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Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes.
Authors:Franklin Sedarat  Eric Lin  Edwin D W Moore  Glen F Tibbits
Affiliation:Cardiac Membrane Research Laboratory, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.
Abstract:Colocalization of dihydropyridine (DHPR) and ryanodine (RyR) receptors, a key determinant of Ca(2+)-induced Ca2+ release, was previously estimated in 3-, 6-, 10-, and 20-day-old rabbit ventricular myocytes by immunocytochemistry and confocal microscopy. We now report on the effects of deconvolution (using a maximum-likelihood estimation algorithm) on the calculation of colocalization indexes. Clusters of DHPR and RyR can be accurately represented as point sources of fluorescence, which enables a model of their relative distributions to be constructed using images of point spread functions to simulate their fluorescence inside a cell. This model was used to investigate the effects of deconvolution on colocalization as a function of separation distance. Deconvolution resulted in significant improvements in both axial and transverse resolutions, producing significant increases in clarity. Comparisons of intensity profiles (full-width half-maximum) pre- and postdeconvolution showed decreased dispersion of the fluorescent signal and a corresponding decrease in false colocalization as determined by fluorescence modeling. This hypothesis was extended to physiological data previously collected. The number of colocalized voxels was quantified after deconvolution, and the degree of colocalization of DHPR with RyR decreased significantly after deconvolution in all age groups: 3 days (62 +/- 2% before deconvolution, 43 +/- 3 after deconvolution) to 20 days old (79 +/- 1% before deconvolution, 63 +/- 2% after deconvolution). The data demonstrate that confocal images should be deconvolved before any quantitative analysis, such as colocalization index determination, to minimize the detrimental effects of out-of-focus light in coincident voxels.
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