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.

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 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.

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.

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

Three-dimensional reconstruction of gene expression patterns during cardiac development

2003 ◽  
Vol 13 (3) ◽  
pp. 187-195 ◽  
Author(s):  
Alexandre T. Soufan ◽  
Jan M. Ruijter ◽  
Maurice J. B. van den Hoff ◽  
Piet A. J. de Boer ◽  
Jaco Hagoort ◽  
...  
Keyword(s):  
Gene Expression ◽  
3D Reconstruction ◽  
Cardiac Development ◽  
3D Visualization ◽  
Expression Patterns ◽  
Genetic Regulation ◽  
Three Dimensional ◽  
Specific Staining ◽  
Dimensional Reconstruction

The study of the genetic regulation of embryonic development requires the three-dimensional (3D) mapping of gene expression at the microscopic level. Despite the recent burst in the number of methods focusing on 3D reconstruction of embryonic specimens, an adequate and accessible 3D reconstruction protocol for the visualization of patterns of gene expression is lacking. In this communication we describe a protocol that was developed for the 3D visualization of patterns of gene expression determined by in situ hybridization (ISH) on serial sections. The method still requires tissue sectioning, due to penetration limits of the specific staining agents into whole embryo preparations. With regard to expenditure of resources, i.e., hardware, software, and time, the protocol is relatively undemanding. Because the variation between specimens requires the visualization of multiple specimens per stage, it was decided to “do more, less well.” The current protocol, therefore, results in reconstructions of sufficient, but not the highest, quality. The use of the protocol is demonstrated on a series of serially sectioned mouse hearts, ranging from embryonic day 8.5 to 14.5. The myocardium of the hearts was identified by ISH using a mixture of specific mRNA probes and reconstructed.

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 Parallelized analysis of spatial gene expression patterns by database integration

2018 ◽  
Author(s):  
Daisuke Miyamoto ◽  
Hidetoshi Ikeno ◽  
Yuko Okamura-Oho ◽  
Akira Sato ◽  
Teiichi Furuichi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Coordinate Space ◽  
Database Integration ◽  
Computational Framework ◽  
Link Type ◽  
Best Fit ◽  
2D Images ◽  
Image Datasets

AbstractWe developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value (δ). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS.We first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

scholarly journals RNA In Situ Hybridization for Detecting Gene Expression Patterns in the Abdomens and Wings of Drosophila Species

2021 ◽  
Vol 4 (1) ◽  
pp. 20
Author(s):  
Mujeeb Shittu ◽  
Tessa Steenwinkel ◽  
William Dion ◽  
Nathan Ostlund ◽  
Komal Raja ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Situ Hybridization ◽  
Expression Patterns ◽  
Drosophila Species ◽  
Gene Expression Patterns ◽  
Probe Design ◽  
Tissue Preparation ◽  
Design And Synthesis ◽  
Rna In Situ Hybridization

RNA in situ hybridization (ISH) is used to visualize spatio-temporal gene expression patterns with broad applications in biology and biomedicine. Here we provide a protocol for mRNA ISH in developing pupal wings and abdomens for model and non-model Drosophila species. We describe best practices in pupal staging, tissue preparation, probe design and synthesis, imaging of gene expression patterns, and image-editing techniques. This protocol has been successfully used to investigate the roles of genes underlying the evolution of novel color patterns in non-model Drosophila species.

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