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

描述（由申请人提供）：生物标本是生物医学界的宝贵资源。该提案的目的是采用微蛋白质阵列和反相蛋白质阵列的组合，以显着增加抗体验证的通量以及可以在生物标本中检查的蛋白质的全面性。我们将使用这个平台来研究约500种蛋白质丰度和修饰状态与分析前变异性的体内和体外围手术期来源的关系。我们将在头颈部肿瘤手术切除的正常过程中记录术前变异性的标准来源，并将这些变量与差异蛋白质表达和修饰联系起来。与此同时，我们将测量定义时间的离体缺血后蛋白质表达和修饰的变化，以模拟离体围手术期分析前变异性的标准来源。总之，我们将制定手术生物标本取出的标准操作规程，并测试采用微蛋白质阵列和反相蛋白质阵列的平台的有效性，以测量约140份生物标本中约500种细胞信号蛋白表达的变化，这些生物标本受到标准围手术期处理变量或分析前变异性的定义和控制来源的影响。在进行替代变量分析以控制已知的生物学协变量（如年龄、sec等）之后，我们将使用线性混合效应模型来确定与分析前变异性相关的基线蛋白质水平。两步三次回归将用于鉴定切除后具有显著离体时间表达变化的蛋白质。我们的模型将用于建立组织质量的指标，并确定指示生物标本质量的蛋白质签名。

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.