scholarly journals The Prospective Study of Epigenetic Regulatory Profiles in Sport and Exercise Monitored Through Chromosome Conformation Signatures

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 905
Author(s):  
Elliott C. R. Hall ◽  
Christopher Murgatroyd ◽  
Georgina K. Stebbings ◽  
Brian Cunniffe ◽  
Lee Harle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Medical Intervention ◽  
Gene Expression Patterns ◽  
Chromosome Conformation ◽  
Exercise Adaptation ◽  
Technological Advances ◽  
Sport And Exercise

The integration of genetic and environmental factors that regulate the gene expression patterns associated with exercise adaptation is mediated by epigenetic mechanisms. The organisation of the human genome within three-dimensional space, known as chromosome conformation, has recently been shown as a dynamic epigenetic regulator of gene expression, facilitating the interaction of distal genomic regions due to tight and regulated packaging of chromosomes in the cell nucleus. Technological advances in the study of chromosome conformation mean a new class of biomarker—the chromosome conformation signature (CCS)—can identify chromosomal interactions across several genomic loci as a collective marker of an epigenomic state. Investigative use of CCSs in biological and medical research shows promise in identifying the likelihood that a disease state is present or absent, as well as an ability to prospectively stratify individuals according to their likely response to medical intervention. The association of CCSs with gene expression patterns suggests that there are likely to be CCSs that respond, or regulate the response, to exercise and related stimuli. The present review provides a contextual background to CCS research and a theoretical framework discussing the potential uses of this novel epigenomic biomarker within sport and exercise science and medicine.

scholarly journals Chromosome conformation and gene expression patterns differ profoundly in human fibroblasts grown in spheroids versus monolayers

Nucleus ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 383-391 ◽  
Author(s):  
Haiming Chen ◽  
Laura Seaman ◽  
Sijia Liu ◽  
Thomas Ried ◽  
Indika Rajapakse
Keyword(s):  
Gene Expression ◽  
Human Fibroblasts ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Chromosome Conformation

scholarly journals ClusterMap: multi-scale clustering analysis of spatial gene expression

2021 ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Haowen Zhou ◽  
Kevin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Computational Framework ◽  
Spatially Resolved ◽  
Tissue Organization

AbstractQuantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

ZebraFISH: Fluorescent In Situ Hybridization Protocol and Three-Dimensional Imaging of Gene Expression Patterns

Zebrafish ◽  
2006 ◽  
Vol 3 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Monique C.M. Welten ◽  
Simon B. de Haan ◽  
Niels van den Boogert ◽  
Jasprien N. Noordermeer ◽  
Gerda E.M. Lamers ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Situ Hybridization ◽  
Fluorescent In Situ Hybridization ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Gene Expression Patterns ◽  
Three Dimensional Imaging ◽  
Hybridization Protocol

scholarly journals Orientation and repositioning of chromosomes correlate with cell geometry–dependent gene expression

2017 ◽  
Vol 28 (14) ◽  
pp. 1997-2009 ◽  
Author(s):  
Yejun Wang ◽  
Mallika Nagarajan ◽  
Caroline Uhler ◽  
G. V. Shivashankar
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Gene Expression Patterns ◽  
Cell Behavior ◽  
Global Changes ◽  
Cell Geometry ◽  
Transcription Inhibition ◽  
Regulate Gene Expression

Extracellular matrix signals from the microenvironment regulate gene expression patterns and cell behavior. Using a combination of experiments and geometric models, we demonstrate correlations between cell geometry, three-dimensional (3D) organization of chromosome territories, and gene expression. Fluorescence in situ hybridization experiments showed that micropatterned fibroblasts cultured on anisotropic versus isotropic substrates resulted in repositioning of specific chromosomes, which contained genes that were differentially regulated by cell geometries. Experiments combined with ellipsoid packing models revealed that the mechanosensitivity of chromosomes was correlated with their orientation in the nucleus. Transcription inhibition experiments suggested that the intermingling degree was more sensitive to global changes in transcription than to chromosome radial positioning and its orientations. These results suggested that cell geometry modulated 3D chromosome arrangement, and their neighborhoods correlated with gene expression patterns in a predictable manner. This is central to understanding geometric control of genetic programs involved in cellular homeostasis and the associated diseases.

scholarly journals ClusterMap: multi-scale clustering analysis of spatial gene expression

2021 ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Haowen Zhou ◽  
Kevin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Computational Framework ◽  
Spatially Resolved ◽  
Tissue Organization

Abstract Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images. 

scholarly journals Mathematical characterization of three-dimensional gene expression patterns

2004 ◽  
Vol 20 (11) ◽  
pp. 1653-1662 ◽  
Author(s):  
L. d. F. Costa ◽  
M. S. Barbosa ◽  
E. T. M. Manoel ◽  
J. Streicher ◽  
G. B. Muller
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Gene Expression Patterns

scholarly journals ClusterMap for multi-scale clustering analysis of spatial gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Jingyi Ren ◽  
Haowen Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Density Peak ◽  
Spatially Resolved ◽  
Tissue Organization

AbstractQuantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.

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