Cell-Based Assays for High-Throughput Screening pp 193-211

Part of the Methods in Molecular Biology book series (MIMB, volume 486)

Extracting Rich Information from Images



Now that automated image-acquisition instruments (high-throughput microscopes) are commercially available and becoming more widespread, hundreds of thousands of cellular images are routinely generated in a matter of days. Each cellular image generated in a high-throughput screening experiment contains a tremendous amount of information; in fact, the name high-content screening (HCS) refers to the high information content inherently present in cell images (J Biomol Screen 2:249–259, 1997). Historically, most of this information is ignored and the visual information present in images for a particular sample is often reduced to a single numerical output per well, usually by calculating the mean per-cell measurement for a particular feature. Here, we provide a detailed protocol for the use of open-source cell image analysis software, CellProfiler, to measure hundreds of features of each individual cell, including the size and shape of each compartment or organelle, and the intensity and texture of each type of staining in each subcompartment. We use as an example publicly available images from a cytoplasm-to-nucleus translocation assay.

Key words

Complex phenotypes Data visualization High-content screening High-throughput data analysis Microscopy Visual phenotypes 


  1. 1.
    Giuliano, K. A., DeBiasio, R. L., Dunlay, R. T., Gough, A., Volosky, J. M., Zock, J., et al (1997) High-content screening: a new approach to easing key bottlenecks in the drug discovery process. J. Biomol. Screen. 2, 249–259.CrossRefGoogle Scholar
  2. 2.
    Kiger, A., Baum, B., Jones, S., Jones, M. R., Coulson, A., Echeverri, C., et al (2003) A functional genomic analysis of cell morphology using RNA interference. J. Biol. 2, 27.PubMedCrossRefGoogle Scholar
  3. 3.
    Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100.PubMedCrossRefGoogle Scholar
  4. 4.
    Lamprecht, M. R., Sabatini, D. M., and Carpenter, A. E. (2007) CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 42, 71–75.PubMedCrossRefGoogle Scholar
  5. 5.
    Root, D. E., Kelley, B. P., and Stockwell, B. R. (2003) Detecting spatial patterns in biological array experiments. J. Biomol. Screen. 8, 393–398.PubMedCrossRefGoogle Scholar
  6. 6.
    Makarenkov, V., Kevorkov, D., Zentilli, P., Gagarin, A., Malo, N., and Nadon, R. (2006) HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data. Bioinformatics 22, 1408–1409.PubMedCrossRefGoogle Scholar
  7. 7.
    Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J., and Nadon, R. (2006) Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24, 167–175.PubMedCrossRefGoogle Scholar
  8. 8.
    Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., and Altschuler, S. J. (2004) Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198.PubMedCrossRefGoogle Scholar
  9. 9.
    Kuiper, N. H. (1962) Tests concerning random points on a circle. Proc. K. Ned. Akad. Wet. Ser. A 63, 38–47.Google Scholar
  10. 10.
    Kullback, S. and Leibler, R. A. (1951) On information and sufficiency. Ann. Math. Stat. 22, 79–86.CrossRefGoogle Scholar
  11. 11.
    Levsky, J. M. and Singer, R. H. (2003) Gene expression and the myth of the average cell. Trends Cell Biol. 13, 4–6.PubMedCrossRefGoogle Scholar
  12. 12.
    Muller, H., Michoux, N., Bandon, D., and Geissbuhler, A. (2004) A review of content-based image retrieval systems in medical applications – clinical benefits and future directions. Int. J. Med. Inform. 73, 1–23.PubMedCrossRefGoogle Scholar
  13. 13.
    Tieu, K. and Viola, P. (2004) Boosting image retrieval. Int. J. Comput. Vis. 56, 17–36.CrossRefGoogle Scholar
  14. 14.
    Jones, T. R., Carpenter, A. E., Sabatini, D. M., and Golland, P. (2006) Methods for high-content, high-throughput image-based cell screening, in Proceedings of the Workshop on Microscopic Image Analysis with Applications in Biology (MIAAB) (Metaxas, D. N., Rittcher, J., and Sebastian, T., eds.) Copenhagen, Denmark, pp. 65–72.Google Scholar
  15. 15.
    Jones, T. R., Carpenter, A. E., and Golland, P. (2005) Voronoi-based segmentation of cells on image manifolds, in Proceedings of the ICCV Workshop on Computer Vision for Biomedical Image Applications (CVBIA) (Liu, T. J., Zhang, C., eds.) Springer, Berlin, Germany, pp. 535–543.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Imaging Platform, Broad Institute of Harvard and MITCambridgeUSA

Personalised recommendations