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NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
by
van Wolfswinkel, Josien C.
, Jacob, Yannick
, Péry, Emilie
, Tatout, Christophe
, Poulet, Axel
, Desset, Sophie
, Thomson, Geoffrey
, Chausse, Frédéric
, Dubos, Tristan
in
3D DNA FISH analysis
/ 3D image analysis
/ Add-in/on software
/ Algorithms
/ Automation
/ Bioinformatics
/ Biomedical and Life Sciences
/ Chromatin
/ Chromocenter
/ Computational biology
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Computer programs
/ Datasets
/ Deoxyribonucleic acid
/ DNA
/ Domains
/ Experiments
/ Gene expression
/ Heterochromatin
/ Heterochromatin organization
/ Image processing
/ Image segmentation
/ Life Sciences
/ Methods
/ Microarrays
/ Morphology
/ Nuclei
/ Nuclei (cytology)
/ Object recognition
/ Software
/ Source code
/ Structure
2022
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NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
by
van Wolfswinkel, Josien C.
, Jacob, Yannick
, Péry, Emilie
, Tatout, Christophe
, Poulet, Axel
, Desset, Sophie
, Thomson, Geoffrey
, Chausse, Frédéric
, Dubos, Tristan
in
3D DNA FISH analysis
/ 3D image analysis
/ Add-in/on software
/ Algorithms
/ Automation
/ Bioinformatics
/ Biomedical and Life Sciences
/ Chromatin
/ Chromocenter
/ Computational biology
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Computer programs
/ Datasets
/ Deoxyribonucleic acid
/ DNA
/ Domains
/ Experiments
/ Gene expression
/ Heterochromatin
/ Heterochromatin organization
/ Image processing
/ Image segmentation
/ Life Sciences
/ Methods
/ Microarrays
/ Morphology
/ Nuclei
/ Nuclei (cytology)
/ Object recognition
/ Software
/ Source code
/ Structure
2022
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NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
by
van Wolfswinkel, Josien C.
, Jacob, Yannick
, Péry, Emilie
, Tatout, Christophe
, Poulet, Axel
, Desset, Sophie
, Thomson, Geoffrey
, Chausse, Frédéric
, Dubos, Tristan
in
3D DNA FISH analysis
/ 3D image analysis
/ Add-in/on software
/ Algorithms
/ Automation
/ Bioinformatics
/ Biomedical and Life Sciences
/ Chromatin
/ Chromocenter
/ Computational biology
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Computer programs
/ Datasets
/ Deoxyribonucleic acid
/ DNA
/ Domains
/ Experiments
/ Gene expression
/ Heterochromatin
/ Heterochromatin organization
/ Image processing
/ Image segmentation
/ Life Sciences
/ Methods
/ Microarrays
/ Morphology
/ Nuclei
/ Nuclei (cytology)
/ Object recognition
/ Software
/ Source code
/ Structure
2022
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NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
Journal Article
NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
2022
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Overview
Background
The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization.
Results
We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of
Arabidopsis thaliana
nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest.
Conclusion and availability
NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from
https://gitlab.com/axpoulet/image2danalysis/-/releases
with source code, documentation and further information avaliable at
https://gitlab.com/axpoulet/image2danalysis
. The images used in this study are publicly available at
https://www.brookes.ac.uk/indepth/images/
and
https://doi.org/10.15454/1HSOIE
.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
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