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Title Systematic AI-assisted Screening of the Cadhesome to Map Epithelial Monolayer Mechanics
Authors Cristina Bertocchi, Juan José Alegría, Sebastián Vásquez-Sepúlveda, Rosario Ibanez-Prat, Aishwarya Srinivasan, Ignacio Arrano-Valenzuela, Barbara Castro-Pereira, Catalina Soto-Montandón, Alejandra Trujillo-Espergel, Ignacio Montenegro-Rojas, Shinji Deguchi, Gareth I. Owen, Pakorn Kanchanawong, Mauricio Cerda, Giovanni Motta, Ronen Zaidel-Bar, Andrea Ravasio
Publication date July 2026
Abstract Cadherin-mediated adhesions serve as key mechanical and
signaling
hubs in epithelial tissues, linking the actin cytoskeleton of adjacent
cells. Their disruption is a hallmark of cancer progression. The
"cadhesome" network comprises over 170 proteins involved in
cadherin-mediated adhesion and force transmission, yet its complexity
hampers functional understanding. We developed a high-throughput platform
combining gene silencing, imaging, and AI-based analysis to profile the role
of each cadhesome component in monolayer formation and mechanical integrity.
Using EpH4 epithelial cells, we analyzed phenotypes under vehicle and
nocodazole-challenge conditions. Machine learning enabled classification of
monolayer disruption, junctional organization, and contractile state. Beyond
confirming known mechanotransduction hubs centered on E-cadherin, EGFR, and
RAC1, our approach systematically uncovered candidate regulators of
monolayer contractile state and stress adaptation, identified
condition-specific roles of poorly characterized proteins, and organized
them into annotated mechanobiological subnetworks that serve as a basis for
hypothesis generation. Presented as a prioritized discovery resource, this
work establishes a scalable strategy to decode mechano-molecular networks
and provides a blueprint for hypothesis-driven investigation of epithelial
mechanics with potential translational relevance.
Pages article 505
Volume 24
Journal name Cell Communication and Signaling
Publisher Springer Nature Switzerland AG (Cham, Switzerland)
Reference URL View reference page