic Network Model ◽  
Chromosome Conformation ◽  
Highly Expressed Genes ◽  
The Difference ◽  
Different Cell Types

Abstract Advances in chromosome conformation capture techniques as well as computational characterization of genomic loci structural dynamics open new opportunities for exploring the mechanistic aspects of genome-scale differences across different cell types. We examined here the dynamic basis of variabilities between different cell types by investigating their chromatin mobility profiles inferred from Hi-C data using an elastic network model representation of the chromatin. Our comparative analysis of sixteen cell lines reveals close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations emerge in the detailed spatial mobilities of genomic loci. Closer examination reveals that the differences in spatial dynamics mainly originate from the difference in the frequencies of their intrinsically accessible modes of motion. Thus, even though the chromosomes of different types of cells have access to similar modes of collective movements, not all modes are deployed by all cells, such that the effective mobilities and cross-correlations of genomic loci are cell-type-specific. Comparison with RNA-seq expression data reveals a strong overlap between highly expressed genes and those distinguished by high mobilities in the present study, in support of the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation.

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

2021 ◽  
Vol 12 (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.

scholarly journals Synchronized cardiac impulses emerge from multi-scale, heterogeneous local calcium signals within and among cells of heart pacemaker tissue

2020 ◽  
Author(s):  
Rostislav Bychkov ◽  
Magdalena Juhaszova ◽  
Kenta Tsutsui ◽  
Christopher Coletta ◽  
Michael D. Stern ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Sinoatrial Node ◽  
Cell Types ◽  
Low Resolution ◽  
Impulse Generation ◽  
Multi Scale ◽  
Complex Processes ◽  
Self Organized ◽  
Complex Process ◽  
Different Cell Types

ABSTRACTBackgroundThe current paradigm of Sinoatrial Node (SAN) impulse generation: (i) is that full-scale action potentials (APs) of a common frequency are initiated at one site and are conducted within the SAN along smooth isochrones; and (ii) does not feature fine details of Ca2+ signalling present in isolated SAN cells, in which small subcellular, subthreshold local Ca2+ releases (LCRs) self-organize to generate cell-wide APs.ObjectivesTo study subcellular Ca2+ signals within and among cells comprising the SAN tissue.MethodsWe combined immunolabeling with a novel technique to detect the occurrence of LCRs and AP-induced Ca2+ transients (APCTs) in individual pixels (chonopix) across the entire mouse SAN images.ResultsAt high magnification, Ca2+ signals appeared markedly heterogeneous in space, amplitude, frequency, and phase among cells comprising an HCN4+/CX43- cell meshwork. The signalling exhibited several distinguishable patterns of LCR/APCT interactions within and among cells. Apparently conducting rhythmic APCTs of the meshwork were transferred to a truly conducting HCN4-/CX43+ network of straited cells via narrow functional interfaces where different cell types intertwine, i.e. the SAN anatomical/functional unit. At low magnification, the earliest APCT of each cycle occurred within a small area of the HCN4 meshwork and subsequent APCT appearance throughout SAN pixels was discontinuous.ConclusionsWe have discovered a novel, microscopic Ca2+ signalling paradigm of SAN operation that has escaped detection using low-resolution, macroscopic tissue isochrones employed in prior studies: APs emerge from heterogeneous subcellular subthreshold Ca2+ signals, resembling multiscale complex processes of impulse generation within clusters of neurons in neuronal networks.Condensed abstractBy combining immunolabeling with a novel optical technique we detected markedly heterogenous Ca2+signals within and among cell clusters of an HCN4+/CX43- meshwork in mouse sinoatrial node. These Ca2+ signals self-organized and transferred, throughout the node, to projections from an HCN4-/CX43+ network connected to a highly organized, rapidly conducting part of the CX43+ network. Thus, APs emerge from heterogeneous, subthreshold Ca2+ signaling not detected in low-resolution macroscopic isochrones. Our discovery requires a fundamental paradigm shift from concentric impulse propagation initiated within a leading site, to a multiscale/complex process, resembling the emergence of organized signals from heterogeneous local signals within neuronal networks.

scholarly journals Identifying regulatory and spatial genomic architectural elements using cell type independent machine and deep learning models

2020 ◽  
Author(s):  
Laura D. Martens ◽  
Oisín Faust ◽  
Liviu Pirvan ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Data Sets ◽  
Chromosome Conformation ◽  
Architectural Elements ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Chromatin Organisation ◽  
Different Cell Types

AbstractChromosome conformation capture methods such as Hi-C enables mapping of genome-wide chromatin interactions and is a promising technology to understand the role of spatial chromatin organisation in gene regulation. However, the generation and analysis of these data sets at high resolutions remain technically challenging and costly. We developed a machine and deep learning approach to predict functionally important, highly interacting chromatin regions (HICR) and topologically associated domain (TAD) boundaries independent of Hi-C data in both normal physiological states and pathological conditions such as cancer. This approach utilises gradient boosted trees and convolutional neural networks trained on both Hi-C and histone modification epigenomic data from three different cell types. Given only epigenomic modification data these models are able to predict chromatin interactions and TAD boundaries with high accuracy. We demonstrate that our models are transferable across cell types, indicating that combinatorial histone mark signatures may be universal predictors for highly interacting chromatin regions and spatial chromatin architecture elements.

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