NucleusJ 1.0, an ImageJ plugin, has been shown to be a useful tool to analyse nuclear morphology and chromatin organisation in plant and animal cells. However, technological improvements of confocal microscopy have speeded-up image acquisition, highlighting the bottleneck in 3D image analysis caused by manual steps in NucleusJ 1.0 and limiting its use for big data analysis. NucleusJ 2.0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-line user interface. Starting with large collection of 3D nuclei, segmentation can be performed by the previously developed Otsu-modified method or by a new 3D gift-wrapping method, taking better account of nuclear indentations and unstained nucleoli. These two complementary methods based on threshold and edgebased detection, are compared for their accuracy by using three types of datasets-digitised spheres, microspheres and plant nuclei-available to the community through an open source repository accessible at https://www.brookes.ac.uk/indepth/images/. A discrete geometric method was introduced to improve the surface area calculation, a key parameter when studying nuclear morphology, replacing an ImageJ default tool by a new one that includes pixel context information. Finally, NucleusJ 2.0 was evaluated using original plant genetic material, in which nuclear morphology and chromatin organisation are strongly affected and by assessing its efficiency on nuclei stained with DNA dyes or after 3D-DNA Fluorescence in situ hybridisation. With these improvements, NucleusJ 2.0 permits the generation of large user-curated datasets that will be useful for software benchmarking or to train convolution neural networks.

No abstract