000135351 001__ 135351
000135351 005__ 20241125101203.0
000135351 0247_ $$2doi$$a10.1093/intbio/zyad002
000135351 0248_ $$2sideral$$a138586
000135351 037__ $$aART-2023-138586
000135351 041__ $$aeng
000135351 100__ $$0(orcid)0000-0002-0163-8378$$aMovilla, Nieves
000135351 245__ $$aA novel integrated experimental and computational approach to unravel fibroblast motility in response to chemical gradients in 3D collagen matrices
000135351 260__ $$c2023
000135351 5203_ $$aAbstract Fibroblasts play an essential role in tissue repair and regeneration as they migrate to wounded areas to secrete and remodel the extracellular matrix. Fibroblasts recognize chemical substances such as growth factors, which enhance their motility towards the wounded tissues through chemotaxis. Although several studies have characterized single-cell fibroblast motility before, the migration patterns of fibroblasts in response to external factors have not been fully explored in 3D environments. We present a study that combines experimental and computational efforts to characterize the effect of chemical stimuli on the invasion of 3D collagen matrices by fibroblasts. Experimentally, we used microfluidic devices to create chemical gradients using collagen matrices of distinct densities. We evaluated how cell migration patterns were affected by the presence of growth factors and the mechanical properties of the matrix. Based on these results, we present a discrete-based computational model to simulate cell motility, which we calibrated through the quantitative comparison of experimental and computational data via Bayesian optimization. By combining these approaches, we predict that fibroblasts respond to both the presence of chemical factors and their spatial location. Furthermore, our results show that the presence of these chemical gradients could be reproduced by our computational model through increases in the magnitude of cell-generated forces and enhanced cell directionality. Although these model predictions require further experimental validation, we propose that our framework can be applied as a tool that takes advantage of experimental data to guide the calibration of models and predict which mechanisms at the cellular level may justify the experimental findings. Consequently, these new insights may also guide the design of new experiments, tailored to validate the variables of interest identified by the model.
000135351 536__ $$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21
000135351 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000135351 590__ $$a1.5$$b2023
000135351 592__ $$a0.568$$b2023
000135351 591__ $$aCELL BIOLOGY$$b190 / 205 = 0.927$$c2023$$dQ4$$eT3
000135351 593__ $$aBiophysics$$c2023$$dQ2
000135351 593__ $$aMedicine (miscellaneous)$$c2023$$dQ2
000135351 593__ $$aBiochemistry$$c2023$$dQ3
000135351 594__ $$a4.9$$b2023
000135351 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135351 700__ $$aGonçalves, Inês G$$uUniversidad de Zaragoza
000135351 700__ $$0(orcid)0000-0002-3784-1140$$aBorau, Carlos$$uUniversidad de Zaragoza
000135351 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, Jose Manuel$$uUniversidad de Zaragoza
000135351 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000135351 773__ $$g14, 8-12 (2023), 212-227$$pIntegr. biol. (Camb.)$$tIntegrative biology (Cambridge)$$x1757-9694
000135351 8564_ $$s1804613$$uhttps://zaguan.unizar.es/record/135351/files/texto_completo.pdf$$yVersión publicada
000135351 8564_ $$s2903592$$uhttps://zaguan.unizar.es/record/135351/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135351 909CO $$ooai:zaguan.unizar.es:135351$$particulos$$pdriver
000135351 951__ $$a2024-11-22-12:12:57
000135351 980__ $$aARTICLE