Cells such as astrocytes and radial glia with many densely ramified, fine processes pose particular challenges for the quantification of structural motility. Here we report the development of a method to calculate a motility index for individual cells with complex, dynamic morphologies. This motility index relies on boxcar averaging of the difference images generated by subtraction of images collected at consecutive time points. An image preprocessing step involving 2D projection, edge detection, and dilation of the raw images is first applied in order to binarize the images. The boxcar averaging of difference images diminishes the impact of artifactual pixel fluctuations while accentuating the group-wise changes in pixel values which are more likely to represent real biological movement. Importantly, this provides a value that correlates with mean process elongation and retraction rates without requiring detailed reconstructions of very complex cells. We also demonstrate that additional increases in the sensitivity of the method can be obtained by denoising images using the temporal frequency power spectra, based on the fact that rapid intensity fluctuations over time are mainly due to imaging artifact. The MATLAB programs implementing these motility analysis methods, complete with user-friendly graphical interfaces, have been made publicly available for download. 1. Introduction The development of advanced imaging methods has revealed that living cells exhibit highly dynamic structural remodeling [1, 2]. The rate and structural details of cellular motility can change over development or in response to the environment in ways that reveal important details about cellular signaling mechanisms [3–5]. Useful commercial and public domain programs are available to identify and measure the movements of relatively large cell structures like amoeboid pseudopodia [6–8] as well as fine features like growth cone filopodia [9], astrocytic protrusions [10], and dendritic spines [11, 12]. Analysis of these kinds of sparsely distributed cellular protrusions may be amenable to the use of image skeletonization or active contour tracing to select the features of interest and to track end-tips for motility analysis. An alternative approach is to measure perimeter shape changes of an entire cell by analyzing the difference between an original image and an “eroded” image from which all fine processes have been filtered away [10]. However, if the features of interest are very dense and spindly, overlapping and oriented in many directions as in the case of astrocytes and
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