Image-based chemical screening

Abstract

Technological advances have made it feasible to conduct high-throughput small-molecule screens based on visual phenotypes of individual cells, using automated imaging and analysis. These screens are rapidly moving from being small, proof-of-principle tests to robust and widespread screens of hundreds of thousands of compounds. Automated imaging screens maximize the information obtained in an initial screen and improve the ability to select high-quality leads. In this Perspective, I highlight the key steps necessary for conducting a high-throughput image-based chemical compound screen.

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Figure 1
Figure 2: A sampling of image-based phenotypes scored in recent screens.
Figure 3: An example of an unusual, subtle phenotype recently screened in our group and scored automatically using CellProfiler for image analysis and CellVisualizer for machine learning–based automated scoring (raw screening images are shown).

References

  1. 1

    Taylor, D.L. & Giuliano, K.A. Multiplexed high content screening assays create a systems cell biology approach to drug discovery. Drug Discov. Today Technol. 2, 149–154 (2005).

    CAS  Article  Google Scholar 

  2. 2

    Lahav, G. et al. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat. Genet. 36, 147–150 (2004).

    CAS  Article  Google Scholar 

  3. 3

    Levsky, J.M. & Singer, R.H. Gene expression and the myth of the average cell. Trends Cell Biol. 13, 4–6 (2003).

    CAS  Article  Google Scholar 

  4. 4

    Giuliano, K.A. et al. High-content screening: a new approach to easing key bottlenecks in the drug discovery process. J. Biomol. Screen. 2, 249–259 (1997).

    CAS  Article  Google Scholar 

  5. 5

    Wilson, C.J. et al. Identification of a small molecule that induces mitotic arrest using a simplified high-content screening assay and data analysis method. J. Biomol. Screen. 11, 21–28 (2006).

    CAS  Article  Google Scholar 

  6. 6

    Li, Z. et al. Identification of gap junction blockers using automated fluorescence microscopy imaging. J. Biomol. Screen. 8, 489–499 (2003).

    CAS  Article  Google Scholar 

  7. 7

    Granas, C. et al. Identification of RAS-mitogen-activated protein kinase signaling pathway modulators in an ERF1 redistribution screen. J. Biomol. Screen. 11, 423–434 (2006).

    Article  Google Scholar 

  8. 8

    Gururaja, T.L. et al. R-253 disrupts microtubule networks in multiple tumor cell lines. Clin. Cancer Res. 12, 3831–3842 (2006).

    CAS  Article  Google Scholar 

  9. 9

    Richards, G.R. et al. A morphology- and kinetics-based cascade for human neural cell high content screening. Assay Drug Dev. Technol. 4, 143–152 (2006).

    CAS  Article  Google Scholar 

  10. 10

    Hoffman, A.F. & Garippa, R.J. A pharmaceutical company user's perspective on the potential of high content screening in drug discovery. Methods Mol. Biol. 356, 19–31 (2007).

    CAS  Article  Google Scholar 

  11. 11

    Carpenter, A.E. & Sabatini, D.M. Systematic genome-wide screens of gene function. Nat. Rev. Genet. 5, 11–22 (2004).

    CAS  Article  Google Scholar 

  12. 12

    Baum, B. & Craig, G. RNAi in a postmodern, postgenomic era. Oncogene 23, 8336–8339 (2004).

    CAS  Article  Google Scholar 

  13. 13

    Moffat, J. & Sabatini, D.M. Building mammalian signalling pathways with RNAi screens. Nat. Rev. Mol. Cell Biol. 7, 177–187 (2006).

    CAS  Article  Google Scholar 

  14. 14

    Zon, L.I. & Peterson, R.T. In vivo drug discovery in the zebrafish. Nat. Rev. Drug Discov. 4, 35–44 (2005).

    CAS  Article  Google Scholar 

  15. 15

    O'Rourke, E.J., Conery, A.L. & Moy, T.I. Whole animal high-throughput screens: the C. elegans model. Methods Mol. Biol. (in the press).

  16. 16

    Avila, E.L. et al. Tools to study plant organelle biogenesis. Point mutation lines with disrupted vacuoles and high-speed confocal screening of green fluorescent protein-tagged organelles. Plant Physiol. 133, 1673–1676 (2003).

    CAS  Article  Google Scholar 

  17. 17

    Bailey, S.N., Sabatini, D.M. & Stockwell, B.R. Microarrays of small molecules embedded in biodegradable polymers for use in mammalian cell-based screens. Proc. Natl. Acad. Sci. USA 101, 16144–16149 (2004).

    CAS  Article  Google Scholar 

  18. 18

    Inglese, J. et al. High-throughput screening assays for the identification of chemical probes. Nat. Chem. Biol. 3, 466–479 (2007).

    CAS  Article  Google Scholar 

  19. 19

    Smith, C. & Eisenstein, M. Automated imaging: data as far as the eye can see. Nat. Methods 2, 547–555 (2005).

    CAS  Article  Google Scholar 

  20. 20

    Gough, A.H. & Johnston, P.A. Requirements, features, and performance of high content screening platforms. Methods Mol. Biol. 356, 41–61 (2007).

    PubMed  Google Scholar 

  21. 21

    Lee, S. & Howell, B.J. High-content screening: emerging hardware and software technologies. Methods Enzymol. 414, 468–483 (2006).

