Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profiling single-EV multi-miRNA signatures by combining total internal reflection fluorescence (TIRF) imaging with a deep learning (DL) algorithm for the first time. This innovative technique allows for the precise characterization of EV miRNAs at the single-vesicle level, overcoming the challenges posed by EV heterogeneity. TIRF with high resolution and a signal-to-noise ratio can simultaneously detect multi-miRNAs in situ in individual EVs. DL algorithm avoids complicated and inaccurate artificial feature extraction, achieving automated high-resolution image analysis. Using this approach, we reveal that the main variation of EVs from 5 cancer cells and normal plasma is the triple-positive EV subpopulation, and the classification accuracy of single triple-positive EVs from 6 sources can reach above 95%. In the clinical cohort, 20 patients (5 lung cancer, 5 breast cancer, 5 cervical cancer, and 5 colon cancer) and 5 healthy controls are predicted with an overall accuracy of 100%. This single-EV strategy provides new opportunities for exploring more specific EV biomarkers to achieve cancer diagnosis and classification.

中文翻译：

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深度学习促进单个细胞外囊泡中多个 miRNA 的分析，用于癌症诊断

细胞外囊泡 microRNA (EV miRNA) 是早期癌症诊断的关键非侵入性生物标志物。然而，基于批量分析的准确癌症诊断受到 EV 之间的异质性的阻碍。在此，我们首次报告了一种通过将全内反射荧光（TIRF）成像与深度学习（DL）算法相结合来分析单EV多miRNA特征的方法。这种创新技术可以在单囊泡水平上精确表征 EV miRNA，克服 EV 异质性带来的挑战。具有高分辨率和信噪比的 TIRF 可以同时检测单个 EV 中的多个 miRNA。 DL算法避免了复杂且不准确的人工特征提取，实现自动化高分辨率图像分析。利用这种方法，我们揭示了来自 5 个癌细胞和正常血浆的 EV 的主要变异是三阳性 EV 亚群，并且来自 6 个来源的单个三阳性 EV 的分类准确率可以达到 95% 以上。在临床队列中，对 20 名患者（5 名肺癌、5 名乳腺癌、5 名宫颈癌和 5 名结肠癌）和 5 名健康对照进行预测，总体准确率为 100%。这种单 EV 策略为探索更具体的 EV 生物标志物以实现癌症诊断和分类提供了新的机会。