Differentiation of human neural stem cells into neural networks on graphene nanogrids

Abstract

Graphene nanogrids (crossed graphene nanoribbons synthesized by the oxidative unzipping of multi-walled carbon nanotubes) on a SiO₂ matrix containing TiO₂ nanoparticles (NPs) were applied as a photocatalytic stimulator in the accelerated differentiation of human neural stem cells (hNSCs) into two-dimensional neural networks. The hydrophilic graphene nanogrids exhibited patterned proliferations of hNSCs (consistent with patterns of the nanogrids), in contrast with the usual random growths occurring on quartz substrates. The number of cell nuclei differentiated on reduced graphene oxide nanoribbon (rGONR) grid/TiO₂ NPs/SiO₂ increased ∼5.9 and 26.8 fold compared to the number of cells on quartz substrates, after three weeks of differentiation, in the dark and under photo stimulation, respectively. The stimulation, originated by the injection of photoexcited electrons from the TiO₂ NPs into the cells on the nanogrids, also resulted in changing the number of differentiated neurons and glial cells in the patterned neural network by factors of ∼1.8 and 0.17, respectively. A higher differentiation on the rGONR grids than rGO sheets was assigned to the physical stress induced by the surface topographic features of the nanogrids. The current–voltage properties of the neural networks differentiated on the electrically disconnected rGONR grids demonstrated effective cell-to-cell and cell-to-rGONR couplings after three weeks of the stimulated differentiation.