Advertisement

Automated, Systematic Determination of Protein Subcellular Location using Fluorescence Microscopy

  • Elvira García Osuna
  • Robert F. Murphy
Part of the Subcellular Biochemistry book series (SCBI, volume 43)

Keywords

Green Fluorescent Protein Subcellular Location Fluorescence Microscope Image Green Fluorescent Protein Fusion Protein Subcellular Location 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boland, M.V., Markey, M.K. and Murphy, R.F. (1997) Classification of protein localization patterns obtained via fluorescence light microscopy. 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.Google Scholar
  2. Boland, M.V., Markey, M.K. and Murphy, R.F. (1998) Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 33, 366–375.PubMedCrossRefGoogle Scholar
  3. Boland, M.V. and Murphy, R.F. (2001) A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells. Bioinformatics 17, 1213–1223.PubMedCrossRefGoogle Scholar
  4. Brunet, S., Thibault, P., Gagnon, E., Kearney, P., Bergeron, J.J. and Desjardins, M. (2003) Organelle proteomics: looking at less to see more. Trends Cell Biol. 13, 629–638.PubMedCrossRefGoogle Scholar
  5. Chen X. and Murphy, R.F. (2005) Objective clustering of proteins based on subcellular location patterns. J. Biomed. Biotechnol. 2005, 87–95.PubMedCrossRefGoogle Scholar
  6. Chen, X., Velliste, M., Weinstein, S., Jarvik, J.W. and Murphy, R.F. (2003) Location proteomics – building subcellular location trees from high resolution 3d fluorescence microscope images of randomly-tagged proteins. Proc. SPIE 4962, 298–306.CrossRefGoogle Scholar
  7. Chou, K.C. and Cai, Y.D. (2003) Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition. J. Cell. Biochem. 90, 1250–1260.PubMedCrossRefGoogle Scholar
  8. Conrad, C., Erfle, H., Warnat, P., Daigle, N., Lorch, T., Ellenberg, J., Pepperkok, R. and Eils, R. (2004) Automatic identification of subcellular phenotypes on human cell arrays. Genome Res. 14, 1130–1136.PubMedCrossRefGoogle Scholar
  9. Danckaert, A., Gonzalez-Couto, E., Bollondi, L., Thompson, N. and Hayes, B. (2002) Automated recognition of intracellular organelles in confocal microscope images. Traffic 3, 66–73.PubMedCrossRefGoogle Scholar
  10. Danuser, G. and Waterman-Storer, C.M. (2006) Quantitative fluorescent speckle microscopy of cytoskeleton dynamics. Annu. Rev. Biophys. Biomol. Struct. 35, 361–387.PubMedCrossRefGoogle Scholar
  11. Ghaemmaghami, S., Huh, W.K., Bower, K., Howson, R.W., Belle, A., Dephoure, N., O’Shea, E.K. and Weissman, J.S. (2003) Global analysis of protein expression in yeast. Nature 425, 737–741.PubMedCrossRefGoogle Scholar
  12. Guda, C., Fahy, E. and Subramaniam, S. (2004) Mitopred: A genome-scale method for prediction of nucleus-encoded mitochondrial proteins. Bioinformatics 20, 1785–1794.PubMedCrossRefGoogle Scholar
  13. Hu, Y., Carmona, J. and Murphy, R.F. (2006) Application of temporal texture features to automated analysis of protein subcellular locations in time series fluorescence microscope images. Proc 2006 IEEE Intl Symp Biomedl Imag 1028–1031.Google Scholar
  14. Huang, K., Lin, J., Gajnak, J.A. and Murphy, R.F. (2002) Image content-based retrieval and automated interpretation of fluorescence microscope images via the protein subcellular location image database. Proc 2002 IEEE Intl Symp Biomedl Imag 325–328.Google Scholar
  15. Huang, K. and Murphy, R.F. (2004a) Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 5, 78.PubMedCrossRefGoogle Scholar
