Automated time-lapse microscopy imaging provides an important and revolutionary method to study dynamic cellular processes and to measure drug response in a dynamic fashion. The availability of fluorescent protein markers makes it possible to monitor mitosis and apoptosis in living cells over extended periods of imaging. Nevertheless, there are significant informatics challenges in processing, modeling, managing, and analyzing large volumes of cellular images generated by time-lapse microscopy in studying dynamic cellular information. The existing bioimaging tools are extremely limited in their scope and capacity for image analysis for live-cell imaging, particularly in respect of time-lapse and high throughput data. Currently, the scientists have to rely on slow, manual analysis to extract information. Thus, image informatics has become the rate-limiting factor in dynamic molecular and cellular imaging studies. This proposal seeks to fill that gap by providing an advanced software package, dynamic cellular image quantitator (D-CELLIQ), with increased capacity to identify and track objects and to analyze and quantitate object features extracted from the large amounts of images generated by time-lapse microscopy, providing a complete picture of the evolution of the features and behaviors of cells in time and space. The hypothesis is that the D-CELLIQ system will be useful to measure cell cycle progression, mitotic timing and initiation of apoptosis in cells observed by time-lapse imaging and to characterize the mechanism of action of novel antimitotic compounds. To test this hypothesis, we aim to define the integrated data processing pipeline and architecture of D-CELLIQ, develop new cellular image analysis and computational modeling tools, and evaluate the utility of the D-CELLIQ with a set of well defined, biological-driven experiments. There are currently no software packages that can perform such analyses, and we therefore plan to make this package freely available to biomedical community.
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