Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11265-008-0205-7
Title: Automated protein distribution detection in high-throughput image-based siRNA library screens
Authors: Law, Y.N.
Ogg, S.
Common, J.
Tan, D.
Lane, E.B.
Yip, A.M. 
Lee, H.K.
Keywords: Fluorescence microscopy
Image analysis
Keratin proteins
Mutant detection
RNA interference
Issue Date: Apr-2009
Source: Law, Y.N., Ogg, S., Common, J., Tan, D., Lane, E.B., Yip, A.M., Lee, H.K. (2009-04). Automated protein distribution detection in high-throughput image-based siRNA library screens. Journal of Signal Processing Systems 55 (1-3) : 1-13. ScholarBank@NUS Repository. https://doi.org/10.1007/s11265-008-0205-7
Abstract: 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. © 2008 Springer Science+Business Media, LLC.
Source Title: Journal of Signal Processing Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/102907
ISSN: 19398018
DOI: 10.1007/s11265-008-0205-7
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