Exploiting Cell-To-Cell Variability To Detect Cellular Perturbations

Gautam Dey, Gagan D Gupta, Balaji Ramalingam, Mugdha Sathe, Satyajit Mayor and Mukund Thattai

PLOS ONE, 2014, vol. 9, issue 3, 1-10

Abstract: Any single-cell-resolved measurement generates a population distribution of phenotypes, characterized by a mean, a variance, and a shape. Here we show that changes in the shape of a phenotypic distribution can signal perturbations to cellular processes, providing a way to screen for underlying molecular machinery. We analyzed images of a Drosophila S2R+ cell line perturbed by RNA interference, and tracked 27 single-cell features which report on endocytic activity, and cell and nuclear morphology. In replicate measurements feature distributions had erratic means and variances, but reproducible shapes; RNAi down-regulation reliably induced shape deviations in at least one feature for 1072 out of 7131 genes surveyed, as revealed by a Kolmogorov-Smirnov-like statistic. We were able to use these shape deviations to identify a spectrum of genes that influenced cell morphology, nuclear morphology, and multiple pathways of endocytosis. By preserving single-cell data, our method was even able to detect effects invisible to a population-averaged analysis. These results demonstrate that cell-to-cell variability contains accessible and useful biological information, which can be exploited in existing cell-based assays.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0090540

DOI: 10.1371/journal.pone.0090540

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