BackgroundThe functionality of a cardiomyocyte is primarily measured by analyzing the electrophysiological properties of the cell. The analysis of the beating behavior of single cardiomyocytes, especially ones derived from stem cells, is challenging but well warranted. In this study, a video-based method that is non-invasive and label-free is introduced and applied for the study of single human cardiomyocytes derived from induced pluripotent stem cells.MethodsThe beating of dissociated stem cell-derived cardiomyocytes was visualized with a microscope and the motion was video-recorded. Minimum quadratic difference, a digital image correlation method, was used for beating analysis with geometrical sectorial cell division and radial/tangential directions. The time series of the temporal displacement vector fields of a single cardiomyocyte was computed from video data. The vector field data was processed to obtain cell-specific, contraction-relaxation dynamics signals. Simulated cardiomyocyte beating was used as a reference and the current clamp of real cardiomyocytes was used to analyze the electrical functionality of the beating cardiomyocytes.ResultsOur results demonstrate that our sectorized image correlation method is capable of extracting single cell beating characteristics from the video data of induced pluripotent stem cell-derived cardiomyocytes that have no clear movement axis, and that the method can accurately identify beating phases and time parameters.ConclusionOur video analysis of the beating motion of single human cardiomyocytes provides a robust, non-invasive and label-free method to analyze the mechanobiological functionality of cardiomyocytes derived from induced pluripotent stem cells. Thus, our method has potential for the high-throughput analysis of cardiomyocyte functions.
This study describes a robust bubble image recognition algorithm that detects the in-focus, ellipse-like bubble images from experimental images with heavily overlapping bubbles. The principle of the overlapping object recognition (OOR) algorithm is that it calculates the overall perimeter of a segment, finds the points at the perimeter that represent the connecting points of overlapping objects, clusters the perimeter arcs that belong to the same object and fits ellipses on the clustered arcs of the perimeter. The accuracy of the algorithm is studied with simulated images of overlapping ellipses, providing an RMS error of 0.9 pixels in size measurement. The algorithm is utilized in measurements of bubble size distributions with a direct imaging (DI) technique in which a digital camera and a pulsed back light are used to detect bubble outlines. The measurement system is calibrated with stagnant bubbles in a gel in order to define the bubble size dependent effective thickness of the measurement volume and the grey scale gradient threshold as a focus criterion. The described concept with a novel bubble recognition algorithm enables DI measurements in denser bubbly flows with increased reliability and accuracy of the measurement results. The measurement technique is applied to the study of the turbulent bubbly flow in a papermaking machine, in the outlet pipe of a centrifugal pump.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.