Biology subjects

Bertocchi, C.

Publications and source records attributed to Bertocchi, C..

4 recordsLinked to original sources

bioRxiv2026

SERPINE1-AP2A1 interplay links substrate stiffness to fibroblast senescence

Cellular senescence is characterized by stable cell-cycle arrest, cytoskeletal remodeling, and altered secretion of senescence-associated secretory phenotype (SASP) factors, including SERPINE1/plasminogen activator inhibitor-1 (PAI-1). Although extracellular matrix (ECM) stiffening has been linked to fibroblast mechanotransduction and SERPINE1-associated remodeling, the molecular pathway connecting substrate stiffness to SERPINE1 regulation in senescent fibroblasts remains incompletely understood. Here, we investigated how defined substrate stiffness affects fibroblast morphology, mechanical phenotype, and SERPINE1 expression, and examined whether the clathrin adaptor AP2A1 participates in this response in replicative senescent human fibroblasts. Using tunable polyacrylamide hydrogels, we found that increasing substrate stiffness enhanced fibroblast spreading, stress fiber thickening, focal adhesion maturation, cellular stiffness, and senescence-associated marker expression. Stiff substrates also increased SERPINE1 expression and its colocalization with actin fibers, with stronger responses observed in senescent than in young fibroblasts. Functional perturbation experiments further suggested that SERPINE1 contributes to stress fiber organization in senescent cells. In addition, AP2A1 colocalized with SERPINE1, and modulation of AP2A1 under knockdown and overexpression conditions altered SERPINE1 signal intensity. Conversely, perturbation of SERPINE1 also affected AP2A1, supporting a potential bidirectional relationship between these two components. Together, these findings identify SERPINE1 as a stiffness-responsive factor associated with senescence-linked cytoskeletal remodeling and support a functional relationship between AP2A1 and SERPINE1 in senescent fibroblasts. These results suggest that the AP2A1-SERPINE1 axis may contribute to the link between extracellular mechanical cues and senescence-associated fibroblast remodeling.

cell biology↗

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↗

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↗