3D RENDITION/QUANTITATIVE ANALYSIS GI2 DEFICIENT MICE

3D 呈现/定量分析 GI2 缺陷小鼠

• 批准号: 7358258
• 负责人: Lutz Birnbaumer
• 金额: $ 2.05万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7358258
• 关键词: 3D; RENDITION; QUANTITATIVE; ANALYSIS; GI2

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. Mice with a disrupted gene encoding (alpha subunit of Gi2 signaling protein) develop inflammatory bowel disease resembling human ulcerative colitis (UC; 1,2) and exhibit reduced number of intestinal Peyers patches. MRM imaging offers a unique opportunity to obtain relevant morphometric data. The changes are 100% penetrant, meaning they occur in all mutant animals, which makes the Gi2 mouse a good model for development and fine tuning of technical and computational refinements needed to make MRM an accessible tool to the scientific community. Problems preparing histological cuts from wild type mice in registry with those prepared from Gi2 mice make it very difficult to both qualitatively and quantitatively describe structural alterations in cerebellums of Gi2 mice. Insight is impossible to obtain using conventional serial histology without a major effort, innumerable manpower hours and manual recording of the data susceptible to errors associated with manual anno

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子弹和调查员（PI）可能已经从其他NIH来源获得了主要资金，因此可以在其他清晰的条目中代表。列出的机构适用于该中心，这不一定是调查员的机构。具有破坏基因编码的小鼠（GI2信号蛋白的α亚基）会出现类似人溃疡性结肠炎（UC; 1,2）的炎症性肠病，并且表现出减少的肠Peyers斑块。 MRM成像提供了获得相关形态计量数据的独特机会。这些变化是100％渗透物，这意味着它们发生在所有突变动物中，这使GI2小鼠成为开发和微调技术和计算改进的良好模型，使MRM成为科学界的可访问工具。 用GI2小鼠制备的野生型小鼠制备组织学削减组织学削减的问题，因此很难在定性和定量地描述GI2小鼠小脑的结构改变。没有大量努力，无数的人力小时和手动记录与手动anno相关的错误的数据记录，就无法使用常规序列组织学获得洞察力