Greco Viviana, Piras Cristian, Pieroni Luisa, Urbani Andrea
Proteomics and metabonomics unit, Fondazione Santa Lucia, IRCCS, Rome, Italy.
Department of Veterinary Medicine, University of Milan, Milan, Italy.
Methods Mol Biol. 2017;1619:3-21. doi: 10.1007/978-1-4939-7057-5_1.
Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.
通过临床蛋白质组学发现生物标志物的血液蛋白质组分析是最具挑战性的任务之一，这是由于样本的复杂性，例如蛋白质浓度动态范围极大且极端异质性，以及需要注意适当的采样和储存条件。血浆和血清的定量和定性蛋白质组学分析对于疾病的早期检测和病理状态评估都可能有用。有两个主要的变异性来源会影响为生物标志物发现和验证而设计的定量实验的精度和准确性。这些来源分为两类，即分析前和分析过程中的，并且常常被忽视；然而，它们可能导致生物标志物研究中出现持续的误差和误解。在本章中，我们回顾了可能影响定量蛋白质组学的关键分析前和分析过程中的变量。根据蛋白质组学界认可的指南，我们针对生物标志物研究提出了一些进行适当蛋白质组学分析的建议和策略。
