The hierarchical packing of euchromatin domains can be described as multiplicative cascades

Image-based analysis of the genome's fractality during the cell cycle

The human genome consists of about 2 m of DNA packed inside the cell nucleus barely 10 μm in diameter. DNA is complexed with histones, forming chromatin fiber, which folds inside the nucleus into loops, topologically associating domains, A/B compartments, and chromosome territories. This organization is knot-free and self-similar across length scales, leading to a hypothesis that the genome presents a fractal globule, which was corroborated by chromosome conformation capture experiments. In addition, many microscopy techniques have been used to obtain the fractal dimension of the genome's spatial distribution from its images. However, different techniques often required that different definitions of fractal dimension be adapted, making the comparison of these results not trivial. In this study, we use spinning disk confocal microscopy to collect high-resolution images of nuclei in live human cells during the cell cycle. We then systematically compare existing image-based fractal analyses-including mass-scaling, box-counting, lacunarity, and multifractal spectrum-by applying them to images of human cell nuclei and investigate changes in the genome's spatial organization during the cell cycle. Our data reveal that different image-based fractal measurements offer distinct metrics, highlighting different features of the genome's spatial organization. Yet, all these metrics consistently indicate the following trend for the changes in the genome's organization during the cell cycle: the genome being compactly packed in early G1 phase, followed by a decondensation throughout the G1 phase, and a subsequent condensation in the S and G2 phases. Our comprehensive comparison of image-based fractal analyses reconciles the perceived discrepancies between different methods. Moreover, our results offer new insights into the physical principles underlying the genome's organization and its changes during the cell cycle.

Fluorescence-based super-resolution-microscopy strategies for chromatin studies

Super-resolution microscopy (SRM) is a prime tool to study chromatin organisation at near biomolecular resolution in the native cellular environment. With fluorescent labels DNA, chromatin-associated proteins and specific epigenetic states can be identified with high molecular specificity. The aim of this review is to introduce the field of diffraction-unlimited SRM to enable an informed selection of the most suitable SRM method for a specific chromatin-related research question. We will explain both diffraction-unlimited approaches (coordinate-targeted and stochastic-localisation-based) and list their characteristic spatio-temporal resolutions, live-cell compatibility, image-processing, and ability for multi-colour imaging. As the increase in resolution, compared to, e.g. confocal microscopy, leads to a central role of the sample quality, important considerations for sample preparation and concrete examples of labelling strategies applicable to chromatin research are discussed. To illustrate how SRM-based methods can significantly improve our understanding of chromatin functioning, and to serve as an inspiring starting point for future work, we conclude with examples of recent applications of SRM in chromatin research.

Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the trade-off margin for high-speed imaging. Recently proposed denoising on the basis of neural networks shows exceptional performance but raises concerns of errors typical of neural networks. Here, we present a work-flow that supports an empirically optimized reduction of exposure times, as well as per-image quality control to exclude images with reconstruction errors. We implement this work-flow on the basis of the denoising tool Noise2Void and assess the molecular state and 3D shape of RNA polymerase II (Pol II) clusters in live zebrafish embryos. Image acquisition speed could be tripled, achieving 2-s time resolution and 350-nm lateral image resolution. The obtained data reveal stereotyped events of approximately 10 s duration: initially, the molecular mark for recruited Pol II increases, then the mark for active Pol II increases, and finally Pol II clusters take on a stretched and unfolded shape. An independent analysis based on fixed sample images reproduces this sequence of events, and suggests that they are related to the transient association of genes with Pol II clusters. Our work-flow consists of procedures that can be implemented on commercial fluorescence microscopes without any hardware or software modification, and should, therefore, be transferable to many other applications.