eprintid: 10088780
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/08/87/80
datestamp: 2020-01-07 13:53:35
lastmod: 2021-09-26 22:39:00
status_changed: 2020-01-07 13:53:35
type: article
metadata_visibility: show
creators_name: Antoniou, AN
creators_name: Powis, SJ
creators_name: Kriston-Vizi, J
title: High-content screening image dataset and quantitative image analysis of Salmonella infected human cells
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D77
keywords: Cellular morphology, Confocal image, Endoplasmic reticulum, HeLa, High-content screening, Image-based screening, Phenotypic screening, Salmonella, Unfolded protein response
note: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/publicdomain/zero/1.0/).
abstract: 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.
date: 2019
date_type: published
official_url: https://doi.org/10.1186/s13104-019-4844-5
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1733398
doi: 10.1186/s13104-019-4844-5
pii: 10.1186/s13104-019-4844-5
lyricists_name: Kriston-Vizi, Janos
lyricists_id: JKRIS94
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: BMC Research Notes
volume: 12
number: 1
article_number: 808
event_location: England
issn: 1756-0500
citation:        Antoniou, AN;    Powis, SJ;    Kriston-Vizi, J;      (2019)    High-content screening image dataset and quantitative image analysis of Salmonella infected human cells.                   BMC Research Notes , 12  (1)    , Article 808.  10.1186/s13104-019-4844-5 <https://doi.org/10.1186/s13104-019-4844-5>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10088780/1/s13104-019-4844-5.pdf