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Abstract In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA. 

ABSTRACT Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community. 

Age-related hearing loss (ARHL) is a common sensory impairment with complex underlying mechanisms. In our previous study, we performed a meta-analysis of genome-wide association studies (GWAS) in mice and identified a novel locus on chromosome 18 associated with ARHL specifically linked to a 32 kHz tone burst stimulus. Consequently, we investigated the role of Formin Homology 2 Domain Containing 3 (Fhod3), a newly discovered candidate gene for ARHL based on the GWAS results. We observed Fhod3 expression in auditory hair cells (HCs) primarily localized at the cuticular plate (CP). To understand the functional implications of Fhod3 in the cochlea, we generated Fhod3 overexpression mice (Pax2-Cre^+/-; Fhod3^Tg/+) (TG) and HC-specific conditional knockout mice (Atoh1-Cre^+/-; Fhod3^fl/fl) (KO). Audiological assessments in TG mice demonstrated progressive high-frequency hearing loss, characterized by predominant loss of outer hair cells, and a decreased phalloidin intensities of CP. Ultrastructural analysis revealed loss of the shortest row of stereocilia in the basal turn of the cochlea, and alterations in the cuticular plate surrounding stereocilia rootlets. Importantly, the hearing and HC phenotype in TG mice phenocopied that of the KO mice. These findings suggest that balanced expression of Fhod3 is critical for proper CP and stereocilia structure and function. Further investigation of Fhod3 related hearing impairment mechanisms may lend new insight towards the myriad mechanisms underlying ARHL, which in turn could facilitate the development of therapeutic strategies for ARHL.