Key result
A deep learning-based classification model successfully distinguished wild type from CPVT-mutated cardiomyocytes with 100% accuracy, and vehicle from isoprenaline-treated cardiomyocytes with 95.6% accuracy.
Why the study?
Despite the increasing use of machine-learning algorithms in biological and medical data, Ca2+ spark images and data have yet to be deeply analyzed.
A novel deep learning approach improves the detection of weak Ca2+ sparks and accurately classifies normal versus diseased single cardiomyocytes based on calcium signaling characteristics.
May advance automated Ca2+ spark analysis in research; leaves open validation for disease models and clinical translation.
Ca2+ sparks are the elementary Ca2+ release events in cardiomyocytes, altered properties of which lead to impaired Ca2+ handling and finally contribute to cardiac pathology under various diseases. Despite increasing use of machine-learning algorithms in deciphering the content of biological and medical data, Ca2+ spark images and data are yet to be deeply learnt and analyzed. In the present study, we developed a deep residual convolutional neural network method to detect Ca2+ sparks. Compared to traditional detection methods with arbitrarily defined thresholds to distinguish signals from noises, our new method detected more Ca2+ sparks with lower amplitudes but similar spatiotemporal distributions, thereby indicating that our new algorithm detected many very weak events that are usually omitted when using traditional detection methods. Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca2+ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. Using this new detection algorithm and classification model, we succeeded in distinguishing wild type (WT) vs RyR2-R2474S± cardiomyocytes with 100% accuracy, and vehicle vs isoprenaline-insulted WT cardiomyocytes with 95.6% accuracy. The model can be extended to judge whether a small number of cardiomyocytes (and so the whole heart) are under a specific cardiac disease. Thus, this study provides a novel and powerful approach for the research and application of calcium signaling in cardiac diseases.
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Yang et al. (2021) studied Catecholaminergic polymorphic ventricular tachycardia (CPVT) (n=502). Deep learning-based automated Ca2+ spark detection and classification vs. Traditional threshold-based detection method was evaluated on Accuracy of classifying single cardiomyocytes (WT vs RS, and VEH vs ISO). A deep learning-based classification model successfully distinguished wild type from CPVT-mutated cardiomyocytes with 100% accuracy, and vehicle from isoprenaline-treated cardiomyocytes with 95.6% accuracy.
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