School of Pharmacy, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang, 110016, China.
Department of Chemistry, Yanbian University, Park Road 977, Yanji, 133002, Jilin Province, China.
Talanta. 2022 Aug 1;245:123452. doi: 10.1016/j.talanta.2022.123452. Epub 2022 Apr 7.
Targeted metabolomics with absolute quantification is widely applied in biomarker identification and drug screening. However, due to the complexity of biological matrix and trace amount of metabolites in plasma, simultaneous quantification of highly polar metabolites in plasma with broad coverage in short time is still challenging. Herein, we proposed a nanoconfined liquid phase nanoextraction (NLPNE) combined with in-fiber derivatization (IFD) strategy that enabled simultaneous quantification of seventy amino-containing analytes in plasma, including amines, nucleosides and their metabolites. Methanol-water (2:1, v/v) was selected as nanoconfined solvent (NCS) to quickly extract highly polar analytes based on the nanoconfinement effect, followed by IFD process directly performed by adding the derivatization reagent benzoyl chloride (BzCl) within 5 min. Besides saving time, this combination strategy was environment-friendly with little organic solvent consumption and cost-effective by using reusable carbon nanofibers. Furthermore, the sensitivity was increased up to 4.92-fold compared with protein precipitation (PP) based conventional derivatization method. Key factors that affected derivatization efficiency including the derivatization time, the amount of derivatization reagent, desorption solution and CNFs, were optimized by response surface methodology (RSM). After systematical method validation, this methodology was applied to determine the multi-metabolites index in plasma of lung cancer using an integrated data processing workflow. Then lung cancer diagnosis model was established through binary logistic regression analysis to make a reference for quick lung cancer screening clinically. Taken together, the NLPNE-IFD LC-MS/MS method for targeted metabolomics enables simultaneous quantification of seventy amino-containing analytes with advantages of broad coverage, high sensitivity, time- and solvent-saving, which could be used on cancer diagnosis clinically.
靶向代谢组学的绝对定量分析广泛应用于生物标志物的鉴定和药物筛选。然而，由于生物基质的复杂性和血浆中代谢物的痕量，在短时间内同时对血浆中具有广泛覆盖的高极性代谢物进行定量分析仍然具有挑战性。在此，我们提出了一种纳米限域液相纳米萃取（NLPNE）与纤维内衍生化（IFD）相结合的策略，该策略能够同时定量分析血浆中的 70 种含氨基酸的分析物，包括胺类、核苷及其代谢物。甲醇-水（2:1，v/v）被选为纳米限域溶剂（NCS），基于纳米限域效应，快速提取高极性分析物，然后在 5 分钟内直接通过添加衍生化试剂苯甲酰氯（BzCl）进行 IFD 过程。除了节省时间外，这种组合策略还具有环保、有机溶剂消耗少和成本效益高的特点，因为它使用了可重复使用的碳纳米纤维。此外，与基于蛋白质沉淀（PP）的常规衍生化方法相比，该方法的灵敏度提高了 4.92 倍。通过响应面法（RSM）优化了影响衍生化效率的关键因素，包括衍生化时间、衍生化试剂用量、解吸液和 CNFs。通过系统的方法验证后，该方法用于通过集成数据分析工作流程确定肺癌患者血浆中的多代谢物指数。然后通过二元逻辑回归分析建立肺癌诊断模型，为临床快速肺癌筛查提供参考。总之，用于靶向代谢组学的 NLPNE-IFD LC-MS/MS 方法能够同时定量分析 70 种含氨基酸的分析物，具有广泛覆盖、高灵敏度、节省时间和溶剂的优点，可用于临床癌症诊断。
