Automated region of interest analysis of dynamic Ca²+ signals in image sequences.

Ca(2+) signals are commonly measured using fluorescent Ca(2+) indicators and microscopy techniques, but manual analysis of Ca(2+) measurements is time consuming and subject to bias. Automated region of interest (ROI) detection algorithms have been employed for identification of Ca(2+) signals in one-dimensional line scan images, but currently there is no process to integrate acquisition and analysis of ROIs within two-dimensional time lapse image sequences. Therefore we devised a novel algorithm for rapid ROI identification and measurement based on the analysis of best-fit ellipses assigned to signals within noise-filtered image sequences. This algorithm was implemented as a plugin for ImageJ software (National Institutes of Health, Bethesda, MD). We evaluated the ability of our algorithm to detect synthetic Gaussian signal pulses embedded in background noise. The algorithm placed ROIs very near to the center of a range of signal pulses, resulting in mean signal amplitude measurements of 99.06 ± 4.11% of true amplitude values. As a practical application, we evaluated both agonist-induced Ca(2+) responses in cultured endothelial cell monolayers, and subtle basal endothelial Ca(2+) dynamics in opened artery preparations. Our algorithm enabled comprehensive measurement of individual and localized cellular responses within cultured cell monolayers. It also accurately identified characteristic Ca(2+) transients, or Ca(2+) pulsars, within the endothelium of intact mouse mesenteric arteries and revealed the distribution of this basal Ca(2+) signal modality to be non-Gaussian with respect to amplitude, duration, and spatial spread. We propose that large-scale statistical evaluations made possible by our algorithm will lead to a more efficient and complete characterization of physiologic Ca(2+)-dependent signaling.