Skip to main content

Thank you for visiting nature.com. 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.

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines

Abstract

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them—the ORACL—best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Overview of method.
Figure 2: Identifying an ORACL that distinguishes among drug classes.
Figure 3: Compound hits across multiple drug classes are identified from a single-pass screen.
Figure 4: Secondary studies validate predictions across diverse drug classes.
Figure 5: The ORACL can identify novel compound groupings.

References

  1. 1

    van 't Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    CAS  Google Scholar 

  2. 2

    Thomas, R.K. et al. High-throughput oncogene mutation profiling in human cancer. Nat. Genet. 39, 347–351 (2007).

    CAS  PubMed  Google Scholar 

  3. 3

    Kolch, W. & Pitt, A. Functional proteomics to dissect tyrosine kinase signalling pathways in cancer. Nat. Rev. Cancer 10, 618–629 (2010).

    CAS  PubMed  Google Scholar 

  4. 4

    Griffin, J.L. & Shockcor, J.P. Metabolic profiles of cancer cells. Nat. Rev. Cancer 4, 551–561 (2004).

    CAS  PubMed  Google Scholar 

  5. 5

    Zhang, J., Yang, P.L. & Gray, N.S. Targeting cancer with small molecule kinase inhibitors. Nat. Rev. Cancer 9, 28–39 (2009).

    PubMed  Google Scholar 

  6. 6

    Kelloff, G.J. & Sigman, C.C. Cancer biomarkers: selecting the right drug for the right patient. Nat. Rev. Drug Discov. 11, 201–214 (2012).

    CAS  PubMed  Google Scholar 

  7. 7

    Sundberg, S.A. High-throughput and ultra-high-throughput screening: solution- and cell-based approaches. Curr. Opin. Biotechnol. 11, 47–53 (2000).

    CAS  PubMed  Google Scholar 

  8. 8

    Mayr, L.M. & Bojanic, D. Novel trends in high-throughput screening. Curr. Opin. Pharmacol. 9, 580–588 (2009).

    CAS  PubMed  Google Scholar 

  9. 9

    Koehn, F.E. High impact technologies for natural products screening. Prog. Drug Res. 65 175, 177–210 (2008).

    Google Scholar 

  10. 10

    Lachance, H., Wetzel, S., Kumar, K. & Waldmann, H. Charting, navigating, and populating natural product chemical space for drug discovery. J. Med. Chem. 55, 5989–6001 (2012).

    CAS  PubMed  Google Scholar 

  11. 11

    Nielsen, T.E. & Schreiber, S.L. Towards the optimal screening collection: a synthesis strategy. Angew. Chem. Int. Ed. Engl. 47, 48–56 (2008).

    CAS  PubMed  Google Scholar 

  12. 12

    O' Connor, C.J., Beckmann, H.S. & Spring, D.R. Diversity-oriented synthesis: producing chemical tools for dissecting biology. Chem. Soc. Rev. 41, 4444–4456 (2012).

    CAS  PubMed  Google Scholar 

  13. 13

    Caie, P.D. et al. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol. Cancer Ther. 9, 1913–1926 (2010).

    CAS  PubMed  Google Scholar 

  14. 14

    Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580–594 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discov. 9, 273–276 (2010).

    CAS  PubMed  Google Scholar 

  16. 16

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

    CAS  PubMed  Google Scholar 

  17. 17

    Chen, B. et al. Small molecule-mediated disruption of Wnt-dependent signaling in tissue regeneration and cancer. Nat. Chem. Biol. 5, 100–107 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    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  PubMed  Google Scholar 

  19. 19

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

    CAS  PubMed  Google Scholar 

  20. 20

    Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  Google Scholar 

  21. 21

    Potts, M.B. et al. Using functional signature ontology (FUSION) to identify mechanisms of action for natural products. Sci. Signal. 6, ra90 (2013).

    PubMed  PubMed Central  Google Scholar 

  22. 22

    Young, D.W. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 59–68 (2008).

    CAS  PubMed  Google Scholar 

  23. 23

    MacDonald, M.L. et al. Identifying off-target effects and hidden phenotypes of drugs in human cells. Nat. Chem. Biol. 2, 329–337 (2006).

    CAS  PubMed  Google Scholar 

  24. 24

    Houle, D., Govindaraju, D.R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855–866 (2010).

    CAS  PubMed  Google Scholar 

  25. 25

    Futamura, Y. et al. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem. Biol. 19, 1620–1630 (2012).

    CAS  PubMed  Google Scholar 

  26. 26

    King, K.R. et al. A high-throughput microfluidic real-time gene expression living cell array. Lab Chip 7, 77–85 (2007).

    CAS  PubMed  Google Scholar 

  27. 27

    Stegmaier, K. et al. Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat. Genet. 36, 257–263 (2004).

    CAS  PubMed  Google Scholar 

  28. 28

    Kawatani, M. et al. Identification of a small-molecule inhibitor of DNA topoisomerase II by proteomic profiling. Chem. Biol. 18, 743–751 (2011).

