A deep learning approach to quantify auditory hair cells

Abstract

Hearing loss affects millions of people worldwide. Yet, there are still no curative therapies for sensorineural hearing loss. Frequent causes of sensorineural hearing loss are due to damage or loss of the sensory hair cells, the spiral ganglion neurons, or the synapses between them. Culturing the organ of Corti allows the study of all these structures in an experimental model, which is easy to manipulate. Therefore, the in vitro culture of the neonatal mammalian organ of Corti remains a frequently used experimental system, in which hair cell survival is routinely assessed. However, the analysis of the surviving hair cells is commonly performed via manual counting, which is a time-consuming process and the inter-rater reliability can be an issue.Here, we describe a deep learning approach to quantify hair cell survival in the murine organ of Corti explants. We used StarDist, a publicly available platform and plugin for Fiji (Fiji is just ImageJ), to train and apply our own custom deep learning model. We successfully validated our model in untreated, cisplatin, and gentamicin treated organ of Corti explants. Therefore, deep learning is a valuable approach for quantifying hair cell survival in organ of Corti explants. Moreover, we also demonstrate how the publicly available Fiji plugin StarDist can be efficiently used for this purpose.