A Chloride Channel Blocker Prevents Inorganic Phosphate Accumulation and Its Effects in the Sarcoplasmic Reticulum of Frog Permeabilized Skeletal Muscle Fibers

Abstract

This study describes the development and characterisation of software to enable automatic detection and analysis of Ca 2þ sparks within x-y image stacks, implemented as a plugin within the open source image analysis platform, ImageJ. The aim was to implement a "conventional" algorithm whereby sparks were identified by applying a threshold (q) to the normalised (F/F 0 ) image: q = background fluorescence within the cell þ SD * 'ε', a user defined variable. A 2 stage interactive method with a graphical user interface (GUI) was used to ensure precise identification of the cell boundary and creation of a binary cell mask, which is subsequently used to exclude all regions outside the cell. The algorithm separates spark detection and analysis, allowing image processing to be applied independently at both stages. Filters also allow exclusion of events based on spark width or morphology. Novel methods are included to allow correction of time dependent changes in background fluorescence (e.g. due to bleaching), which would otherwise compromise spark detection by thresholding. The main outputs (amplitude, width, duration and spark mass) are presented in tabular form. In addition, an interactive GUI allows each spark to be examined, along with its measurements, and the associated Gaussian curve fit. A "Kill" button allows obvious errors in detection to be excluded. The performance of the algorithm was tested both on synthesised images (values of ε ranging from 3.0-4.2 and signal to noise ratios of 2, 3 or 4) and on x-y confocal fluorescence images from fluo-3 loaded rat ventricular myocytes. In both cases the performance was comparable to that reported previously for threshold based detection methods applied to line-scan images.