Biology subjects

Ravasio, A.

Publications and source records attributed to Ravasio, A..

5 recordsLinked to original sources

bioRxiv2025

MODELING CELL MIGRATORY PERSISTENCE THROUGH TEMPORAL CORRELATIONS AND ANGULAR NOISE

The persistence of cell migration is a fundamental property of motile behavior, enabling cells to maintain directionality while adapting to fluctuations and external cues. This feature underlies essential processes such as development, immune responses, and cancer invasion. Classical mathematical models have offered key insights into directed migration, yet they often neglect temporal correlations arising from cellular mechanisms that stabilize polarity and protrusion dynamics, processes not well captured by simple white noise. Here, we introduce an agent-based model based on stochastic differential equations (SDEs) that integrates fractional Brownian motion (fBm) to explicitly incorporate translational autocorrelation in cell trajectories. We simulate migration as a function of angular reorientation (Dr) and the strength of correlated noise (H). In this framework, temporal correlation stabilizes trajectory features inherited from initial conditions, whereas angular reorientation introduces variability that enables transitions between erratic and directed motion. Our simulations show that, unlike models driven by white noise, positive correlation markedly enhances persistence even under strong angular reorientation. Moreover, the combination of Dr and H gives rise to emergent behaviors, particularly in the presence of taxis, where persistence and responsiveness are jointly tuned. These results identify correlated noise as a proxy for intrinsic cellular memory and provide a versatile computational framework to interpret the diversity and complexity of migratory behaviors. Significance StatementCell migration drives key biological processes such as immune surveillance, development, and cancer invasion. Most models reduce motility to random walks perturbed by white noise, overlooking temporal correlations that arise from intrinsic cellular memory. By integrating fractional Brownian motion into agent-based modeling, we show how correlated translational noise interacts with angular diffusion to produce emergent behaviors, including overshooting, exploratory loops, and persistent trajectories. Our framework unifies these outcomes under a single mechanistic description and highlights how intrinsic noise modulates taxis, exploration, and persistence. This approach provides mathematicians and cell biologists with a versatile tool to test how cells balance stability and adaptability in dynamic environments.

biophysics↗

bioRxiv2024

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

Cadherin-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. TeaserAI-aided screening identifies key regulators of epithelial tissue mechanics, uncovering potential therapeutic targets in cancer.

cell biology↗

bioRxiv2023

Microfabrication-based engineering of biomimetic dentin-like constructs to simulate dental aging

1.Human dentin is a highly organized dental tissue displaying a complex microarchitecture consisting of micrometer-sized tubules encased in a mineralized type-I collagen matrix. As such, it serves as an important substrate for the adhesion of microbial colonizers and oral biofilm formation in the context of dental caries disease, including root caries in the elderly. Despite this issue, there remains a current lack of effective biomimetic in-vitro dentin models that facilitate the study of oral microbial adhesion by considering the surface architecture at the micro- and nanoscales. Therefore, the aim of this study was to develop a novel in-vitro microfabricated biomimetic dentin surface that simulates the complex surface microarchitecture of exposed dentin. For this, a combination of soft lithography microfabrication and biomaterial science approaches were employed to construct a micropitted PDMS substrate functionalized with mineralized type-I collagen. These dentin analogues were subsequently glycated with methylglyoxal (MGO) to simulate dentin matrix aging in-vitro and analyzed utilizing an interdisciplinary array of techniques including atomic force microscopy (AFM), elemental analysis, and electron microscopy. AFM force-mapping demonstrated that the nanomechanical properties of the biomimetic constructs were within the expected biological parameters, and that mineralization was mostly predominated by hydroxyapatite deposition. Finally, dual-species biofilms of Streptococcus mutans and Candida albicans were grown and characterized on the biofunctionalized PDMS microchips, demonstrating biofilm specific morphologic characteristics and confirming the suitability of this model for the study of early biofilm formation under controlled conditions. Overall, we expect that this novel biomimetic dentin model could serve as an in-vitro platform to study oral biofilm formation or dentin-biomaterial bonding in the laboratory without the need for animal or human tooth samples in the future.

bioengineering↗

bioRxiv2019

Mechanical Roles of Vinculin/β-catenin interaction in Adherens Junction

Mechanical force transmission through the adherens junctions (AJs) are highly regulated processes essential for multicellular organization of tissues. AJ proteins such as E-cadherin, -catenin, and vinculin have been shown to be sensing or bearing mechanical forces being transmitted between the actin cytoskeleton and the intercellular contacts. However, the molecular organization and connectivity of these components remains not well understood. Using a super-resolution microscopy approach, we report that vinculin, once activated, could form a direct structural connection with {beta}-catenin, which can bypass -catenin, one of the main mechanotransducers in AJs. Direct vinculin/{beta}-catenin interaction is capable of supporting mechanical tension and contributes to the stabilization of the cadherin-catenin complexes. These findings suggest a multi-step model for the force-dependent reinforcement of AJs whereby -catenin may serve as the initial catalytic activator of vinculin, followed by vinculin translocation to form a direct link between E-cadherin-bound {beta}-catenin and the actin cytoskeleton.

cell biology↗

bioRxiv2019

Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity

Eph receptors, a family of receptor tyrosine kinases, play a crutial role in the assembly and maintenance of healthy tissues. Dysfunction in Eph signaling are causally and correlatively associated with cancer progression. In breast cancer cells, dysregulated Eph signaling has been largely linked to alterations in receptor clustering abilities. In the present study, we implemented a single-cell assay and a scoring scheme to systematically probe the spatial organization of activated EphA receptor in carcinoma cells of different origin. Using this assay, we found that cancer cells retained EphA clustering phenotype upon cell division for several generations and degree of clustering reported for population as well as single-cell migration potential. Finally, using patient-derived cancer cell lines, we probed the evolution of EphA signalling in cancer cell populations that underwent metastatic transformation and acquisition of drug resistance. Taken together, our simple and scalable approach provides a reliable quantitation of EphA associated gene expression and phenotypes in multiple carcinomas and can assay the heterogeneity of cancer cell populations in a cost- and time-effective manner.

cancer biology↗