NODeJ: an ImageJ plugin for 3D segmentation of nuclear objects

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.

The INDEPTH (Impact of Nuclear Domains on Gene Expression and Plant Traits) Academy: a community resource for plant science

“…As an example of use of this resource, Dubos et al (2020) uploaded all their raw and segmented images to make them available to the community. Our datasets are useful for software benchmarking ( Bilalovic et al , 2020 ; Dubos et al , 2021 , Preprint) and to train future DL-based algorithms as discussed by Dimiter Prodanov (Interuniversity Microelectronics Centre-IMEC, Belgium) and Sumit Vohra (Zuse Institute, Germany) during one of the INDEPTH Academy Masterclasses (Active Segmentation Tool for plant image analysis, www.youtube.com/watch?v=jt2P7SQSuz4 ). To exemplify this, we initiated the preliminary steps toward the benchmarking of DL methods dedicated to image segmentation.…”