QUANTIFICATION OF THE SYNAPTOSOMAL PROTEOME OF THE RAT CEREBELLUM DURING POST-NA

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 利用多维蛋白质识别技术(MudPIT)等质谱学技术对哺乳动物大脑进行大规模蛋白质组学分析已成功地识别了成百上千种蛋白质。然而，缺乏大规模有效量化大脑中蛋白质表达水平的策略。在这里，我们展示了一种新的脑蛋白质组学量化策略，称为SILAM(哺乳动物稳定同位素标记)。我们利用代谢性标记的(15)N大鼠脑作为内部标准，对出生后发育过程中小脑突触体部分进行MudPIT定量分析。我们量化了四个发育时间点的1138个蛋白质的表达水平，其中196个蛋白质变化被确定为具有统计学意义。在观察到的发育变化中，超过50%以前使用其他蛋白质定量技术进行了报道，我们还发现蛋白质是潜在的神经发育的新调节因子。我们报道了第一个大规模的小脑突触发育的蛋白质组学分析，我们展示了一个有用的量化策略来研究神经疾病的动物模型。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Large-scale proteomic analysis of the mammalian brain has been successfully performed with mass spectrometry techniques, such as Multidimensional Protein Identification Technology (MudPIT), to identify hundreds to thousands of proteins. Strategies to efficiently quantify protein expression levels in the brain in a large-scale fashion, however, are lacking. Here, we demonstrate a novel quantification strategy for brain proteomics called SILAM (Stable Isotope Labeling in Mammals). We utilized a (15)N metabolically labeled rat brain as an internal standard to perform quantitative MudPIT analysis on the synaptosomal fraction of the cerebellum during post-natal development. We quantified the protein expression level of 1138 proteins in four developmental time points, and 196 protein alterations were determined to be statistically significant. Over 50% of the developmental changes observed have been previously reported using other protein quantification techniques, and we also identified proteins as potential novel regulators of neurodevelopment. We report the first large-scale proteomic analysis of synaptic development in the cerebellum, and we demonstrate a useful quantitative strategy for studying animal models of neurological disease.