EpiGraph: an open-source platform to quantify epithelial organization

Evolving topological order in the postnatal visceral pleura

BACKGROUND

Changes in epithelial cell shape reflects optimal cell packing and the minimization of surface free energy, but also cell-cell interactions, cell proliferation, and cytoskeletal rearrangements.

RESULTS

Here, we studied the structure of the rat pleura in the first 15 days after birth. After pleural isolation and image segmentation, the analysis demonstrated a progression of epithelial order from postnatal day 1 (P1) to P15. The cells with the largest surface area and greatest shape variability were observed at P1. In contrast, the cells with the smallest surface area and most shape consistency were observed at P15. A comparison of polygonal cell geometries demonstrated progressive optimization with an increase in the number of hexagons (six-sided) as well as five-sided and seven-sided polygons. Analysis of the epithelial organization with Voronoi tessellations and graphlet motif frequencies demonstrated a developmental path strikingly distinct from mathematical and natural reference paths. Graph Theory analysis of cell connectivity demonstrated a progressive decrease in network heterogeneity and clustering coefficient from P1 to P15.

CONCLUSIONS

We conclude that the rat pleura undergoes a striking change in pleural structure from P1 to P15. Further, a geometric and network-based approach can provide a quantitative characterization of these developmental changes.

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.

DISSECT is a tool to segment and explore cell and tissue mechanics in highly deformed 3D epithelia

Understanding morphogenesis strongly relies on the characterization of tissue topology and mechanical properties deduced from imaging data. The development of new imaging techniques offers the possibility to go beyond the analysis of mostly flat surfaces and image and analyze complex tissue organization in depth. An important bottleneck in this field is the need to analyze imaging datasets and extract quantifications not only of cell and tissue morphology but also of the cytoskeletal network's organization in an automatized way. Here, we describe a method, called DISSECT, for DisPerSE (Discrete Persistent Structure Extractor)-based Segmentation and Exploration of Cells and Tissues, that offers the opportunity to extract automatically, in strongly deformed epithelia, a precise characterization of the spatial organization of a given cytoskeletal network combined with morphological quantifications in highly remodeled three-dimensional (3D) epithelial tissues. We believe that this method, applied here to Drosophila tissues, will be of general interest in the expanding field of morphogenesis and tissue biomechanics.

Tissues as networks of cells: towards generative rules of complex organ development

Network analysis is a well-known and powerful tool in molecular biology. More recently, it has been introduced in developmental biology. Tissues can be readily translated into spatial networks such that cells are represented by nodes and intercellular connections by edges. This discretization of cellular organization enables mathematical approaches rooted in network science to be applied towards the understanding of tissue structure and function. Here, we describe how such tissue abstractions can enable the principles that underpin tissue formation and function to be uncovered. We provide an introduction into biologically relevant network measures, then present an overview of different areas of developmental biology where these approaches have been applied. We then summarize the general developmental rules underpinning tissue topology generation. Finally, we discuss how generative models can help to link the developmental rule back to the tissue topologies. Our collection of results points at general mechanisms as to how local developmental rules can give rise to observed topological properties in multicellular systems.

Geometric and network organization of visceral organ epithelium

Mammalian epithelia form a continuous sheet of cells that line the surface of visceral organs. To analyze the epithelial organization of the heart, lung, liver and bowel, epithelial cells were labeled in situ, isolated as a single layer and imaged as large epithelial digitally combine montages. The stitched epithelial images were analyzed for geometric and network organization. Geometric analysis demonstrated a similar polygon distribution in all organs with the greatest variability in the heart epithelia. Notably, the normal liver and inflated lung demonstrated the largest average cell surface area (p < 0.01). In lung epithelia, characteristic wavy or interdigitated cell boundaries were observed. The prevalence of interdigitations increased with lung inflation. To complement the geometric analyses, the epithelia were converted into a network of cell-to-cell contacts. Using the open-source software EpiGraph, subgraph (graphlet) frequencies were used to characterize epithelial organization and compare to mathematical (Epi-Hexagon), random (Epi-Random) and natural (Epi-Voronoi5) patterns. As expected, the patterns of the lung epithelia were independent of lung volume. In contrast, liver epithelia demonstrated a pattern distinct from lung, heart and bowel epithelia (p < 0.05). We conclude that geometric and network analyses can be useful tools in characterizing fundamental differences in mammalian tissue topology and epithelial organization.

