Morphometric Analysis of Brains and Neurons

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DESCRIPTION (provided by applicant): Modern biomedical imaging technologies allow for generation of high-resolution digital 3D images of many microscopic biological objects. However, effective strategies remain to be developed for automatic quantitative and statistical analysis of such microscopic structures' 3D morphologies. This proposal is aimed at developing algorithms for automatic morphometric analysis of neurons in intact brains. Quantitative and statistical characterization of individual neurons' spatial locations and their 3D projection patterns is not only essential for understanding brains' complexity, diversity, and plasticity with single-cell resolution, but also critical for elucidating subtle cellular pathological mechanisms underlying various neurological/mental/psychological disorders. New expertise will be explored to advance technologies in multiple areas of biomedical imaging, such as image computation and simulations of complex tissues. A GAL4-independent binary transcriptional system has been developed to label specifically the entire morphologies of the Drosophila olfactory learning and memory center, the mushroom bodies (MBs). In conjunction with MARCM (Mosaic Analysis with a Repressible Cell Marker) technologies, one can independently label various single MB neurons and the whole MBs in the same brains. Meanwhile, new algorithms have been developing to conduct automatic morphing (morphological deformation & matching) of irregular-shaped 3D objects. A virtual average MB will be constructed via statistical characterization of pair-wise morphing among multiple "standard" MBs. Morphometric analysis of distinct MBs and spatial mapping of individual MB neurons will then involve establishing point-to-point correspondence between the MBs of interest or the MBs, in which specific single MB neurons are differentially labeled, and the statistical model MB. Thus, one may be able to detect automatically and describe quantitatively any given MB's structural deviations and to identify individual MB neurons based on their 3D neuronal location/projection patterns.