Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Cell and Tissue Imaging, Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Molecules. 2018 Apr 11;23(4):882. doi: 10.3390/molecules23040882.
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.
冷冻电镜断层扫描（cryo-ET）是一种强大的方法，可以在其天然细胞和组织环境中可视化超分子复合物（如细胞骨架）的三维组织。由于其电子剂量最小，并且在数据收集过程中由于缺失楔形而产生重建伪影，因此 cryo-ET 通常会导致显示各向异性 XY 与 Z 分辨率的嘈杂密度图。分子拥挤进一步加剧了自动检测超分子复合物（如毛细胞静纤毛中的肌动蛋白束）的挑战。静纤毛对于内耳感觉上皮毛细胞的机电转导过程至关重要。鉴于肌动蛋白束的复杂性和密集排列，传统的细丝检测和追踪方法在这些情况下都失败了。在这项研究中，我们引入了 BundleTrac，这是一种有效追踪束中数百根细丝的方法。BundleTrac 与手动追踪静纤毛中的肌动蛋白丝的比较表明，BundleTrac 准确地构建了 330 根细丝中的 326 根（98.8%），对于 330 根细丝的整体交叉距离为 1.3 体素。BundleTrac 是一种有效的半自动建模方法，其中为每根细丝提供一个种子点，其余的细丝由计算来识别。我们还展示了一种使用多项式回归来解决密度图的分辨率和高噪声各向异性环境的去噪方法的潜力。
