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An Automated, Label‐free Method for Quantifying Intercellular Gap Formation in Images of Cell Monolayers

Within cell monolayers, specifically the vascular endothelium, intercellular gap formation is an important phenomenon involved in response to infection, wound healing, force transduction, and barrier regulation. Because intercellular gap formation is studied extensively in the context of biological research, there exists a need for analysis tools that enable the quantification of intercellular gaps. However, existing tools to assess cell gaps are limited by the requirement of exogenous cell markers and fluorescent probes to label cellular regions, or by cell damage‐associated markers that incompletely reflect the dynamic process of physiological gap formation and repair. Currently, there is no method of quantifying intercellular gaps in unlabeled light micrographs. Therefore, we developed an automated, algorithmic solution to the quantification of cell gaps, which works by iterative contrasting of cellular and non‐cellular regions within images. We vetted this algorithm by analyzing dose‐dependent cytotoxicity responses in pulmonary microvascular endothelial cell (PMVEC) monolayers after Pseudomonas aeruginosa infection, and compared our data to the conventional lactate dehydrogenase (LDH) cell cytotoxicity assay. We found that our algorithm was comparably sensitive to the LDH assay. However, it was faster and required no additional reagents. Additionally, our approach eliminated the user bias inherent to manual analysis techniques, and was robust to deviations in image background intensity, color, blur, and cell morphology. We conclude that this novel technique represents an accurate and sensitive approach to the measurement of intercellular gap formation. We next plan to develop and implement a user interface in order to disseminate this technology to the scientific community at large.