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Large scale, unbiased analysis of elementary calcium signaling events in cardiac myocytes.
- Source :
-
Journal of molecular and cellular cardiology [J Mol Cell Cardiol] 2019 Oct; Vol. 135, pp. 79-89. Date of Electronic Publication: 2019 Aug 13. - Publication Year :
- 2019
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Abstract
- The identification of spatiotemporally restricted Ca <superscript>2+</superscript> signals, Ca <superscript>2+</superscript> sparks, was instrumental for our understanding of cardiac Ca <superscript>2+</superscript> homeostasis. High-speed 2D confocal imaging enables acquisition of such Ca <superscript>2+</superscript> sparks with high-content information but their full appreciation is constrained by the lack of unbiased and easy-to-use analysis tools. We developed a software toolset for unbiased and automatic Ca <superscript>2+</superscript> spark analysis for huge data sets of subcellular Ca <superscript>2+</superscript> signals. iSpark was developed to be scanner and detector independent. In myocytes from hearts subjected to various degrees of hypertrophy we acquired >5.000.000 Ca <superscript>2+</superscript> sparks from 14 mice. The iSpark-enabled analysis of this large Ca <superscript>2+</superscript> spark data set showed that the highly organized distribution of Ca <superscript>2+</superscript> sparks present in healthy cells disarrayed concomitant with the development of aberrant transverse tubules and disease severity. Thus, iSpark represents a versatile and universal tool for analyzing local Ca <superscript>2+</superscript> signaling in healthy as well as diseased, aberrant local Ca <superscript>2+</superscript> signal transduction. The results from the unbiased analysis of large data sets provide a deeper insight into possible mechanisms contributing to the onset and progression of cardiac diseases such as hypertrophy.<br /> (Copyright © 2019 Elsevier Ltd. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1095-8584
- Volume :
- 135
- Database :
- MEDLINE
- Journal :
- Journal of molecular and cellular cardiology
- Publication Type :
- Academic Journal
- Accession number :
- 31419438
- Full Text :
- https://doi.org/10.1016/j.yjmcc.2019.08.004