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一种新型的自动化图像分析管道,用于定量分析培养的人类细胞内质网形态变化。

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

机构信息

UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.

Cell Screening Laboratory, UCD School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland.

出版信息

BMC Bioinformatics. 2021 Sep 8;22(1):427. doi: 10.1186/s12859-021-04334-x.

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.

摘要

背景

在内质网(ER)哺乳动物细胞中,包含一个广泛分布在细胞质中的高度复杂的网状形态。这个细胞器对生物学家特别感兴趣,因为它的功能障碍与许多疾病有关,这些疾病通常表现为网状网络的结构和组织发生变化。由于其复杂的形态,缺乏用于定量描述该细胞器的图像分析方法,以及重要的是对其进行任何改变的方法。

结果

在这项工作中,我们详细介绍了一种利用自动化高内涵筛选显微镜捕捉细胞荧光标记各种 ER 标志物的图像的方法,然后对其进行定量分析。我们提出,两个关键指标,即密集 ER 的面积和网状元素之间的多边形区域的面积,分别为测量粗糙 ER 和光滑 ER 的数量提供了基础。我们证明,可以在我们的自动化图像分析管道中定量测量和比较许多不同的内质网药理学扰动。此外,我们表明,这种方法可以在商业和开放访问的图像分析软件中实现,结果具有可比性。

结论

我们提出,这种方法有可能应用于大规模的遗传和化学扰动的背景下,以评估贴壁细胞培养中内质网的组织。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334a/8425006/3be43043b335/12859_2021_4334_Fig1_HTML.jpg

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