Scutoids are a geometrical solution to three-dimensional packing of epithelia

As animals develop, tissue bending contributes to shape the organs into complex three-dimensional structures. However, the architecture and packing of curved epithelia remains largely unknown. Here we show by means of mathematical modelling that cells in bent epithelia can undergo intercalations along the apico-basal axis. This phenomenon forces cells to have different neighbours in their basal and apical surfaces. As a consequence, epithelial cells adopt a novel shape that we term “scutoid”. The detailed analysis of diverse tissues confirms that generation of apico-basal intercalations between cells is a common feature during morphogenesis. Using biophysical arguments, we propose that scutoids make possible the minimization of the tissue energy and stabilize three-dimensional packing. Hence, we conclude that scutoids are one of nature's solutions to achieve epithelial bending. Our findings pave the way to understand the three-dimensional organization of epithelial organs.España Ministerio de Ciencia y Tecnología BFU2013-48988-C2-1-P and BFU2016-8079

Tutorial sobre la Plataforma Educativa ECONET - Moodle de la FCE

Tal y como se especifica en la página de presentación de la FCE una de nuestras tareas consiste en ofrecer servicio de apoyo pedagógico y técnico para ayudar en el uso de las Nuevas Tecnologías y las redes en la enseñanza universitaria. En esta línea de actuación se enmarca el alojamiento en un servidor específico de la FCE de asignaturas, eventos... de nuestra Universidad. En años anteriores se ha desarrollado una Plataforma propia que ha sido y es una herramienta válida para los usuarios. No obstante, se ha optado por la incorporación de otra Plataforma y que por su especificidad se denomina ECONet- Moodle la cual dispone de una integración de funcionalidades muy demandada por el profesorado

EpiGraph: an open-source platform to quantify epithelial organization

Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required.España Ministerio de Economia, Industria y Competitividad BFU2016-74975-PEspaña, Programa Ramón y Cajal (PI13/ 01347

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

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.This work is supported by the project PID2019-103900GB-I00 funded by MCIN/AEI /10.13039/501100011033 and Programa Operativo FEDER Andalucía 2014–2020 (US-1380953) to L.M.E. Work by L.M.E. and J.A.A.-S.R. has been funded by the Junta de Andalucía (Consejerı´a de economı´a, conocimiento, empresas y Universidad) grant PY18-631 co-funded by FEDER funds. A.T. has been funded by a ‘‘Contrato predoctoral PIF’’ from Universidad de Sevilla. C.G.-V. has been funded by a ‘‘Contrato predoctoral para la formacio´ n de doctores’’ BES-2017-082306. G.B. was supported by a Comunidad de Madrid contract (CAM) and by an FPI grant from MINECO (BES-2022-077789). F.M.-B. was supported by MICINN (PID2020-120367GB-I00) and Fundacio´ n Ramo´ n Areces (CIVP18A3904). P.G.-G. has been funded by Margarita Salas Fellowship – NextGenerationEU. C.H.F.-E. has been funded by Marı´a Zambrano Fellowship – NextGenerationEU. I.A.-C. would like to acknowledge that his work has been partially supported by the University of the Basque Country UPV/EHU grant GIU19/027 and by grant PID2021-126701OB-I00, funded by MCIN/AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe." L.M.E. also wants to thank PIE-202120E047 – Conexiones-Life network for networking and input

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

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

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

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.Ministerio de Ciencia e Innovación PID2019-103900GB-I00, PID2020-120367GB-I00, PID2021-126701OB-I00Junta de Andalucía US-1380953, PY18-631Ministerio de Economía y Competitividad BES-2022-07778

Supplemental information CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Document S1. Figures S1–S6 Table S1. Extracted features from 353 curated cysts (104 cysts at 4 days, 103 cysts at 7 days, 116 cysts at 10 days), related to Figure 2 Table S2. Hyperparameter search space for our proposed 3D ResU-Net, related to Figure 1 Table S3. Performance evaluation of our pipeline (CartoCell) on images of different epithelial tissues and comparison with other state-of-the-art segmentation methods, using the evaluation metrics described in STAR Methods, related to Figure 1 Table S4. Relative error between features extracted using automatically segmented cysts and manually curated cysts (STAR Methods), related to Figure 1 Table S5. Cyst morphology and scutoid location statistics, related to Figure 2 Table S6. Comparison of morphology and packing features of normoxic and hypoxic MDCK cysts, related to Figure 2 Table S7. Classification of the developmental stages of Drosophila egg chambers employed, related to Figure 3 Document S2. Article plus supplemental informationPeer reviewe

