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

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.

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。

糖蛋白组学作为蛋白质组学的一个重要分支，是当前蛋白质组学研究的热点和难点。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。围绕上述问题，本项目开展了面向糖蛋白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。经过4年的实施，项目在以下几个方面取得进展：（1）将仿生识别技术与硼酸亲和色谱技术相结合，成功制备了一系列糖蛋白质印迹整体材料，实现了痕量血清中低丰度糖蛋白质的高效、高选择性分离富集；（2）将纳米技术与亲和色谱技术相结合，合成了一系列功能化磁性(介孔)纳米复合材料，实现了低丰度糖蛋白/磷酸化蛋白/组蛋白/糖肽等生物标志物的高效、高选择性富集；（3）将纳米技术与整体柱制备技术相结合，成功制备了一系列基于纳米掺杂的新型色谱固定相/毛细管整体柱，实现了（糖）蛋白/肽段的高效、高选择性、高通量、快速分离；（4）利用室温自组装技术，成功制备了一系列具有高催化活性的酶-无机盐杂化纳米花酶反应器，实现了（糖）蛋白质高效、快速酶解；（5）开发了一系列基于纳米基质（球状、片状）的表面辅助激光解吸离子化质谱技术，实现了低分子肽段（MW<600 Da）的高灵敏检测；（6）在此基础上，整合蛋白质预富集、分离及检测系统，成功构建了集成化的多维液相色谱分离分析平台，初步实现了复杂生物体系中（糖）蛋白质的高效、高通量分离，为后续的糖蛋白组学研究提供强有力的技术支撑。该项目部分研究成果荣获2016年度中国分析测试协会科学技术奖（CAIA奖）一等奖。项目执行期内累计发表SCI论文28篇，包括Mass Spectrom. Rev., Anal. Chem., Chem. Commun., ACS Appl. Mater. Interfaces, J. Mater. Chem., B, J. Chromatogr. A等国际权威刊物，研究成果多次被期刊作为亮点或封面形式进行专题报道和评述。申请国家发明专利9项，其中已授权4项。

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