Automated Layer Analysis (ALAn): An Image Analysis Tool for the Unbiased Characterization of Mammalian Epithelial Architecture in Culture

• Published in: Bio-protocol Vol. 14; no. 8; p. e4971
• Main Authors: Cammarota, Christian, Bergstralh, Dan T, Finegan, Tara M
• Format: Journal Article
• Language: English
• Published: United States Bio-Protocol 20.04.2024; Bio-protocol LLC
• Subjects: Biology; Methods; Cell culture; MDCK cells; Quantitative microscopy analysis; 3D Architecture; Epithelial biology; Image Analysis; Epithelia
• ISSN: 2331-8325; 2331-8325
• 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.