A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line
We present a high-content screen (HCS) for the simultaneous analysis of multiple phenotypes in HeLa cells expressing an autophagy reporter (mcherry-LC3) and one of 209 GFP-fused proteins from the Crohn’s Disease (CD)-associated bacterium, Adherent Invasive E. coli (AIEC) strain LF82. Using automated...
| Main Authors: | , |
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| Format: | Article |
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Nature Publishing Group
2018
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| Online Access: | https://eprints.nottingham.ac.uk/51012/ |
| _version_ | 1848798391716282368 |
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| author | Collins, Adam Huett, Alan |
| author_facet | Collins, Adam Huett, Alan |
| author_sort | Collins, Adam |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present a high-content screen (HCS) for the simultaneous analysis of multiple phenotypes in HeLa cells expressing an autophagy reporter (mcherry-LC3) and one of 209 GFP-fused proteins from the Crohn’s Disease (CD)-associated bacterium, Adherent Invasive E. coli (AIEC) strain LF82. Using automated confocal microscopy and image analysis (CellProfiler), we localised GFP fusions within cells, and monitored their effects upon autophagy (an important innate cellular defence mechanism), cellular and nuclear morphology, and the actin cytoskeleton. This data will provide an atlas for the localisation of 209 AIEC proteins within human cells, as well as a dataset to analyse their effects upon many aspects of host cell morphology. We also describe an open-source, automated, image-analysis workflow to identify bacterial effectors and their roles via the perturbations induced in reporter cell lines when candidate effectors are exogenously expressed. |
| first_indexed | 2025-11-14T20:19:02Z |
| format | Article |
| id | nottingham-51012 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:19:02Z |
| publishDate | 2018 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-510122020-05-04T19:36:38Z https://eprints.nottingham.ac.uk/51012/ A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line Collins, Adam Huett, Alan We present a high-content screen (HCS) for the simultaneous analysis of multiple phenotypes in HeLa cells expressing an autophagy reporter (mcherry-LC3) and one of 209 GFP-fused proteins from the Crohn’s Disease (CD)-associated bacterium, Adherent Invasive E. coli (AIEC) strain LF82. Using automated confocal microscopy and image analysis (CellProfiler), we localised GFP fusions within cells, and monitored their effects upon autophagy (an important innate cellular defence mechanism), cellular and nuclear morphology, and the actin cytoskeleton. This data will provide an atlas for the localisation of 209 AIEC proteins within human cells, as well as a dataset to analyse their effects upon many aspects of host cell morphology. We also describe an open-source, automated, image-analysis workflow to identify bacterial effectors and their roles via the perturbations induced in reporter cell lines when candidate effectors are exogenously expressed. Nature Publishing Group 2018-05-15 Article PeerReviewed Collins, Adam and Huett, Alan (2018) A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line. Scientific Data, 5 . 180081/1-180081/12. ISSN 2052-4463 https://www.nature.com/articles/sdata201881 doi:10.1038/sdata.2018.81 doi:10.1038/sdata.2018.81 |
| spellingShingle | Collins, Adam Huett, Alan A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title | A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title_full | A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title_fullStr | A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title_full_unstemmed | A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title_short | A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line |
| title_sort | multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a hela cell line |
| url | https://eprints.nottingham.ac.uk/51012/ https://eprints.nottingham.ac.uk/51012/ https://eprints.nottingham.ac.uk/51012/ |