Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning

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 medi...

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Autores principales:	Shengqi Yang, Ran Li, Jiliang Chen, Zhen Li, Zhangqin Huang, Wenjun Xie
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Ca2+ sparks deep learning automated detection classifying single cardiomyocyte cardiac diseases Physiology QP1-981
Acceso en línea:	https://doaj.org/article/565bad70ae1c46d4bc8f3f9321de3ded
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id	oai:doaj.org-article:565bad70ae1c46d4bc8f3f9321de3ded
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spelling	oai:doaj.org-article:565bad70ae1c46d4bc8f3f9321de3ded2021-11-08T13:04:23ZCalcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning1664-042X10.3389/fphys.2021.770051https://doaj.org/article/565bad70ae1c46d4bc8f3f9321de3ded2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphys.2021.770051/fullhttps://doaj.org/toc/1664-042XCa2+ 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.Shengqi YangRan LiJiliang ChenZhen LiZhangqin HuangWenjun XieFrontiers Media S.A.articleCa2+ sparksdeep learningautomated detectionclassifying single cardiomyocytecardiac diseasesPhysiologyQP1-981ENFrontiers in Physiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ca2+ sparks deep learning automated detection classifying single cardiomyocyte cardiac diseases Physiology QP1-981
spellingShingle	Ca2+ sparks deep learning automated detection classifying single cardiomyocyte cardiac diseases Physiology QP1-981 Shengqi Yang Ran Li Jiliang Chen Zhen Li Zhangqin Huang Wenjun Xie Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
description	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.
format	article
author	Shengqi Yang Ran Li Jiliang Chen Zhen Li Zhangqin Huang Wenjun Xie
author_facet	Shengqi Yang Ran Li Jiliang Chen Zhen Li Zhangqin Huang Wenjun Xie
author_sort	Shengqi Yang
title	Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
title_short	Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
title_full	Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
title_fullStr	Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
title_full_unstemmed	Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning
title_sort	calcium spark detection and event-based classification of single cardiomyocyte using deep learning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/565bad70ae1c46d4bc8f3f9321de3ded
work_keys_str_mv	AT shengqiyang calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning AT ranli calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning AT jiliangchen calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning AT zhenli calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning AT zhangqinhuang calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning AT wenjunxie calciumsparkdetectionandeventbasedclassificationofsinglecardiomyocyteusingdeeplearning
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Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning

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