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

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


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.


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



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.

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Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Albert Gough
    • 1
    • 2
    Email author
  • 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

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