Video image-based analysis of single human induced pluripotent stem cell derived cardiomyocyte beating dynamics using digital image correlation.
BACKGROUND
The 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.


METHODS
The 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.


RESULTS
Our 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.


CONCLUSIONS
Our 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.
Computional Biophysics and Imaging Group, Department of Electronics and Communications Engineering, and BioMediTech, Tampere University of Technology, Tampere, Finland. antti.l.ahola@tut.fi.
Antti
A
Ahola
Anna L
AL
Kiviaho
Kim
K
Larsson
Markus
M
Honkanen
Katriina
K
Aalto-Setälä
Jari
J
Hyttinen
D002454:Cell Differentiation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D057026:Induced Pluripotent Stem Cells / Q000166:cytology*; D018715:Microscopy, Video / Q000379:methods*; D032383:Myocytes, Cardiac / Q000166:cytology*; D059010:Single-Cell Analysis / Q000379:methods*