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在线胰蛋白酶消化结合液相色谱-串联质谱分析进行蛋白质定量的要点

Nuts and Bolts of Protein Quantification by Online Trypsin Digestion Coupled LC-MS/MS Analysis.

作者信息

Toth Christopher A, Kuklenyik Zsuzsanna, Barr John R

机构信息

Division of Laboratory Sciences, Centers for Disease Control and Prevention, Atlanta, GA, USA.

出版信息

Methods Mol Biol. 2019;1871:295-311. doi: 10.1007/978-1-4939-8814-3_19.

DOI:10.1007/978-1-4939-8814-3_19
PMID:30276747
Abstract

Protein digestion coupled to liquid chromatography and tandem mass spectrometry (LC-MS/MS) detection enables multiplexed quantification of proteins in complex biological matrices. However, the reproducibility of enzymatic digestion of proteins to produce proteotypic target peptides is a major limiting factor of assay precision. Online digestion using immobilized trypsin addresses this problem through precise control of digestion conditions and time. Because online digestion is typically for a short time, the potential for peptide degradation, a major source of measurement bias, is significantly reduced. Online proteolysis requires minimal sample preparation and is easily coupled to LC-MS/MS systems, further reducing potential method variability. We describe herein a method optimized for the multiplexed quantification of several apolipoproteins in human serum using on-column digestion. We highlight key features of the method that enhance assay accuracy and precision. These include the use of value-assigned serum as calibrators and stable isotope-labeled (SIL) peptide analogs as internal standards. We also comment on practical aspects of column switching valve design, instrument maintenance, tandem mass spectrometry data acquisition, and data processing.

摘要

蛋白质消化与液相色谱和串联质谱(LC-MS/MS)检测相结合,能够对复杂生物基质中的蛋白质进行多重定量分析。然而,蛋白质酶解产生蛋白型靶肽的重现性是分析精度的主要限制因素。使用固定化胰蛋白酶进行在线消化,可通过精确控制消化条件和时间来解决这一问题。由于在线消化通常时间较短,肽降解(测量偏差的主要来源)的可能性会显著降低。在线蛋白水解所需的样品制备最少,并且易于与LC-MS/MS系统联用,进一步降低了潜在的方法变异性。我们在此描述一种经过优化的方法,用于使用柱上消化对人血清中的几种载脂蛋白进行多重定量分析。我们强调了该方法中提高分析准确性和精密度的关键特征。这些特征包括使用赋值血清作为校准物,以及使用稳定同位素标记(SIL)肽类似物作为内标。我们还对柱切换阀设计、仪器维护、串联质谱数据采集和数据处理的实际方面进行了评论。

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