A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning
Shinji Urata,
Tadatsune Iida,
Yuri Suzuki,
Shiou-Yuh Lin,
Yu Mizushima,
Chisato Fujimoto,
Yu Matsumoto,
Tastuya Yamasoba
Affiliations
Shinji Urata
Department of Cellular Neurobiology, University of Tokyo, Tokyo, JapanDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Tadatsune Iida
Department of Cellular Neurobiology, University of Tokyo, Tokyo, Japan
Yuri Suzuki
Department of Cellular Neurobiology, University of Tokyo, Tokyo, Japan
Shiou-Yuh Lin
Department of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Yu Mizushima
Department of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Chisato Fujimoto
Department of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Yu Matsumoto
Department of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Tastuya Yamasoba
Department of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
Here, we describe a sorbitol-based optical clearing method, called modified Sca/eS that can be used to image all hair cells (HCs) in the mouse cochlea. This modification of Sca/eS is defined by three steps: decalcification, de-lipidation, and refractive index matching, which can all be completed within 72 h. Furthermore, we established automated analysis programs that perform machine learning-based pattern recognition. These programs generate 1) a linearized image of HCs, 2) the coordinates of HCs, 3) a holocochleogram, and 4) clusters of HC loss. In summary, a novel approach that integrates modified Sca/eS and programs based on machine learning facilitates quantitative and comprehensive analysis of the physiological and pathological properties of all HCs.
