Zhang Ruiyuan, Guo Yu, Huang Chen, Fang Jixiang
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Small. 2025 Feb;21(6):e2409806. doi: 10.1002/smll.202409806. Epub 2024 Dec 26.
The heterogeneity of extracellular vesicles (EVs) surface information represents different functions, which is neglected in previous studies. In this study, a label-free SERS analysis approach is demonstrated to study fundamental EV biological and physical information heterogeneity by matching specific sizes of nano-enhanced particles. This strategy reveals informative, comprehensive, and high-quality SERS spectra of the overall exosome surface, and effectively circumvents the key information loss caused by the spatial resistance of NPs binding to the 293 exosomes' concave structure. The classification of normal and cancerous cell-derived exosomes by PCA method, the accuracy is improved from 91.2% to 95.1% by optimizing sizes of nano-enhanced particles. In addition, stem cell-derived EVs of diverse sizes and morphologies similarly show acuity of spectrum variation to NPs size, which is conductive to qualitative studies. This new strategy will offer a widened in-depth understanding of the surface information, size, and morphology of EVs, which can be applied to the study of biological functions.
细胞外囊泡（EVs）表面信息的异质性代表着不同的功能，这一点在以往的研究中被忽视了。在本研究中，展示了一种无标记表面增强拉曼光谱（SERS）分析方法，通过匹配纳米增强颗粒的特定尺寸来研究EVs基本的生物学和物理信息异质性。该策略揭示了整体外泌体表面丰富、全面且高质量的SERS光谱，并有效规避了纳米颗粒（NPs）与293外泌体凹面结构结合的空间阻力所导致的关键信息丢失。通过主成分分析（PCA）方法对正常细胞和癌细胞来源的外泌体进行分类，通过优化纳米增强颗粒的尺寸，准确率从91.2%提高到了95.1%。此外，不同大小和形态的干细胞来源的EVs同样显示出光谱变化对NPs尺寸的敏感性，这有助于定性研究。这种新策略将为深入理解EVs的表面信息、大小和形态提供更广阔的视角，可应用于生物学功能的研究。
