BMC BioinformaticsBioinformaticsPeer-reviewed journal article
NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects
Tristan DubosiD, Axel PouletiD, Geoffrey ThomsoniD, Emilie PéryiD, Frédéric ChausseiD, Christophe TatoutiD, Sophie DessetiD, Josien C. van WolfswinkeliD, Yannick JacobiD
Abstract
BackgroundThe 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. ResultsWe 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 allows 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 availabilityNODeJ 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.
- organism: Arabidopsis
- assay: imaging
- research focus: cell type annotation
- research focus: spatial domain detection
- research focus: software / tool
- approach: experimental assay
- Citations
- 15
- FWCI
- 1.66
- Field percentile
- top 16%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context
A. Vijayan, T. A. Mody, … K. Schneitz·DevelopmentPeer-reviewed·Jul 18, 2024
- NucLogic: Logic-based geometric 3D nuclear segmentation for fast and robust analysis of challenging biological imaging data
Sebastian Gerhard Van Dijk, Mario Ledesma-Terrón, … Saskia J.E. Suijkerbuijk·bioRxiv·Oct 6, 2026
- Nessys: a novel method for accurate nuclear segmentation in 3D
Guillaume Blin, Daina Sadurska, … Sally Lowell·PLoS BiologyPeer-reviewed·Aug 9, 2019
- NISNet3D: Three-Dimensional Nuclear Synthesis and Instance Segmentation for Fluorescence Microscopy Images
L. Wu, A. Chen, … E. J. Delp·bioRxiv·Jun 10, 2022
- PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies
Grzegorz Bokota, Jacek Sroka, … Dariusz Plewczyński·BMC BioinformaticsPeer-reviewed·Feb 17, 2021
- Segmentor: A tool for manual refinement of 3D microscopy annotations
David Borland, Carolyn M. McCormick, … Jason L. Stein·BMC BioinformaticsPeer-reviewed·May 22, 2021