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

<jats:title>Abstract</jats:title><jats:sec> <jats:title>Objectives</jats:title> <jats:p><jats:italic>Salmonella</jats:italic> bacteria can induce the unfolded protein response, a cellular stress response to misfolding proteins within the endoplasmic reticulum. <jats:italic>Salmonella</jats:italic> 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 <jats:italic>Salmonella</jats:italic> infections of host cells.</jats:p> </jats:sec><jats:sec> <jats:title>Data description</jats:title> <jats:p>High-content screening confocal fluorescence microscopic image set of <jats:italic>Salmonella</jats:italic> 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 <jats:italic>Salmonella</jats:italic> mCherry protein in each well. Totally 93,300 confocal fluorescence microscopic images were published in this dataset. An <jats:italic>ImageJ</jats:italic> 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 <jats:italic>Salmonella</jats:italic> bacteria was calculated. Statistical analysis was performed by an <jats:italic>R</jats:italic> script, quantifying infected and non-infected cells for wild-type and Δ<jats:italic>sifA</jats:italic> mutant cells. The dataset can be further used by researchers working with big data of endoplasmic reticulum fluorescence microscopic images, <jats:italic>Salmonella</jats:italic> bacterial infection images and human cancer cells.</jats:p> </jats:sec>