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Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model

Citation

Rugonyi, Sandra (2020), Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model, Dryad, Dataset, https://doi.org/10.5061/dryad.hdr7sqvg5

Abstract

Cardiac pumping depends on the morphological structure of the heart, but also on its sub-cellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three dimensional (3D) heart structure, assessing malformations; and its ultrastructure, assessing disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). We achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation on the same sample, enabling correlative multiscale imaging. Our approach enables multiscale studies in models of congenital heart disease and beyond.

Methods

Data deposited is from a multiscale imaging procedure, described in the eLife paper. It consists of cardiac imaging data from two chicken embryonic hearts: 1) a normal, control (CON) heart; and 2) a heart with a malformation resembling tetralogy of Fallot (TOF) in humans. The heart is already fully formed, so these images are equivalent to a human fetal heart of 5-6 months of gestation. The whole heart was imaged using 3D micro-computing tomography (microCT).  Then regions of interest (ROIs) were chosen for imaging the sub-cellular architecture (ultrastructure) using 3D serial block-face scanning electron microscop7 (SBF-SEM images). From selected SBF-SEM images, we segmented the cell nuclei, mitochondria and myofibrils, as well as the extracellular space using the Dragonfly software (ORS, Canada). Datasets include microCT images, SBF-SEM images, segmented SBF-SEM images. Protocols employed are fully disclosed and detailed in the manuscript methods section.

Funding

National Institutes of Health, Award: R01 HL094570

OHSU University Shared Resource Pilot Funding Program

School of Medicine, Oregon Health and Science University

OHSU University Shared Resource Pilot Funding Program