The localisation of apico-basal polarity proteins along the Z-axis of epithelial cells is well understood while their distribution in the plane of the epithelium is poorly characterised. Here we provide a systematic description of the planar localisation of apico-basal polarity proteins in the Drosophila ommatidial epithelium. We show that the adherens junction proteins Shotgun and Armadillo, as well as the baso-lateral complexes, are bilateral, i.e. present on both sides of cell interfaces. In contrast, we report that other key adherens junction proteins, Bazooka and the myosin regulatory light chain (Spaghetti squash) are unilateral, i.e. present on one side of cell interfaces. Furthermore, we demonstrate that planar cell polarity (PCP) and not the apical determinants Crumbs and Par-6 control Bazooka unilaterality in cone cells. Altogether, our work unravels an unexpected organisation and combination of apico-basal, cytoskeletal and planar polarity proteins that is different on either side of cell-cell interfaces and unique for the different contacts of the same cell.

Epithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually implies extensive manual correction, even with semi-automated tools. Here we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

Epithelia are dynamic tissues that self-remodel during their development. At morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually implies extensive manual correction, even with semi-automated tools. Here we present EPySeg, an open source, coding-free software that uses deep learning to segment epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By alleviating human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

Modern scientific publications often require complex figures with multiple panels and graphs, demanding consistent formatting across diverse data types. Crucial to this formatting is the precise handling of scale, pixel size, and aspect ratio, especially for microscopy and imaging data where these factors are key to accurate representation and comparison of results. Hence, creating figures can be time-consuming and tedious. We present PyFigures, a Python-based software designed to streamline the process of creating scientific figures, particularly for microscopy and imaging data. PyFigures offers automated layout handling, flexible figure structuring, automated lettering, and template creation. It supports multi-dimensional images, various input formats, and multiple export options. The tool's integration with Python[1] and Matplotlib[2] allows for custom data visualization, while its text-based serialization ensures long-term compatibility. PyFigures aims to simplify daily figure creation tasks while providing the flexibility needed for publication-quality images. To learn more about PyFigures or to try it out, visit our GitHub page: https://github.com/baigouy/PyFigures

Accurate quantification of spatial distances between fluorescent signals in multi-channel 3D microscopy is essential for understanding genomic organization and gene regulation. However, chromatic aberration introduces systematic spatial offsets between channels that significantly bias distance measurements, particularly at short genomic distances. We present FISH-Dist, an automated computational pipeline for quantitative distance measurements in 3D fluorescence in situ hybridization (FISH) experiments acquired on standard confocal microscopes. Our method combines deep learning-based spot segmentation, 3D Gaussian fitting for sub-pixel localization, and two complementary chromatic aberration correction approaches: affine (ACC) and linear (LCC). We validated the pipeline by measuring the lengths of DNA origami nanorulers and systematically evaluated FISH probe design parameters, including probe spacing, density, and target sequence length. FISH-Dist achieves sub-pixel accuracy in signal detection and substantially reduces inter-channel distance measurement errors. This enables a reproducible quantification of spatial relationships in 3D FISH datasets. Unlike existing tools optimized for long-range chromosomal interactions or requiring super-resolution microscopy, FISH-Dist specifically addresses the technical challenges of standard confocal imaging at short genomic distances, where chromatic aberration has a proportionally greater impact on measurement accuracy.