Novel Magnetic-Bead-Assisted Sequential Extraction Method for Liquid Chromatography-Mass Spectrometry (MS)/MS of Components with Diverse Properties: Gastrin Determination as a Case Study

化学 色谱法 分析物 质谱法 胃泌素 样品制备 萃取(化学) 固相萃取 液相色谱-质谱法 分泌物 生物化学
作者
Xiaoli Ma,Yutong Zou,DanChen Wang,Danni Mu,Jian Zhong,Fengying Gong,Huijuan Zhu,Ailing Song,Songlin Yu,Ling Qiu
出处
期刊:Analytical Chemistry [American Chemical Society]
被引量:11
标识
DOI:10.1021/acs.analchem.2c02970
摘要

Sample preparation is the rate-limiting step in liquid chromatography-mass spectrometry (LC-MS)/MS-based clinical analysis when target analytes possess significantly different properties. Repeated solid-phase extraction (SPE) processes are typically required, resulting in low throughput and excessive consumption of labor, materials, and samples. In this study, we developed and validated a feasible and productive method to enrich target analytes with different properties during a single operation, while sufficiently removing matrix interferences to meet LC-MS/MS requirements. Gastrin determination was selected as the subject of this study. An automated magnetic-bead-assisted sequential extraction (MBASE) workflow was developed to simultaneously isolate nonsulfated gastrin-17 (G17ns), sulfated gastrin-17 (G17s), nonsulfated gastrin-34 (G34ns), and sulfated gastrin-34 (G34s) from human serum. It performs two different ion-exchange-based magnetic-bead extraction steps on one sample aliquot to produce one combined extract for LC-MS/MS analysis. When compared with the traditional SPE process, the MBASE workflow saves over 75% time and labor expenses as well as over 90% material cost, while providing even higher extraction efficiency. The MBASE LC-MS/MS method was validated as accurate and robust. Clinical sample test results demonstrated that the conventional chemiluminescence immunoassay method significantly under-estimated total gastrins in human serum, and the MBASE LC-MS/MS method could serve as an ideal tool to provide a comprehensive and accurate gastrin profile.
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