A Self-Calibrating Surface-Enhanced Raman Scattering-Active System for Bacterial Phenotype Detection.

Pathogen detection is of significant importance in human health and safety due to the high morbidity and mortality induced by bacterial infections. Therefore, the development of rapid, sensitive, and selective methods for the discrimination of pathogens is the key to improve the patient survival rates. In this work, we develop a new self-calibrating surface-enhanced Raman scattering (SERS)-based sensor that enables sensitive and reproducible pathogen detection in practical samples. The assay makes use of gold nanoflowers (AuNFs) consisting of three components: a solid Au core of ∼15 nm, a hollow gap of ∼1 nm, and a flower-like Au shell. We have demonstrated that the sensitive and quantitative analysis of biomolecules can be achieved by the target-dependent, sequence-specific DNA hybridization assembly between AuNFs with a built-in internal standard. We further demonstrate that this kind of reliable SERS sensor is able to distinguish different bacteria with sensitivity down to single bacterium. We expect that the established quantitative SERS technique could provide a promising tool for widespread applications in biomedical research and clinical diagnostics.