Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with “hotspots” generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

中文翻译：

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利用深度学习驱动的 3D 环绕增强 SERS 平台对外泌体蛋白进行无标记多重分析，用于早期癌症诊断

通过 SERS 鉴定血浆外泌体的蛋白质谱可能是早期癌症诊断的一种有前景的策略。然而，由于传统胶体纳米晶体或二维SERS基质检测到的外泌体拉曼信号不完整且复杂，因此通过SERS同时检测多种外泌体蛋白仍然具有挑战性。在此，我们开发了一种新型三维（3D）周围增强SERS平台，称为3D se-SERS，用于外泌体蛋白的多重检测。在这种 3D se-SERS 中，蛋白质和外泌体被金纳米粒子产生的“热点”覆盖，这些“热点”密集地三维包围着分析物，提供敏感且全面的 SERS 信号。将此3D se-SERS与深度学习模型相结合，我们成功定量分析了患者血浆外泌体表面的CD63、CD81、CD9、CD151、CD171、TSPAN8和PD-L1等七种蛋白质，可以预测疾病的发生和发生。肺癌的进展。这种集成深度学习技术的 3D se-SERS 得益于高灵敏度和显着的多重分析能力，可对蛋白质和外泌体进行综合分析，展示了深度学习驱动的 3D se-SERS 技术在基于血浆外泌体的早期癌症诊断中的潜力。