Comparison of polynomial fitting versus single time point analysis of ECIS data for barrier assessment. Issue 19 (4th October 2021) - Suresh, Karthik; Servinsky, Laura; Johnston, Laura; Punjabi, Naresh M.; Dudek, Steven M.; Damarla, Mahendra
Abstract: Electrical cell‐substrate impedance sensing (ECIS) is an in vitro methodology for measuring the barrier integrity of a variety of cell types, including pulmonary endothelial cells. These experiments are frequently used for in vitro assessment of lung injury. The data derived from ECIS experiments consists of repeated measures of resistance across an endothelial monolayer. As such, these data reflect the dynamic changes in electrical resistance that occur over time. Currently methodologies for assessing ECIS data rely on single point assessments of barrier function, such as the maximal drop in trans‐endothelial electrical resistance (TERMax ). However, this approach ignores the myriad of changes in resistance that occur before and after the TERMax data point. Herein, we utilize polynomial curve fitting on experimentally generated ECIS data, thus allowing for comparing ECIS experiments by examining the mean polynomial coefficients between groups. We show that polynomial curves accurately fit a variety of ECIS data, and that concordance between TERMax and coefficient analysis varies by type of stimulus, suggesting that TERMax differences may not always correlate with a significant difference in the overall shape of the ECIS profile. Lastly, we identify factors that impact coefficient values obtained in our analyses, including the length of time devoted to baseline measurements before addition of stimuli. Polynomial coefficient analysis is another tool that can be used for more comprehensive interrogation of ECIS data to better understand the biological underpinnings that lead to changes in barrier dysfunction in vitro . Abstract : Electrical cell‐substrate impedance sensing (ECIS) is a common method for assessing cell permeability. Herein, we use polynomial fitting to improve fitting and quantification of resistance measurements generated by ECIS experiments.