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

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

The Ste20 kinase misshapen is essential for the invasive behaviour of ovarian epithelial cells in Drosophila

7 páginas, 5 figuras.Stationary-to-migratory transitions of epithelial cells have a key role in development and tumour progression. Border cell migration is a powerful system in which to investigate this transition in living organisms. Here, we identify the Ste20-like kinase misshapen (msn) as a novel regulator of border-cell migration in Drosophila. Expression of msn in border cells is independent of the transcription factor slow border cells and of inputs from all pathways that are known to control border-cell migration. The msn gene functions to modulate the levels and/or distribution of Drosophila E-cadherin to promote the invasive migratory behaviour of border cells.Funding from the Spanish Ministerio de Ciencia e Innovación (MICINN), the EMBO Young Investigator Programme and the Junta de Andalucía is acknowledged.Peer reviewe

GTP exchange factor Vav regulates guided cell migration by coupling guidance receptor signalling to local Rac activation

et al.Guided cell migration is a key mechanism for cell positioning in morphogenesis. The current model suggests that the spatially controlled activation of receptor tyrosine kinases (RTKs) by guidance cues limits Rac activity at the leading edge, which is crucial fo establishing and maintaining polarized cell protrusions at the front. However, little is known about the mechanisms by which RTKs control the local activation of Rac. Here, usinga multidisciplinary approach, we identify the GTP exchange factor (GEF) Vav as a key regulator of Rac activity downstream of RTKs in a developmentally regulated cell migration event, that of the Drosophila border cells (BCs). We show that elimination of the vav gene impairs BC migration. Live imaging analysis reveals that vav is required for the stabilization and maintenance of protrusions at the front of the BC cluster. In addition, activation of the PDGF/VEGF-related receptor (PVR) by its ligand the PDGF/PVF1 factor brings about activation of Vav protein by direct interaction with the intracellular domain of PVR. Finally, FRET analyses demonstrate that Vav is required in BCs for the asymmetric distribution of Rac activity at the front. Our results unravel an important role for the Vav proteins as signal transducers that couple signalling downstream of RTKs with local Rac activation during morphogenetic movements.Research in M.D.M.-B. laboratory is funded by the Spanish Ministerio de Ciencia y Tecnología (MCYT) [grant numbers BFU2004-02840/BMC, BFU2010-16669 and CSD-2007-00008 to M.D.M.-B.], by the EMBO Young Investigator Programme and by the Junta de Andalucía [grant numbers P06-CVI-01592 and P09-CVI-5058]. C.H.F.-E. is supported by a fellowship funded by the Consolider Project Grant [grant number CSD-2007-00008] and M.M. was supported by a Juan de la Cierva contract. The institutional support from the Junta de Andalucía to the CABD is acknowledged. S.K. is supported by the German-Israeli-Foundation (GIF). G.E. is funded by the Canadian Institutes for Health Research [grant number MOP-114899] by the Natural Sciences and Engineering Research Council of Canada and holds a Canada Research Chair (Tier II) in Vesicular Trafficking and Cell Signaling.Peer Reviewe