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

Abstract

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.

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging

“…A number of machine-learning based methods have been developed for the segmentation of cells (Stringer et al, 2021), mitochondria (Fischer et al, 2020; Lefebvre et al, 2021), and nuclei (Hollandi et al, 2020), which provide robust and precise classification of cell structures. However, to date, thresholding remains the standard method of use for ER segmentation (English and Voeltz 2013; Pain et al, 2019; Garcia-Pardo et al, 2021), a method which lacks both sensitivity and specificity and thus quantitative conclusions are hard to draw, especially in situations where image quality is compromised by noise. Alternative methods are based on labour intensive manual labelling of image data to generate specialised datasets for training of machine learning algorithms.…”

OrgaMeas: A pipeline that integrates all the processes of organelle image analysis

“…Although many tools for biomedical image analysis have been reported, such as those for cell segmentation, ROI setting, and organelle segmentation, most of these were developed as stand-alone tools for application to a single process, and therefore the research community is demanding analysis pipelines that seamlessly cover the entire image analysis process. Although such pipelines already exist, they were developed using conventional image processing methods that are available within ImageJ/Fiji and are prone to errors (Garcia-Pardo et al, 2021; Klickstein et al, 2020). To address this issue, we took advantage of the power of deep learning and developed OrgaMeas as an organelle image analysis pipeline with high accuracy, high throughput, and low bias ( Fig.…”

nERdy: network analysis of endoplasmic reticulum dynamics

“…In follow-up work, ERnet-v2 [28], the authors used a Swin Transformer-based model to extract tubules, sheets, and sheet-based-tubules (SBTs) from the ER structure and provided quantitative measures to understand the topology of the ER network in a supervised manner. Garcia et al [29] developed a pipeline to quantitatively measure the areas of rough and smooth ER to assess the impact of different pharmacological perturbations on ER morphology. Analysis of ER dynamics is non-trivial because of the low signal-to-noise ratio and highly variable fluorescence intensity distribution over space and time [30].…”