The complex three-dimensional organization of epithelial tissues

Understanding the cellular organization of tissues is key to developmental biology. In order to deal with this complex problem, researchers have taken advantage of reductionist approaches to reveal fundamental morphogenetic mechanisms and quantitative laws. For epithelia, their two-dimensional representation as polygonal tessellations has proved successful for understanding tissue organization. Yet, epithelial tissues bend and fold to shape organs in three dimensions. In this context, epithelial cells are too often simplified as prismatic blocks with a limited plasticity. However, there is increasing evidence that a realistic approach, even from a reductionist perspective, must include apico-basal intercalations (i.e. scutoidal cell shapes) for explaining epithelial organization convincingly. Here, we present an historical perspective about the tissue organization problem. Specifically, we analyze past and recent breakthroughs, and discuss how and why simplified, but realistic, in silico models require scutoidal features to address key morphogenetic events.

EpiGraph: an open-source platform to quantify epithelial organization

Summary

Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required.

Availability and implementation

EpiGraph is available at https://imagej.net/EpiGraph and the code is accessible (https://github.com/ComplexOrganizationOfLivingMatter/Epigraph) under GPLv3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

[BUB1 gene is highly expressed in gastric cancer:analysis based on Oncomine database and bioinformatics]

OBJECTIVE

To investigate the expression of BUB1 gene in gastric cancer.

METHODS

Oncomine, GEPIA, BioGPS and Kaplan-Meier Plotter databases were used to analyze the difference of BUB1 gene expression between gastric cancer tissue and normal gastric tissue. The association of BUB1 expression level with the prognosis of gastric cancer patients was also analyzed. The Cancer Cell Line Encyclopedia (CCLE) was explored to analyze the expression of BUB1 in T cells and B cells in gastric cancer patients, and the String database was used to generate the network map of BUB1-related proteins and functional annotation of gene ontology (GO). The related pathways of KEGG were analyzed. Tumor immune assessment resource (TIMER) database was used to analyze the expression of BUB1 in immune infiltration and its effect on prognosis of gastric cancer patients. To further verify the results of gene chip analysis in Oncomine database, we collected 30 pairs of surgical specimens of gastric adenocarcinoma and adjacent tissues from patients admitted to the First Affiliated Hospital of Chengdu Medical College from March, 2018 to July, 2019. The results of BUB1 gene expression in Oncomine database were verified by PCR and immunohistochemistry.

RESULTS

Oncomine, GEPIA and BioGPS analyses showed that BUB1 was highly expressed in gastric cancer compared with normal gastric tissue. Kaplan-Meier survival analysis showed that the progression-free survival time (HR=0.52, 95% CI:0.41-0.67, P &lt; 0.05) and the overall survival time (HR=0.67, 95% CI:0.55-0.82, P &lt; 0.05) were prolonged in gastric cancer patients with a high expression of BUB1. Through String data collection, BUB1-related proteins were mainly enriched in 13 cellular components, 4 molecular functions and 12 biological processes, involving 4 signal pathways. TIMER database analysis showed that CD4⁺ T cells and macrophages with high expressions of BUB1 mRNA in the immune microenvironment were associated with a favorable 5-year survival outcome of patients with gastric cancer. In the surgical specimens, real-time quantitative PCR showed that the expression level of BUB1 mRNA was significantly higher in gastric cancer tissues than in the adjacent gastric mucosa tissues, and immunohistochemical results demonstrated positive BUB1 staining in the gastric cancer tissues.

CONCLUSIONS

BUB1 gene is highly expressed in gastric cancer. BUB1 may reduce tumor immunosuppression and helps to evaluate the prognosis of patients with gastric cancer.