Imaging fluorescent determination of energetics of chloroplast division and related matters

During exploration of imaging photosynthetic fluorimetry of Arabidopsis thaliana mutants, we discovered a novel phenomenon wherein photosynthetic efficiency (defined in Ning et al., 1995) is shown to plot in discrete groups. This exploration resulted first in the development of a spectrofluorometric method that apparently allows for in vivo observation of division of chloroplast populations in leaves of Arabidopsis thaliana mutants and in the wild-type.; Testing the phenomenon, we examined leaves of monocot plants in which the progression of leaf development and greening follows a linear course upwards along the leaf. The monocots chosen were sugarcane and especially Amaryllis; data from wheat, Narcissus, and other plants are mentioned but not described here. The above results showed that in these plants, chloroplast division phenomenon occurred only where chloroplast division is localized. We found this is also consistent with the postulate that the biphasic energetics observed correspond to the division of this organelle.; To verify the phenomenon further, we performed preliminary confocal microscopy studies in Amaryllis; we saw what seemed to be chloroplast division in the zones where the leaves showed the multiple photosynthetic efficiencies and these results supported the concept that our spectroscopic technique is a real and useful method to observe chloroplast division. Here we also present a novel statistical approach allowing quantification of probability in two-dimensional in vivo fluorescence spectroscopy of these biological samples.; To automate detection of chloroplast division for future use, we develop a digital image processing program we called a software "tool". This tool analyzes photographs of confocal images, identifies chloroplast division and shows statistical information of identified chloroplasts. The statistical information includes distribution of intensity, area and perimeter of each identified chloroplast. We used several image processing techniques to analyze confocal images, including filtering images, object extracting and algorithms in graph theory. We have implemented a "friendly" GUI (Graphical User Interface) that enables a user to perform operations such as correction, addition and deletion of group(s) easily during the execution of the program. By employing an innovative configuration of image analysis techniques, this software tool is able to identify where chloroplast division is occurring and answer related experimental questions.