A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells

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 th...

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Bibliographic Details
Published in:BMC bioinformatics 2021-09, Vol.22 (Suppl 3), p.1-427, Article 427
Main Authors: Garcia-Pardo, M. Elena, Simpson, Jeremy C, O'Sullivan, Niamh C
Format: Article
Language:English
Subjects:
Analysis
Automated image analysis
Automation
Binding sites
Cytology
Cytoplasm
Design
Endoplasmic reticulum
Endoplasmic reticulum morphology
ER function
High-content imaging
Human cell culture
Image analysis
Image processing
Mammalian cells
Methodology
Methods
Morphology
Perturbation
Physiological aspects
Pipelines
Proteins
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Summary: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. 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. 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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04334-x