自动层分析(ALAn):用于无偏见描述哺乳动物培养上皮结构的图像分析工具

IF 1 Q3 BIOLOGY
Christian Cammarota, Dan T Bergstralh, Tara M Finegan
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引用次数: 0

摘要

培养的哺乳动物细胞是研究上皮生物学和力学的常见模型系统。上皮通常被认为是假二维的,因此要根据顶端组织表面进行成像和分析。我们发现,即使在小培养孔内,上皮单层的三维结构也会千差万别,在组织平面上看似有序的上皮细胞层,在顶端-基底平面上会出现严重的混乱。应通过三维分析上皮细胞的形状来了解培养组织的结构和成熟度,从而准确比较不同实验之间的差异。在此,我们介绍了使用我们的图像分析管道--自动层分析(ALAn)--的详细规程,该管道是为定量表征培养上皮细胞层的结构而开发的。ALAn 基于一套规则,这些规则适用于使用共聚焦显微镜对培养细胞层成像所获得的培养层顶端-基底(深度)维度 DNA 和肌动蛋白信号的空间分布。ALAn 可促进不同实验、研究和实验室之间的可重复性,为用户提供上皮结构和成熟度的定量、无偏见表征。主要特点 - 本方案旨在以自动、无偏见的方式对上皮单层进行空间分析。- ALAn 需要两个输入:使用共聚焦荧光显微镜获得的培养细胞中细胞核和肌动蛋白的空间分布。- ALAn 代码使用 Python3 编写,采用 Jupyter Notebook 交互格式。- 已优化用于 Marbin-Darby Canine Kidney (MDCK) 细胞,并成功用于表征人类 MCF-7 乳腺衍生细胞和 Caco-2 结肠癌细胞。- 本方案利用 Imaris 软件分割细胞核,但也可采用其他方法。ALAn 需要细胞核的中心坐标和体积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Layer Analysis (ALAn): An Image Analysis Tool for the Unbiased Characterization of Mammalian Epithelial Architecture in Culture.

Cultured mammalian cells are a common model system for the study of epithelial biology and mechanics. Epithelia are often considered as pseudo-two dimensional and thus imaged and analyzed with respect to the apical tissue surface. We found that the three-dimensional architecture of epithelial monolayers can vary widely even within small culture wells, and that layers that appear organized in the plane of the tissue can show gross disorganization in the apical-basal plane. Epithelial cell shapes should be analyzed in 3D to understand the architecture and maturity of the cultured tissue to accurately compare between experiments. Here, we present a detailed protocol for the use of our image analysis pipeline, Automated Layer Analysis (ALAn), developed to quantitatively characterize the architecture of cultured epithelial layers. ALAn is based on a set of rules that are applied to the spatial distributions of DNA and actin signals in the apical-basal (depth) dimension of cultured layers obtained from imaging cultured cell layers using a confocal microscope. ALAn facilitates reproducibility across experiments, investigations, and labs, providing users with quantitative, unbiased characterization of epithelial architecture and maturity. Key features • This protocol was developed to spatially analyze epithelial monolayers in an automated and unbiased fashion. • ALAn requires two inputs: the spatial distributions of nuclei and actin in cultured cells obtained using confocal fluorescence microscopy. • ALAn code is written in Python3 using the Jupyter Notebook interactive format. • Optimized for use in Marbin-Darby Canine Kidney (MDCK) cells and successfully applied to characterize human MCF-7 mammary gland-derived and Caco-2 colon carcinoma cells. • This protocol utilizes Imaris software to segment nuclei but may be adapted for an alternative method. ALAn requires the centroid coordinates and volume of nuclei.

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