Although oxidative stress processes based on reactive oxygen species have been the research focus, analyzing this phenomenon at the single-cell level is challenging. A microfluidic platform combined with highly sensitive surface-enhanced Raman scattering (SERS) technology was constructed. The secretion of the vascular endothelial growth factor (VEGF) and the extracellular microenvironmental pH fluctuations of a single cell during the oxidative stress process were assessed and analyzed. The immune sandwich structure formed between the capture probe and the reporter probe with the linkage of secreted VEGF was built above the probed cell surface in each drop. The self-driven collection behavior of the magnetic bead-based capture probe toward the cell surface significantly amplified the SERS signals of the reporter probe, thus improving the sensitivity and accuracy of detection. 4-Mercaptopyridine (4-Mpy) responding sensitively to pH was applied to label the reporter probe, which endows the extracellular microenvironmental pH sensing during oxidative stress events according to relative SERS intensity. Combined with machine learning to analyze the spectral characteristics, the distinction between normal cells and different types of tumor cells was finally realized. This study makes full use of the advantages of machine learning to process spectral data to explore deep information and provides an auxiliary tool for diagnosing cancer and other medical conditions in the future.

中文翻译：

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Microdroplet-SERS 平台，用于氧化应激事件中单细胞分泌的 VEGF 和细胞外 pH 分析


尽管基于活性氧的氧化应激过程一直是研究重点，但在单细胞水平上分析这种现象具有挑战性。构建了结合高灵敏度表面增强拉曼散射（SERS）技术的微流控平台。评估和分析氧化应激过程中单细胞血管内皮生长因子（VEGF）的分泌和细胞外微环境pH波动。捕获探针和报告探针之间形成的免疫夹心结构与分泌的 VEGF 连接，在每滴中被探测的细胞表面上方构建。基于磁珠的捕获探针对细胞表面的自驱动收集行为显着放大了报告探针的SERS信号，从而提高了检测的灵敏度和准确性。对 pH 敏感的 4-巯基吡啶 (4-Mpy) 用于标记报告探针，根据相对 SERS 强度，在氧化应激事件期间赋予细胞外微环境 pH 感测。结合机器学习分析光谱特征，最终实现正常细胞与不同类型肿瘤细胞的区分。这项研究充分利用机器学习处理光谱数据的优势来探索深层信息，为未来诊断癌症和其他医疗状况提供辅助工具。