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Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data?

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Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data? / Ismaili, Djemail; Geelhoed, Bastiaan; Christ, Torsten.

In: PROG BIOPHYS MOL BIO, Vol. 157, 11.2020, p. 33-39.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Ismaili, D, Geelhoed, B & Christ, T 2020, 'Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data?', PROG BIOPHYS MOL BIO, vol. 157, pp. 33-39. https://doi.org/10.1016/j.pbiomolbio.2020.05.003

APA

Ismaili, D., Geelhoed, B., & Christ, T. (2020). Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data? PROG BIOPHYS MOL BIO, 157, 33-39. https://doi.org/10.1016/j.pbiomolbio.2020.05.003

Vancouver

Ismaili D, Geelhoed B, Christ T. Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data? PROG BIOPHYS MOL BIO. 2020 Nov;157:33-39. https://doi.org/10.1016/j.pbiomolbio.2020.05.003

Bibtex

@article{4ef38495b331446f9ea866d729aef59e,
title = "Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data?",
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 Ca2+ 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.",
author = "Djemail Ismaili and Bastiaan Geelhoed and Torsten Christ",
note = "Copyright {\textcopyright} 2020 Elsevier Ltd. All rights reserved.",
year = "2020",
month = nov,
doi = "10.1016/j.pbiomolbio.2020.05.003",
language = "English",
volume = "157",
pages = "33--39",
journal = "PROG BIOPHYS MOL BIO",
issn = "0079-6107",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Ca2+ currents in cardiomyocytes: How to improve interpretation of patch clamp data?

AU - Ismaili, Djemail

AU - Geelhoed, Bastiaan

AU - Christ, Torsten

N1 - Copyright © 2020 Elsevier Ltd. All rights reserved.

PY - 2020/11

Y1 - 2020/11

N2 - 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 Ca2+ 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.

AB - 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 Ca2+ 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.

U2 - 10.1016/j.pbiomolbio.2020.05.003

DO - 10.1016/j.pbiomolbio.2020.05.003

M3 - SCORING: Journal article

C2 - 32439316

VL - 157

SP - 33

EP - 39

JO - PROG BIOPHYS MOL BIO

JF - PROG BIOPHYS MOL BIO

SN - 0079-6107

ER -