Extracellular matrix density regulates the formation of tumour spheroids through cell migration

Goncalves, IG (Universidad de Zaragoza) ; Garcia-Aznar, JM (Universidad de Zaragoza)

Resumen: Author 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.

Idioma: Inglés

DOI: 10.1371/journal.pcbi.1008764

Año: 2021

Publicado en: PLOS COMPUTATIONAL BIOLOGY 17, 2 (2021)
ISSN: 1553-734X

Factor impacto JCR: 4.779 (2021)

Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 9 / 57 = 0.158 (2021) - Q1 - T1
Categ. JCR: BIOCHEMICAL RESEARCH METHODS rank: 20 / 79 = 0.253 (2021) - Q2 - T1

Factor impacto CITESCORE: 6.6 - Agricultural and Biological Sciences (Q1) - Environmental Science (Q1) - Neuroscience (Q1) - Computer Science (Q1) - Mathematics (Q1) - Biochemistry, Genetics and Molecular Biology (Q2)

Factor impacto SCIMAGO: 1.96 - Cellular and Molecular Neuroscience (Q1) - Computational Theory and Mathematics (Q1) - Modeling and Simulation (Q1) - Genetics (Q1) - Ecology, Evolution, Behavior and Systematics (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/742684/EU/Catalytic Dual-Function Devices Against Cancer/CADENCE
Financiación: info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PRX18/00147
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)