Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data?. (November 2020)

• Record Type: Journal Article
• Authors: Ismaili, Djemail; Geelhoed, Bastiaan; Christ, Torsten
• Is Part Of: Progress in biophysics and molecular biology. Volume 157(2020)
• Page Start: 33
• Page End: 39
• Publication Date: 2020-11
• Subjects: Ca2+ current -- Cardiomyocytes -- Capacitance -- Variability -- Data transformation; Cm Cell capacitance -- ICa Ca2+ current -- LV Left ventricle -- AF Atrial fibrillation -- SR Sinus rhythm; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biophysics -- Periodicals; Molecular Biology -- Periodicals; Biophysique -- Périodiques; Biochimie -- Périodiques; 571.4
• DOI: 10.1016/j.pbiomolbio.2020.05.003
• Languages: English
• ISSNs: 0079-6107

Abstract

Abstract: Objectives: Variability of ion currents is major issue when used for significance testing. One of the simplest approach to reduce variability is normalization to cell membrane size. However, efficacy of Ca 2+ currents (ICa ) normalization is unknown. Beside absolute variability, the type of distribution since non-Gaussian distribution makes application of nonparametric test necessary. Methods: We retrospectively analyzed individual ICa amplitudes measured in ventricular cardiomyocytes from mice, rats and humans and in atrial cardiomyocytes from humans in sinus rhythm and in atrial fibrillation. ICa was normalized to cell membrane size, estimated from capacitance transients. In addition, data were Log transformed to reach Gaussian distribution. Normalized and transformed data were analyzed for variability and applicability of parametric vs. nonparametric tests. Results: There was strong correlation between ICa and cell membrane size. However, correlation coefficient was rather low. Normalizing ICa had an inconsistent effect on variability. Variability of ICa in cells from the same patient/animal was not different cardiomyocytes from humans, rat and mice. Calculation of mean values based on mean values of cells from individuals (patients or animals) vs. mean values calculated for all cells drastically reduces statistical power to detect differences between the groups. Log transformation of ICa allowed application of much higher sensitive parametric testing, compensating loss of power. Conclusion: Impact of cell membrane size to ICa is low and may limit efficacy of normalization of ICa to reduce variability. In contrast, Log transformation of ICa data reduces variability and can increase statistical power to detect difference between ICa datasets.