000102108 001__ 102108
000102108 005__ 20230519145404.0
000102108 0247_ $$2doi$$a10.1371/journal.pcbi.1008764
000102108 0248_ $$2sideral$$a124331
000102108 037__ $$aART-2021-124331
000102108 041__ $$aeng
000102108 100__ $$aGoncalves, IG$$uUniversidad de Zaragoza
000102108 245__ $$aExtracellular matrix density regulates the formation of tumour spheroids through cell migration
000102108 260__ $$c2021
000102108 5060_ $$aAccess copy available to the general public$$fUnrestricted
000102108 5203_ $$aAuthor summary Multicellular organisms are composed of cells found in a scaffold known as the extracellular matrix, which interacts with cells. There is still a need to understand how the properties of this matrix, namely, its mechanical properties, regulate the organization of cellular systems. However, recent works have proven the relevance of the matrix, with a particular emphasis in tumour biology studies. Furthermore, to accelerate and reduce the costs of these studies, several computational frameworks have been presented to simulate the collective behaviour of the matrix. Hence, in this work, we introduce a model based on experimental data, which highlights the role of the mechanical properties of the matrix in individual and collective cell migration. We clearly show how the extracellular matrix induces the formation of large tumour clusters. Moreover, the model that we present accurately describes general trends of the experimental results used for model calibration; the model also has the potential to be extended to study matrices with different properties and different cell lines. In this work, we show how the mechanical properties of the cellular microenvironment modulate the growth of tumour spheroids. Based on the composition of the extracellular matrix, its stiffness and architecture can significantly vary, subsequently influencing cell movement and tumour growth. However, it is still unclear exactly how both of these processes are regulated by the matrix composition. Here, we present a centre-based computational model that describes how collagen density, which modulates the steric hindrance properties of the matrix, governs individual cell migration and, consequently, leads to the formation of multicellular clusters of varying size. The model was calibrated using previously published experimental data, replicating a set of experiments in which cells were seeded in collagen matrices of different collagen densities, hence producing distinct mechanical properties. At an initial stage, we tracked individual cell trajectories and speeds. Subsequently, the formation of multicellular clusters was also analysed by quantifying their size. Overall, the results showed that our model could accurately replicate what was previously seen experimentally. Specifically, we showed that cells seeded in matrices with low collagen density tended to migrate more. Accordingly, cells strayed away from their original cluster and thus promoted the formation of small structures. In contrast, we also showed that high collagen densities hindered cell migration and produced multicellular clusters with increased volume. In conclusion, this model not only establishes a relation between matrix density and individual cell migration but also showcases how migration, or its inhibition, modulates tumour growth.
000102108 536__ $$9info:eu-repo/grantAgreement/EC/H2020/742684/EU/Catalytic Dual-Function Devices Against Cancer/CADENCE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 742684-CADENCE$$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/PRX18/00147$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21
000102108 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000102108 590__ $$a4.779$$b2021
000102108 592__ $$a1.96$$b2021
000102108 594__ $$a6.6$$b2021
000102108 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b9 / 57 = 0.158$$c2021$$dQ1$$eT1
000102108 593__ $$aCellular and Molecular Neuroscience$$c2021$$dQ1
000102108 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b20 / 79 = 0.253$$c2021$$dQ2$$eT1
000102108 593__ $$aComputational Theory and Mathematics$$c2021$$dQ1
000102108 593__ $$aModeling and Simulation$$c2021$$dQ1
000102108 593__ $$aGenetics$$c2021$$dQ1
000102108 593__ $$aEcology, Evolution, Behavior and Systematics$$c2021$$dQ1
000102108 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000102108 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, JM$$uUniversidad de Zaragoza
000102108 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000102108 773__ $$g17, 2 (2021)$$pPLoS Comput. Biol.$$tPLOS COMPUTATIONAL BIOLOGY$$x1553-734X
000102108 8564_ $$s2707590$$uhttps://zaguan.unizar.es/record/102108/files/texto_completo.pdf$$yVersión publicada
000102108 8564_ $$s2450827$$uhttps://zaguan.unizar.es/record/102108/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000102108 909CO $$ooai:zaguan.unizar.es:102108$$particulos$$pdriver
000102108 951__ $$a2023-05-18-13:44:39
000102108 980__ $$aARTICLE