A method for characterizing phenotypic changes in highly variable cell populations and its application to high content screening of Arabidopsis thaliana protoplasts. Issue 4 (28th February 2017)

• Record Type: Journal Article
• Authors: Johnson, Gregory R.; Kangas, Joshua D.; Dovzhenko, Alexander; Trojok, Rüdiger; Voigt, Karsten; Majarian, Timothy D.; Palme, Klaus; Murphy, Robert F.
• Is Part Of: Cytometry. Volume 91:Issue 4(2017)
• Page Start: 326
• Page End: 335
• Publication Date: 2017-02-28
• Subjects: high content screening -- cellular heterogeneity -- subcellular location -- fluorescence microscopy; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23067
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: Quantitative image analysis procedures are necessary for the automated discovery of effects of drug treatment in large collections of fluorescent micrographs. When compared to their mammalian counterparts, the effects of drug conditions on protein localization in plant species are poorly understood and underexplored. To investigate this relationship, we generated a large collection of images of single plant cells after various drug treatments. For this, protoplasts were isolated from six transgenic lines of A. thaliana expressing fluorescently tagged proteins. Eight drugs at three concentrations were applied to protoplast cultures followed by automated image acquisition. For image analysis, we developed a cell segmentation protocol for detecting drug effects using a Hough transform‐based region of interest detector and a novel cross‐channel texture feature descriptor. In order to determine treatment effects, we summarized differences between treated and untreated experiments with an L1 Cramér‐von Mises statistic. The distribution of these statistics across all pairs of treated and untreated replicates was compared to the variation within control replicates to determine the statistical significance of observed effects. Using this pipeline, we report the dose dependent drug effects in the first high‐content Arabidopsis thaliana drug screen of its kind. These results can function as a baseline for comparison to other protein organization modeling approaches in plant cells. © 2017 International Society for Advancement of Cytometry Abstract : Murphy et al. developed quantitative image analysis procedures to analyze a large collection of single cells of Arabidopsis thaliana after various drug treatments. Pipeline overview. Four steps were performed: (1) detection of spherical regions of interest, (2) computation of intra and inter‐channel texture features, (3) determination of a measure of discrimination, deltaF, between sets of replicates (e.g., between untreated and untreated or between untreated and treated with a given drug concentration), and (4) determination of the area of overlap between the CDFs for different comparisons. The third step consists of two parts: (a) Fitting a SVM to separate the two replicates, and project every point on to the hyperplane normal, and (b) Determining the CDF with respect to signed distance from the hyperplane. A condition was considered to have an effect if the area of overlap in step 4 was statistically significant.