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Updated: Sep 19, 2026

Rapid Detection of Helicobacter pylori Virulence and Typing Using Quantum Dot Labeling Technology
Published on: June 13, 2025
D2O-Labeled Single-Cell Raman Spectroscopy Reveals the Evolutionary Trajectory of Helicobacter pylori During the
Jiayu Zeng1, Pan Sheng1, Ye Qiu1
1Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo and Biosensing, College of Chemistry and Chemical Engineering, College of Environmental Science and Engineering of Hunan Province, Hunan University, Changsha, China.
Abstract:
Helicobacter pylori is a carcinogenic pathogen with a high gastric cancer risk. The rapid emergence of antibiotic resistance severely compromises treatment efficacy. However current conventional approaches fail to capture early molecular events preceding detectable resistance. Here, we used a gradient intermittent antibiotic exposure model to simulate stepwise resistance evolution and longitudinally acquired single-cell Raman spectra across adaptive cycles. By integrating two-dimensional correlation Raman spectroscopy (2D-COS), we resolved the temporal sequence of molecular alterations during the phenotypic silent window. The earliest responses involved adjustments of pre-existing biomass structures. Specifically, symmetric CH2 and CH3 vibrations changed first, reflecting membrane lipid and protein conformational shifts, followed by carbohydrate backbone and alkyl vibrations indicating initial metabolic adjustments. During a structural reorganization phase, amide III shifted earlier than amide I, suggesting protein conformational adjustment precedes content increase, while lipid chain vibration emerged concurrently. Subsequently, nucleic acid base and pyrimidine ring vibrations increased, culminating in the C-D band representing newly synthesized biomass accumulation. This sequential cascade reveals that structural adaptation occurs prior to metabolic activation. Our work provides a detailed molecular basis for resistance evolution within the silent window and highlights single-cell Raman-based metabolic profiling for early detection.
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