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 reported periodontal pathogens. They 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 using our designed primers as well as molecular beacon based fluorescence spectroscopy.  While effective, these detection methods involve critical preparation steps, and signal readout requires labeled reporter molecules. In this project, we applied surface-enhanced Raman spectroscopy (SERS) to achieve label-free detection of these oral bacteria. The results show that individual SERS spectra of the same species obtained at different scans and under different preparations are very similar; whereas the SERS spectra of different species are distinctive concerning band locations and intensities. This suggests the effectiveness of SERS in bacterial identification. To achieve fast detection, a large set of spectra of each species, collected at 30 s per spectrum, are used for machine learning to establish predictive models. Using these models, species identification in a bacterial mixture was successfully achieved. This sensing modality can be applied to the detection of other oral bacteria. When integrated on a microfluidic chip with a tailored detection chamber for high throughput, it will enable label-free, rapid, reusable, and multiplex detection at high sensitivity and high specificity, and will facilitate the development of a portable salivary sensor device.

ML-enabled identification of periodontal pathogens via surface enhanced Raman spectroscopy

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.