ArticleMolecules (Basel, Switzerland)2018
Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Evaluating Deep Learning Architectures for Actin Filament Segmentation under Varying Noise Conditions in Simulated Cryo-ET Tomograms.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Keratan sulfate, an electrosensory neurosentient bioresponsive cell instructive glycosaminoglycan.Glycobiology · 2024Review
- Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell withInternational journal of molecular sciences · 2023Article
- Automatic extraction of actin networks in plants.PLoS computational biology · 2023Article
- Tracing Randomly Oriented Filaments in a Simulated Actin Network Tomogram.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms.Biomolecules · 2022Article
- Article
- Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review.Computational and structural biotechnology journal · 2021Review
- Correction of Missing-Wedge Artifacts in Filamentous Tomograms by Template-Based Constrained Deconvolution.Journal of chemical information and modeling · 2020Article
- A cryo-tomography-based volumetric model of the actin core of mouse vestibular hair cell stereocilia lacking plastin 1.Journal of structural biology · 2020Article
- Cilia in the developing zebrafish ear.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- A Pattern Recognition Tool for Medium-resolution Cryo-EM Density Maps and Low-resolution Cryo-ET Density maps.Bioinformatics research and applications : 14th International Symposium, ISBRA 2018, Beijing, China, June 8-11, 2018, Proceedings. ISBRA (Conference) (14th : 2018 : Beijing, China) · 2018Article
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Authors and funding
6 authors at 3 institutions in 1 country.
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Abstract
Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.