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汇集房水样本:二维液相色谱-串联质谱策略的偏差?

Pooling aqueous humor samples: bias in 2D-LC-MS/MS strategy?

机构信息

Universite Pierre et Marie Curie-Paris6, UMR S 945, Paris F-75013, France.

出版信息

J Proteome Res. 2010 Feb 5;9(2):789-97. doi: 10.1021/pr9006602.

DOI:10.1021/pr9006602
PMID:19929026
Abstract

The proteomic analysis of body fluids presents a major challenge in studies of human diseases. Traditional techniques for protein separation require large volumes and large amounts of protein, which may be difficult to obtain for certain fluids, such as the aqueous humor (AH). Two-dimensional liquid chromatography (2D-LC-MS/MS), adapted for peptides separation from complex protein mixtures, provides an alternative approach in proteomic analysis with a potential utility in biomarker research. We investigated several different 2D-LC-MS/MS methods for use with the AH of patients with cataract, traditionally used as a control group in studies of ocular diseases. We compared analyses of individual samples with analyses of pools of proteins or peptides, and found that the investigation strategy used strongly influenced protein identification. We identified 71 proteins related to extracellular proteins highly abundant in serum (e.g., albumin or transferrin) and involved in various functions, such as transport and metabolism, together with intracellular (myeloblastin) or organelle-specific proteins (cytochrome c). An evaluation of the advantages and disadvantages of each method suggested that individual analyses and the use of peptide mixtures should be favored as complementary techniques in the search for biomarkers in ocular diseases.

摘要

体液的蛋白质组学分析在人类疾病研究中是一个主要的挑战。传统的蛋白质分离技术需要大量的体积和蛋白质,而对于某些液体,如房水 (AH),可能难以获得。二维液相色谱 (2D-LC-MS/MS) 适用于从复杂蛋白质混合物中分离肽,为蛋白质组学分析提供了一种替代方法,在生物标志物研究中有潜在的应用价值。我们研究了几种不同的 2D-LC-MS/MS 方法,用于白内障患者的 AH,AH 通常被用作眼部疾病研究中的对照组。我们比较了对单个样本的分析与对蛋白质或肽的混合样本的分析,发现使用的研究策略强烈影响蛋白质的鉴定。我们鉴定了 71 种与血清中高度丰富的细胞外蛋白 (如白蛋白或转铁蛋白) 相关的蛋白质,以及涉及各种功能的蛋白质,如运输和代谢,以及细胞内 (髓样细胞) 或细胞器特异性蛋白质 (细胞色素 c)。对每种方法的优缺点进行评估后表明,应将单独的分析和使用肽混合物作为互补技术,用于眼部疾病生物标志物的研究。

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