Suppr超能文献

基于外泌体的骨肉瘤诊断的异质性分析。

Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis.

机构信息

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

出版信息

Anal Chem. 2024 Jun 11;96(23):9486-9492. doi: 10.1021/acs.analchem.4c00941. Epub 2024 May 30.

Abstract

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors ( = 37), nonmetastatic OS patients ( = 13), and metastatic patients ( = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

摘要

骨肉瘤(OS)是最常见的原发性骨肿瘤,通常诊断较晚,预后不良。目前,针对早期 OS 检测,缺乏具有高可信度的有效生物标志物或诊断方法,尤其是转移性 OS。肿瘤来源的细胞外囊泡(EVs)作为液体活检中用于早期癌症诊断的有前途的生物标志物而备受关注。在此,我们报告了一种基于等离子体成像的生物传感技术,称为通过单 EV 计数的亚群蛋白分析(SPASEC),用于进行基于大小的 EV 亚群分析。在我们的 SPASEC 平台中,在涂有 OS 特异性抗体的等离子体传感器芯片上准确地对 EV 进行大小测量和计数。随后,根据大小将 EV 分类为不同的亚群,并对每个大小依赖的亚群的膜蛋白进行分析。我们测量了来自 OS 细胞系和临床血浆样本的每个 EV 亚群中 EV 标志物(CD63、BMP2、GD2 和 N-钙黏蛋白)的异质表达水平。使用线性判别分析(LDA)模型,将四个标志物的组合应用于对健康供体( = 37)、非转移性 OS 患者( = 13)和转移性患者( = 12)进行分类,曲线下面积分别为 0.95、0.92 和 0.99。SPASEC 为早期 OS 诊断提供了准确的 EV 传感技术。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验