Abstract
Computational modeling is an important tool to advance our knowledge on cardiac diseases and their underlying mechanisms. Computational models of conduction in cardiac tissues require identification of parameters. Our knowledge on these parameters is limited, especially for diseased tissues. Here, we assessed and quantified parameters for computational modeling of conduction in cardiac tissues. We used a rabbit model of myocardial infarction (MI) and an imaging-based approach to derive the parameters. Left ventricular tissue samples were obtained from fixed control hearts (animals: 5) and infarcted hearts (animals: 6) within 200 μm (region 1), 250–750 μm (region 2) and 1,000–1,250 μm (region 3) of the MI border. We assessed extracellular space, fibroblasts, smooth muscle cells, nuclei and gap junctions by a multi-label staining protocol. With confocal microscopy we acquired three-dimensional (3D) image stacks with a voxel size of 200 × 200 × 200 nm. Image segmentation yielded 3D reconstructions of tissue microstructure, which were used to numerically derive extracellular conductivity tensors. Volume fractions of myocyte, extracellular, interlaminar cleft, vessel and fibroblast domains in control were (in %) 65.03 ± 3.60, 24.68 ± 3.05, 3.95 ± 4.84, 7.71 ± 2.15, and 2.48 ± 1.11, respectively. Volume fractions in regions 1 and 2 were different for myocyte, myofibroblast, vessel, and extracellular domains. Fibrosis, defined as increase in fibrotic tissue constituents, was (in %) 21.21 ± 1.73, 16.90 ± 9.86, and 3.58 ± 8.64 in MI regions 1, 2, and 3, respectively. For control tissues, image-based computation of longitudinal, transverse and normal extracellular conductivity yielded (in S/m) 0.36 ± 0.11, 0.17 ± 0.07, and 0.1 ± 0.06, respectively. Conductivities were markedly increased in regions 1 (+75, +171, and +100%), 2 (+53, +165, and +80%), and 3 (+42, +141, and +60%). Volume fractions of the extracellular space including interlaminar clefts strongly correlated with conductivities in control and MI hearts. Our study provides novel quantitative data for computational modeling of conduction in normal and MI hearts. Notably, our study introduces comprehensive statistical information on tissue composition and extracellular conductivities on a microscopic scale in the MI border zone. We suggest that the presented data fill a significant gap in modeling parameters and extend our foundation for computational modeling of cardiac conduction.
Highlights
Cardiac diseases, such as myocardial infarction (MI), are often associated with an increased risk of arrhythmia, which results from remodeling of cellular electrophysiology and tissue electrical properties
Only models of myocyte electrophysiology are considered, because myocytes occupy most of the volume in cardiac tissues and with some exceptions, the contribution of other cells to conduction is thought to be marginal throughout the normal heart
Various experimental findings suggest that non-myocytes, in particular, fibroblasts, myofibroblasts and macrophages, contribute to cardiac conduction and arrhythmia (Gaudesius et al, 2003; Miragoli et al, 2006; Zlochiver et al, 2008; Quinn et al, 2016; Hulsmans et al, 2017)
Summary
Cardiac diseases, such as myocardial infarction (MI), are often associated with an increased risk of arrhythmia, which results from remodeling of cellular electrophysiology and tissue electrical properties. Various experimental findings suggest that non-myocytes, in particular, fibroblasts, myofibroblasts and macrophages, contribute to cardiac conduction and arrhythmia (Gaudesius et al, 2003; Miragoli et al, 2006; Zlochiver et al, 2008; Quinn et al, 2016; Hulsmans et al, 2017). Electrophysiological models of these cells have been developed and their effects on conduction can be simulated using multi-domain models
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