3D atlas of the rat inner ear from iDISCO+ cleared temporal bone

Background. Atlases are an invaluable tool in neuroscience and otolaryngology, greatly aiding planning of experiments and surgical intervention, and experimental and clinical data interpretation. The rat is a major animal model for hearing and balance studies, but no complete high resolution 3D reconstruction of the microscopic inner ear structures is available for this animal, at difference from other mammals. On the other hand, among animal models used in hearing research, a detailed atlas for the central auditory system is publicly available only for the rat. Therefore, we reconstructed membranous, nerve and bony structures in the rat inner ear at 4 micron resolution. Methods. We performed iDISCO+ based clearing and immunostaining of decalcified rat temporal bones, which were imaged with fluorescence lightsheet microscopy (mesoSPIM) at 4 micron resolution. Image stacks were segmented in a semiautomated way, and all visible inner ear structures were reconstructed in 3D. Structures obtained from fluorescent stacks were compared with analogous structure obtained from iodine-enhanced microCT segmentations at 6 micron resolution. Results. Cleared bony labyrinth showed minimal shape distortions, as shown by alignment with microCT labyrinth. Membrane labyrinth could display variable collapse of the superior division, especially at the level of the roof of canal ampullae, whereas the inferior division (saccule and cochlea) and sensory regions appeared less fragile, most likely due to the shape of endolymph compartments and to the presence of structural reinforcements. Autofluorescence and collagen IV signal tracing allowed reconstruction of several inner ear structures, including the thin reuniens, saccular and endolymphatic ducts, the utriculoendolymphatic valve, and the geometry of otolithic organ maculae. Microwave-aided decalcification yielded sharper images. Conclusion. We have reconstructed 20 membranous, nerve and bony structures in the rat inner ear, which are available in a shared repository for download and can be used for modeling of auditory and vestibular sensory functions and for training of automated segmentation machine learning tools.