    CAS  PubMed  Google Scholar 

  29. 29

    Muroi, M. et al. Application of proteomic profiling based on 2D-DIGE for classification of compounds according to the mechanism of action. Chem. Biol. 17, 460–470 (2010).

    CAS  PubMed  Google Scholar 

  30. 30

    Bantscheff, M. et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25, 1035–1044 (2007).

    CAS  PubMed  Google Scholar 

  31. 31

    Feng, Y., Mitchison, T.J., Bender, A., Young, D.W. & Tallarico, J.A. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat. Rev. Drug Discov. 8, 567–578 (2009).

    CAS  PubMed  Google Scholar 

  32. 32

    Roti, G. & Stegmaier, K. Genetic and proteomic approaches to identify cancer drug targets. Br. J. Cancer 106, 254–261 (2012).

    CAS  PubMed  Google Scholar 

  33. 33

    Fuchs, F. et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol. Syst. Biol. 6, 370 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. 34

    Bakal, C., Aach, J., Church, G. & Perrimon, N. Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316, 1753–1756 (2007).

    CAS  PubMed  Google Scholar 

  35. 35

    Taylor, D.L. Past, present, and future of high content screening and the field of cellomics. Methods Mol. Biol. 356, 3–18 (2007).

    CAS  PubMed  Google Scholar 

  36. 36

    Gustafsdottir, S.M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 8, e80999 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. 37

    Wawer, M.J. et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc. Natl. Acad. Sci. USA 111, 10911–10916 (2014).

    CAS  PubMed  Google Scholar 

  38. 38

    Cohen, A.A. et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008).

    CAS  PubMed  Google Scholar 

  39. 39

    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  PubMed  Google Scholar 

  40. 40

    Johnson, R.A. & Wichern, D.W. Applied Multivariate Statistical Analysis. 3rd edn. (Prentice Hall, Englewood Cliffs, N.J., 1992).

  41. 41

    Martin, C.J. et al. Molecular characterization of macbecin as an Hsp90 inhibitor. J. Med. Chem. 51, 2853–2857 (2008).

    CAS  PubMed  Google Scholar 

  42. 42

    Reddy, P. et al. Histone deacetylase inhibitor suberoylanilide hydroxamic acid reduces acute graft-versus-host disease and preserves graft-versus-leukemia effect. Proc. Natl. Acad. Sci. USA 101, 3921–3926 (2004).

    CAS  PubMed  Google Scholar 

  43. 43

    Wójcik, C. et al. Valosin-containing protein (p97) is a regulator of endoplasmic reticulum stress and of the degradation of N-end rule and ubiquitin-fusion degradation pathway substrates in mammalian cells. Mol. Biol. Cell 17, 4606–4618 (2006).

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Kuhn, D.J. et al. Potent activity of carfilzomib, a novel, irreversible inhibitor of the ubiquitin-proteasome pathway, against preclinical models of multiple myeloma. Blood 110, 3281–3290 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Chen, D., Frezza, M., Schmitt, S., Kanwar, J. & Dou, Q.P. Bortezomib as the first proteasome inhibitor anticancer drug: current status and future perspectives. Curr. Cancer Drug Targets 11, 239–253 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Kim, T.S. et al. Interaction of Hsp90 with ribosomal proteins protects from ubiquitination and proteasome-dependent degradation. Mol. Biol. Cell 17, 824–833 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Moffat, J.G., Rudolph, J. & Bailey, D. Phenotypic screening in cancer drug discovery- past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).

    CAS  PubMed  Google Scholar 

  48. 48

    Kangas, J.D., Naik, A.W. & Murphy, R.F. Efficient discovery of responses of proteins to compounds using active learning. BMC Bioinformatics 15, 143 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Sigal, A. et al. Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nat. Methods 3, 525–531 (2006).

    CAS  PubMed  Google Scholar 

  50. 50

    Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Sugiyama, M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027–1061 (2007)<>.

    Google Scholar 

  52. 52

    Schäfer, J. & Strimmer, K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4, e32 (2005).

    Google Scholar 

  53. 53

    Wu, J., Hu, C.P., Gu, Q.H., Li, Y.P. & Song, M. Trichostatin A sensitizes cisplatin-resistant A549 cells to apoptosis by up-regulating death-associated protein kinase. Acta Pharmacol. Sin. 31, 93–101 (2010).

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Ono, M. et al. Sensitivity to gefitinib (Iressa, ZD1839) in non-small cell lung cancer cell lines correlates with dependence on the epidermal growth factor (EGF) receptor/extracellular signal-regulated kinase 1/2 and EGF receptor/Akt pathway for proliferation. Mol. Cancer Ther. 3, 465–472 (2004).

