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

Journal Article · · Molecules
 [1];  [2];  [3];  [3];  [4];  [1]
  1. Old Dominion Univ., Norfolk, VA (United States). Dept. of Computer Science
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging Division. Cell and Tissue Imaging; Univ. of California, Berkeley, CA (United States). Dept. of Molecular and Cell Biology
  3. Old Dominion Univ., Norfolk, VA (United States). Dept. of Mechanical and Aerospace Engineering
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging Division. Cell and Tissue Imaging
+ Show Author Affiliations

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1628487
Journal Information:
Molecules, Vol. 23, Issue 4; ISSN 1420-3049
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

Functional Consequences of Keratan Sulfate Sulfation in Electrosensory Tissues and in Neuronal Regulation journal March 2019
Cilia in the developing zebrafish ear journal December 2019

Figures / Tables (8)

Figure 1(p. 3)figure Figure 1
Figure 2(p. 4)figure Figure 2
Figure 3(p. 5)figure Figure 3
Figure 4(p. 6)figure Figure 4
Figure 5(p. 7)figure Figure 5
Figure 6(p. 8)figure Figure 6
Table 1(p. 8)table Table 1
Figure 7(p. 10)figure Figure 7

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
59 BASIC BIOLOGICAL SCIENCES
Biochemistry & Molecular Biology
Chemistry
cryo-electron tomography
image
density
filament
pattern recognition
segmentation
stereocilia
actin
model building
volumetric model