Expansion of Signal Transduction Pathways in Fungi by Extensive Genome Duplication

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Expansion of Signal Transduction Pathways in Fungi by Extensive Genome Duplication

[EN] Plants and fungi use light and other signals to regulate development, growth, and metabolism. The fruiting bodies of the fungus Phycomyces blakesleeanus are single cells that react to environmental cues, including light, but the mechanisms are largely unknown [1]. The related fungus Mucor circinelloides is an opportunistic human pathogen that changes its mode of growth upon receipt of signals from the environment to facilitate pathogenesis [2]. Understanding how these organisms respond to environmental cues should provide insights into the mechanisms of sensory perception and signal transduction by a single eukaryotic cell, and their role in pathogenesis. We sequenced the genomes of P. blakesleeanus and M. circinelloides and show that they have been shaped by an extensive genome duplication or, most likely, a whole-genome duplication (WGD), which is rarely observed in fungi [3-6]. We show that the genome duplication has expanded gene families, including those involved in signal transduction, and that duplicated genes have specialized, as evidenced by differences in their regulation by light. The transcriptional response to light varies with the developmental stage and is still observed in a photoreceptor mutant of P. blakesleeanus. A phototropic mutant of P. blakesleeanus with a heterozygous mutation in the photoreceptor gene madA demonstrates that photosensor dosage is important for the magnitude of signal transduction. We conclude that the genome duplication provided the means to improve signal transduction for enhanced perception of environmental signals. Our results will help to understand the role of genome dynamics in the evolution of sensory perception in eukaryotes.European funds (European Regional Development Fund, ERDF); Spanish Ministerio de Economı´a y Competitividad; Junta de Andalucí

Curvature in Biological Systems: Its Quantification, Emergence, and Implications across the Scales

© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Surface curvature both emerges from, and influences the behavior of, living objects at length scales ranging from cell membranes to single cells to tissues and organs. The relevance of surface curvature in biology is supported by numerous experimental and theoretical investigations in recent years. In this review, first, a brief introduction to the key ideas of surface curvature in the context of biological systems is given and the challenges that arise when measuring surface curvature are discussed. Giving an overview of the emergence of curvature in biological systems, its significance at different length scales becomes apparent. On the other hand, summarizing current findings also shows that both single cells and entire cell sheets, tissues or organisms respond to curvature by modulating their shape and their migration behavior. Finally, the interplay between the distribution of morphogens or micro-organisms and the emergence of curvature across length scales is addressed with examples demonstrating these key mechanistic principles of morphogenesis. Overall, this review highlights that curved interfaces are not merely a passive by-product of the chemical, biological, and mechanical processes but that curvature acts also as a signal that co-determines these processes.A.P.G.C. and P.R.F. acknowledge the funding from Fundação para a Ciência e Tecnologia (Portugal), through IDMEC, under LAETA project UIDB/50022/2020. T.H.V.P. acknowledges the funding from Fundação para a Ciência e Tecnologia (Portugal), through Ph.D. Grant 2020.04417.BD. A.S. acknowledges that this work was partially supported by the ATTRACT Investigator Grant (no. A17/MS/11572821/MBRACE, to A.S.) from the Luxembourg National Research Fund. The author thanks Gerardo Ceada for his help in the graphical representations. N.A.K. acknowledges support from the European Research Council (grant 851960) and the Gravitation Program “Materials Driven Regeneration,” funded by the Netherlands Organization for Scientific Research (024.003.013). M.B.A. acknowledges support from the French National Research Agency (grant ANR-201-8-CE1-3-0008 for the project “Epimorph”). G.E.S.T. acknowledges funding by the Australian Research Council through project DP200102593. A.C. acknowledges the funding from the Deutsche Forschungsgemeinschaft (DFG) Emmy Noether Grant CI 203/-2 1, the Spanish Ministry of Science and Innovation (PID2021-123013O-BI00) and the IKERBASQUE Basque Foundation for Science.Peer reviewe