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A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells
BACKGROUND: In mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425006/ https://www.ncbi.nlm.nih.gov/pubmed/34496765 http://dx.doi.org/10.1186/s12859-021-04334-x |
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author | Garcia-Pardo, M. Elena Simpson, Jeremy C. O’Sullivan, Niamh C. |
author_facet | Garcia-Pardo, M. Elena Simpson, Jeremy C. O’Sullivan, Niamh C. |
author_sort | Garcia-Pardo, M. Elena |
collection | PubMed |
description | BACKGROUND: In mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as changes to the structure and organisation of the reticular network. Due to its complex morphology, image analysis methods to quantitatively describe this organelle, and importantly any changes to it, are lacking. RESULTS: In this work we detail a methodological approach that utilises automated high-content screening microscopy to capture images of cells fluorescently-labelled for various ER markers, followed by their quantitative analysis. We propose that two key metrics, namely the area of dense ER and the area of polygonal regions in between the reticular elements, together provide a basis for measuring the quantities of rough and smooth ER, respectively. We demonstrate that a number of different pharmacological perturbations to the ER can be quantitatively measured and compared in our automated image analysis pipeline. Furthermore, we show that this method can be implemented in both commercial and open-access image analysis software with comparable results. CONCLUSIONS: We propose that this method has the potential to be applied in the context of large-scale genetic and chemical perturbations to assess the organisation of the ER in adherent cell cultures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04334-x. |
format | Online Article Text |
id | pubmed-8425006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84250062021-09-10 A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells Garcia-Pardo, M. Elena Simpson, Jeremy C. O’Sullivan, Niamh C. BMC Bioinformatics Methodology Article BACKGROUND: In mammalian cells the endoplasmic reticulum (ER) comprises a highly complex reticular morphology that is spread throughout the cytoplasm. This organelle is of particular interest to biologists, as its dysfunction is associated with numerous diseases, which often manifest themselves as changes to the structure and organisation of the reticular network. Due to its complex morphology, image analysis methods to quantitatively describe this organelle, and importantly any changes to it, are lacking. RESULTS: In this work we detail a methodological approach that utilises automated high-content screening microscopy to capture images of cells fluorescently-labelled for various ER markers, followed by their quantitative analysis. We propose that two key metrics, namely the area of dense ER and the area of polygonal regions in between the reticular elements, together provide a basis for measuring the quantities of rough and smooth ER, respectively. We demonstrate that a number of different pharmacological perturbations to the ER can be quantitatively measured and compared in our automated image analysis pipeline. Furthermore, we show that this method can be implemented in both commercial and open-access image analysis software with comparable results. CONCLUSIONS: We propose that this method has the potential to be applied in the context of large-scale genetic and chemical perturbations to assess the organisation of the ER in adherent cell cultures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04334-x. BioMed Central 2021-09-08 /pmc/articles/PMC8425006/ /pubmed/34496765 http://dx.doi.org/10.1186/s12859-021-04334-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Methodology Article Garcia-Pardo, M. Elena Simpson, Jeremy C. O’Sullivan, Niamh C. A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title | A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title_full | A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title_fullStr | A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title_full_unstemmed | A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title_short | A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
title_sort | novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425006/ https://www.ncbi.nlm.nih.gov/pubmed/34496765 http://dx.doi.org/10.1186/s12859-021-04334-x |
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