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dc.contributor.authorAntoniou, Antony N.
dc.contributor.authorPowis, Simon J.
dc.contributor.authorKriston-Vizi, Janos
dc.identifier.citationAntoniou , A N , Powis , S J & Kriston-Vizi , J 2019 , ' High-content screening image dataset and quantitative image analysis of Salmonella infected human cells ' , BMC Research Notes , vol. 12 , 808 .
dc.identifier.otherPURE: 264666249
dc.identifier.otherPURE UUID: 9b1c8edd-7871-4bda-98a3-8a47e096d15b
dc.identifier.otherRIS: urn:5B2843C79C4EBD179C7538E194DF34D4
dc.identifier.otherRIS: Antoniou2019
dc.identifier.otherORCID: /0000-0003-4218-2984/work/66398255
dc.identifier.otherScopus: 85076698107
dc.identifier.otherWOS: 000512732000002
dc.descriptionThis work was supported by the Medical Research Council Core funding the MRC LMCB (MC_U12266B) (JKV) and the EU FP7 Marie-Curie International Reintegration Grant PIRG08-GA-2010-276811 (JKV). ANA was funded by ARUK Fellowships Non-Clinical Career Development Fellowship Ref No: 18440. ANA and SJP were also in part funded by ARUK (Grant 21261).en
dc.description.abstractObjectives 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.
dc.relation.ispartofBMC Research Notesen
dc.rightsCopyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. To view a copy of this licence, visit
dc.subjectUnfolded protein responseen
dc.subjectEndoplasmic reticulumen
dc.subjectHigh-content screeningen
dc.subjectImage-based screeningen
dc.subjectPhenotypic screeningen
dc.subjectConfocal imageen
dc.subjectCellular morphologyen
dc.subjectQR180 Immunologyen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleHigh-content screening image dataset and quantitative image analysis of Salmonella infected human cellsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Centre for Biophotonicsen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.description.statusPeer revieweden

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