3D MOUSE BRAIN PROTEOME MAPPING OF NORMAL AND PARKINSON'S DISEASE MOUSE MODELS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Researchers from UCLA and PNNL are collaborating to develop the first integrated three-dimensional (3-D) model of the transcriptome and proteome constructs of the mouse brain in order to reveal the expression patterns of mRNA and proteins throughout the normal brain and to compare the expression patterns of normal regions to similar regions apparently damaged from disease, drug abuse, or other trauma. A two-fold strategy is being employed to: 1) systematically map the protein and mRNA expression levels in spatially registered volume elements (known as voxels) with 1 cubic millimeter size, and 2) investigate the regulation of mRNA and proteins in brain regions (e.g., striatum) known to be specifically affected by certain diseases. Our initial pilot study will be on methamphetamine and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced brain damage, which produces physiological affects similar to Parkinson's Disease (PD) and primarily damages the striatum of the brain. The goal is to identify and quantify the differential abundance of proteins within the damaged striatum and establish a list of proteins that are most intimately associated with PD-type symptoms. In order to quantitatively map protein expression levels within voxels, we are developing new automated microscale sample-processing and high throughput analysis platform for efficient processing and analyzing the ~600 voxels obtained from a single mouse brain to generate 3-D maps of protein distributions.