Functionalized graphene oxide triggers the neurogenic potential on neuroblastoma cell line (SH-SY5Y) and human amniotic fluid stem cells (hAFSCs)
Year: 2025
Type: article
Abstract: Hydrogels scaffolds based on GO/rGO embedded in polyethylene glycol (PEG) and gelatin-methacrylate (GelMA) polymeric matrices, were synthetized by simple techniques and characterized. The methacrylation process from gelatin to obtain GelMA was confirmed by UV–VIS and 1 H NMR spectroscopies, suggested a successful methacrylate process during the preparation process of GelMA with a degree of substitution (DS) of GelMA close to 100 %. The characterization of hydrogels by Fourier Transform Infrared Spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and Differential Scanning Calorimetry (DSC), confirmed the incorporation and well-integration of graphenic materials into polymeric matrices. The biological results using neuroblastoma cell line (SH-SY5Y) showed a higher number of synaptic structure cells seeded on rGO and derivatives than cells on GO and derivatives. The evaluation of molecular changes induced by GO/rGO and derivatives on the human amniotic fluid stem cells (hAFSCs) differentiation, using three key neuronal markers (MAP-2, PSD-95, Synapsin) confirmed that protein levels were higher in cells grown on graphene compared to the CTR condition. The analysis for SOX-2, as stemness marker, confirmed the loss of stemness for hAFSCs in favor of a neuronal-like phenotype. These results suggest potentially materials for treating neurodegenerative diseases and nervous system injuries. • Scalable synthesis of PEG/GelMA hydrogels embedded with graphene oxide (GO) or reduced graphene oxide (rGO). • rGO boosts neuronal differentiation. • Neuroblastoma cells and human amniotic stem cells on rGO substrates showed elevated synaptic markers and reduced stemness. • GO/rGO-PEG/GelMA composites are proposed as 3D-printable inks for neural repair
Institutions: University of L'Aquila, Temple University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
OpenAlex ID: W4414801481
