Research Project
10410746
Project Summary: The sensory cells of the inner ear, the hair cells, carry a bundle of precisely organized stereocilia bundles on their surface. Stereocilia are microvilli-like protrusions detecting sub-nanometer vibrations enabling our perception of sound. This incredible sensitivity requires an ensemble of proteins, some of which form mechanical linkages at the surface of stereocilia. These linkages have long been visualized with electron microscopy, however, the specific function of some remain elusive. This proposal focuses on a specific novel protein, Polycystic Kidney and Hepatic Disease 1-Like 1 (PKHD1L1), which is thought to be a major component of the stereocilia surface coat and is hypothesized to facilitate stereocilia bundle cohesion. To understand the spatiotemporal distribution of the protein throughout development, as well as the mechanism by which it causes deafness when mutated, multiple high-resolution light and electron microscopy experiments using different imaging modalities are being performed, resulting in a plethora of 2D and 3D images containing a wealth of data which unfortunately are costly to analyze in their entirety. Often generated to answer one question, such data sets could be shared with the scientific community for other groups to reuse for their research needs. As data collection becomes increasingly more expensive and time consuming, more groups join in sharing their raw data sets with the scientific community, in various formats often unsuitable for easy analysis. Advances in machine learning for computer vision has made it feasible to automate these manual analysis tasks, however the development of such models is heavily bottlenecked by the availability of training data which has been properly prepared and annotated. This supplemental proposal aims to increase the availability of such training data by annotating and depositing a wealth of imaging data in such a way that they may be readily used for the training of machine learning models. We will annotate three types of imaging data we collect from wild-type and ko mice to study the role of PKHD1L1 in hair cells: 1) cochlear hair cells on 3D confocal Z-stacks, 2) hair cell stereocilia and mitochondria in our 3D focused ion beam scanning electron micrograph volumes, and finally 3) stereocilia (in 2D) on our scanning electron micrographs of cochlear hair cell bundles. These annotations will be evaluated by our collaborators specializing in machine learning volumetric image segmentation to help us assess data biases, assess generalizability of our data annotation, and demonstrate the usability of the data in AI/ML applications through mini-AI/ML applications as a proof of concept. A special attention will be given to documenting our data and demonstrating their usability by retraining open-source machine learning algorithms both, with the help of our collaborators, and in-house. The data, annotations, documentation, and example use cases will be publicly hosted in open repositories. There is currently a dearth of machine learning work in the hearing field. By publicly releasing machine learning ready image data, our work will encourage the development of models that have the potential to dramatically increase the efficiency and throughput of future image analysis.
