Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Image-based multivariate profiling of drug responses from single cells


Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10–15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Representing multivariate changes in cellular phenotypes by human-interpretable vectors.
Figure 2: Drugs effects can be detected by a small number of features.
Figure 3: Titration clustering reveals multiphasic drug effects.
Figure 4: Common phenotypic changes are shared by different compounds from the same category.
Figure 5: The category of a new d-profile can be inferred from its closest characterized d-profiles.

Similar content being viewed by others


  1. Boland, M.V. & Murphy, R.F. After sequencing: quantitative analysis of protein localization. IEEE Eng. Med. Biol. Mag. 18, 115–119 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Lang, P., Yeow, K., Nichols, A. & Scheer, A. Cellular imaging in drug discovery. Nat. Rev. Drug Discov. 5, 343–356 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Price, J.H. et al. Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools. J. Cell. Biochem. Suppl. 39, 194–210 (2002).

    Article  PubMed  Google Scholar 

  4. Zhou, X. & Wong, S.T.C. Informatics challenges of high-throughput microscopy. IEEE Signal Process. Mag. 23, 63–72 (2006).

    Article  Google Scholar 

  5. Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Tanaka, M. et al. An unbiased cell morphology-based screen for new, biologically active small molecules. PLoS Biol. 3, e128 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Boland, M.V. & Murphy, R.F. 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 (2001).

    Article  CAS  PubMed  Google Scholar 

  8. Conrad, C. et al. Automatic identification of subcellular phenotypes on human cell arrays. Genome Res. 14, 1130–1136 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Neumann, B. et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3, 385–390 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Ohya, Y. et al. High-dimensional and large-scale phenotyping of yeast mutants. Proc. Natl. Acad. Sci. USA 102, 19015–19020 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gasparri, F., Mariani, M., Sola, F. & Galvani, A. Quantification of the proliferation index of human dermal fibroblast cultures with the ArrayScan high-content screening reader. J. Biomol. Screen. 9, 232–243 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Giuliano, K.A. et al. Systems cell biology knowledge created from high content screening. Assay Drug Dev. Technol. 3, 501–514 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. Vapnik, V.N. Statistical Learning Theory. (John Wiley & Sons, New York, 1998).

    Google Scholar 

  14. Duda, R.O., Hart, P.E. & Stork, D.G. Pattern Classification 2nd edn. (John Wiley & Sons, New York, 2001).

    Google Scholar 

  15. Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).

    Article  Google Scholar 

  16. Haralick, R.M. Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979).

    Article  Google Scholar 

  17. Teh, C.H. & Chin, R.T. On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Mach. Intell. 10, 496–513 (1988).

    Article  Google Scholar 

  18. Lundholt, B.K., Scudder, K.M. & Pagliaro, L. A simple technique for reducing edge effect in cell-based Assays. J. Biomol. Screen. 8, 566–570 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Malo, N., Hanley, J.A., Cerquozzi, S., Pelletier, J. & Nadon, R. Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24, 167–175 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (Springer-Verlag, New York, 2001).

    Book  Google Scholar 

  21. Yeung, T.K., Germond, C., Chen, X. & Wang, Z. The mode of action of taxol: apoptosis at low concentration and necrosis at high concentration. Biochem. Biophys. Res. Commun. 263, 398–404 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Yoo, C.B. & Jones, P.A. Epigenetic therapy of cancer: past, present and future. Nat. Rev. Drug Discov. 5, 37–50 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Perlman, Z.E., Mitchison, T.J. & Mayer, T.U. High-content screening and profiling of drug activity in an automated centrosome-duplication assay. ChemBioChem 6, 145–151 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Bollag, D.M. et al. Epothilones, a new class of microtubule-stabilizing agents with a taxol-like mechanism of action. Cancer Res. 55, 2325–2333 (1995).

    CAS  PubMed  Google Scholar 

  25. Panda, D., Rathinasamy, K., Santra, M.K. & Wilson, L. Kinetic suppression of microtubule dynamic instability by griseofulvin: implications for its possible use in the treatment of cancer. Proc. Natl. Acad. Sci. USA 102, 9878–9883 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Clemons, P.A. Complex phenotypic assays in high-throughput screening. Curr. Opin. Chem. Biol. 8, 334–338 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Dove, A. Drug screening-beyond the bottleneck. Nat. Biotechnol. 17, 859–863 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Maciag, K. et al. Systems-level analyses identify extensive coupling among gene expression machines. Mol. Syst. Biol. [online] 2, 0003 (2006) (doi:10.1038/msb4100045).

    Article  CAS  Google Scholar 

  29. Hanley, J.A. & McNeil, B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982).

    Article  CAS  PubMed  Google Scholar 

Download references


We thank S. Johnson from the Texas Advanced Computing Center for high-performance computing technical assistance, and all members of the Altschuler and Wu lab at the UT Southwestern Medical Center for stimulating discussions. This research was supported by the Endowed Scholars program at UT Southwestern Medical Center and the Welch Foundation (I-1619, I-1644).

Author information

Authors and Affiliations



L.-H.L. designed, implemented and performed the profiling methods. All authors contributed to the conception of the overall approach, statistical analysis of the methods and writing of the manuscript.

Note: Supplementary information is available on the Nature Methods website.

Corresponding author

Correspondence to Steven J Altschuler.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Distributions of the number of selected features. (PDF 67 kb)

Supplementary Fig. 2

Common phenotypic changes for the DNA-SC35-anillin, DNA-p53-cFos, and DNA-MT-actin marker sets. (PDF 1442 kb)

Supplementary Fig. 3

Some of the common phenotypic changes are cell-cycle independent. (PDF 161 kb)

Supplementary Data (PDF 3334 kb)

Supplementary Methods (PDF 86 kb)

Supplementary Note (PDF 342 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Loo, LH., Wu, L. & Altschuler, S. Image-based multivariate profiling of drug responses from single cells. Nat Methods 4, 445–453 (2007).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing