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Coarse-grained elastic network modelling: A fast and stable numerical tool to characterize mesenchymal stem cells subjected to AFM nanoindentation measurements

Authors :
Michele Gattullo
Michele Fiorentino
Elisa Migliorini
Elisabetta Ada Cavalcanti-Adam
Antonio Boccaccio
Vito Modesto Manghisi
Lorenzo Vaiani
A. E. Uva
Polytechnic University of Bari
Biomimétisme et Médecine Régénératrice (BRM)
Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-BioSanté (UMR BioSanté)
Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG)
Direction de Recherche Fondamentale (CEA) (DRF (CEA))
Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA))
Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG)
Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)
Max Planck Institute for Medical Research [Heidelberg]
Max-Planck-Gesellschaft
European Project: 658334,H2020,H2020-MSCA-IF-2014,OsteoNano(2015)
Biomimetism and regenerative medicine (BRM)
Migliorini, Elisa
Spatial nanoscale control of growth and adhesion factors to enhance the osteogenic differentiation of mesenchymal stem cells - OsteoNano - - H20202015-05-01 - 2017-04-30 - 658334 - VALID
Source :
Materials Science & Engineering C-Biomimetic and Supramolecular Systems, Materials Science and Engineering: C, Materials Science and Engineering: C, Elsevier, 2021, 121, pp.111860. ⟨10.1016/j.msec.2020.111860⟩, Materials Science and Engineering: C, 2021, 121, pp.111860. ⟨10.1016/j.msec.2020.111860⟩
Publication Year :
2021

Abstract

International audience; The knowledge of the mechanical properties is the starting point to study the mechanobiology of mesenchymal stem cells and to understand the relationships linking biophysical stimuli to the cellular differentiation process. In experimental biology, Atomic Force Microscopy (AFM) is a common technique for measuring these mechanical properties. In this paper we present an alternative approach for extracting common mechanical parameters, such as the Young's modulus of cell components, starting from AFM nanoindentation measurements conducted on human mesenchymal stem cells. In a virtual environment, a geometrical model of a stem cell was converted in a highly deformable Coarse-Grained Elastic Network Model (CG-ENM) to reproduce the real AFM experiment and retrieve the related force-indentation curve. An ad-hoc optimization algorithm perturbed the local stiffness values of the springs, subdivided in several functional regions, until the computed force-indentation curve replicated the experimental one. After this curve matching, the extraction of global Young's moduli was performed for different stem cell samples. The algorithm was capable to distinguish the material properties of different subcellular components such as the cell cortex and the cytoskeleton. The numerical results predicted with the elastic network model were then compared to those obtained from hertzian contact theory and Finite Element Method (FEM) for the same case studies, showing an optimal agreement and a highly reduced computational cost. The proposed simulation flow seems to be an accurate, fast and stable method for understanding the mechanical behavior of soft biological materials, even for subcellular levels of detail. Moreover, the elastic network modelling allows shortening the computational times to approximately 33% of the time required by a traditional FEM simulation performed using elements with size comparable to that of springs.

Details

Language :
English
ISSN :
09284931
Database :
OpenAIRE
Journal :
Materials Science & Engineering C-Biomimetic and Supramolecular Systems, Materials Science and Engineering: C, Materials Science and Engineering: C, Elsevier, 2021, 121, pp.111860. ⟨10.1016/j.msec.2020.111860⟩, Materials Science and Engineering: C, 2021, 121, pp.111860. ⟨10.1016/j.msec.2020.111860⟩
Accession number :
edsair.doi.dedup.....9387dd05f6e47b415bbd5cf49b3c392b