High-content screening image dataset and quantitative image analysis of Salmonella infected human cells

Antoniou, Antony, Powis, Simon J. and Kriston-Vizi, Janos (2019) High-content screening image dataset and quantitative image analysis of Salmonella infected human cells. BMC Research Notes, 12 (1). p. 808. ISSN 1756-0500

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OBJECTIVES: Salmonella bacteria can induce the unfolded protein response, a cellular stress response to misfolding proteins within the endoplasmic reticulum. Salmonella can exploit the host unfolded protein response leading to enhanced bacterial replication which was in part mediated by the induction and/or enhanced endo-reticular membrane synthesis. We therefore wanted to establish a quantitative confocal imaging assay to measure endo-reticular membrane expansion following Salmonella infections of host cells.

DATA DESCRIPTION: High-content screening confocal fluorescence microscopic image set of Salmonella infected HeLa cells is presented. The images were collected with a PerkinElmer Opera LX high-content screening system in seven 96-well plates, 50 field-of-views and DAPI, endoplasmic reticulum tracker channels and Salmonella mCherry protein in each well. Totally 93,300 confocal fluorescence microscopic images were published in this dataset. An ImageJ high-content image analysis workflow was used to extract features. Cells were classified as infected and non-infected, the mean intensity of endoplasmic reticulum tracker under Salmonella bacteria was calculated. Statistical analysis was performed by an R script, quantifying infected and non-infected cells for wild-type and ΔsifA mutant cells. The dataset can be further used by researchers working with big data of endoplasmic reticulum fluorescence microscopic images, Salmonella bacterial infection images and human cancer cells.

Item Type: Article
Uncontrolled Keywords: Salmonella, Unfolded protein response, Endoplasmic reticulum, High-content screening, Image-based screening, Phenotypic screening, Confocal image, Cellular morphology, HeLa
Subjects: C500 Microbiology
C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 28 Jul 2020 11:20
Last Modified: 31 Jul 2021 12:02
URI: http://nrl.northumbria.ac.uk/id/eprint/43901

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