Advertisement

Selected Applications of Graph-Based Tracking Methods for Cancer Research

  • Pascal Vallotton
  • Lilian Soon
Chapter
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)

Abstract

Cell appearance is highly variable, particularly for cancer cells. Consequently, massive amounts of image data need to be screened rapidly and reproducibly to ascertain phenotypic information of interest. This demands a high level of automation and chosen image analysis techniques.A very relevant phenotype for cancer is motile behaviour as it is a fundamental ingredient of its development. We aim to measure normal and abnormal motile processes, identify small molecules and genotypes that modulate these processes, and develop mathematical models of the disease towards identifying specific targets or target sets. Here, we describe the use and principles underlying representative software tools to read motile phenotypes from time-lapse data, with emphasis on improved graph-based tracking methods.

Keywords

Object Tracking Cell Tracking Manual Tracking Bipartite Match Cell Front 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alberts, B., et al. Molecular Biology of the Cell (Garland, 2002).Google Scholar
  2. Duffy, M.J., McGowan, P.M. & Gallagher, W.M. Cancer invasion and metastasis: changing views. J Pathol. 214, 283–293. (2008).PubMedCrossRefGoogle Scholar
  3. Fok, S., et al. Planar microfluidic chamber for generation of stable and steep chemoattractant gradients. Biophysical Journal 95, 1523–1530. (2008).PubMedCrossRefGoogle Scholar
  4. Grunwald, D., et al. Probing intranuclear environments at the single-molecule level. Biophys J. 94, 2847–2858. Epub 2007 Dec 2847. (2008).Google Scholar
  5. Hadjidemetriou, H., Gabrielli, B., Mele, K. & Vallotton, P. Detection and tracking of cell divisions in phase contrast video microscopy, in MICCAI (New York, 2008).Google Scholar
  6. Kaitna, R. Experimental study on rheologic behaviour of debris flow material. Acta Geotechnica 2, 71–85. (2007).CrossRefGoogle Scholar
  7. Li, K., Wu, X., Chen, D.Z. & Sonka, M. Optimal surface segmentation in volumetric images–a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell. 28, 119–134. (2006).PubMedCrossRefGoogle Scholar
  8. Li Jeon, N., et al. Neutrophil chemotaxis in linear and complex gradients of interleukin-8 formed in a microfabricated device. Nat Biotechnol. 20, 826–830. Epub 2002 Jul 2001. (2002).Google Scholar
  9. Lindeberg, T. Scale-Space Theory in Computer Vision (Kluwer Academic Publishers, 1994).Google Scholar
  10. Petri Seiler, K., Kuehn, H., Pat Happ, M., Decaprio, D. & Clemons, P.A. Using ChemBank to probe chemical biology. Curr Protoc Bioinformatics. Chapter, Unit 14–15. (2008).Google Scholar
  11. Sedgewick, R. Algorithms in C++ Part 5: Graph Algorithms (Addison-Wesley, Boston, 2002).Google Scholar
  12. Siebrasse, J.P., Grunwald, D. & Kubitscheck, U. Single-molecule tracking in eukaryotic cell nuclei. Anal Bioanal Chem. 387, 41–44. Epub 2006 Oct 2011. (2007).Google Scholar
  13. Sintorn, I.M., Homman-Loudiyi, M., Soderberg-Naucler, C. & Borgefors, G. A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images. Comput Methods Programs Biomed. 76, 95–102. (2004).PubMedCrossRefGoogle Scholar
  14. Small, J.V. & Resch, G.P. The comings and goings of actin: coupling protrusion and retraction in cell motility. Curr Opin Cell Biol. 17, 517–523. (2005).PubMedCrossRefGoogle Scholar
  15. Soille, P. Morphological Image Analysis (Springer, 2004).Google Scholar
  16. Soon, L.L. A discourse on cancer cell chemotaxis: where to from here? Iubmb Life 59, 60–67. (2007).PubMedCrossRefGoogle Scholar
  17. Soon, L., Mouneimne, G., Segall, J., Wyckoff, J. & Condeelis, J. Description and characterization of a chamber for viewing and quantifying cancer cell chemotaxis. Cell Motil Cytoskeleton. 62, 27–34. (2005).PubMedCrossRefGoogle Scholar
  18. Tchou-Wong, K.M., et al. Rapid chemokinetic movement and the invasive potential of lung cancer cells; a functional molecular study. BMC Cancer. 6, 151. (2006).Google Scholar
  19. Vallotton, P., Ponti, A., Waterman-Storer, C.M., Salmon, E.D. & Danuser, G. Recovery, visualization, and analysis of actin and tubulin polymer flow in live cells: a fluorescent speckle microscopy study. Biophys J. 85, 1289–1306. (2003).PubMedCrossRefGoogle Scholar
  20. Vallotton, P., Gupton, S.L., Waterman-Storer, C.M. & Danuser, G. Simultaneous mapping of filamentous actin flow and turnover in migrating cells by quantitative fluorescent speckle microscopy. Proc Natl Acad Sci U S A. 101, 9660–9665. Epub 2004 Jun 9621. (2004).Google Scholar
  21. Watanabe, N. & Mitchison, T.J. Single-molecule speckle analysis of actin filament turnover in lamellipodia. Science. 295, 1083–1086. (2002).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.CSIRO Mathematical and Information SciencesNorth RydeAustralia

Personalised recommendations