Zhang Zhen, Nishimura Yukako, Kanchanawong Pakorn
Mechanobiology Institute, National University of Singapore, 117411 Singapore.
Mechanobiology Institute, National University of Singapore, 117411 Singapore
Mol Biol Cell. 2017 Jan 15;28(2):333-345. doi: 10.1091/mbc.E16-06-0421. Epub 2016 Nov 16.
Microtubule filaments form ubiquitous networks that specify spatial organization in cells. However, quantitative analysis of microtubule networks is hampered by their complex architecture, limiting insights into the interplay between their organization and cellular functions. Although superresolution microscopy has greatly facilitated high-resolution imaging of microtubule filaments, extraction of complete filament networks from such data sets is challenging. Here we describe a computational tool for automated retrieval of microtubule filaments from single-molecule-localization-based superresolution microscopy images. We present a user-friendly, graphically interfaced implementation and a quantitative analysis of microtubule network architecture phenotypes in fibroblasts.
微管丝形成了普遍存在的网络，这些网络决定了细胞中的空间组织。然而，微管网络的复杂结构阻碍了对其进行定量分析，限制了我们对其组织与细胞功能之间相互作用的深入理解。尽管超分辨率显微镜极大地促进了微管丝的高分辨率成像，但从这些数据集中提取完整的丝网络仍具有挑战性。在这里，我们描述了一种用于从基于单分子定位的超分辨率显微镜图像中自动检索微管丝的计算工具。我们展示了一个用户友好的、具有图形界面的实现方式，并对成纤维细胞中的微管网络结构表型进行了定量分析。
