We have studied three-dimensional reconstruction methods to estimate the cell volume of astroglial cells in primary culture. The studies are based on fluorescence imaging and optical sectioning. An automated image-acquisition system was developed to collect two-dimensional microscopic images. Images were reconstructed by the Linear Maximum a Posteriori method and the non-linear Maximum Likelihood Expectation Maximization (ML-EM) method. In addition, because of the high computational demand of the ML-EM algorithm, we have developed a fast variant of this method. (1) Advanced image analysis techniques were applied for accurate and automated cell volume determination. (2) The sensitivity and accuracy of the reconstruction methods were evaluated by using fluorescent micro-beads with known diameter. The algorithms were applied to fura-2-labeled astroglial cells in primary culture exposed to hypo- or hyper-osmotic stress. The results showed that the ML-EM reconstructed images are adequate for the determination of volume changes in cells or parts thereof.
Cell volume changes are often associated with important physiological and pathological processes in the cell. These changes may be the means by which the cell interacts with its surrounding. Astroglial cells change their volume and shape under several circumstances that affect the central nervous system. Following an incidence of brain damage, such as a stroke or a traumatic brain injury, one of the first events seen is swelling of the astroglial cells. In order to study this and other similar phenomena, it is desirable to develop technical instrumentation and analysis methods capable of detecting and characterizing dynamic cell shape changes in a quantitative and robust way. We have developed a technique to monitor and to quantify the spatial and temporal volume changes in a single cell in primary culture. The technique is based on two- and three-dimensional fluorescence imaging. The temporal information is obtained from a sequence of microscope images, which are analyzed in real time. The spatial data is collected in a sequence of images from the microscope, which is automatically focused up and down through the specimen. The analysis of spatial data is performed off-line and consists of photobleaching compensation, focus restoration, filtering, segmentation and spatial volume estimation.
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