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.
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