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Browsing by Author "Kim, Jinkyung"
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Item ANKRD24 organizes TRIOBP to reinforce stereocilia insertion points(JCB, 2022-02-17) Krey, Jocelyn F.; Liu, Chang; Belyantseva, Inna A.; Bateschell, Michael; Dumont, Rachel A.; Goldsmith, Jennifer; Chatterjee, Paroma; Morrill, Rachel S.; Fedorov, Lev M.; Foster, Sarah; Kim, Jinkyung; Nuttall, Alfred L.; Jones, Sherri M.; Choi, Dongseok; Friedman, Thomas B.; Ricci, Anthony J.; Zhao, Bo; Barr-Gillespie, Peter G.; Otolaryngology -- Head and Neck Surgery, School of MedicineThe stereocilia rootlet is a key structure in vertebrate hair cells, anchoring stereocilia firmly into the cell’s cuticular plate and protecting them from overstimulation. Using superresolution microscopy, we show that the ankyrin-repeat protein ANKRD24 concentrates at the stereocilia insertion point, forming a ring at the junction between the lower and upper rootlets. Annular ANKRD24 continues into the lower rootlet, where it surrounds and binds TRIOBP-5, which itself bundles rootlet F-actin. TRIOBP-5 is mislocalized in Ankrd24KO/KO hair cells, and ANKRD24 no longer localizes with rootlets in mice lacking TRIOBP-5; exogenous DsRed–TRIOBP-5 restores endogenous ANKRD24 to rootlets in these mice. Ankrd24KO/KO mice show progressive hearing loss and diminished recovery of auditory function after noise damage, as well as increased susceptibility to overstimulation of the hair bundle. We propose that ANKRD24 bridges the apical plasma membrane with the lower rootlet, maintaining a normal distribution of TRIOBP-5. Together with TRIOBP-5, ANKRD24 organizes rootlets to enable hearing with long-term resilience.Item Large-scale annotated dataset for cochlear hair cell detection and classification(bioRxiv, 2023-09-01) Buswinka, Christopher J.; Rosenberg, David B.; Simikyan, Rubina G.; Osgood, Richard T.; Fernandez, Katharine; Nitta, Hidetomi; Hayashi, Yushi; Liberman, Leslie W.; Nguyen, Emily; Yildiz, Erdem; Kim, Jinkyung; Jarysta, Amandine; Renauld, Justine; Wesson, Ella; Thapa, Punam; Bordiga, Pierrick; McMurtry, Noah; Llamas, Juan; Kitcher, Siân R.; López-Porras, Ana I.; Cui, Runjia; Behnammanesh, Ghazaleh; Bird, Jonathan E.; Ballesteros, Angela; Vélez-Ortega, A. Catalina; Edge, Albert S. B.; Deans, Michael R.; Gnedeva, Ksenia; Shrestha, Brikha R.; Manor, Uri; Zhao, Bo; Ricci, Anthony J.; Tarchini, Basile; Basch, Martin; Stepanyan, Ruben S.; Landegger, Lukas D.; Rutherford, Mark; Liberman, M. Charles; Walters, Bradley J.; Kros, Corné J.; Richardson, Guy P.; Cunningham, Lisa L.; Indzhykulian, Artur A.; Otolaryngology -- Head and Neck Surgery, School of MedicineOur 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.