Evolutionarily conserved protein motifs drive interactions between the plant nucleoskeleton and nuclear pores.

The nucleoskeleton forms a filamentous meshwork under the nuclear envelope and contributes to the regulation of nuclear shape and gene expression. To understand how the Arabidopsis (Arabidopsis thaliana) nucleoskeleton physically connects to the nuclear periphery in plants, we investigated the Arabidopsis nucleoskeleton protein KAKU4 and sought for functional regions responsible for its localization at the nuclear periphery. We identified 3 conserved peptide motifs within the N-terminal region of KAKU4, which are required for intermolecular interactions of KAKU4 with itself, interaction with the nucleoskeleton protein CROWDED NUCLEI (CRWN), localization at the nuclear periphery, and nuclear elongation in differentiated tissues. Unexpectedly, we find these motifs to be present also in NUP82 and NUP136, 2 plant-specific nucleoporins from the nuclear pore basket. We further show that NUP82, NUP136, and KAKU4 have a common evolutionary history predating nonvascular land plants with KAKU4 mainly localizing outside the nuclear pore suggesting its divergence from an ancient nucleoporin into a new nucleoskeleton component. Finally, we demonstrate that both NUP82 and NUP136, through their shared N-terminal motifs, interact with CRWN and KAKU4 proteins revealing the existence of a physical continuum between the nuclear pore and the nucleoskeleton in plants.

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

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 .

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.

Automated 3D bio-imaging analysis of nuclear organization by NucleusJ 2.0.

NucleusJ 1.0, an ImageJ plugin, is a useful tool to analyze nuclear morphology and chromatin organization in plant and animal cells. NucleusJ 2.0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-lineuser 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 are compared for their accuracy by using three types of datasets available to the community at https://www.brookes.ac.uk/indepth/images/ . Finally, NucleusJ 2.0 was evaluated using original plant genetic material by assessing its efficiency on nuclei stained with DNA dyes or after 3D-DNA Fluorescence in situ hybridization. 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.

RT States: systematic annotation of the human genome using cell type-specific replication timing programs.

MOTIVATION: The replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus. RESULTS: In this study, we explored recurrent and spatially coherent combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We found that a Hidden Markov Model with 15 hidden states provide a good model to describe these genome-wide RT profiling data. Each of the hidden state represents a unique combination of RT profiles across different cell types which we refer to as 'RT states'. To understand the biological properties of these RT states, we inspected their relationship with chromatin states, gene expression, functional annotation and 3D chromosomal organization. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome. AVAILABILITY AND IMPLEMENTATION: R scripts for inferring HMM models and Perl scripts for further analysis are available https://github.com/PouletAxel/script_HMM_Replication_timing. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic and epigenetic variation in 5S ribosomal RNA genes reveals genome dynamics in Arabidopsis thaliana.

Organized in tandem repeat arrays in most eukaryotes and transcribed by RNA polymerase III, expression of 5S rRNA genes is under epigenetic control. To unveil mechanisms of transcriptional regulation, we obtained here in depth sequence information on 5S rRNA genes from the Arabidopsis thaliana genome and identified differential enrichment in epigenetic marks between the three 5S rDNA loci situated on chromosomes 3, 4 and 5. We reveal the chromosome 5 locus as the major source of an atypical, long 5S rRNA transcript characteristic of an open chromatin structure. 5S rRNA genes from this locus translocated in the Landsberg erecta ecotype as shown by linkage mapping and chromosome-specific FISH analysis. These variations in 5S rDNA locus organization cause changes in the spatial arrangement of chromosomes in the nucleus. Furthermore, 5S rRNA gene arrangements are highly dynamic with alterations in chromosomal positions through translocations in certain mutants of the RNA-directed DNA methylation pathway and important copy number variations among ecotypes. Finally, variations in 5S rRNA gene sequence, chromatin organization and transcripts indicate differential usage of 5S rDNA loci in distinct ecotypes. We suggest that both the usage of existing and new 5S rDNA loci resulting from translocations may impact neighboring chromatin organization.