肿瘤源性细胞外囊泡（EVs）有望用于早期癌症监测。然而，从患者的液体活检中分离和分析EVs是具有挑战性的。为此，我们设计了一种基于Förster共振能量转移（FRET）信号的EV膜蛋白检测系统（EV-MPDS），通过其中的适配体量子点和AIEgen染料之间的FRET信号，消除了EV的提取和纯化，从而方便地诊断肺癌。在80个临床样本的队列中，这种系统相对于ELISA检测方法，在癌症诊断的准确性（100%对比65%）和敏感性（100%对比55%）方面表现出增强的效果。通过使用机器学习分析系统全面分析五个生物标志物，提高了早期筛查的准确性（从96.4%提高到100%）。基于FRET的肿瘤EV-MPDS因此是一种无需分离、低体积（1 μL）且高精准度的方法，为肺癌诊断和早期筛查提供了潜在机会。

Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.