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Molecules 2018, 23(4), 882; https://doi.org/10.3390/molecules23040882

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

1
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
2
Cell and Tissue Imaging, Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
3
Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
4
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Amarda Shehu and Nurit Haspel
Received: 23 December 2017 / Revised: 14 March 2018 / Accepted: 22 March 2018 / Published: 11 April 2018
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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. View Full-Text
Keywords: cryo-electron tomography; image; density; filament; pattern recognition; segmentation; stereocilia; actin; model building; volumetric model cryo-electron tomography; image; density; filament; pattern recognition; segmentation; stereocilia; actin; model building; volumetric model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sazzed, S.; Song, J.; Kovacs, J.A.; Wriggers, W.; Auer, M.; He, J. Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia. Molecules 2018, 23, 882.

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