The ability to control the differentiation of stem cells into specific neuronal types has a tremendous potential for the treatment of neurodegenerative diseases. In vitro neuronal differentiation can be guided by the interplay of biochemical and biophysical cues. Different strategies to increase the differentiation yield have been proposed, focusing everything on substrate topography, or, alternatively on substrate stiffness. Both strategies demonstrated an improvement of the cellular response. However it was often impossible to separate the topographical and the mechanical contributions. Here we investigate the role of the mechanical properties of nanostructured substrates, aiming at understanding the ultimate parameters which govern the stem cell differentiation. To this purpose a set of different substrates with controlled stiffness and with or without nanopatterning are used for stem cell differentiation. Our results show that the neuronal differentiation yield depends mainly on the substrate mechanical properties while the geometry plays a minor role. In particular nanostructured and flat polydimethylsiloxane (PDMS) substrates with comparable stiffness show the same neuronal yield. The improvement in the differentiation yield obtained through surface nanopatterning in the submicrometer scale could be explained as a consequence of a substrate softening effect. Finally we investigate by single cell force spectroscopy the neuronal precursor adhesion on the substrate immediately after seeding, as a possible critical step governing the neuronal differentiation efficiency. We observed that neuronal precursor adhesion depends on substrate stiffness but not on surface structure, and in particular it is higher on softer substrates. Our results suggest that cell-substrate adhesion forces and mechanical response are the key parameters to be considered for substrate design in neuronal regenerative medicine.
|Titolo:||Nanomechanics controls neuronal precursors adhesion and differentiation|
|Autori:||Migliorini, E; Ban, Jelena; Grenci, G; Andolfi, L; Pozzato, A; Tormen, M; Torre, Vincent; Lazzarino, M.|
|Data di pubblicazione:||2013|
|Digital Object Identifier (DOI):||10.1002/bit.24880|
|Appare nelle tipologie:||1.1 Journal article|