RefCell: multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’

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Shen, Yang
Kubben, Nard
Candia, Julián
Morozov, Alexandre V.
Misteli, Tom
Losert, Wolfgang
Shen, Y., Kubben, N., Candia, J. et al. RefCell: multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’. BMC Bioinformatics 19, 427 (2018).
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. 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. 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.