Extracting microtubule networks from superresolution single-molecule localization microscopy data

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.