Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning.
Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning.
复制标题
钙火花检测和基于事件的单个心肌细胞的分类使用深度学习。
DOI：
10.3389/fphys.2021.770051
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发表时间：
2021
影响因子：
4
通讯作者：
Xie W
中科院分区：
文献类型：
--
作者：
Yang S;Li R;Chen J;Li Z;Huang Z;Xie W
钙火花是心肌细胞中基本的钙释放事件，其特性改变会导致钙处理功能受损，最终在多种疾病状态下促使心脏病变的发生。尽管机器学习算法在解读生物和医学数据内容方面的应用日益广泛，但钙火花图像和数据仍有待深入学习和分析。在本研究中，我们开发了一种深度残差卷积神经网络方法来检测钙火花。与传统的通过任意设定阈值来区分信号与噪声的检测方法相比，我们的新方法能够检测到更多幅度较低但时空分布相似的钙火花，这表明新算法能够检测到许多在使用传统检测方法时通常会被忽略的非常微弱的事件。此外，我们提出了一种基于事件的逻辑回归和二元分类模型，利用钙火花特征对单个心肌细胞进行分类，而到目前为止，这些特征通常仅用于正常组和疾病组之间的简单统计分析和比较。利用这种新的检测算法和分类模型，我们成功地以100%的准确率区分了野生型（WT）和RyR2 - R2474S±心肌细胞，以及以95.6%的准确率区分了载体处理组和异丙肾上腺素损伤的野生型心肌细胞。该模型可扩展用于判断少量心肌细胞（乃至整个心脏）是否处于某种特定的心脏疾病状态。因此，本研究为心脏疾病中钙信号的研究和应用提供了一种新颖且强大的方法。
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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影响因子：
3.4
作者：
Steele, Elliot M.;Steele, Derek S.
通讯作者：
Steele, Derek S.
影响因子：
3.4
作者：
Cheng, H;Song, LS;Stern, MD
通讯作者：
Stern, MD
影响因子：
5.3
作者：
Zhang, Xiaohong;Jiang, Liqing;Lu, Xinhong
通讯作者：
Lu, Xinhong
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
DOI：
10.1085/jgp.113.2.187
发表时间：
1999-02
期刊：
The Journal of general physiology
影响因子：
--
作者：
Lacampagne A;Ward CW;Klein MG;Schneider MF
通讯作者：
Schneider MF