Cammarota Christian, Bergstralh Dan T, Finegan Tara M
Department of Physics & Astronomy, University of Rochester, Rochester, NY, USA.
Department of Biology, University of Rochester, Rochester, NY, USA.
Bio Protoc. 2024 Apr 20;14(8):e4971. doi: 10.21769/BioProtoc.4971.
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.
培养的哺乳动物细胞是研究上皮生物学和力学的常用模型系统。上皮组织通常被视为伪二维结构,因此是相对于顶端组织表面进行成像和分析的。我们发现,即使在小培养孔内,上皮单层的三维结构也可能有很大差异,而且在组织平面上看似有组织的层在顶-基平面上可能显示出严重的无序。应在三维空间中分析上皮细胞的形状,以了解培养组织的结构和成熟度,从而在实验之间进行准确比较。在这里,我们提供了一份详细的方案,介绍如何使用我们开发的图像分析流程——自动层分析(ALAn),用于定量表征培养的上皮层结构。ALAn基于一组规则,这些规则应用于通过共聚焦显微镜对培养的细胞层进行成像获得的培养层顶-基(深度)维度中DNA和肌动蛋白信号的空间分布。ALAn有助于在不同实验、研究和实验室之间实现可重复性,为用户提供上皮结构和成熟度的定量、无偏表征。关键特性 • 本方案旨在以自动化和无偏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Jupyter Notebook交互式格式用Python3编写。 •针对马-达犬肾(MDCK)细胞进行了优化,并成功应用于表征源自人MCF-7乳腺的细胞和Caco-2结肠癌细胞。 • 本方案利用Imaris软件对细胞核进行分割,但也可适用于其他方法。ALAn需要细胞核的质心坐标和体积。