Dynamic region of interest histometric analysis of endogenous calcium activity in intact lung tissue

Calcium (Ca2+) signaling in endothelial cells plays an important role in regulation of wide range of physiological processes in the pulmonary microcirculation. A dysregulation in the intracellular Ca2+ concentration can serve as an initiator of different pathological conditions like lung endothelial barrier disruption, edema, inflammation, etc. Normal physiologic signaling is localized spatio-temporally and signaling 'signatures' define the specificity of outcomes within the cells. However, analyzing the signaling dynamics acquired through imaging of live tissues has been challenging owing to the intricate patterns of the distinct signals. Moreover, signal analysis tools based on whole-field or static region of interest (ROI) assessments may under- or overestimate measurements of signaling parameters with respect to event origination, spread and duration. In the current study we designed an algorithm for detection and analysis of these biological signaling events based on dynamic ROI tracking, where time-dependent polygonal ROIs are automatically assigned to the changing perimeters of detected signaling events. This approach allowed for robust tracking of signals and quantification of the signaling event parameters over time We next applied this algorithm on image sequences of lung slices isolated from genetically encoded mice expressing the endothelial specific cdh5GCaMP8 (GFP-based) calcium indicator, and observed an inherent dynamic Ca2+ signaling profile within the pulmonary microvascular endothelium. To investigate the versatility of our algorithm, we further treated the lung slices with acetylcholine (ACh) or subjected them to Ca2+-free (CAF) medium, to examine the emerging change in patterns of the profiles from the inherent basal dynamics. Under both these conditions, our software revealed distinct changes in event parameters with respect to event amplitude, duration and spread of the signaling events. Thus, our algorithm allowed us to identify distinct Ca2+ signaling patterns associated with various stimuli, thereby enabling identification of these signatures under a wide variety of sub-cellular/pathologic challenges.