Novel isotopic N,N-Dimethyl Leucine (iDiLeu) Reagents Enable Absolute Quantification of Peptides and Proteins Using a Standard Curve Approach

化学 分析物 色谱法 选择性反应监测 定量蛋白质组学 质谱法 标准曲线 试剂 等压标记 三级四极质谱仪 串联质谱法 分析化学(期刊) 蛋白质组学 蛋白质质谱法 生物化学 有机化学 基因
作者
Tyler Greer,Christopher B. Lietz,Feng Xiang,Lingjun Li
出处
期刊:Journal of the American Society for Mass Spectrometry [American Chemical Society]
卷期号:26 (1): 107-119 被引量:41
标识
DOI:10.1007/s13361-014-1012-y
摘要

Absolute quantification of protein targets using liquid chromatography-mass spectrometry (LC-MS) is a key component of candidate biomarker validation. One popular method combines multiple reaction monitoring (MRM) using a triple quadrupole instrument with stable isotope-labeled standards (SIS) for absolute quantification (AQUA). LC-MRM AQUA assays are sensitive and specific, but they are also expensive because of the cost of synthesizing stable isotope peptide standards. While the chemical modification approach using mass differential tags for relative and absolute quantification (mTRAQ) represents a more economical approach when quantifying large numbers of peptides, these reagents are costly and still suffer from lower throughput because only two concentration values per peptide can be obtained in a single LC-MS run. Here, we have developed and applied a set of five novel mass difference reagents, isotopic N,N-dimethyl leucine (iDiLeu). These labels contain an amine reactive group, triazine ester, are cost effective because of their synthetic simplicity, and have increased throughput compared with previous LC-MS quantification methods by allowing construction of a four-point standard curve in one run. iDiLeu-labeled peptides show remarkably similar retention time shifts, slightly lower energy thresholds for higher-energy collisional dissociation (HCD) fragmentation, and high quantification accuracy for trypsin-digested protein samples (median errors <15%). By spiking in an iDiLeu-labeled neuropeptide, allatostatin, into mouse urine matrix, two quantification methods are validated. The first uses one labeled peptide as an internal standard to normalize labeled peptide peak areas across runs (<19% error), whereas the second enables standard curve creation and analyte quantification in one run (<8% error).
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