High-Dimensional Profiling: The Theta Comparative Cell Scoring Method

  • Scott J. Warchal
  • John C. Dawson
  • Neil O. Carragher
Part of the Methods in Molecular Biology book series (MIMB, volume 1787)


Principal component analysis enables dimensional reduction of multivariate datasets that are typical in high-content screening. A common analysis utilizing principal components is a distance measurement between a perturbagen—such as small-molecule treatment or shRNA knockdown—and a negative control. This method works well to identify active perturbagens, though it cannot discern between distinct phenotypic responses. Here, we describe an extension of the principal component analysis approach to multivariate high-content screening data to enable quantification of differences in direction in principal component space. The theta comparative cell scoring method can identify and quantify differential phenotypic responses between panels of cell lines to small-molecule treatment to support in vitro pharmacogenomics and drug mechanism-of-action studies.

Key words

Phenotypic screening High-content analysis Cell-based profiling 



This work was supported by a Cancer Research UK Ph.D. Studentship award to the Cancer Research UK Edinburgh Centre.


  1. 1.
    Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10:507–519CrossRefPubMedCentralGoogle Scholar
  2. 2.
    Ljosa V, Caie PD, Ter Horst R, Sokolnicki KL, Jenkins EL, Daya S et al (2013) Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J Biomol Screen 18:1321–1329CrossRefGoogle Scholar
  3. 3.
    Singh S, Carpenter AE, Genovesio A (2014) Increasing the content of high-content screening: an overview. J Biomol Screen 19:640–650CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Reisen F, Sauty de Chalon A, Pfeifer M, Zhang X, Gabriel D, Selzer P (2015) Linking phenotypes and modes of action through high-content screen fingerprints. Assay Drug Dev Technol 13:150810081821009CrossRefGoogle Scholar
  5. 5.
    Kümmel A, Selzer P, Siebert D, Schmidt I, Reinhardt J, Götte M et al (2012) Differentiation and visualization of diverse cellular phenotypic responses in primary high-content screening. J Biomol Screen 17:843–849CrossRefPubMedCentralGoogle Scholar
  6. 6.
    Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW et al (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–575CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S et al (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Perlman Z, Slack M, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ (2004) Multidimensional drug profiling by automated microscopy. Science 306:1194–1199CrossRefPubMedCentralGoogle Scholar
  9. 9.
    Vincent F, Loria P, Pregel M, Stanton R, Kitching L, Nocka K et al (2015) Developing predictive assays: the phenotypic screening “rule of 3”. Sci Transl Med 7:293ps15CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Tanaka M, Bateman R, Rauh D, Vaisberg E, Ramachandani S, Zhang C et al (2005) An unbiased cell morphology-based screen for new, biologically active small molecules. PLoS Biol 3:0764–0776CrossRefGoogle Scholar
  11. 11.
    Caie PD, Walls RE, Ingleston-Orme A, Daya S, Houslay T, Eagle R et al (2010) High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol Canc Ther 9:1913–1926CrossRefGoogle Scholar
  12. 12.
    Gustafsdottir SM, Ljosa V, Sokolnicki KL, Wilson JA, Walpita D, Kemp MM et al (2013) Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 8:e80999CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Warchal SJ, Dawson JC, Carragher NO (2016) Development of the theta comparative cell scoring method to quantify diverse phenotypic responses between distinct cell types. Assay Drug Dev Technol 14:395–406CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Bray M-A, Fraser AN, Hasaka TP, Carpenter AE (2012) Workflow and metrics for image quality control in large-scale high-content screens. J Biomol Screen 17:266–274CrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Scott J. Warchal
    • 1
  • John C. Dawson
    • 1
  • Neil O. Carragher
    • 1
  1. 1.Cancer Research UK Edinburgh CentreInstitute of Genetics and Molecular Medicine, University of EdinburghEdinburghUK

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