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Features of DNA Signals Codistribution in High-Content Screening of Drug-Induced Demethylation in Cancer Cells

  • Arkadiusz Gertych
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)

Abstract

The main aim of this work is to introduce new features for the automated evaluation of fluorescence signals of methylated and global DNA, to quickly and objectively measure changes resulting from a demethylating anti-cancer drug application in cell nuclei. Drug response can be monitored in a high-content fashion by means of three dimensional (3-D) image cytometry. This technique enables extraction of basic DNA signal intensities that can be converted to more sophisticated features such as a codistribution and its derivatives. In this work, a slope of nuclear DNA codistribution patterns extracted from individual cells, and whole cell populations and divergences between DNA codistributions of untreated as well as drug treated cells were analysed. A progressive decrease of the slope and an increase of the divergence observed for low drug concentrations suggest that these features can be essential for high-content screening of agents with demethylating potency.

Keywords

high-content screening bioimage informatics DNA methylation demethylating drugs quantitative immunofluorescence 

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References

  1. 1.
    Perlman, Z.E., Slack, M.D., Feng, Y., Mitchison, T.J., Wu, L.F., Altschuler, S.J.: Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004)PubMedCrossRefGoogle Scholar
  2. 2.
    Tkaczuk, K.H., Tait, N.S., Ioffe, O., Tan, M., Goloubeva, O.G., Lesko, S.A., Deamond, S.F., Zhou, D., Lum, Z.P., Sutula, M.J., Van Echo, D., Tsó, P.O.: Computer assisted quantitative immunofluorescence of tumor tissue marker expression and clinical outcome to chemotherapy in advanced breast cancer patients. Discov. Med. 62, 33–40 (2011)Google Scholar
  3. 3.
    Hoffman, A.F., Garippa, R.: A pharmaceutical company user’s perspective on the potential of high content screening in drug discovery. Methods. Mol. Biol. 356, 19–31 (2007)PubMedCrossRefGoogle Scholar
  4. 4.
    Herceg, Z., Hainauta, P.: Genetic and epigenetic alterations as biomarkers for cancer detection, diagnosis and prognosis. Mol. Oncol. 1, 26–41 (2007)PubMedCrossRefGoogle Scholar
  5. 5.
    Villar-Garea, A., Esteller, M.: DNA demethylating agents and chromatin-remodelling drugs: which, how and why? Curr. Drug. Metab. 1, 11–31 (2003)Google Scholar
  6. 6.
    Lübbert, M.: DNA methylation inhibitors in the treatment of leukemias, myelodysplastic syndromes and hemoglobinopathies: clinical results and possible mechanisms of action. Curr. Top. Microbiol. Immunol. 249, 135–164 (2000)PubMedCrossRefGoogle Scholar
  7. 7.
    Gama-Sosa, M.A., Slagel, V.A., Trewyn, R.W., Oxenhandler, R., Kuo, K.C., Gehrke, C.W., Ehrlich, M.: The 5-methylcytosine content of DNA from human tumors. Nuclecic Acids Res. 11, 6883–6894 (1983)CrossRefGoogle Scholar
  8. 8.
    Feinberg, A.P., Vogelstein, B.: Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301, 89–92 (1983)PubMedCrossRefGoogle Scholar
  9. 9.
    Ting, A., McGarvey, K., Baylin, S.: The cancer epigenome-components and functional correlates. Genes. Dev. 20, 3215–3231 (2006)PubMedCrossRefGoogle Scholar
  10. 10.
    Tajbakhsh, J., Wawrowsky, K., Gertych, A., Bar-Nur, O., Vishnevsky, E., Lindsley, E., Farkas, D.L.: Characterization of tumor cells and stem cells by differential nuclear methylation imaging. In: Farkas, D.L., Nicolau, D., Leif, R.C. (eds.) Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues, vol. 6859, p. 68590F. SPIE, San Jose (2008)Google Scholar
  11. 11.
    Gertych, A., Wawrowsky, K., Lindsley, E., Vishnewsky, E., Farkas, D., Tajbakhsh, J.: Automated quantification of DNA demethylation effects in cells via 3D mapping of nuclear signatures and population homogeneity assessment. Cytom. Part A 75, 569–583 (2009)CrossRefGoogle Scholar
  12. 12.
    Gertych, A., Farkas, D., Tajbakhsh, J.: Measuring topology of low-intensity DNA methylation sites for high-throughput assessment of epigenetic drug-induced effects in cancer cells. Exp. Cell. Res. 316, 3150–3160 (2010)PubMedCrossRefGoogle Scholar
  13. 13.
    Christman, J.K.: 5-Azacytidine and 5-aza-2-́deoxycytidine as inhibitors of DNA methylation: mechanistic studies and their implications for cancer therapy. Oncogene 21, 5483–5495 (2002)PubMedCrossRefGoogle Scholar
  14. 14.
    Zinchuk, V., Zinchuk, O., Okada, T.: Quantitative Colocalization Analysis of Multicolor Confocal Immunofluorescence. Microscopy Images: Pushing Pixels to Explore Biological Phenomena. Acta Histochem. Cytochem. 40, 101–111 (2007)Google Scholar
  15. 15.
    Carpenter, A.E.: Image-based chemical screening. Nature Chemical Biology 3, 461–465 (2007)PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arkadiusz Gertych
    • 1
  1. 1.Bioinformatics Lab, Department of SurgeryCedars-Sinai Medical CenterLos AngelesUSA

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