RefCell: Multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'

Yang Shen, Nard Kubben, Julián Candia, Alexandre V. Morozov, Tom Misteli, Wolfgang Losert

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs. Results: Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample. Conclusions: We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Original languageEnglish (US)
Article number427
JournalBMC Bioinformatics
Issue number1
StatePublished - Nov 16 2018

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


  • Heterogeneity
  • Image-based high-throughput screen
  • Single-cell analysis


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