Abstract

Sensorineural hearing loss is a common disability found worldwide which is associated with a degeneration of spiral ganglion neurons (SGN). It is a challenge to restore SGN due to the permanent degeneration and viability of SGN is requisite for patients to receive an advantage from hearing aid devices. Human dental pulp stem cells (DPSC) and stem cells from human exfoliated deciduous teeth (SHED) are self-renewing stem cells that originate from the neural crest during development. These stem cells have a high potential for neuronal differentiation. This is primarily due to their multilineage differentiation potential and their relative ease of access. Previously, we have shown the ability of these stem cell types to differentiate into spiral ganglion neuron-like cells. In this study, we induced the cells into neural precursor cells (NPC) and cocultured with auditory brainstem slice (ABS) encompassing cochlear nucleus by the Stoppini method. We also investigated their ability to differentiate after 2 weeks and 4 weeks in coculture. Neuronal differentiation of DPSC-NPC and SHED-NPC was higher expression of specific markers to SGN, TrkB, and Gata3, compared to monoculture. The cells also highly expressed synaptic vesicle protein (SV2A) and exhibited intracellular calcium oscillations. Our findings demonstrated the possibility of using DPSCs and SHEDs as an autologous stem cell-based therapy for sensorineural hearing loss patients.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.