    CAS  Article  Google Scholar 

  22. 22

    Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7, 690–696 (2006).

    CAS  Article  Google Scholar 

  23. 23

    Paran, Y. et al. High-throughput screening of cellular features using high-resolution light-microscopy; application for profiling drug effects on cell adhesion. J. Struct. Biol. 158, 233–243 (2007).

    CAS  Article  Google Scholar 

  24. 24

    Liebel, U. et al. A microscope-based screening platform for large-scale functional protein analysis in intact cells. FEBS Lett. 554, 394–398 (2003).

    CAS  Article  Google Scholar 

  25. 25

    Wheeler, D.B. et al. RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells. Nat. Methods 1, 127–132 (2004).

    CAS  Article  Google Scholar 

  26. 26

    Lundholt, B.K., Heydorn, A., Bjorn, S.P. & Praestegaard, M. A simple cell-based HTS assay system to screen for inhibitors of p53-Hdm2 protein-protein interactions. Assay Drug Dev. Technol. 4, 679–688 (2006).

    CAS  Article  Google Scholar 

  27. 27

    Vogt, A. et al. Cell-active dual specificity phosphatase inhibitors identified by high-content screening. Chem. Biol. 10, 733–742 (2003).

    CAS  Article  Google Scholar 

  28. 28

    Baatz, M., Arini, N., Schape, A., Binnig, G. & Linssen, B. Object-oriented image analysis for high content screening: detailed quantification of cells and sub cellular structures with the Cellenger software. Cytometry A 69, 652–658 (2006).

    Article  Google Scholar 

  29. 29

    Prigozhina, N.L. et al. Plasma membrane assays and three-compartment image cytometry for high content screening. Assay Drug Dev. Technol. 5, 29–48 (2007).

    CAS  Article  Google Scholar 

  30. 30

    Pipalia, N.H., Huang, A.Y., Ralph, H., Rujoi, M. & Maxfield, F.R. Automated microscopy screening for compounds that partially revert cholesterol accumulation in Niemann-pick C cells. J. Lipid Res. 47, 284–301 (2006).

    CAS  Article  Google Scholar 

  31. 31

    Eggert, U.S. & Mitchison, T.J. Small molecule screening by imaging. Curr. Opin. Chem. Biol. 10, 232–237 (2006).

    CAS  Article  Google Scholar 

  32. 32

    Eggert, U.S. et al. Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol. 2, e379 (2004).

    Article  Google Scholar 

  33. 33

    Yarrow, J.C., Totsukawa, G., Charras, G.T. & Mitchison, T.J. Screening for cell migration inhibitors via automated microscopy reveals a Rho-kinase inhibitor. Chem. Biol. 12, 385–395 (2005).

    CAS  Article  Google Scholar 

  34. 34

    Kau, T.R. et al. A chemical genetic screen identifies inhibitors of regulated nuclear export of a Forkhead transcription factor in PTEN-deficient tumor cells. Cancer Cell 4, 463–476 (2003).

    CAS  Article  Google Scholar 

  35. 35

    Pelish, H.E. et al. Secramine inhibits Cdc42-dependent functions in cells and Cdc42 activation in vitro. Nat. Chem. Biol. 2, 39–46 (2006).

    CAS  Article  Google Scholar 

  36. 36

    Corcoran, L.J., Mitchison, T.J. & Liu, Q. A novel action of histone deacetylase inhibitors in a protein aggresome disease model. Curr. Biol. 14, 488–492 (2004).

    CAS  Article  Google Scholar 

  37. 37

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

    CAS  Article  Google Scholar 

  38. 38

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

    Article  Google Scholar 

  39. 39

    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).

    CAS  Article  Google Scholar 

  40. 40

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

    CAS  Article  Google Scholar 

  41. 41

    Abramoff, M.D., Magalhaes, P.J. & Ram, S.J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).

    Google Scholar 

  42. 42

    Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  Google Scholar 

  43. 43

    Makarenkov, V. et al. HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data. Bioinformatics 22, 1408–1409 (2006).

    CAS  Article  Google Scholar 

  44. 44

    Boutros, M., Bras, L.P. & Huber, W. Analysis of cell-based RNAi screens. Genome Biol. 7, R66 (2006).

    Article  Google Scholar 

  45. 45

    Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006).

    CAS  Article  Google Scholar 

  46. 46

    Carpenter, A.E. Data analysis: extracting rich information from images. Methods Mol. Biol. (in the press).

  47. 47

    Perrimon, N., Friedman, A., Mathey-Prevot, B. & Eggert, U.S. Drug-target identification in Drosophila cells: combining high-throughout RNAi and small-molecule screens. Drug Discov. Today 12, 28–33 (2007).

    CAS  Article  Google Scholar 

  48. 48

    MacKeigan, J.P., Murphy, L.O. & Blenis, J. Sensitized RNAi screen of human kinases and phosphatases identifies new regulators of apoptosis and chemoresistance. Nat. Cell Biol. 7, 591–600 (2005).

    CAS  Article  Google Scholar 

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Acknowledgements

The author sincerely thanks M. Vokes for research and artwork, and N. Tolliday and L. Verplank for helpful comments.

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Carpenter, A. Image-based chemical screening. Nat Chem Biol 3, 461–465 (2007). https://doi.org/10.1038/nchembio.2007.15

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