The oral microbiota is the second-most complex microbial community in the human body. Among reported periodontal pathogens, Actinobacillus actinomycetemcomitans, (Aa) is one of the most convincingly implicated as etiological agents in periodontitis. It possesses a large number of virulence factors with a wide range of activities which enable it to colony at 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 device to quantitatively detect this and other micro-organisms in human saliva. We isolated DNA from Aa. and quantitatively determined it by real-time PCR using our designed primers. The linear detection range was 2.3x101 to 2.2x104 CFU/mL. The primer’s specificity was confirmed by detecting Aa. in the presence of 200 folds of E-Coli, another gram-negative bacterium. We also analyzed four volunteers Saliva samples and were able to detect Aa at 1.43 x103, 5.00 x103, 6.09 x103, 2.06 x104 CFU/mL, respectively. However, most detection methods involve critical preparation steps, and the signal readout requires labeled reporter molecules. In this project, we aim to apply hole-enhanced Raman spectroscopy to establish a high throughput, hyperspectral imaging approach. Specifically, nanoholes in thin gold films were fabricated by colloidal lithography. By collecting a large number of Raman spectra of pure Aa, predictive models for Aa is established by coupling with machine learning. The pathogen is expected to be further tested in the concentration range from 10 to 107 CFU/mL to establish the detection limit, optimize the sensitivity and specificity in a mixture of oral bacteria. The sensing modality can be applied to the detection of other oral bacteria. When integrated on a microfluidic chip, it will enable label-free, rapid, reusable, multiplex detection at high sensitivity and high specificity, and will facilitate the development of a portable salivary sensor device.

Development of salivary sensor for detection of Actinobacillus actinomycetemcomitans by hole-enhanced Raman spectroscopy with machine learning

ACS Spring 2022

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