Smoothelin-like 2 Inhibits Coronin-1B to Stabilize the Apical Actin Cortex during Epithelial Morphogenesis

The actin cortex is involved in many biological processes and needs to be significantly remodeled during cell differentiation. Developing epithelial cells construct a dense apical actin cortex to carry out their barrier and exchange functions. The apical cortex assembles in response to three-dimensional (3D) extracellular cues, but the regulation of this process during epithelial morphogenesis remains unknown. Here, we describe Smoothelin-like 2 (SMTNL2) function, a member of the smooth-muscle related Smoothelin protein family, in apical cortex maturation. SMTNL2 is induced during the development of multiple epithelial tissues and localizes to the apical and junctional actin cortex in intestinal and kidney epithelial cells. SMTNL2 deficiency leads to membrane herniations in the apical domain of epithelial cells, indicative of cortex abnormalities. We find that SMTNL2 binds to actin filaments and is required to slow down the turnover of apical actin. We also characterize the SMTNL2 proximal interactome and find that SMTNL2 executes its functions partly through inhibition of Coronin-1B. While Coronin-1B-mediated actin dynamics are required for early morphogenesis, its sustained activity is detrimental for the mature apical shape. SMTNL2 binds to Coronin-1B through its N-terminal coiled-coil region and negates its function to stabilize the apical cortex. In sum, our results unveil a mechanism for regulating actin dynamics during epithelial morphogenesis, providing critical insights on the developmental control of the cellular corte

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

Intercalate or invaginate: PI(3,4,5)P3 governs a membrane constriction switch in cell shaping

Although contractile processes, from tissue invagination to cell intercalation, utilize diverse ratcheting mechanisms, little is known about how ratcheting becomes engaged at specific cell surfaces. In this issue of Developmental Cell, Maio et al. demonstrate that PI(3,4,5)P3 is a paramount regulator of the Sbf/RabGEF-Rab35 ratchet mechanism