Automatic Detection and Classification of Ca2+ Release Events in Confocal Line- and Framescan Images
Analysis of Ca2+ signaling in cardiac cells is always a trade-off between acquisition speed and signal-to-noise ratio. This becomes especially apparent in confocal microscopy, during fast 2D scanning or when recording fluorescence signals from the sarcoplasmic reticulum, for example. Methods have been developed to remedy this via 'denoising' the image by fitting each pixel with a transient function. So far, adoption of such methods has been hindered by a number of limitations (e.g., inability to fit local, concurrent and consecutive events) and the limited availability of a customizable implementation.
Here we present a novel method for performing per-pixel denoising of confocal frame- and linescans. Our algorithm permits the extraction of spatiotemporally overlapping events (e.g., a Ca2+ spark occurring during the decaying phase of a Ca2+ wave) and is able to detect various different types of events within a pixel time course. The method estimates a non-constant baseline for each pixel, negating the necessity of using background regions or self-ratio methods prior to performing the analysis. Furthermore, by applying a clustering algorithm, detected single-pixel events are grouped into physiologically relevant events spanning multiple pixels (sparks, waves, puffs,transients, etc.), from which traditional parameters such as FDHM, FWHM, amplitude, wave speed, rise and decay times, can be easily extracted.
The method has been implemented as a cross-platform open source software with a comprehensive and easy to use graphical user interface. We have applied our method to analyzing linescans of repetitive Ca2+ sparks from individual RyR clusters in isolated ventricular cardiomyocytes; high-speed (150 frames/sec) framescans containing alterations in Ca2+ release events in atrial myocytes; and parallel analysis of Ca2+ release dynamics in the sarcoplasmic reticulum and cytosol.
Elementary Ca2+ signaling events occur in many different cell types and exhibit a variety of spatiotemporal features, according to which they were classified. For such events an ever growing nomenclature has been developed (e.g. Ca2+ sparks, puffs, blinks, quarks, etc., for review see Niggli, E., and N. Shirokova. 2007. A guide to sparkology: The taxonomy of elementary cellular Ca2+ signaling events. Cell Calcium. 42:379–387.). More complex Ca2+ signals, such as Ca2+ waves and whole-cell Ca2+ transients are often composed of a variable number of such elementary events.
Numerous methods exist for analysing Ca2+ sparks and other types of elementary and global Ca2+ signals in confocal linescan images. These employ various different approaches: noise tresholding (Ríos 2001, Picht 2007), wavelet transform (Szabó 2010), etc.
Recently, a method was developed by Tian et al., (Tian 2012), where the fluorescence time trace in each pixel is fitted and provides a practically noise free approximation of the original fluorescence data. This pixel-by-pixel method had, however, several limitations which made it impractical to be used for Ca2+ release event detection.
Here we extend the method by Tian et al., in several ways. The new method allows for Ca2+ release event classification based on pixel-by-pixel denoising of the original signal.