Detection and Location of Hybridization Domains on Chromosomes by Image Cytometry

Laura Mascio1

1 Lawrence Livermore National Laboratory, Livermore, California
Publication Name:  Current Protocols in Cytometry
Unit Number:  Unit 10.9
DOI:  10.1002/0471142956.cy1009s03
Online Posting Date:  May, 2001
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Abstract

Because of time considerations in performing image analysis for detection of very small amounts of fluorescence probe, there are advantages in using automated detection systems. This unit provides considerable detail on several algorithms used for location of hybridization domains that reduce the tedium and improve objectivity and repeatability. Not only are these algorithms described and discussed in detail, but sources for the programs are also provided. Here is a carefully crafted textual and graphical description of how to go about learning the art of automated image analysis.

     
 
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Table of Contents

  • Basic Protocol 1: Using Custom Algorithms to Detect and Localize Hybridization Domains in Metaphase Chromosomes
  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol 1: Using Custom Algorithms to Detect and Localize Hybridization Domains in Metaphase Chromosomes

  Materials
  • DNA sample counterstained with 4′,6‐diamidino‐2‐phenylindole (DAPI) or propidium iodide (PI) and hybridized with probes of interest (units 8.1 8.3)
  • Fluorescence microscope with filter wheel containing single‐bandpass filters at the excitation source and corresponding triple‐ (or double‐) bandpass emission filters
  • High‐resolution CCD or other digital camera
  • Computer for controlling image acquisition and performing image analysis
  • Software for image processing and analysis (e.g., Khoros from http://www.khoral.com, NIH image from http://rsb.info.nih.gov/NIH‐image/download.html, or SCIL‐Image from http://www.tno.nl/instit/tpd/product/scil/; or see references in protocol steps for instructions on generating the appropriate algorithms)
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Figures

Videos

Literature Cited

   Dorst, L. and Smeulders, A.W.M. 1987. Length estimators for digitized contours. Comput. Graphics Image Process. 40:311‐333.
   Freeman, H. 1970. Boundary encoding and processing. In Picture Processing and Psychopictorics (B.S. Lipkin and A. Rosenfeld, eds.) pp. 241‐266. Academic Press, New York.
   Giardina, C.R. and Dougherty, E.R. 1998. Morphological Methods in Image and Signal Processing. PrenticeHall, Englewood Cliffs, N.J.
   Kallioniemi, A., Kallioniemi, O.‐P., Mascio, L., Sudar, D., Pinkel, D., Deaven, L., and Gray, J.W. 1994. Physical mapping of chromosome 17 cosmids. Genomics 20:125‐128.
   Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M. 1976. Processing of RI‐angiocardiographic images. In Digital Processing of Biomedical Images (K. Preston and M. Onoe, eds.) pp.187‐203. Plenum, New York.
   Lawrence, J.B. 1990. A fluorescence in situ hybridization approach for gene mapping and the study of nuclear organization genome analysis. Genet. Phys. Mapping 1:1‐39.
   Lichter, P., Tang, C., Call, K., Hermanson, G., Evans, G.A., Housman, D., and Ward, D.C. 1990. High‐resolution mapping of human chromosome 11 by in situ hybridization with cosmid clones. Science 247:64‐69.
   Mascio, L.N., Verbeek, P.W., Sudar, D., Kuo, W.‐L., and Gray, J.W. 1995. Semiautomated DNA probe mapping using digital imaging microscopy: I. System development. Cytometry 19:51‐59.
   Pinkel, D., Landegent, J., Collins, C., Fuscoe, J., Segraves, R., Lucas, J., and Gray, J.W. 1988. Fluorescence in situ hybridization with human chromosome‐specific libraries: Detection of trisomy 21 and translocations of chromosome 4. Proc. Natl. Acad. Sci. U.S.A. 85:9138‐9142.
   Ridler, T.W. and Calvard, S. 1978. Picture thresholding using an iterative selection method. IEEE Trans. Systems Man Cybernet. 8:630‐632.
   Sakamoto, M., Pinkel, D., Mascio, L., Sudar, D., Peters, D., Kuo, W.‐L., Yamakawa, K., Nakamura, Y., Drabkin, H., Jericevic, Z., Smith, L., and Gray, J.W. 1995. Semi‐automated DNA probe mapping using digital imaging microscopy. II. System performance. Cytometry 19:60‐69.
   Serra, J. 1982. Image Analysis and Mathematical Morphology. Academic Press, London.
   Verbeek, P.W., Vrooman, H.A., and Van Vliet, L.J. 1988. Low‐level image processing by max‐min filters. Signal Process. 17:249‐258.
   Verwer, B.J.H. 1988. Improved metrics in image processing applied to the Hildritch skeleton. Proceedings of the 9th International Conference on Pattern Recognition. Rome, Italy, Nov. 14‐17, 1988, pp. 137‐142. Computer Society Press, Washington, D.C.
   Vossepoel, A.M. and Smeulders, A.W.M. 1982. Vector code probabilities and metrication error in the representation of straight lines of finite length. Comput. Vision Graphics Image Process. 20:347‐364.
Key Reference
   Mascio, L.N., Verbeek, P.W., Sudar, D., Kuo, W.‐L., and Gray, J.W. 1995. See above
  This paper presents and discusses the algorithm described in this unit.
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