拉曼光谱在生物传感和临床研究方面取得了重大进展。在这里，我们描述了表面增强拉曼光谱 (SERS) 如何在机器学习 (ML) 的帮助下扩展其功能，从而在单细胞水平上对转录组、蛋白质组和代谢组进行可解释的洞察。我们首先回顾了纳米光子学（包括等离子体、超材料和超表面）的进步如何增强拉曼散射，以实现快速、强的无标记光谱。然后，我们讨论用于精确且可解释的谱分析的机器学习方法，包括神经网络、扰动和梯度算法以及迁移学习。我们提供了使用纳米光子学和机器学习进行单细胞拉曼表型分析的说明性示例，包括细菌抗生素敏感性预测、干细胞表达谱、癌症诊断以及免疫治疗功效和毒性预测。最后，我们讨论了单细胞拉曼光谱的未来令人兴奋的前景，包括拉曼仪器、自动驾驶实验室、拉曼数据库和用于揭示生物学见解的机器学习。

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.