CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia
2023
Andrés San Román, Jesús Ángel | Gordillo Vázquez, Carmen María | Franco Barranco, Daniel | Morato Concejero, Laura | Huertas Fernández-Espartero, Cecilia | Baonza, Gabriel | Tagua Jáñez, Antonio Jesús | Vicente Munuera, Pablo | Palacios Barea, Ana María | Gavilán Dorronzoro, María de la Paz | Martín Belmonte, Fernando | Annese, Valentina | Gómez Gálvez, Pedro | Arganda Carreras, Ignacio | Escudero Cuadrado, Luis María | Universidad de Sevilla. Departamento de Biología Celular | Ministerio de Ciencia e Innovación (MICIN). España | Junta de Andalucía | Ministerio de Economía y Competitividad (MINECO). España
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.
显示更多 [+] 显示较少 [-]Ministerio de Ciencia e Innovación PID2019-103900GB-I00, PID2020-120367GB-I00, PID2021-126701OB-I00
显示更多 [+] 显示较少 [-]Junta de Andalucía US-1380953, PY18-631
显示更多 [+] 显示较少 [-]Ministerio de Economía y Competitividad BES-2022-077789
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