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利用代谢标记评估定量蛋白质组学中最大技术差异源的流程。

Pipeline to assess the greatest source of technical variance in quantitative proteomics using metabolic labelling.

机构信息

Centre for Proteomics, Cambridge System Biology Centre, Department of Biochemistry, University of Cambridge, CB2 1QR, UK.

出版信息

J Proteomics. 2012 Dec 21;77:441-54. doi: 10.1016/j.jprot.2012.09.020. Epub 2012 Oct 16.

Abstract

The biological variance in protein expression of interest to biologists can only be accessed if the technical variance of the protein quantification method is low compared with the biological variance. Technical variance is dependent on the protocol employed within a quantitative proteomics experiment and accumulated with every additional step. The magnitude of additional variance incurred by each step of a protocol should be determined to enable design of experiments maximally sensitive to differential protein expression. Metabolic labelling techniques for MS based quantitative proteomics enable labelled and unlabelled samples to be combined at the tissue level. It has been widely assumed, although not yet empirically verified, that early combination of samples minimises technical variance in relative quantification. This study presents a pipeline to determine the variance incurred at each stage of a common quantitative proteomics protocol involving metabolic labelling. We apply this pipeline to determine whether early combination of samples in a protocol leads to significant reduction in experimental variance. We also identify which stage within the protocol is associated with maximum variance. This provides a blueprint by which the variance associated with each stage of any protocol can be dissected and utilised to influence optimal experimental design.

摘要

如果蛋白质定量方法的技术变异与生物学变异相比较低,则可以研究生物学家感兴趣的蛋白质表达的生物学变异。技术变异取决于定量蛋白质组学实验中采用的方案,并在每个附加步骤中积累。应确定方案中每个步骤引起的额外方差的大小,以实现对差异蛋白质表达最大程度敏感的实验设计。基于 MS 的定量蛋白质组学的代谢标记技术可使标记和未标记的样品在组织水平上混合。虽然尚未经过经验验证,但人们普遍假设,尽早将样品混合可最大程度地减少相对定量中的技术变异。本研究提出了一种确定涉及代谢标记的常见定量蛋白质组学方案中每个阶段产生的变异的流程。我们应用此流程来确定在方案中尽早混合样品是否会导致实验变异显著减少。我们还确定了方案中哪个阶段与最大方差相关。这为剖析与任何方案的每个阶段相关的方差并利用其来影响最佳实验设计提供了蓝图。

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