Computer Algorithms for Automated Detection and Analysis of Local Ca2+ Releases in Spontaneously Beating Cardiac Pacemaker Cells

TL;DR

This paper introduces a novel algorithm for automatic detection and analysis of local Ca2+ releases in live, spontaneously beating cardiac pacemaker cells, effectively eliminating motion artifacts and enabling detailed spatiotemporal characterization.

Contribution

The authors developed a new 2D algorithm that tracks cell motion, filters noise, and detects LCRs with parameters like period, duration, and path area, advancing calcium imaging analysis.

Findings

Successfully detected LCRs in sinoatrial node cells

Identified splitting and merging behaviors of LCRs

Potential applications in other cell types and calcium signals

Abstract

Local Ca 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. Here 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 Ca 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. 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 Ca transient. Our algorithm provides major LCR parameters such as period, signal mass, duration, and path area. As LCRs propagate within cells, the algorithm identifies splitting and merging behaviors, indicating the importance of Ca-induced-Ca-release for the fate of LCRs and for generating a powerful ensemble Ca signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal Ca release events from recording noise and global signals. While the algorithms 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 Ca wavelets (abortive waves), sparks and embers in muscle cells and Ca puffs and syntillas in neurons.