To identify small molecules that shield mammalian sensory hair cells from the ototoxic side effects of aminoglycoside antibiotics, 10,240 compounds were initially screened in zebrafish larvae selecting those that protected lateral-line hair cells against neomycin and gentamicin. When the 64 hits from this screen were retested in mouse cochlear cultures, 8 protected outer hair cells (OHCs) from gentamicin in vitro without causing hair-bundle damage. These 8 hits share structural features and all block, to varying degrees, the OHC's mechano-electrical transducer (MET) channel, a known route of aminoglycoside entry into hair cells. Further characterisation of one of the strongest MET-channel blockers, UoS-7692, revealed it additionally protects against kanamycin and tobramycin, and does not abrogate the bactericidal activity of gentamicin. UoS-7692 behaves, like the aminoglycosides, as a permeant blocker of the MET channel, significantly reduces gentamicin-Texas Red loading into OHCs, and preserves lateral-line function in neomycin-treated zebrafish. Trans-tympanic injection of UoS-7692 protects mouse OHCs from furosemide/kanamycin exposure in vivo and partially preserves hearing. The results confirm the hair-cell MET channel as a viable target for the identification of compounds that protect the cochlea from aminoglycosides, and provide a series of hit compounds that will inform the design of future otoprotectants.

CEACAM16 is a non-collagenous protein of the tectorial membrane, an extracellular structure of the cochlea essential for normal hearing. Dominant and recessive mutations in CEACAM16 have been reported to cause postlingual and progressive forms of deafness in humans. In a previous study of young Ceacam16 βgal/βgal null mutant mice on a C57Bl/6J background, the incidence of spontaneous otoacoustic emissions (SOAEs) was greatly increased relative to Ceacam16 +/+ and Ceacam16 +/βgal mice, but auditory brain-stem responses (ABRs) and distortion product otoacoustic emissions (DPOAEs) were near normal, indicating auditory thresholds were not significantly affected. To determine if the loss of CEACAM16 leads to hearing loss at later ages in this mouse line, cochlear structure and auditory function were examined in Ceacam16 +/+ , Ceacam16 +/βgal and Ceacam16 βgal/βgal mice at 6 and 12 months of age and compared to that previously described at 1 month. Analysis of older Ceacam16 βgal/βgal mice reveals a progressive loss of matrix from the core of the tectorial membrane that is more extensive in the apical, low-frequency regions of the cochlea. In Ceacam16 βgal/βgal mice at 6–7 months, the DPOAE magnitude at 2f1-f2 and the incidence of SOAEs both decrease relative to young animals. By ∼12 months, SOAEs and DPOAEs are not detected in Ceacam16 βgal/βgal mice and ABR thresholds are increased by up to ∼40 dB across frequency, despite a complement of hair cells similar to that present in Ceacam16 +/+ mice. Although SOAE incidence decreases with age in Ceacam16 βgal/βgal mice, it increases in aging heterozygous Ceacam16 +/βgal mice and is accompanied by a reduction in the accumulation of CEACAM16 in the tectorial membrane relative to controls. An apically-biased loss of matrix from the core of the tectorial membrane, similar to that observed in young Ceacam16 βgal/βgal mice, is also seen in Ceacam16 +/+ and Ceacam16 +/βgal mice, and other strains of wild-type mice, but at much later ages. The loss of Ceacam16 therefore accelerates age-related degeneration of the tectorial membrane leading, as in humans with mutations in CEACAM16 , to a late-onset progressive form of hearing loss.

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT’s core codebase can be trained to develop a custom deep learning detection model for any object on an image.