    CAS  PubMed  Google Scholar 

  55. 55

    Chen, M.C. et al. The HDAC inhibitor, MPT0E028, enhances erlotinib-induced cell death in EGFR-TKI-resistant NSCLC cells. Cell Death Dis. 4, e810 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Altschuler and Wu laboratories for critical feedback; U. Alon (Weizmann Institute of Science, Rehovot, Israel), and members of his laboratory for providing the CD tag plasmid and guidance on its use; G. DeMartino (University of Texas Southwestern Medical Center, Dallas) for useful conversations reagents for the proteasome validation and the Ub-R clone of HeLa cells; and S. Wei for help with HTS experiments. This research was partially supported by the US National Institutes of Health grants CA133253 (S.J.A.), R01CA184984 (L.F.W.) and the Institute of Computational Health Sciences at UCSF (S.J.A., L.F.W.).

Author information

Affiliations

Authors

Contributions

J.K., Q.W. and S.L. generated the reporter library; A.D.C. built pSEG; J.K. designed the experiments; J.K. and C.-H.H. performed the experiments; B.A.P. helped perform the HTS experiments; C.-H.H. performed the data analysis; J.K., C.-H.H., L.F.W. and S.J.A. wrote the manuscript; and L.F.W. and S.J.A. guided all aspects of this study.

Corresponding authors

Correspondence to Steven J Altschuler or Lani F Wu.

Ethics declarations

Competing interests

S.J.A. and L.F.W. have submitted a patent application.

Integrated supplementary information

Supplementary Figure 1 Our reporter cell lines display diverse response to drugs from different functional classes.

Six reporter cell lines and the parent cell lines (untagged) were treated with various cancer drugs and imaged every 12 hrs for 48 hrs. Each row is a reporter cell line with the name of the YFP-tagged protein, and columns are drugs from 6 drug classes. Images at 48 hrs are shown. Blue: CFP. Green: YFP. Red: mCherry. The scale bar is 10 µm.

Supplementary Figure 2 Overview of profile computation.

1. Phenotypic features were extracted for each cell under DMSO control or drug/compound perturbations (e.g. Gemcitabine). 2. Drug/compound effects on each feature at population level (compared to DMSO) were summarized by KS statistics. 3. A phenotypic profile of a drug/compound (at single time point) consists of KS statistics of all features, and can be visualized as a point in high-dimensional feature space. 4. Phenotypic profiles can be extended to multiple time point. Dynamics of drug/compound effects were visualized as “time-traces” in the feature space.

Supplementary Figure 3 Phenotypic profiles show similarity within drug-class.

Phenotypic responses based on combining six different reporter cell lines (Supplementary figure 1) were computed. Each heat map summarizes phenotypic responses to one drug, and each row of 5 heat maps represents five different drugs in one drug class (e.g. DNA). To determine the y-axis sort order for each heat map, we: concatenated profiles from six reporters, sorted the features for camptothecin (CPT) from most green to most red, and then applied this sort order to all other drugs. The x-axis on each heat map is increasing time (0hr, 12hr,…, 48hr). Our figure shows that profiles are similar within drug classes (within each row of heat maps), and distinct between classes (across rows of heat maps), particularly at later times.

Supplementary Figure 4 Time traces of reporter-based phenotypic profiles.

The phenotypic profiles for six reporter cell lines (Supplementary figures 1, 3) were re-visualized as time-varying response curves in 3-D (and in stereo) using multidimensional scaling. Shown are phenotypic profiles for 30 drugs x 2 replicate experiments (solid lines and dash lines).

Supplementary Figure 5 Time points of 24 and/or 48 hrs are sufficient to discriminate drug classes.

Prediction accuracies were calculated using 10-fold cross-validation with phenotypic profiles built from the six reporter cell lines at 0hr only, 24hr only, 48hr only or all time points.

Supplementary Figure 6 Combining 24 and 48 hrs provides higher prediction accuracy.

Prediction accuracy was calculated using 10-fold cross validation for each of the 7 reference plates in the primary screen. The last column is the averaged prediction accuracy across the 7 reference plates.

Supplementary Figure 7 Prediction accuracy of NCI oncology set.

We calibrated confidence values for drug-class prediction using the NCI oncology set, which has “ground truth” annotation. As the confidence threshold increases (from blue to red; see color bar), the number of classified compounds decreases (x-axis). Accuracy (y-axis) was calculated as the proportion of classified compounds that were predicted correctly. The arrow indicates the confidence threshold = 0.1, which was used in our analysis.

Supplementary Figure 8 Statistics of primary hits across screened libraries.

A. Number of primary hits and hit rate in each library. B. Predicted drug classes for primary hits in each screened library.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 1235 kb)

Supplementary Tables 1–6

Table 1 Feature list Table 2 Reference drug list Table 3 NCI oncology drug annotation and prediction Table 4 Literature supported compounds used as new reference drugs Table 5 Recall and new predictions Table 6 Validated hits and structure (XLSX 297 kb)

Supplementary Code (ZIP 399 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kang, J., Hsu, CH., Wu, Q. et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat Biotechnol 34, 70–77 (2016). https://doi.org/10.1038/nbt.3419

Download citation

Further reading

Search

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