The microtubule cytoskeleton in living cells generate and resist mechanical forces to mediate fundamental cell processes, including cell division and migration. Recent advances in digital fluorescence microscopy have enabled the direct observation of bending of individual microtubules in living cells, which has enabled quantitative estimation of the mechanical state of the microtubule array. Although a variety of mechanisms have been proposed, the precise origins of microtubule deformation in living cells remain largely obscure. To investigate these mechanisms and their relative importance in cellular processes, a method is needed to accurately quantify microtubule bending within living cells. Here we describe a method for quantification of bending, using digital fluorescence microscope images to estimate the distribution of curvature in the microtubule. Digital images of individual microtubules can be used to obtain a set of discrete x-y coordinates along the microtubule contour, which is then used to estimate the curvature distribution. Due to system noise and digitization error, the estimate will be inaccurate to some degree. To quantify the inaccuracy, a computational model is used to simulate both the bending of thermally driven microtubules and their observation by digital fluorescence microscopy. This allows for direct comparison between experimental and simulated images, a method which we call model convolution microscopy. We assess the accuracy of various methods and present a suitable method for estimating the curvature distribution for thermally driven semiflexible polymers. Finally, we discuss extensions of the method to quantify microtubule curvature in living cells.
|Original language||English (US)|
|Title of host publication||Cell Mechanics|
|Editors||YuLi Wang, Dennis Discher|
|Number of pages||32|
|State||Published - 2007|
|Name||Methods in Cell Biology|
Bibliographical noteFunding Information:
D.M.K. and E.T. acknowledge support from the National Science Foundation under Grant No. DMR‐0513393 and ND EPSCoR through NSF grant EPS‐0132289. D.J.O. acknowledges support from NSF grants BES 9984955, BES 0119481, and NIGMS R01GM71522.
Copyright 2008 Elsevier B.V., All rights reserved.