A multivalent polyanion-dispersed carbon nanotube toward highly bioactive nanostructured fibrous stem cell scaffolds
Source abstract
Abstract
Abstract The combination of physical and chemical cues from nanostructured biomaterials is quite promising but has rarely been reported in regulating the fate of stem cells. In this study, we propose to synthesize multivalent polyanion-dispersed carbon nanotubes (CNTs) for fabrication of nanostructured fibrous scaffolds with capabilities on controlling the fate of induced pluripotent stem cells (IPS). First, the CNTs are noncovalently functionalized by hyperbranched polyglycerol sulfate (hPGS) via sonication, and the stability and biocompatibility of hPGS-dispersed CNTs are compared with commercial dispersants. Then, the nanostructured fibrous scaffolds are prepared by coating hPGS-dispersed CNTs onto the electrospun polycaprolactone (PCL) fiber substrates to achieve PCL-CNT-hPGS. Our results show that these fibrous scaffolds can serve as a biocompatible platform for promoting the adhesion and proliferation of IPS cells. Meanwhile, the immunostaining shows that the nanostructured fibrous scaffolds induce higher neural differentiation efficiency compared to the control substrates. Moreover, we also validate that the aligned fibrous scaffolds can guide the orientation of generated neurites. Overall, a new multivalent polyanion-dispersed CNT has been utilized to construct highly bioactive nanostructured fibrous scaffolds, which not only combine physical and chemical cues to offer beneficial microenvironments for controlling the fate of IPS cells, but also present a new avenue for constructing carbon nanomaterials functionalized composites in tissue regeneration, bionic, biomedical, and bioelectronics.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Neuroscience and Neural Engineering · Graphene and Nanomaterials Applications · 3D Printing in Biomedical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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