Differentiation capacity of dental pulp stem cell into inner ear hair cell using an in vitro assay: a preliminary step toward treating sensorineural hearing loss
Purpose: Sensorineural hearing loss (SNHL) is commonly caused by the death or dysfunction of cochlear cell types as a result of their lack of regenerative capacity. However, regenerative medicine, such as stem cell therapy, has become a promising tool to cure many diseases, including hearing loss. In this study, we determined whether DPSCs could differentiate into cochlear hair cell in vitro. Methods: DPSCs derived from human third molar dental pulp were induced into NSCs using a medium containing basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF) for 7 days, and then into cochlear hair cell using a medium containing EGF and IGF-1 for the next 14 days. We used the neuroepithelial protein marker nestin and cochlear hair cell marker myosin VIIa as the markers for cells differentiation. Cells expressing the positive markers under the microscope were confirmed to have differentiated into cochlear hair cell. Results: DPSCs were successfully induced to differentiate into NSCs, with mean 24% nestin-positive cells. We found that DPSC-derived NSCs have a great capacity in differentiating into inner ear hair cell-like cells with an average of 81% cells presenting myosin VIIa. Thus, DPSCs have high potential to serve as a good resource for SNHL treatment. Conclusion: We found the high potential of DPSCs to differentiate into NSC. The ability of DPSCs in differentiating into neural lineage cell made them a good candidate for regenerative therapy in neural diseases, such as SNHL.Keywords: Cochlear hair cell; Dental pulp stem cells; Sensorineural hearing loss.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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