谷歌浏览器插件
订阅小程序
在清言上使用

On-line duplex molecularly imprinted solid-phase extraction for analysis of low-abundant biomarkers in human serum by liquid chromatography-tandem mass spectrometry

Journal of Chromatography A(2021)

引用 7|浏览28
暂无评分
摘要
In the present work, a pair of molecularly imprinted polymers (MIPs) targeting distinct peptide targets were packed into trap columns and combined for automated duplex analysis of two low abundant small cell lung cancer biomarkers (neuron-specific enolase [NSE] and progastrin-releasing peptide [ProGRP]). Optimization of the on-line molecularly imprinted solid-phase extraction (MISPE) protocol ensured that the MIPs had the necessary affinity and selectivity towards their respective signature peptide targets - NLLGLIEAK (ProGRP) and ELPLYR (NSE) - in serum. Two duplex formats were evaluated: a physical mix-ture of the two MIPs (1:1 w/w ratio) inside a single trap column, and two separate MIP trap columns connected in series. Both duplex formats enabled the extraction of the peptides from serum. However, the trap columns in series gave superior extraction efficiency (85.8 +/- 3.8% and 49.1 +/- 6.7% for NLLGLIEAK and ELPLYR, respectively). The optimized protocol showed satisfactory intraday (RSD <23.4 %) and interday (RSD <14.6%) precision. Duplex analysis of NSE and ProGRP spiked into digested human serum was linear (R-2 >= 0.98) over the disease range (0.3-30 nM). The estimated limit of detection (LOD) and limit of quan-tification (LOQ) were 0.11 nM and 0.37 nM, respectively, for NSE, and 0.06 nM and 0.2 nM, respectively, for ProGRP. Both biomarkers were determined at clinically relevant levels. To the best of our knowledge, the present work is the first report of an automated MIP duplex biomarker analysis. It represents a proof of concept for clinically viable duplex analysis of low abundant biomarkers present in human serum or other biofluids. (C) 2021 The Author(s). Published by Elsevier B.V.
更多
查看译文
关键词
On-line SPE,Multiplexing,Molecularly imprinted polymers,LC-MS/MS,Low-abundant biomarkers,Targeted bottom-up proteomics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要