Skip to main content
SpringerLink
Log in

Automated Protein Distribution Detection in High-Throughput Image-Based siRNA Library Screens

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12

Similar content being viewed by others

References

  1. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  2. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd ed., p. 680). New York: Wiley-Interscience.

    Google Scholar 

  3. Moll, R., Franke, W. W., Schiller, D. L., Geiger, B., & Krepler, R. (1982). The catalog of human cytokeratins: Patterns of expression in normal epithelia, tumors and cultured cells. Cell, 31, 11–24.

    Article  Google Scholar 

  4. Fuchs, E., & Weber, K. (1994). Intermediate filaments: Structure, dynamics, function, and disease. Annual Reviews of Biochemical, 63, 345–382.

    Google Scholar 

  5. Irvine, A. D., & McLean, W. H. (1999). Human keratin diseases: The increasing spectrum of disease and subtlety of the phenotype-genotype correlation. British Journal of Dermatology, 140, 815–828.

    Article  Google Scholar 

  6. Szeverenyi, I., Cassidy, A. J., Chung, C. W., Lee, B. T., Common, J. E., Ogg, S. C., et al. (2007). The human intermediate filament database: Comprehensive information on a gene family involved in many human diseases. Human Mutation, 29, 351–360.

    Article  Google Scholar 

  7. Ku, N. O., Liao, J., Chou, C. F., & Omary, M. B. (1996). Implications of intermediate filament protein phosphorylation. Cancer Metastasis Reviews, 15, 429–444.

    Article  Google Scholar 

  8. Herrmann, H., Hesse, M., Reichenzeller, M., Aebi, U., & Magin, T. M. (2003). Functional complexity of intermediate filament cytoskeletons: From structure to assembly to gene ablation. International Review of Cytology, 223, 83–175.

    Article  Google Scholar 

  9. Coulombe, P. A., & Omary, M. B. (2002). ‘Hard’ and ‘soft’ principles defining the structure, function and regulation of keratin intermediate filaments. Current Opinion in Cell Biology, 14, 110–122.

    Article  Google Scholar 

  10. Coulombe, P. A., Hutton, M. E., Letai, A., Hebert, A., Paller, A. S., & Fuchs, E. (1991). Point mutations in human keratin 14 genes of epidermolysis bullosa simplex patients: Genetic and functional analyses. Cell, 66, 301–311.

    Article  Google Scholar 

  11. Toivola, D. M., Zhou, Q., English, L. S., & Omary, M. B. (2002). Type II keratins are phosphorylated on a unique motif during stress and mitosis in tissues and cultured cells. Molecular Biology of the Cell, 13, 1857–1870.

    Article  Google Scholar 

  12. Windoffer, R., & Leube, R. E. (2004). Imaging of keratin dynamics during the cell cycle and in response to phosphatase inhibition. Methods in Cell Biology, 78, 321–352.

    Article  Google Scholar 

  13. Neumann, B., Held, M., Liebel, U., Erfle, H., Rogers, P., Pepperkok, R., et al. (2006). High-throughput RNAi screening by time-lapse imaging of live human cells. Nature Methods, 3(5), 385–390.

    Article  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.

  16. Chen, X., Velliste, M., & Murphy, R. F. (2006). Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics. Cytometry A, 69A(7), 631–640.

    Article  Google Scholar 

  17. Conrad, C., Erfle, H., Warnat, P., Daigle, N., Lörch, T., Ellenberg, J., et al. (2004). Automatic identification of subcellular phenotypes on human cell arrays. Genome Research, 14, 1130–1136.

    Article  Google Scholar 

  18. Chen, X., & Murphy, R. F. (2005). Objective clustering of proteins based on subcellular location patterns. Journal of Biomedicine and Biotechnology, 2005(2), 87–95.

    Article  MATH  Google Scholar 

  19. Bakal, C., Aach, J., Church, G., & Perrimon, N. (2007). Quantitative morphological signatures define local signaling networks regulating cell morphology. Science, 316, 1753–1756.

    Article  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.

  21. Glory, E., & Murphy, R. F. (2007). Automated subcellular location determination and high-throughput microscopy. Developmental Cell, 12, 7–16.

    Article  Google Scholar 

  22. Burrus, C. S., & Copinath, R. A. (1997). Introduction to wavelets and wavelet transforms (p. 268). NJ: Prentice Hall.

    Google Scholar 

  23. Starck, J. L., Murtagh, F., & Bijaoui, A. (1995). Multiresolution support applied to image filtering and restoration. Graphical Models and Image Processing, 57(5), 420–431.

    Article  Google Scholar 

  24. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–714.

    Article  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.

  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 

  29. Olivo-Marin, J. C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition, 35, 1989–1996.

    Article  MATH  Google Scholar 

Download references

Acknowledgement

This work was supported (in part) by the Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore.

Author information

Authors and Affiliations

  1. Imaging Informatics Group, Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore

    Yan Nei Law & Hwee Kuan Lee

  2. Institute of Medical Biology, A*STAR, 8 Biomedical Grove, #06-06 Immunos, Singapore, 138665, Singapore

    Stephen Ogg, John Common, David Tan & E. Birgitte Lane

  3. Department of Mathematics, National University of Singapore, 2 Science Drive 2, Singapore, 117543, Singapore

    Andy M. Yip

Authors
  1. Yan Nei Law
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Ogg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Common
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Birgitte Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andy M. Yip
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hwee Kuan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hwee Kuan Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Law, Y.N., Ogg, S., Common, J. et al. Automated Protein Distribution Detection in High-Throughput Image-Based siRNA Library Screens. J Sign Process Syst Sign Image Video Technol 55, 1–13 (2009). https://doi.org/10.1007/s11265-008-0205-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-008-0205-7

Keywords

Search

Navigation