Maltsev Alexander V, Parsons Sean P, Kim Mary S, Tsutsui Kenta, Stern Michael D, Lakatta Edward G, Maltsev Victor A, Monfredi Oliver
Laboratory of Cardiovascular Science, NIA/NIH, Baltimore, Maryland, United States of America.
Farncombe Institute, McMaster University, Hamilton, Ontario, Canada.
PLoS One. 2017 Jul 6;12(7):e0179419. doi: 10.1371/journal.pone.0179419. eCollection 2017.
Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle cells and Ca2+ puffs and syntillas in neurons.
局部钙离子释放(LCRs)是心脏起搏细胞功能中涉及的关键事件。然而,尚未开发出用于在活的、自发搏动的起搏细胞中自动检测和分析LCRs的特定算法。在本研究中,我们使用高速二维相机在从兔子和豚鼠分离的自发收缩的窦房(SA)结细胞中测量LCRs,并开发了一种能够在二维空间和时间上检测和分析LCRs的新算法。我们的算法沿着收缩细胞的中线跟踪点。它将这些点用作仿射变换的坐标系,生成细胞不收缩的变换图像序列。此后,通过应用一系列空间滤波器,从记录噪声中分离出动作电位诱导的钙离子瞬变和LCRs。通过应用于每个像素(细胞位置)的差分(逐帧)灵敏度算法检测LCR的产生和消失事件。当LCR的信号在足够大的区域内变化足够快时,就检测到一个LCR。当LCR的幅度大幅衰减或合并到上升的全细胞钙离子瞬变中时,认为其已经消失。最终,我们的算法提供了主要的LCR参数,如周期、信号质量、持续时间和传播路径面积。随着LCRs在活细胞内传播,该算法识别分裂和合并行为,表明局部传播的钙离子诱导钙离子释放对LCRs的命运和产生强大的整体钙离子信号的重要性。因此,我们的新计算机算法消除了运动伪影,并从记录噪声和全局信号中检测二维局部时空事件。虽然这些算法是为检测窦房结细胞中的LCRs而开发的,但它们有可能用于生物物理学和细胞生理学的其他应用,例如,检测肌肉细胞中的钙离子小波(流产波)、火花和余烬,以及神经元中的钙离子阵发和小串。