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Imaging microtubule shape fluctuations and networks for studying cytoskeleton network structure and mechanics
(2019-11-14) Himmelsbach, Michael S; Florin, Ernst-Ludwig; Alvarado, José; Baker, Aaron; Fink, Manfred; Gordon, Vernita
A large part of the mechanical integrity and biological functionality of eukaryotic cells relies on a network of filaments called the cytoskeleton. The overall mechanical properties of the cytoskeleton have their origin in the properties of individual filaments and the network architecture. Microtubules are the stiffest type of cytoskeletal filament with a persistence length in the millimeter range. These semiflexible filaments form networks where bending elasticity contributes to the force distribution and the overall mechanical properties. Long-standing efforts to model individual cytoskeletal components have resulted in a number of predictions that are not testable by video microscopy with a camera, and the mechanics of individual microtubules are not well understood. As a result, interpreting emergent mechanical properties of microtubule networks remains difficult. Here we introduce two novel methods which addresses the need to study structural and mechanical properties of single filaments and networks. The first method allows high bandwidth tracking of microtubule shape fluctuations using light scattered by the filament to study dynamics of individual microtubules. By assessing the light scattered by single and multiple filaments, the possibility for in situ characterization the molecular scale architecture is demonstrated. The power spectral density of thermally fluctuating microtubules can be measured with high precision over a wide range of frequencies (1–104 Hz). The filament dynamics are interpreted in terms of the wormlike chain model for semiflexible filaments. The second method, thermal noise imaging, is a three-dimensional scanning probe technique that utilizes the confined thermal motion of a 200 nm optically trapped particle as a noninvasive probe. We achieved 10 nm precision in localizing the filament axes. The presence of filaments leads to excluded volumes in the region scanned by the probe. Quantitative information about crosslinking geometry and filament curvature can be extracted from the data. Transverse filament fluctuations lead to a reduction in the excluded volume. Since such fluctuations depend on filament stiffness, network architecture and the force transmitted through each filament, thermal noise imaging provides a novel way to study biopolymer network structure and force distribution label-free with nanometer scale resolution in three dimensions.