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

Nature Biotechnology volume 34, pages 70–77 (2016)Cite this article

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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.

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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.

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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.).

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Author notes

  1. Jungseog Kang and Chien-Hsiang Hsu: These authors contributed equally to this work.

Authors and Affiliations

  1. Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Jungseog Kang, Chien-Hsiang Hsu, Qi Wu, Shanshan Liu, Adam D Coster, Steven J Altschuler & Lani F Wu

  2. Department of Arts and Science, New York University-Shanghai, Shanghai, China

    Jungseog Kang

  3. Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Chien-Hsiang Hsu

  4. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.,

    Chien-Hsiang Hsu, Steven J Altschuler & Lani F Wu

  5. Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Bruce A Posner

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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.

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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)

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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

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