Micro-western array methodology for assessment of preanalytical variability in bi

描述(申请人提供)：生物标本对生物医学界来说是一种无价的资源。这一建议的目的是利用微蛋白质阵列和反相蛋白质阵列的组合来显著提高抗体验证的吞吐量以及可在生物显微镜中检测的蛋白质的全面性。我们将使用这个平台来检查大约500个蛋白质丰度和修饰状态与分析前可变性的体内和体外来源的关系。我们将记录头颈部肿瘤手术切除正常过程中每一次手术变异的标准来源，并将这些变异与不同的蛋白表达和修饰联系起来。同时，我们将测量在确定的体外缺血时间后蛋白质表达和修饰的变化，以模拟体外围术期分析前变异性的标准来源。综上所述，我们将开发外科生物标本去除的标准操作程序，并测试使用微蛋白阵列和反相蛋白质阵列的平台的有效性，该平台用于测量受标准围手术期处理变量或特定和受控来源的分析前可变性影响的大约140个生物标本中约500个细胞信号蛋白的表达变化。在对已知生物协变量(如年龄、秒等)进行替代变量分析以控制之后，我们将使用线性混合效应建模来确定与分析前可变性相关的基线蛋白质水平。两步三次回归将被用来识别切除后具有显著体外时间表达变化的蛋白质。我们的模型将被用来建立组织质量的度量标准，并识别指示生物样品质量的蛋白质特征。

DESCRIPTION (provided by applicant): Biospecimens are an invaluable resource to the biomedical community. The objective of this proposal is to employ a combination of micro-western arrays and reverse phase protein arrays to dramatically increase the throughput of antibody validation as well as the comprehensiveness of proteins that can be examined in biospecimens. We will use this platform to examine the relationship of about 500 protein abundances and modification states with sources of in- and ex-vivo peri-operative sources of preanalytical variability. We will record standard sources of per-operative variability during the normal course of surgical resection of head-and- neck tumors and relate these variables to differential protein expression and modification. In parallel, we will measure changes in protein expression and modification following defined times of ex-vivo ischemia to mimic a standard source of ex-vivo peri-operative pre-analytical variability. In summary, we will develop standard operating procedures for surgical biospecimen removal and test the efficacy of a platform employing micro- western arrays and reverse phase protein arrays for measuring changes in the expression of about 500 cell signaling proteins in approximately 140 biospecimens subjected either to standard perioperative handling variables or to defined and controlled sources of preanalytical variability. Following surrogate variable analysis to control for known biological covariates (such as age, sec, etc.), we will use linear mixed effects modeling to identify baseline protein levels associated with preanalytical variability. Two-step, cubic regression will be used to identify proteins with significant ex-vivo temporal expression changes following resection. Our model will be used to establish metrics of tissue quality and to identify protein signatures indicative of biospecimen quality.