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

描述（由申请人提供）：生物样本是生物医学界的宝贵资源。该提案的目标是采用微免疫阵列和反相蛋白质阵列的组合来显着提高抗体验证的通量以及可在生物样本中检查的蛋白质的全面性。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体内和体外围手术期分析前变异来源的关系。我们将记录头颈肿瘤手术切除正常过程中术中变异的标准来源，并将这些变量与差异蛋白表达和修饰联系起来。同时，我们将测量在规定的离体缺血时间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的标准来源。总之，我们将制定手术生物样本取出的标准操作程序，并测试使用微免疫阵列和反相蛋白阵列的平台的功效，该平台用于测量约 140 个生物样本中约 500 种细胞信号蛋白表达的变化，这些生物样本要么受到标准围手术期处理变量的影响，要么受到定义和控制的分析前变异源的影响。通过替代变量分析来控制已知的生物协变量（例如年龄、秒数等），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识别切除后具有显着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指标并识别指示生物样本质量的蛋白质特征。

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.