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Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins
The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization chang...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935485/ https://www.ncbi.nlm.nih.gov/pubmed/29620521 http://dx.doi.org/10.7554/eLife.31872 |
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author | Lu, Alex X Chong, Yolanda T Hsu, Ian Shen Strome, Bob Handfield, Louis-Francois Kraus, Oren Andrews, Brenda J Moses, Alan M |
author_facet | Lu, Alex X Chong, Yolanda T Hsu, Ian Shen Strome, Bob Handfield, Louis-Francois Kraus, Oren Andrews, Brenda J Moses, Alan M |
author_sort | Lu, Alex X |
collection | PubMed |
description | The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology. |
format | Online Article Text |
id | pubmed-5935485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-59354852018-05-07 Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins Lu, Alex X Chong, Yolanda T Hsu, Ian Shen Strome, Bob Handfield, Louis-Francois Kraus, Oren Andrews, Brenda J Moses, Alan M eLife Cell Biology The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology. eLife Sciences Publications, Ltd 2018-04-05 /pmc/articles/PMC5935485/ /pubmed/29620521 http://dx.doi.org/10.7554/eLife.31872 Text en © 2018, Lu et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Cell Biology Lu, Alex X Chong, Yolanda T Hsu, Ian Shen Strome, Bob Handfield, Louis-Francois Kraus, Oren Andrews, Brenda J Moses, Alan M Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title | Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title_full | Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title_fullStr | Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title_full_unstemmed | Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title_short | Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
title_sort | integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins |
topic | Cell Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935485/ https://www.ncbi.nlm.nih.gov/pubmed/29620521 http://dx.doi.org/10.7554/eLife.31872 |
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