Advertisement

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

  • Albert Gough
  • Tong Ying Shun
  • D. Lansing Taylor
  • Mark Schurdak
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1745)

Abstract

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12–26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Keywords

Cellular heterogeneity High-content screening Systems biology Drug discovery Phenotypic profiling 

Notes

Acknowledgments

This work was funded by a Pennsylvania Department of Health CURE research Grant (SAP# 4100054875) and used the University of Pittsburgh Cancer Institute (UPCI) Chemical Biology Facility that is supported in part by award P30CA047904, the NIH-National Cancer Institute, Cancer Center Support Grant, to the UPCI. The data analyzed in this project was generated under the NExT-CBC agreement number 29XS131-TO6 funded by the National Cancer Institute to D.L. Taylor. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supplementary material

421126_1_En_2_MOESM1_ESM.zip (8.4 mb)
MIMB-Supplemental-Material (ZIP 8,573kb)

References

  1. 1.
    Tawfik DS (2010) Messy biology and the origins of evolutionary innovations. Nat Chem Biol 6(10):692–696.  https://doi.org/10.1038/nchembio.441 CrossRefPubMedGoogle Scholar
  2. 2.
    Gough A, Stern AM, Maier J, Lezon T, Shun T-Y, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL (2017) Biologically relevant heterogeneity: metrics and practical insights. SLAS Discov 22(3):213–237.  https://doi.org/10.1177/2472555216682725 PubMedGoogle Scholar
  3. 3.
    Huang S (2009) Non-genetic heterogeneity of cells in development: more than just noise. Development 136(23):3853–3862.  https://doi.org/10.1242/dev.035139 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Snijder B, Pelkmans L (2011) Origins of regulated cell-to-cell variability. Nat Rev Mol Cell Biol 12(2):119–125.  https://doi.org/10.1038/nrm3044 CrossRefPubMedGoogle Scholar
  5. 5.
    Altschuler SJ, Wu LF (2010) Cellular heterogeneity: when do differences make a difference? Cell 141(4):559–563CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Singh DK, Ku CJ, Wichaidit C, Steininger RJ 3rd, Wu LF, Altschuler SJ (2010) Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol Syst Biol 6:369CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gough AH, Chen N, Shun TY, Lezon TR, Boltz RC, Reese CE, Wagner J, Vernetti LA, Grandis JR, Lee AV, Stern AM, Schurdak ME, Taylor DL (2014) Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery. PLoS One 9(7):e102678.  https://doi.org/10.1371/journal.pone.0102678 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gascoigne KE, Taylor SS (2008) Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs. Cancer Cell 14(2):111–122.  https://doi.org/10.1016/j.ccr.2008.07.002 CrossRefPubMedGoogle Scholar
  9. 9.
    Janiszewska M, Liu L, Almendro V, Kuang Y, Paweletz C, Sakr RA, Weigelt B, Hanker AB, Chandarlapaty S, King TA, Reis-Filho JS, Arteaga CL, Park SY, Michor F, Polyak K (2015) In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. Nat Genet 47(10):1212–1219.  https://doi.org/10.1038/ng.3391 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Niepel M, Spencer SL, Sorger PK (2009) Non-genetic cell-to-cell variability and the consequences for pharmacology. Curr Opin Chem Biol 13(5–6):556–561.  https://doi.org/10.1016/j.cbpa.2009.09.015 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zhao B, Pritchard JR, Lauffenburger DA, Hemann MT (2014) Addressing genetic tumor heterogeneity through computationally predictive combination therapy. Cancer Discov 4(2):166–174.  https://doi.org/10.1158/2159-8290.CD-13-0465 CrossRefPubMedGoogle Scholar
  12. 12.
    Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT (2013) Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci USA 110(2):E170–E179.  https://doi.org/10.1073/pnas.1210419110 CrossRefPubMedGoogle Scholar
