Mr. Will Ludington Profile

Will Ludington

at Univ of California San Francisco

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Proceedings Article | 4 March 2009 Paper

Automated analysis of intracellular motion using kymographs in 1, 2, and 3 dimensions

William Ludington, Wallace Marshall

Proceedings Volume 7184, 71840Y (2009) https://doi.org/10.1117/12.812419

KEYWORDS: Particles, Cameras, Radon transform, Microscopy, Detection and tracking algorithms, Motion analysis, Image processing, Visualization, Statistical analysis, 3D image processing

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In this paper we use kymographs and computational image processing to convert 3-D video microscopy data of intracellular motion into 1-D time series data for further analysis. Because standard tools exist for time series analysis, this method allows us to produce robust quantitative results from otherwise visual data. The kymograph-based approach has an additional advantage over standard particle-tracking and flow-based image quantification algorithms in that we can average out camera noise over the spatial axis of the kymograph. The method has the disadvantage that it removes all spatial information. For this reason we see this method as a complement to rather than a replacement of standard tracking algorithms. The standard problem we are trying to address in our work is how fluorescent proteins in one cellular compartment are injected into another cellular compartment. The proteins travel at constant speed along a fixed spatial path, so a 2-D kymograph produced from a trace along this fixed path will tell us about the injection history into this second compartment. Our algorithm works by first taking a Radon transform of the input 2-D kymograph. We next make synthetic kymographs by backprojection. The angle with the best correlation between the original kymograph and the backprojection determines the dominant speed of the moving particles as well as the angle of the 1-D projected time series. Time series are then analyzed with standard tools to determine the peak size distribution, the peak interval distribution, the autocorrelation and the power spectrum.

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