Browsing by Author "Bird, Jonathan E."

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    A Myosin Nanomotor Essential for Stereocilia Maintenance Exp 1 ands the Etiology of 2 Hereditary Hearing Loss DFNB3
    (bioRxiv, 2025-02-21) Behnammanesh, Ghazaleh; Dragich, Abigail K.; Liao, Xiayi; Hadi, Shadan; Kim, Mi-Jung; Perrin, Benjamin; Someya, Shinichi; Frolenkov, Gregory I.; Bird, Jonathan E.; Biology, School of Science
    Cochlear hair cells transduce sound using stereocilia, and disruption to these delicate mechanosensors is a significant cause of hearing loss. Stereocilia architecture is dependent upon the nanomotor myosin 15. A short isoform (MYO15A-2) drives stereocilia development by delivering an elongation-promoting complex (EC) to stereocilia tips, and an alternatively spliced long isoform (MYO15A-1) tunes postnatal size in shorter stereocilia, which possess mechanosensitive ion channels. Disruption of these functions causes two distinct stereocilia pathologies, which underly human autosomal recessive non-syndromic hearing loss DFNB3. Here, we characterize a new isoform, MYO15A-3, that increases expression in postnatal hair cells as the developmental MYO15A-2 isoform wanes reciprocally. We show the critical EC complex is initially delivered by MYO15A-2, followed by a postnatal handover to MYO15A-3, which continues to deliver the EC. In a Myo15a-3 mutant mouse, stereocilia develop normally with correct EC targeting, but lack the EC postnatally and do not maintain their adult architecture, leading to progressive hearing loss. We conclude MYO15A-3 delivers the EC in postnatal hair cells and that the EC and MYO15A-3 are both required to maintain stereocilia integrity. Our results add to the spectrum of stereocilia pathology underlying DFNB3 hearing loss and reveal new molecular mechanisms necessary for resilient hearing during adult life.
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    Actin at stereocilia tips is regulated by mechanotransduction and ADF/cofilin
    (Elsevier, 2021-03) McGrath, Jamis; Tung, Chun-Yu; Liao, Xiayi; Belyantseva, Inna A.; Roy, Pallabi; Chakraborty, Oisorjo; Li, Jinan; Berbari, Nicolas F.; Faaborg-Andersen, Christian C.; Barzik, Melanie; Bird, Jonathan E.; Zhao, Bo; Balakrishnan, Lata; Friedman, Thomas B.; Perrin, Benjamin J.; Biology, School of Science
    Stereocilia on auditory sensory cells are actin-based protrusions that mechanotransduce sound into an electrical signal. These stereocilia are arranged into a bundle with three rows of increasing length to form a staircase-like morphology that is required for hearing. Stereocilia in the shorter rows, but not the tallest row, are mechanotransducing because they have force-sensitive channels localized at their tips. The onset of mechanotransduction during mouse postnatal development refines stereocilia length and width. However, it is unclear how actin is differentially regulated between stereocilia in the tallest row of the bundle and the shorter, mechanotransducing rows. Here, we show actin turnover is increased at the tips of mechanotransducing stereocilia during bundle maturation. Correspondingly, from birth to postnatal day 6, these stereocilia had increasing amounts of available actin barbed ends, where monomers can be added or lost readily, as compared with the non-mechanotransducing stereocilia in the tallest row. The increase in available barbed ends depended on both mechanotransduction and MYO15 or EPS8, which are required for the normal specification and elongation of the tallest row of stereocilia. We also found that loss of the F-actin-severing proteins ADF and cofilin-1 decreased barbed end availability at stereocilia tips. These proteins enriched at mechanotransducing stereocilia tips, and their localization was perturbed by the loss of mechanotransduction, MYO15, or EPS8. Finally, stereocilia lengths and widths were dysregulated in Adf and Cfl1 mutants. Together, these data show that actin is remodeled, likely by a severing mechanism, in response to mechanotransduction.
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    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 Medicine
    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.
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    Large-scale annotated dataset for cochlear hair cell detection and classification
    (Springer Nature, 2024-04-23) 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; Wang, Haobing; 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, Martín L.; Stepanyan, Ruben; Landegger, Lukas D.; Rutherford, Mark A.; 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 Medicine
    Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they 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. Machine learning can be used to automate 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, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or 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 give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.