Biology subjects

Andres-San Roman, J. A.

Publications and source records attributed to Andres-San Roman, J. A..

4 recordsLinked to original sources

bioRxiv2025

A Natural Programmable Metamaterial Controls 3D Curvature of Compound Eyes

The panoramic vision of the convex compound eyes, common to insects and crustaceans, relies on micrometer-scale curvature variations1. These variations create specialized visual zones adapted to specific tasks, including detecting prey, mates, or predators2,3. However, the mechanisms by which such fine-scale curvature is encoded during development remain unknown. Here we show that the developing eye of Drosophila melanogaster functions as a natural metamaterial that programs the organs precise 3D curvature. We discover a supracellular triangular mesh in the basal retina with a specific pattern of triangles sizes. Computational simulations demonstrate its role directing the small scale curvature variations of the eye. Genetic disruption of this micropattern prevents local curvature establishment. Furthermore, the presence of a homologous mesh-curvature relationship in Drosophila mauritiana indicates evolutionary conservation of this mechanism. These results reveal a novel mechanism of morphogenesis control in which the supracellular 2D patterning give rise to a biological programmable metamaterial that encodes 3D curvature with great precision4. Our in vivo finding offers a novel framework for the design of shape-programmable 3D biological surfaces with broad implications from synthetic morphogenesis to clinical applications.

developmental biology↗

bioRxiv2024

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium.

Tissue morphogenesis is intimately linked to the changes in shape and organisation of individual cells. In curved epithelia, cells can intercalate along their own apicobasal axes adopting a shape named "scutoid" that allows energy minimization in the tissue. Although several geometric and biophysical factors have been associated with this 3D reorganisation, the dynamic changes underlying scutoid formation in 3D epithelial packing remain poorly understood. Here we use live-imaging of the sea star embryo coupled with deep learning-based segmentation, to dissect the relative contributions of cell density, tissue compaction, and cell proliferation on epithelial architecture. We find that tissue compaction, which naturally occurs in the embryo, is necessary for the appearance of scutoids. Physical compression experiments identify cell density as the factor promoting scutoid formation at a global level. Finally, the comparison of the developing embryo with computational models indicates that the increase in the proportion of scutoids is directly associated with cell divisions. Our results suggest that apico-basal intercalations appearing just after mitosis may help accommodate the new cells within the tissue. We propose that proliferation in a compact epithelium induces 3D cell rearrangements during development. Summary statementThe study uses sea star embryogenesis as a model of a proliferating epithelium to highlight how cell division induces 3D cell rearrangements during development.

developmental biology↗

bioRxiv2024

BiaPy: A unified framework for versatile bioimage analysis with deep learning

BiaPy is an open-source library and application that streamlines the use of common deep learning approaches for bioimage analysis. Designed to simplify technical complexities, it offers an intuitive interface, zero-code notebooks, and Docker integration, catering to both users and developers. While focused on deep learning workflows for 2D and 3D image data, it enhances performance with multi-GPU capabilities, memory optimization, and scalability for large datasets. Although BiaPy does not encompass all aspects of bioimage analysis, such as visualization and manual annotation tools, it empowers researchers by providing a ready-to-use environment with customizable templates that facilitate sophisticated bioimage analysis workflows.

bioinformatics↗

bioRxiv2023

CartoCell, a high-throughput pipeline for accurate 3D image analysis, unveils cell morphology patterns in epithelial cysts.

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our Single-cell Cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues. MOTIVATIONA major bottleneck in developing neural networks for cell segmentation is the need for labor-intensive manual curation in order to develop a training dataset. The present work addresses this limitation by developing an automated image analysis pipeline that utilizes small datasets to generate accurate labels of cells in complex, 3D epithelial contexts. The overall goal is to provide an automatic and feasible method to achieve high-quality epithelial reconstructions and to enable high-content analysis of morphological features, which can improve our understanding of how these tissues self-organize.

developmental biology↗