面向糖蛋白质组学的多维毛细管液相色谱-质谱自动化分析平台集成方法研究

规模化糖蛋白分离鉴定及其糖基化位点确定是当前糖蛋白质组研究的重点和难点。在以质谱测序为基础的糖蛋白质组分析方法中，传统基于糖蛋白富集、酶解和糖肽分离的分析策略多采用离线模式进行，存在着样品易丢失、分析费时以及难以实现自动化等问题。针对上述困难，本项目拟发展一种在线的、集糖蛋白富集、酶解、糖肽分离、质谱鉴定于一体的自动化分析平台，其核心技术包括：（1）结合仿生识别技术，发展蛋白质分子印迹整体柱，在线选择性去除高丰度蛋白；（2）结合亲和色谱技术，发展硼酸功能化整体柱，在线特异性富集低丰度糖蛋白；（3）结合纳米技术，发展基于纳米基质掺杂的亲水整体柱/酶反应柱，高效分离/或酶解糖蛋白；（4）集成多级样品预处理、分离及鉴定单元，构建自动化的糖蛋白分析平台。通过本项目实施，有望实现更全面、更低丰度糖蛋白质的鉴定，提高分析的通量和自动化水平，为进一步发现具有潜在生物学意义的功能糖蛋白提供有力的技术支撑。

Large-scale separation and identification of glycoproteins and their glycosylation sites still remain an unsolved problem in current glycoproteome research. Although different analytical strategies have been recently developed for the identification of glycoproteins, more attention are paid on the following procedures, including off-line glycoprotein prefractionation, in-solution digestion and glycopeptide separation before MS identification, which often suffer from some drawbacks such as sample loss, time-consuming and difficulty in automation. To solve these problems, a highly integrated, automated platform of multi-dimensional capillary liquid chromatography-mass spectrometry is proposed for glycoproteome analysis in this proposal, which consists of targeted depletion in-line with glycoprotein fractionation, online digestion, glycopeptide separation and MS identification. In addition, the corresponding monolithic capillary columns, as the key element of the automated platform, are also problems needed to be solved in this proposal, which included as follows: (1) developing a series of molecularly imprinted monolithic columns adopted as in-tube solid-phase microextraction (In-tube SPME) for on-line selective depletion of high-abundance proteins based on the principle of biomimetic recognition; (2) developing a series of boronic acid-functionalized monolithic columns for on-line selective enrichment of low-abundance glycoproteins based on the principle of affinity recognition; (3) developing a series of nanomatrix-doped hydrophilic monolithic columns and enzymatic-immobilized monolithic columns for highly efficient separation and digestion of glycoproteins; (4) constructing an automated platform integrated with the multistep sample pretreatment, separation and MS identification units. Through this proposal, it can be expected to achieve more comprehensive identification of low-abundance glycoproteins, improving the throughput analysis and level of automation. More importantly, it can also provide a strong technical support for further discovery of glycoproteins with potential biological function.