  13. 13.
    Zhang J-H, Chung TDY, Oldenburg KR (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4(2):67–73.  https://doi.org/10.1177/108705719900400206 CrossRefPubMedGoogle Scholar
  14. 14.
    Steininger RJ, Rajaram S, Girard L, Minna JD, Wu LF, Altschuler SJ (2015) On comparing heterogeneity across biomarkers. Cytometry A 87(6):558–567.  https://doi.org/10.1002/cyto.a.22599. CrossRefPubMedGoogle Scholar
  15. 15.
    Ruiz C, Li J, Luttgen MS, Kolatkar A, Kendall JT, Flores E, Topp Z, Samlowski WE, McClay E, Bethel K, Ferrone S, Hicks J, Kuhn P (2015) Limited genomic heterogeneity of circulating melanoma cells in advanced stage patients. Phys Biol 12(1):016008.  https://doi.org/10.1088/1478-3975/12/1/016008 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Balluff B, Frese CK, Maier SK, Schone C, Kuster B, Schmitt M, Aubele M, Hofler H, Deelder AM, Heck A Jr, Hogendoorn PC, Morreau J, Maarten Altelaar AF, Walch A, McDonnell LA (2015) De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry. J Pathol 235(1):3–13.  https://doi.org/10.1002/path.4436 CrossRefPubMedGoogle Scholar
  17. 17.
    Schwarz RF, Trinh A, Sipos B, Brenton JD, Goldman N, Markowetz F (2014) Phylogenetic quantification of intra-tumour heterogeneity. PLoS Comput Biol 10(4):e1003535.  https://doi.org/10.1371/journal.pcbi.1003535 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Haney SA (2014) Rapid assessment and visualization of normality in high-content and other cell-level data and its impact on the interpretation of experimental results. J Biomol Screen.  https://doi.org/10.1177/1087057114526432
  19. 19.
    Loo LH, Lin HJ, Steininger RJ 3rd, Wang Y, Wu LF, Altschuler SJ (2009) An approach for extensibly profiling the molecular states of cellular subpopulations. Nat Methods 6(10):759–765CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gingold JA, Coakley ES, Su J, Lee DF, Lau Z, Zhou H, Felsenfeld DP, Schaniel C, Lemischka IR (2015) Distribution Analyzer, a methodology for identifying and clustering outlier conditions from single-cell distributions, and its application to a Nanog reporter RNAi screen. BMC Bioinformatics 16:225.  https://doi.org/10.1186/s12859-015-0636-7 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Bhang HE, Ruddy DA, Krishnamurthy Radhakrishna V, Caushi JX, Zhao R, Hims MM, Singh AP, Kao I, Rakiec D, Shaw P, Balak M, Raza A, Ackley E, Keen N, Schlabach MR, Palmer M, Leary RJ, Chiang DY, Sellers WR, Michor F, Cooke VG, Korn JM, Stegmeier F (2015) Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat Med 21(5):440–448.  https://doi.org/10.1038/nm.3841 CrossRefPubMedGoogle Scholar
  22. 22.
    Gough A, Shun TY, Lansing Taylor D, Schurdak M (2016) A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens. Methods 96:12–26.  https://doi.org/10.1016/j.ymeth.2015.10.007 CrossRefPubMedGoogle Scholar
  23. 23.
    Young IT (1977) Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources. J Histochem Cytochem 25(7):935–941CrossRefPubMedGoogle Scholar
  24. 24.
    Polyak K (2014) Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat Med 20(4):344–346.  https://doi.org/10.1038/nm.3518 CrossRefPubMedGoogle Scholar
  25. 25.
    Kleppe M, Levine RL (2014) Tumor heterogeneity confounds and illuminates: assessing the implications. Nat Med 20(4):342–344.  https://doi.org/10.1038/nm.3522 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Albert Gough
    • 1
    • 2
  • Tong Ying Shun
    • 1
  • D. Lansing Taylor
    • 1
    • 2
    • 3
  • Mark Schurdak
    • 1
    • 2
    • 3
  1. 1.Drug Discovery InstituteUniversity of PittsburghPittsburghUSA
  2. 2.Department of Computational and Systems BiologyUniversity of PittsburghPittsburghUSA
  3. 3.Pittsburgh Cancer InstituteUniversity of PittsburghPittsburghUSA

Personalised recommendations