Development of Automatic Image Analysis Algorithms for Protein Localization Studies in Budding Yeast

K. Logg, M. Kvarnström, A. Diez, K. Bodvard, M. Käll. In D. L. Farkas, R. C. Leif, D. V. Nicolau, editors, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V, Proceedings of SPIE, February 2007, 6441.


Microscopy of fluorescently labeled proteins has become a standard technique for live cell imaging. However, it is still a challenge to systematically extract quantitative data from large sets of images in an unbiased fashion, which is particularly important in high-throughput or time-lapse studies. Here we describe the development of a software package aimed at automatic quantification of abundance and spatio-temporal dynamics of fluorescently tagged proteins in vivo in the budding yeast Saccharomyces cerevisiae, one of the most important model organisms in proteomics. The image analysis methodology is based on first identifying cell contours from bright field images, and then use this information to measure and statistically analyse protein abundance in specific cellular domains from the corresponding fluorescence images. The applicability of the procedure is exemplified for two nuclear localized GFP-tagged proteins, Mcm4p and Nrm1p.

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