Increasing Sensitivity of Ca2+ Spark Detection in Noisy Images by Application of a Matched-Filter Object Detection Algorithm

Abstract

Microscopic calcium (Ca2+) events (such as Ca2+ sparks) are an important area for study, as they help clarify the mechanism(s) underlying intracellular signaling. In the heart, Ca2+ sparks occur as a result of Ca2+ release from the sarcoendoplasmic reticulum, via ryanodine receptor channels. Measurement of Ca2+ spark properties can provide valuable information about the control of ryanodine receptor channel gating in situ, but requires high spatiotemporal resolution imaging, which produces noisy datasets that are problematic for spark detection. Automated detection algorithms may overcome visual detection bias, but missed and false-positive events can distort the distribution of measured Ca2+ spark properties. We present a sensitive and reliable method for the automated detection of Ca2+ sparks in datasets obtained using confocal line-scanning or total internal reflection fluorescence microscopy. This matched-filter detection algorithm (MFDA) employs a user-defined object, chosen to mimic Ca2+ spark properties, and the experimental dataset is searched for instances of the object. Detection certainty is provided by nonparametric statistical testing. The supplied codes can also refine the search object on the basis of those detected to further increase detection sensitivity. In comparison to a commonly used, intensity-thresholding algorithm, the MFDA is more sensitive and reliable, particularly at low signal/noise ratios. The MFDA can also be easily adapted to other signal-detection problems in noisy datasets.