Abstract
Intracellular calcium handling is a complex nonlinear process that regulates cardiac contraction and rhythm, and cardiac arrhythmias have been associated with alterations in the calcium homeostasis. The spatiotemporal behavior of anomalous calcium handling includes a rich variety of local and global spontaneous release events ranging form isolated calcium sparks to multiple calcium waves originating at the same time in different parts of a single cardiomyocyte.We have developed a novel method for automatic detection and characterization of spontaneous calcium release events from a sequence of fluorescence microscopy images. First, a mask of the cell's shape is defined using a thresholding method in order to eliminate the effects of experimental background noise. A total fluorescence signal is then obtained by averaging pixel values within the mask for each frame, which is normalized to baseline fluorescence measured in frames without activity.The resulting signal presents a non-stationary behavior revealing the occurrence of different types of events. A wavelet-based detection method is used in order to identify independent global events in the average signal. Each event is then classified into one of three predefined types: a) single wave propagating across the cell, b) multiple simultaneous local events or c) a train of propagating waves (calcium bursts).Since the average signal does not contain local information it cannot be used to fully distinguish between these three event types. Therefore, we developed a classification method that uses a motion-tracking algorithm to identify each of the local release events contributing to a particular global event. This approach allows determining the trajectory, size, and propagation velocity of each local event and provides a reliable classification of global events. Furthermore, the method quantifies the contribution of each event type to the total calcium leakage during an experiment.
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