ArticleScientific data2020
Serial scanning electron microscopy of anti-PKHD1L1 immuno-gold labeled mouse hair cell stereocilia bundles.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- VASCilia is an open-source, deep learning-based tool for 3D analysis of cochlear hair cell stereocilia bundles.PLoS biology · 2026Article
- VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles.bioRxiv : the preprint server for biology · 2025Article
- Mini-PCDH15 gene therapy rescues hearing in a mouse model of Usher syndrome type 1F.Nature communications · 2023Article
- Electron Microscopy Techniques for Investigating Structure and Composition of Hair-Cell Stereociliary Bundles.Frontiers in cell and developmental biology · 2021Article
- Serial scanning electron microscopy of anti-PKHD1L1 immuno-gold labeled mouse hair cell stereocilia bundles.Scientific data · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Serial electron microscopy techniques have proven to be a powerful tool in biology. Unfortunately, the data sets they generate lack robust and accurate automated segmentation algorithms. In this data descriptor publication, we introduce a serial focused ion beam scanning electron microscopy (FIB-SEM) dataset consisting of six outer hair cell (OHC) stereocilia bundles, and the supranuclear part of the hair cell bodies. Also presented are the manual segmentations of stereocilia bundles and the gold bead labeling of PKHD1L1, a coat protein of hair cell stereocilia important for hearing in mice. This depository includes all original data and several intermediate steps of the manual analysis, as well as the MATLAB algorithm used to generate a three-dimensional distribution map of gold labels. They serve as a reference dataset, and they enable reproduction of our analysis, evaluation and improvement of current methods of protein localization, and training of algorithms for accurate automated segmentation.
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Registered trials
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