Advances in tissue clearing and three-dimensional microscopy require new tools to analyze the resulting large volumes with single-cell resolution. Many existing nuclei detection approaches fail when applied to the developing heart, with its high cell density, and elongated myocytes. We propose a new regression-based convolutional neural network that detect nuclei centroids in whole DAPI-stained embryonic quail hearts. High nuclei detection accuracy was obtained in two different hearts where our algorithm outperformed other deep learning approaches. Once nuclei were identified we were also able to extract properties such as orientation and size, which enables future studies of heart development and disease.
Due to its accessibility, hemodynamics can be imaged in early stage quail embryo hearts longitudinally. Our group has linked abnormal shear stress patterns caused by regurgitant blood flow with resultant congenital heart defects (CHDs). To understand the mechanisms behind the development of these CHDs, it is imperative to image molecular expression at the sites of abnormal shear stress. However, molecular probes for the quail model are not extensive and 3D imaging is needed to accurately identify regions of interest in the looping heart. In this study, we present HCR FISH probes that target the shear stress responsive genes
Optical coherence tomography (OCT) has been applied to investigate heart development because of its
capability to image both structure and function of tiny beating embryonic hearts. Labeling heart
structures is necessary for quantifying mechanical functions such as cardiac motion, wall strain, blood
flow and shear stress, of looping hearts. Since manual segmentation is time-consuming and labor-
intensive, this study aimed to use deep learning to automatically extract dynamic shapes including the
myocardium, the endocardial cushions, and the lumen of beating embryonic hearts from 4-D OCT
images. This will benefit research on heart development, especially studies requiring large cohorts of
embryos.
Optical coherence tomography (OCT) has been applied for understanding heart development because of its capability of imaging both the structure and function of tiny beating embryonic hearts. Labeling endocardial cushions is necessary for quantifying morphological characteristics of the looping hearts. Since manual segmentation is time-consuming and prone to subjectivity, this study aims to use V-net to automatically segment endocardial cushions from OCT images. This will benefit research on heart development, especially studies requiring large cohorts of embryos, for example those investigating the teratogenic effects of ethanol or drugs and the prevention of these effects on the developing hearts.
Accurately quantifying embryonic cardiac electrophysiology is important not only for understanding the complex interplay between the conduction system and development of other cardiac tissues, but also for assessing how the conduction system is altered in disease and how this alteration, in turn, can cause congenital heart defects. Optical mapping (OM) using fluorescent voltage-sensitive dyes is a powerful tool for measuring electrical activities of early intact beating embryonic hearts with a large field-of-view. However, conventional OM provides a two-dimensional (2-D) representation of a three-dimensional (3-D) sample, and thus information acquired is incomplete and dependent upon the orientation of the sample, which makes it hard to compare one heart to another. To overcome this limitation, volumetric OM using light-sheet microscopy has been developed. However, the methods for calculating conduction velocity (CV) from volumetric data are lacking. Here, we extend this approach to measure 3-D CV. First, the activation time for each voxel is determined by fitting the upstroke of the action potential trace in time. From this, we find the spatial gradient of activation time. Finally, CV is calculated based on this spatial gradient of activation time. This approach was validated using a 3-D simulated digital phantom, and embryonic quail hearts at looping stages. The conduction patterns in looping hearts are consistent with previously observed features: conduction is faster in the ventricle region and slower at the atrioventricular junction and outflow tract; the inner curvature of the ventricle has slower conduction than the outer curvature.
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