AI-assisted imaging screening reveals mechano-molecular tissue organizers and network of signaling hubs

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dc.catalogadoryvc
dc.contributor.authorBertocchi, Cristina
dc.contributor.authorAlegría, Juan José
dc.contributor.authorVásquez Sepúlveda, Sebastian Ignacio
dc.contributor.authorIbañez Prat, Rosario
dc.contributor.authorAishwarya, Srinivasan
dc.contributor.authorArraño Valenzuela, Ignacio Alberto
dc.contributor.authorCastro Pereira, Barbara Helen
dc.contributor.authorSoto Montandon, Catalina Andrea
dc.contributor.authorTrujillo Espergel, Alejandra Isabel
dc.contributor.authorOwen, Gareth Ivor
dc.contributor.authorKanchanawong, Pakorn
dc.contributor.authorCerda, Mauricio
dc.contributor.authorMotta, Giovanni
dc.contributor.authorZaidel Bar, Ronen
dc.contributor.authorRavasio, Andrea
dc.date.accessioned2025-03-17T19:42:54Z
dc.date.available2025-03-17T19:42:54Z
dc.date.issued2024
dc.description.abstractCadherin-mediated adhesions are crucial mechanical and signaling hubs that connect cells within a tissue and probe the mechanics of the surrounding environment. They constitute a physical link between the actin cytoskeleton of neighboring cells, providing the mechanical coordination needed for morphogenetic processes, tissue homeostasis, collective migration, and regeneration. Disruptions in adhesion mechanisms are closely linked to the breakdown of epithelial structure and the emergence of disease-related traits characteristic of cancer progression. The cadhesome network comprises over 170 structural and regulatory proteins involved in cadherin-mediated adhesion. While this network is essential for coordinating tissue responses to mechanical stress, its complexity has historically limited our understanding of how individual components contribute to force transmission and tissue homeostasis. Recent technological advances offer tools to investigate large molecular networks in cellular function and pathology (functional omics). Leveraging these advances, we developed an experimental and analytical platform combining high-throughput gene silencing, imaging, and artificial intelligence (AI) to systematically profile each role of each protein in tissue formation, mechanical stability, and response to induced tension. Using EpH4 cells as an epithelial tissue model, we performed systematic silencing in triplicate, capturing a range of tissue phenotypes under baseline and tension-inducing conditions. Machine learning methods were used to analyze complex imaging data, quantify tissue ruptures, characterize junctional organization, and measure tension states of the tissue. By incorporating machine learning algorithms, we automated image feature extraction, clustering, and classification, enabling an unprecedented quantitative evaluation of tissue mechanics at scale. Our machine learning models allowed us to identify significant patterns, including protein specific responses to tension and their roles in tissue-level mechanical integrity. Finally, we constructed a protein interaction network detailing the roles of each protein, their physical interactions, and known links to cancer. The network analysis revealed three prominent mechanotransductive and signaling subnetworks centered around E-cadherin, EGFR, and RAC1. Our study provides a foundational framework for investigating mechanosensing proteins and it offers a scalable blueprint for discovering potential therapeutic targets in diseases like cancer, where tissue mechanics play a crucial role.
dc.description.funderANID SCIA; ACT 192015 (AR, CB, MC); ANID/Fondequip; EQM210101 (AR, CB); ANID/Fondecyt 1210872 (AR, CB); ANID/Nucleo Milenio; NCN2024_068 (AR, MC); ANID Fondequip; EQM210020 (MC, CB) ANID/Fondecyt; No. 1221696 (MC); ANID/Fondecyt; No. 1230919 (MC); ANID/Fondecyt; No. 1211988 (MC); Pontificia Universidad Católica de Chile Puente; 2024-3 (CB); National Research Foundation Singapore; Project NRF-MSG-2023-0001 (PK)
dc.format.extent54 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1101/2024.11.14.623670
dc.identifier.urihttps://doi.org/10.1101/2024.11.14.623670
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102695
dc.information.autorucFacultad de Ciencias Biológicas; Bertocchi, Cristina; 0000-0003-0907-1318; 1078032
dc.information.autorucFacultad de Ciencias Biológicas; Vásquez Sepúlveda, Sebastian Ignacio; S/I; 1027863
dc.information.autorucEscuela de Ingeniería; Ibañez Prat, Rosario; S/I; 245617
dc.information.autorucEscuela de Ingeniería; Arraño Valenzuela, Ignacio Alberto; S/I; 1064584
dc.information.autorucFacultad de Ciencias Biológicas; Castro Pereira, Barbara Helen; S/I; 1135375
dc.information.autorucEscuela de Ingeniería; Soto Montandon, Catalina Andrea; S/I; 1026995
dc.information.autorucFacultad de Ciencias Biológicas; Trujillo Espergel, Alejandra Isabel; S/I; 1047248
dc.information.autorucFacultad de Ciencias Biológicas; Owen, Gareth Ivor; 0000-0003-3807-6054; 1000459
dc.information.autorucInstituto de Ingeniería Biológica y Médica; Ravasio, Andrea; S/I; 1071356
dc.language.isoen
dc.nota.accesocontenido completo
dc.rightsacceso abierto
dc.rights.licenseCC BY-NC-ND Atribución-NoComercial-SinDerivadas Internacional 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleAI-assisted imaging screening reveals mechano-molecular tissue organizers and network of signaling hubs
dc.typepreprint
sipa.codpersvinculados1078032
sipa.codpersvinculados1027863
sipa.codpersvinculados245617
sipa.codpersvinculados1064584
sipa.codpersvinculados1135375
sipa.codpersvinculados1026995
sipa.codpersvinculados1047248
sipa.codpersvinculados1000459
sipa.codpersvinculados1071356
sipa.trazabilidadORCID;2025-03-03
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