System for high-throughput, automated design-based stereology
DESCRIPTION (provided by applicant): We propose a commercial product composed of a computerized microscope system that allows investigators to perform automated cell counting and measuring using design-based stereology (DBS), as well as automated reconstruction of cytoarchitecture (ROC), on tissue sections from the nervous system. DBS has become the standard methodology for quantitative histology used in biomedical research. ROC is an emerging technique to get novel insight into the functional architectureof entire cortical areas. However, despite significant progress obtained with computer- based microscopy systems that perform DBS and ROC, both methods remain very labor intensive and time consuming to perform. The proposed project shall overcome this limitation by introducing a novel image representation approach in three-dimensional microscopic imaging, as well as novel data collection methods. We believe that introduction of the robust commercial software solution proposed here will greatly improve throughput for DBS studies, and enable new studies in basic neuroscience and pharmaceutical and biotechnology research and development which are not currently feasible due to the significant amount of work involved. Thus, this novel software solution will provide an improved basis for developing novel treatment strategies for complex CNS diseases. PUBLIC HEALTH RELEVANCE: Design-based stereology and cytoarchitectonic studies yield important information about the organization and composition of the nervoussystem, however these analyses are tedious and labor- intensive. This project commercializes an innovative system for performing high throughput design- based stereology and automated cytoarchitecture reconstruction to rapidly obtain information about number and distribution of neurons, glia and other cells and structures of interest in the central nervous system. High throughput, precise, accurate, and unbiased quantification at an accelerated rate over current methods will enable important new studies inneuroscience research that are not feasible at this time.
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