Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Abstract

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software 1 to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

An mTOR-independent Macroautophagy Activator Ameliorates Tauopathy and Prionopathy Neurodegeneration Phenotypes

“…108 We thought it was possible that CCT020312 induces macroautophagy via PERK activation. However, a 4-h treatment of HEK293T cells with 1µM CCT020312 – a dose that is lower than the onethat has been reported to activate PERK in MEF cells, 115 but near the maximum dose used in our experiments to demonstrate macroautophagy activation – did not activate PERK as measured by RNA-Seq analysis of PERK-regulated target genes (Figure S3A, S3B). PERK was also not activated by treatment with CCT020312 (1µM) for 18 h in HEK293T cells expressing activating transcription factor 4 fused to Firefly luciferase (ATF4-Fluc), ATF4 being a PERK transcriptional reporter (Figure S3C).…”

Epigenetic silencing of HTATIP2 in glioblastoma contributes to treatment resistance by enhancing nuclear translocation of the DNA repair protein MPG

“…As a result, 2 CSV files were generated. From that, the P enrichment score or ratio of cytoplasmic/nuclear MPG [ 34 , 35 ] was plotted by Prism for the final graph. Accordingly, a positive (cytoplasmic) enrichment score indicated that most cells present in the tested cell population displayed predominantly cytoplasmic MPG localization.…”

Epigenetic silencing of HTATIP2 in glioblastoma enhances nuclear translocation of the DNA-repair protein MPG affecting treatment resistance

“…As result, 2 CSV les were generated. From that, the p enrichment score or ratio of cytoplasmic/nuclear MPG 28,29 was plotted by Prism for the nal graph. Accordingly, a positive (cytoplasmic) enrichment score indicated that a majority of cells with predominantly cytoplasmic MPG localization was present in the tested cell population.…”