Hao Yinyin, Bao Yajing, Huang Xueying, Hu Yijun, Xiong Bo
School of Mathematics and Statistics, Wuhan University Wuhan China.
Key Laboratory of Pesticides & Chemical Biology, Ministry of Education, Institute of Public Health and Molecular Medicine Analysis, College of Chemistry, Central China Normal University Wuhan China
RSC Adv. 2018 Nov 28;8(70):39811-39817. doi: 10.1039/c8ra08276f.
In order to address time-consuming sample pre-treatment and separation prior to mass spectrometry (MS) identifications, highly integrated chips were developed, but damage to any functional unit in these chips would result in complete replacement. Herein, we propose a modular microfluidic platform comprising pre-treatment, liquid chromatography (LC) separation and nanoelectrospray ionization (nESI) chips for on-line enrichment, separation and nESI MS detection of pesticide metabolites and peptides. The pre-treatment chip is applicable in enriching pyridalyl and its metabolites, and it achieves optimal desalination efficiency, 98.5%, for polymerase chain reaction products. Additionally, the LC separation chip was fully characterised, and it demonstrated satisfactory separation efficiency, quantification ability and pressure durability. Finally, the modular microfluidic platform was used to identify the peptides in trypsin-digested casein. Four additional peptides were identified, indicating an improvement in detection ability compared with using off-line zip tips coupled with MS investigations. Because the proposed modular platform can significantly reduce manual work, it would be a potential tool to achieve high throughput and automatic MS identifications with low sample consumptions.
为了解决质谱(MS)鉴定之前耗时的样品预处理和分离问题,人们开发了高度集成的芯片,但这些芯片中任何功能单元的损坏都将导致整个芯片被替换。在此,我们提出了一种模块化微流控平台,该平台由预处理、液相色谱(LC)分离和纳米电喷雾电离(nESI)芯片组成,用于农药代谢物和肽的在线富集、分离和nESI MS检测。预处理芯片适用于富集哒嗪硫磷及其代谢物,对于聚合酶链反应产物,它实现了98.5%的最佳脱盐效率。此外,对LC分离芯片进行了全面表征,其表现出令人满意的分离效率、定量能力和耐压性。最后,使用该模块化微流控平台鉴定了胰蛋白酶消化的酪蛋白中的肽。另外鉴定出了四种肽,这表明与使用离线ZipTip结合MS分析相比,检测能力有所提高。由于所提出的模块化平台可以显著减少人工操作,它将成为一种以低样品消耗实现高通量和自动MS鉴定的潜在工具。