The oral microbiota is the second-most complex microbial community in the human body. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg) and Streptococcus mutans (Sm) are among the reported periodontal pathogens. They are biological nanorods with dimensions of 0.8-1.5 µm in length and 0.4-0.8 µm in width. The pathogens possess a large number of virulence factors with a wide range of activities, which enable them to colonize in the oral cavity, invade periodontal tissues, initiate connective tissue destruction, and interfere with tissue repair. It is imperative to develop an easy, fast, and sensitive method to quantitatively detect these and other periodontal causative microorganisms in human saliva. We were able to detect the bacteria by real-time qPCR and molecular beacon. While effective, these detection methods involve critical preparation steps and require labeled reporters. In this work, we applied surface-enhanced Raman spectroscopy (SERS) to achieve label-free detection. High-resolution spectra showed that the high signal-to-noise ratio SERS spectra of different species are similar yet distinctive concerning band locations and intensities. To achieve fast detection, a large set of low-resolution spectra, collected at 30 s per spectrum, were used for machine learning to establish predictive models. Using these models, species identification in a pathogen mixture was achieved at >95% accuracy. To achieve effective detection of these and other pathogens in saliva, we explored the complexity of saliva by analyzing protein, DNA, oral bacteria, and selected periodontal pathogen levels in human saliva. Additionally, low-speed centrifugation and slight dilution were found to effectively remove large particles and reduce saliva viscosity while retain oral bacteria and pathogens in the liquid biopsy. It will facilitate the detection of diverse biomarkers in saliva with various sensing modalities, including the Raman-signal based label-free detection of periodontal pathogens.

Zhenghao Zhao

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