Behnammanesh, G. | Search bioRxiv

bioRxiv2024

A Mutation in Tmem135 Causes Progressive Sensorineural Hearing Loss

Transmembrane protein 135 (TMEM135) is a 52 kDa protein with five predicted transmembrane domains that is highly conserved across species. Previous studies have shown that TMEM135 is involved in mitochondrial dynamics, thermogenesis, and lipid metabolism in multiple tissues; however, its role in the inner ear or the auditory system is unknown. We investigated the function of TMEM135 in hearing using wild-type (WT) and Tmem135FUN025/FUN025 (FUN025) mutant mice on a CBA/CaJ background, a normal-hearing mouse strain. Although FUN025 mice displayed normal auditory brainstem response (ABR) at 1 month, we observed significantly elevated ABR thresholds at 8, 16, and 64 kHz by 3 months, which progressed to profound hearing loss by 12 months. Consistent with our auditory testing, 13-month-old FUN025 mice exhibited a severe loss of outer hair cells and spiral ganglion neurons in the cochlea. Our results using BaseScope in situ hybridization indicate that TMEM135 is expressed in the inner hair cells, outer hair cells, and supporting cells. Together, these results demonstrate that the FUN025 mutation in Tmem135 causes progressive sensorineural hearing loss, and suggest that TMEM135 is crucial for maintaining key cochlear cell types and normal sensory function in the aging cochlea.

neuroscience↗

bioRxiv2023

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90,000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

neuroscience↗