适体
化学
检出限
滚动圆复制
电感耦合等离子体质谱法
粒子(生态学)
质谱法
胶体金
纳米技术
生物物理学
色谱法
纳米颗粒
分析化学(期刊)
DNA
分子生物学
生物化学
材料科学
DNA复制
海洋学
生物
地质学
作者
Xuewei Zhang,Guanxiao Qi,Shuai Chen,Yong‐Liang Yu,Jian‐Hua Wang
标识
DOI:10.1021/acs.analchem.4c02066
摘要
Tumor-derived extracellular vesicles (TEVs) are rich in cellular information and hold great promise as a biomarker for noninvasive cancer diagnosis. However, accurate measurement of TEVs presents challenges due to their low abundance and potential interference from a high number of EVs derived from normal cells. Herein, an aptamer-proximity-ligation-activated rolling circle amplification (RCA) method for EV membrane recognition, coupled with single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) for the quantification of TEVs, is developed. When DNA-labeled ultrasmall gold nanoparticle (AuNP) probes bind to the long chains formed by RCA, they aggregate to form large particles. Notably, small AuNPs scarcely produce pulse signals in sp-ICP-MS, thereby detecting TEVs in a wash-free manner. By leveraging the strong binding affinity of aptamers, dual aptamers for EpCAM and PD-L1 recognition, and the sp-ICP-MS technique, this method offers remarkable sensitivity and selectivity in tracing TEVs. Under optimized conditions, the present method shows a favorable linear relationship between the pulse signal frequency of sp-ICP-MS and TEV concentration within the range of 105–107 particles/mL, along with a detection limit of 1.1 × 104 particles/mL. The pulse signals from sp-ICP-MS combined with machine learning algorithms are used to discriminate cancer patients from healthy donors with 100% accuracy. Due to its simple and fast operation and excellent sensitivity and accuracy, this approach holds significant potential for diverse applications in life sciences and personalized medicine.
科研通智能强力驱动
Strongly Powered by AbleSci AI