Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia.
Department of Biochemistry and Molecular Biology, Department of Physiology and Biomedical Engineering, Department of Transplantation, Mayo Clinic, Jacksonville, FL 32224, USA.
Int J Mol Sci. 2023 May 9;24(10):8506. doi: 10.3390/ijms24108506.
Extracellular vesicles (EVs) play important roles in (patho)physiological processes by mediating cell communication. Although EVs contain glycans and glycosaminoglycans (GAGs), these biomolecules have been overlooked due to technical challenges in comprehensive glycome analysis coupled with EV isolation. Conventional mass spectrometry (MS)-based methods are restricted to the assessment of N-linked glycans. Therefore, methods to comprehensively analyze all glyco-polymer classes on EVs are urgently needed. In this study, tangential flow filtration-based EV isolation was coupled with glycan node analysis (GNA) as an innovative and robust approach to characterize most major glyco-polymer features of EVs. GNA is a molecularly bottom-up gas chromatography-MS technique that provides unique information that is unobtainable with conventional methods. The results indicate that GNA can identify EV-associated glyco-polymers that would remain undetected with conventional MS methods. Specifically, predictions based on GNA identified a GAG (hyaluronan) with varying abundance on EVs from two different melanoma cell lines. Enzyme-linked immunosorbent assays and enzymatic stripping protocols confirmed the differential abundance of EV-associated hyaluronan. These results lay the framework to explore GNA as a tool to assess major glycan classes on EVs, unveiling the EV glycocode and its biological functions.
细胞外囊泡 (EVs) 通过介导细胞通讯在 (病理) 生理过程中发挥重要作用。尽管 EVs 包含聚糖和糖胺聚糖 (GAGs),但由于综合糖组分析与 EV 分离相结合的技术挑战,这些生物分子一直被忽视。基于常规质谱 (MS) 的方法仅限于评估 N-连接聚糖。因此,迫切需要开发全面分析 EV 上所有糖聚合物类别的方法。在这项研究中,基于切向流过滤的 EV 分离与聚糖节点分析 (GNA) 相结合,作为一种创新且强大的方法来表征 EV 上大多数主要糖聚合物特征。GNA 是一种分子自上而下的气相色谱-MS 技术,可提供传统方法无法获得的独特信息。结果表明,GNA 可以识别与 EV 相关的聚糖聚合物,这些聚糖聚合物将无法通过传统 MS 方法检测到。具体而言,基于 GNA 的预测鉴定了两种不同黑色素瘤细胞系的 EV 上具有不同丰度的 GAG(透明质酸)。酶联免疫吸附测定和酶剥离方案证实了 EV 相关透明质酸的差异丰度。这些结果为探索 GNA 作为评估 EV 上主要聚糖类别的工具奠定了基础,揭示了 EV 糖码及其生物学功能。