Spiner, deep learning-based automated detection of spiral ganglion neurons in intact cochleae

Tissue clearing and light-sheet fluorescence microscopy were applied for 3D profiling of intact cochleae. However, the spiral ganglion neurons (SGNs) remain relatively understudied compared to hair cells and supporting cells, especially in large animal models. Here, we: (1) introduced collagenase treatment to the current protocol of tissue clearing to enhance uniform antibody staining of SGNs within the pig cochlea and (2) adopted a deep learning object detection model to locate and count SGNs in large 3D datasets via Spiner (Spiral ganglion neuron profiler). Using this approach, Type I SGNs in intact gerbil and pig cochleae were detected and counted in 3D, and Spiner counts were consistent with manual counts. We believe broad adaptation of the method will improve understanding of the SGN population and their role in hearing loss. Codes for a user-friendly web interface were provided for model running and fine-tuning, making it accessible to those without programming experience.