Combining Glucose Units, m/z, and Collision Cross Section Values: Multiattribute Data for Increased Accuracy in Automated Glycosphingolipid Glycan Identifications and Its Application in Triple Negative Breast Cancer

Glycan head-groups attached to glycosphingolipids (GSLs) found in the cell membrane bilayer can alter in response to external stimuli and disease, making them potential markers and/or targets for cellular disease states. To identify such markers, comprehensive analyses of glycan structures must be undertaken. Conventional analyses of fluorescently labeled glycans using hydrophilic interaction high-performance liquid chromatography (HILIC) coupled with mass spectrometry (MS) provides relative quantitation and has the ability to perform automated glycan assignments using glucose unit (GU) and mass matching. The use of ion mobility (IM) as an additional level of separation can aid the characterization of closely related or isomeric structures through the generation of glycan collision cross section (CCS) identifiers. Here, we present a workflow for the analysis of procainamide-labeled GSL glycans using HILIC-IM-MS and a new, automated glycan identification strategy whereby multiple glycan attributes are combined to increase accuracy in automated structural assignments. For glycan matching and identification, an experimental reference database of GSL glycans containing GU, mass, and CCS values for each glycan was created. To assess the accuracy of glycan assignments, a distance-based confidence metric was used. The assignment accuracy was significantly better compared to conventional HILIC-MS approaches (using mass and GU only). This workflow was applied to the study of two Triple Negative Breast Cancer (TNBC) cell lines and revealed potential GSL glycosylation signatures characteristic of different TNBC subtypes.

结合于细胞膜双层中糖鞘脂（glycosphingolipids, GSLs）的聚糖头部基团，可随外界刺激与疾病进程发生动态变化，因此可作为细胞疾病状态的潜在标志物或治疗靶点。为鉴定此类疾病相关标志物，需对聚糖结构开展系统性全面分析。传统荧光标记聚糖分析策略采用亲水相互作用高效液相色谱（hydrophilic interaction high-performance liquid chromatography, HILIC）与质谱（mass spectrometry, MS）联用技术，可实现聚糖相对定量，并能够通过葡萄糖单位（glucose unit, GU）与质量匹配完成自动化聚糖注释。将离子迁移（ion mobility, IM）作为额外分离维度，可通过生成聚糖碰撞截面（collision cross section, CCS）标识符，助力密切相关同分异构聚糖结构的表征与区分。本研究提出一套针对普鲁卡因胺（procainamide）标记糖鞘脂聚糖的分析流程，采用HILIC-IM-MS技术，并整合全新的自动化聚糖鉴定策略——通过结合多种聚糖属性以提升自动化结构注释的准确性。为实现聚糖匹配与鉴定，本研究构建了包含每种聚糖的GU值、精确质量数与CCS值的糖鞘脂聚糖实验参考数据库。为评估聚糖注释的准确性，研究采用基于距离的置信度评估指标。与仅采用质量与GU匹配的传统HILIC-MS方法相比，本流程的注释准确性显著提升。该流程被应用于两株三阴性乳腺癌（Triple Negative Breast Cancer, TNBC）细胞系的研究，成功揭示了不同三阴性乳腺癌亚型特征性的糖鞘脂糖基化特征谱。