Analysis of image-based phenotypic parameters for high throughput gene perturbation assays. (October 2015)

• Record Type: Journal Article
• Authors: Song, Mee; Jeong, Euna; Lee, Tae-Kyu; Tsoy, Yury; Kwon, Yong-Jun; Yoon, Sukjoon
• Is Part Of: Computational biology and chemistry. Volume 58(2015)
• Page Start: 192
• Page End: 198
• Publication Date: 2015-10
• Subjects: siRNA screening -- Gene perturbation -- Image-based assay -- Phenotypic parameter; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.07.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Image-based multiplexing assay was attempted for siRNA library screening. Diverse orthogonal phenotypic parameters were retrieved from a single screening. Image-based cell count provided better resolution than the viability measure. Geneset analysis revealed biological implications of various image-based parameters. Abstract: Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.