Saftics Andras, Purnell Benjamin, Beres Balint, Thompson S, Jiang Nan, Ghaeli Ima, Lima Carinna, Armstrong Brian, Van Keuren-Jensen Kendall, Jovanovic-Talisman Tijana
Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, California 91010, United States.
Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, H-1111, Hungary.
Anal Chem. 2025 Jan 28;97(3):1654-1664. doi: 10.1021/acs.analchem.4c04614. Epub 2025 Jan 13.
Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content. Recent advances in single EV methods have addressed some of these challenges by providing sensitive tools for assessing individual vesicles; one example is our recently developed Single Extracellular VEsicle Nanoscopy (SEVEN) approach. However, these tools are typically not universally available to the general research community, as they require highly specialized equipment. Here, we show how single EV studies may be democratized via a novel method that employs super-resolution radial fluctuations (SRRF) microscopy and advanced data analysis. SRRF is compatible with a wide range of microscopes and fluorophores. We herein quantified individual EVs by combining affinity isolation (analytical protocol based on SEVEN) with SRRF microscopy and new analysis algorithms supported by machine learning-based EV assessment. Using SEVEN, we first optimized the workflow and validated the data obtained on wide-field and total internal reflection fluorescence microscopes. We further demonstrated that our approach, which we call the SEVEN-Universal Protocol (SEVEN-UP), can robustly assess the number, size, and content of plasma and recombinant EVs. Finally, we used the platform to assess RNA in EVs from conditioned cell culture media. Using SYTO RNASelect dye, we found that 18% of EVs from HEK 293T cells appear to contain RNA; these EVs were significantly larger compared with the general EV population. Altogether, we developed an economical, multiparametric, single EV characterization approach for the research community.
细胞外囊泡(EVs)是所有细胞释放的膜包裹纳米颗粒,紧密参与关键细胞功能。此外,EVs最近已成为令人兴奋的治疗方式、递送载体和生物标志物来源。然而,EVs难以表征,因为它们通常在大小、来源和分子含量上较小且具有异质性。单EV方法的最新进展通过提供评估单个囊泡的灵敏工具解决了其中一些挑战;一个例子是我们最近开发的单细胞外囊泡纳米显微镜(SEVEN)方法。然而,这些工具通常并非普通研究群体普遍可用,因为它们需要高度专业化的设备。在这里,我们展示了如何通过一种采用超分辨率径向涨落(SRRF)显微镜和先进数据分析的新方法,使单EV研究民主化。SRRF与广泛的显微镜和荧光团兼容。我们在此通过将亲和分离(基于SEVEN的分析方案)与SRRF显微镜以及基于机器学习的EV评估支持的新分析算法相结合,对单个EV进行了量化。使用SEVEN,我们首先优化了工作流程,并验证了在宽场和全内反射荧光显微镜上获得的数据。我们进一步证明,我们称之为SEVEN通用方案(SEVEN-UP)的方法能够稳健地评估血浆和重组EVs的数量、大小和内容。最后,我们使用该平台评估条件细胞培养基中EVs的RNA。使用SYTO RNASelect染料,我们发现来自HEK 293T细胞的18%的EVs似乎含有RNA;这些EVs与一般EV群体相比明显更大。总之,我们为研究群体开发了一种经济、多参数的单EV表征方法。