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High-content screening image dataset and quantitative image analysis of Salmonella infected human cells
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 inductio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915951/ https://www.ncbi.nlm.nih.gov/pubmed/31843016 http://dx.doi.org/10.1186/s13104-019-4844-5 |
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author | Antoniou, Antony N. Powis, Simon J. Kriston-Vizi, Janos |
author_facet | Antoniou, Antony N. Powis, Simon J. Kriston-Vizi, Janos |
author_sort | Antoniou, Antony N. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6915951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69159512019-12-30 High-content screening image dataset and quantitative image analysis of Salmonella infected human cells Antoniou, Antony N. Powis, Simon J. Kriston-Vizi, Janos BMC Res Notes Data Note 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. BioMed Central 2019-12-16 /pmc/articles/PMC6915951/ /pubmed/31843016 http://dx.doi.org/10.1186/s13104-019-4844-5 Text en © The Author(s) 2019 Open AccessThis 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. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Data Note Antoniou, Antony N. Powis, Simon J. Kriston-Vizi, Janos High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title | High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title_full | High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title_fullStr | High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title_full_unstemmed | High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title_short | High-content screening image dataset and quantitative image analysis of Salmonella infected human cells |
title_sort | high-content screening image dataset and quantitative image analysis of salmonella infected human cells |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6915951/ https://www.ncbi.nlm.nih.gov/pubmed/31843016 http://dx.doi.org/10.1186/s13104-019-4844-5 |
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