  16. Huang, K. and Murphy, R.F. (2004b) From quantitative microscopy to automated image understanding. J. Biomed. Opt. 9, 893–912.PubMedCrossRefGoogle Scholar
  17. Huh, W.K., Falvo, J.V., Gerke, L.C., Caroll, A.S., Howson, R.W., Weissman, J.S. and O’Shea, E.K. (2003) Global analysis of the protein localization in budding yeast. Nature 425, 686–691.PubMedCrossRefGoogle Scholar
  18. Jarvik, J.W., Adler, S.A., Telmer, C.A., Subramaniam, V. and Lopez, A.J. (1996) Cd-tagging: A new approach to gene and protein discovery and analysis. Bio Techniques 20, 896–904.Google Scholar
  19. Jarvik, J.W., Fisher, G.W., Shi, C., Hennen, L., Hauser, C., Adler, S. and Berget, P.B. (2002) In vivo functional proteomics: Mammalian genome annotation using cd-tagging. BioTechniques 33, 852–867.PubMedGoogle Scholar
  20. King, E.B., Kromhout, L.K., Chew, K.L., Mayall, B.H., Petrakis, N.L., Jensen, R.H. and Young, I.T. (1984) Analytic studies of foam cells from breast cancer precursors. Cytometry 5, 124–130.PubMedCrossRefGoogle Scholar
  21. Lu, Z., Szafron, D., Greiner, R., Lu, P., Wishart, D.S., Poulin, B., Anvik, J., Macdonell, C. and Eisner, R. (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20, 547–556.PubMedCrossRefGoogle Scholar
  22. Machacek, M. and Danuser, G. (2006) Morphodynamic profiling of protrusion phenotypes. Biophys. J. 90, 1439–1452.PubMedCrossRefGoogle Scholar
  23. Murphy, R.F. (2004) Automated interpretation of subcellular location patterns. 2004 IEEE International Symposium on Biomedical Imaging (ISBI-2004),Google Scholar
  24. Murphy, R.F., Velliste, M. and Porreca, G. (2003) Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. J. VLSI Sig. Proc. 35, 311–321.CrossRefGoogle Scholar
  25. Nakai, K. (2000) Protein sorting signals and prediction of subcellular localization. Adv. Protein Chem. 54, 277–344.PubMedCrossRefGoogle Scholar
  26. Park, K.J. and Kanehisa, M. (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 19, 1656–1663.PubMedCrossRefGoogle Scholar
  27. Sigal, A., Milo, R., Cohen, A., Geva-Zatorsky, N., Klein, Y., Alaluf, I., Swerdlin, N., Perzov, N., Danon, T., Liron, Y., Raveh, T., Carpenter, A.E., Lahav, G. and Alon, U. (2006) Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nat. Methods 3, 525–531.PubMedCrossRefGoogle Scholar
  28. Simpson, J.C., Wellenreuther, R., Poustka, A., Pepperkok, R. and Wiemann, S. (2000) Systematic subcellular localization of novel proteins identified by large-scale cdna sequencing. EMBO Rep. 1, 287–292.PubMedCrossRefGoogle Scholar
  29. Uhlen, M., Bjorling, E., Agaton, C., Szigyarto, C.A.-K., Amini, B., Andersen, E., Andersson, A.-C., Angelidou, P., Asplund, A., Cerjan, D., Ekstrom, M., Elobeid, A. and Eriksson, C. (2005) A human protein atlas for normal and cancer tissues based on antibody proteomics. Am. Soc. Biochem. Mol. Biol. 4, 1920–1932.Google Scholar
  30. Young, I.T., Verbeek, P.W. and Mayall, B.H. (1986) Characterization of chromatin distribution in cell nuclei. Cytometry 7, 467–474.PubMedCrossRefGoogle Scholar
  31. Zhao, T., Velliste, M., Boland, M.V. and Murphy, R.F. (2005) Object type recognition for automated analysis of protein subcellular location. IEEE Trans. Image Process. 14, 1351–1359.PubMedCrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Elvira García Osuna
    • 1
  • Robert F. Murphy
    • 1
  1. 1.Carnegie Mellon University Pittsburgh

Personalised recommendations