Automated Protein Distribution Detection in High-Throughput Image-Based siRNA Library Screens
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The availability of RNA interference (RNAi) libraries, automated microscopy and computational methods enables millions of biochemical assays to be carried out simultaneously. This allows systematic, data driven high-throughput experiments to generate biological hypotheses that can then be verified with other techniques. Such high-throughput screening holds great potential for new discoveries and is especially useful in drug screening. In this study, we present a computational framework for an automatic detection of changes in images of in vitro cultured keratinocytes when phosphatase genes are silenced using RNAi technology. In these high-throughput assays, the change in pattern only happens in 1–2% of the cells and fewer than one in ten genes that are silenced cause phenotypic changes in the keratin intermediate filament network, with small keratin aggregates appearing in cells in addition to the normal reticular network seen in untreated cells. By taking advantage of incorporating prior biological knowledge about phenotypic changes into our algorithm, it can successfully filter out positive ‘hits’ in this assay which is shown in our experiments. We have taken a stepwise approach to the problem, combining different analyses, each of which is well-designed to solve a portion of the problem. These include, aggregate enhancement, edge detection, circular object detection, aggregate clustering, prior to final classification. This strategy has been instrumental in our ability to successfully detect cells containing protein aggregates.
KeywordsKeratin proteins RNA interference Mutant detection Fluorescence microscopy Image analysis
This work was supported (in part) by the Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore.
- 2.Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed., p. 680). New York: Wiley-Interscience.Google Scholar
- 4.Fuchs, E., & Weber, K. (1994). Intermediate filaments: Structure, dynamics, function, and disease. Annual Reviews of Biochemical, 63, 345–382.Google Scholar
- 14.Yarrow, J. C., Perlman, Z. E., Kirchhausen, T., & Mitchison, T. J. (2003). Phenotypic screening of small molecule libraries by high throughput cell imaging. Combinatorial Chemistry & High Throughput Screening, 6(4), 279–286.Google Scholar
- 15.Kneller, A. (2006). The new age of bioimaging. Paradigm, Fall, pp. 18–25.Google Scholar
- 20.Jones, T. R., Carpenter, A. E., Sabatini, D. M., & Golland, P. (2006). Methods for high-content, high-throughput image-based cell screening. Proceedings of the Workshop on Microscopic Image Analysis with Applications in Biology, pp. 65–72.Google Scholar
- 22.Burrus, C. S., & Copinath, R. A. (1997). Introduction to wavelets and wavelet transforms (p. 268). NJ: Prentice Hall.Google Scholar
- 25.Jain, A. K. (1988). Fundamentals of digital image processing (p. 592). NJ: Prentice Hall.Google Scholar
- 26.Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231.Google Scholar
- 27.Bright, X., & Steel, E. B. (1987). Two-dimensional top hat filter for extracting spots and spheres from digital images. Journal of Microscopy, 146(2), 191–200.Google Scholar
- 28.Breen, X., Joss, G. H., & Williams, K. L. (1991). Locating objects of interest within biological objects: the top hat box filter. Journal of Computer-Assisted Microscopy, 3(2), 97–102.Google Scholar