Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images

  • Robert F. Murphy
  • Meel Velliste
  • Gregory Porreca


The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.

protein localization subcellular location features fluorescence microscopy pattern recognition location proteomics 


  1. 1.
    M.V. Boland, M.K. Markey, and R.F. Murphy, “Classification of Protein Localization Patterns Obtained via Fluorescence Light Microscopy,” in 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 1997, pp. 594–597.Google Scholar
  2. 2.
    M.V. Boland, M.K. Markey, and R.F. Murphy, “Automated Recognition of Patterns Characteristic of Subcellular Structures in Fluorescence Microscopy Images,” Cytometry, vol. 33, 1998, pp. 366–375.CrossRefGoogle Scholar
  3. 3.
    R.F. Murphy, M.V. Boland, and M. Velliste, “Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images,” in Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology,San Diego, 2000, pp. 251–259.Google Scholar
  4. 4.
    M.V. Boland and R.F. Murphy, “A Neural Network Classifier Capable of Recognizing the Patterns of All Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells,” Bioinformatics, vol. 17, 2001, pp. 1213–1223.CrossRefGoogle Scholar
  5. 5.
    J.W. Jarvik, S.A. Adler, C.A. Telmer, V. Subramaniam, and A.J. Lopez, “CD-Tagging: A New Approach to Gene and Protein Discovery and Analysis,” Biotechniques, vol. 20, 1996, pp. 896–904.Google Scholar
  6. 6.
    C.A. Telmer, P.B. Berget, B. Ballou, R.F. Murphy, and J.W. Jarvik, “Epitope Tagging Genomic DNA Using a CD-Tagging Tn10 Minitransposon,” Biotechniques, vol. 32, 2002, pp. 422–430.Google Scholar
  7. 7.
    J.W. Jarvik, G.W. Fisher, C. Shi, L. Hennen, C. Hauser, S. Adler, and P.B. Berget, “In Vivo Functional Proteomics: Mammalian Genome Annotation Using CD-Tagging,” BioTechniques, vol. 33, 2002, pp. 852–867.Google Scholar
  8. 8.
    M.M. Rolls, P.A. Stein, S.S. Taylor, E. Ha, F. McKeon, and T.A. Rapoport, “A Visual Screen of a GFP-Fusion Library Identifies a New Type of Nuclear Envelope Membrane Protein,” J. Cell Biol., vol. 146, 1999, pp. 29–44.CrossRefGoogle Scholar
  9. 9.
    A. Kumar, K.-H. Cheung, P. Ross-Macdonald, P.S.R. Coelho, P. Miller, and M. Snyder, “TRIPLES:ADatabase of Gene Function in Saccharomyces Cerevisiae,” Nucleic Acids Research, vol. 28, 2000, pp. 81–84.CrossRefGoogle Scholar
  10. 10.
    G. Habeler, K. Natter, G.G. Thallinger, M.E. Crawford, S.D. Kohlwein, and Z. Trajanoski, “YPL.db: The Yeast Protein Localization Database,” Nucleic Acids Research, vol. 30, 2002, pp. 80–83.CrossRefGoogle Scholar
  11. 11.
    R.I. Jennrich, “Stepwise Discriminant Analysis,” in Statistical Methods for Digital Computers, K. Enslein, A. Ralston, and H.S. Wilf (Eds.), John Wiley & Sons: New York, 1977, pp. 77–95.Google Scholar
  12. 12.
    A. Danckaert, E. Gonzalez-Couto, L. Bollondi, N. Thompson, and B. Hayes, “Automated Recognition of Intracellular Organelles in Confocal Microscope Images,” Traffic, vol. 3, 2002, pp. 66–73.CrossRefGoogle Scholar
  13. 13.
    R.F. Murphy, M. Velliste, J. Yao, and G. Porreca, “Searching Online Journals for Fluorescence Microscope Images Depicting Protein Subcellular Locations,” in Proceedings of the 2nd IEEE International Symposium on Bio-Informatics and Biomedical Engineering (BIBE 2001), Bethesda, MD, USA, 2001, pp. 119–128.Google Scholar
  14. 14.
    M.K. Markey, M.V. Boland, and R.F. Murphy, “Towards Objective Selection of Representative Microscope Images,” Biophys. J., vol. 76, 1999, pp. 2230–2237.CrossRefGoogle Scholar
  15. 15.
    E.J.S. Roques and R.F. Murphy, “Objective Evaluation of Differences in Protein Subcellular Distribution,” Traffic, vol. 3, 2002, pp. 61–65.CrossRefGoogle Scholar
  16. 16.
    M. Velliste and R.F. Murphy, “Automated Determination of Protein Subcellular Locations from 3D Fluorescence Microscope Images,” in Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging (ISBI-2002), Bethesda, MD, USA, 2002, pp. 867–870.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Robert F. Murphy
    • 1
  • Meel Velliste
    • 1
  • Gregory Porreca
    • 1
  1. 1.Departments of Biological Sciences and Biomedical EngineeringCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations