Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy

Abstract

Techniques for continuously monitoring the formation of subgingival biofilm, in relation to the determination of species and their accumulation over time in gingivitis and periodontitis, are limited. In recent years, advancements in the field of optical spectroscopic techniques have provided an alternative for analyzing three-dimensional microbiological structures, replacing the traditional destructive or biofilm staining techniques. In this work, we have demonstrated that the use of confocal Raman spectroscopy coupled with multivariate analysis provides an approach to spatially differentiate bacteria in an in vitro model simulating a subgingival dual-species biofilm. The present study establishes a workflow to evaluate and differentiate bacterial species in a dual-species in vitro biofilm model, using confocal Raman microscopy (CRM). Biofilm models of Actinomyces denticolens and Streptococcus oralis were cultured using the “Zürich in vitro model” and were analyzed using CRM. Cluster analysis was used to spatially differentiate and map the biofilm model over a specified area. To confirm the clustering of species in the cultured biofilm, confocal laser scanning microscopy (CLSM) was coupled with fluorescent in vitro hybridization (FISH). Additionally, dense bacteria interface area (DBIA) samples, as an imitation of the clusters in a biofilm, were used to test the developed multivariate differentiation model. This confirmed model was successfully used to differentiate species in a dual-species biofilm and is comparable to morphology. The results show that the developed workflow was able to identify main clusters of bacteria based on spectral “fingerprint region” information from CRM. Using this workflow, we have demonstrated that CRM can spatially analyze two-species in vitro biofilms, therefore providing an alternative technique to map oral multi-species biofilm models.

In Situ Raman Analysis of Biofilm Exopolysaccharides Formed in Streptococcus mutans and Streptococcus sanguinis Commensal Cultures

“…In order to reverse the spectral features into useful biochemical information at the molecular level in such complex systems, bioinformatics, statistics, and machine-learning algorithms should be constructed that enable physically sound spectral deconvolutions empowering the Raman method. A widely adopted method to assist Raman diagnosis and prognostic assessments of pathogenic oral flora relies on principal component analyses (PCA)-based chemometrics [ 23 , 97 , 98 , 99 , 100 ]. In case of speciation deficiencies by the PCA method, Raman barcoding has been proposed, which allows obtaining a greater depth in capturing structural details than merely comparing spectral morphologies as done in PCA analyses [ 26 , 27 , 29 ].…”

Raman Spectroscopy: A Potential Diagnostic Tool for Oral Diseases

“…The potential of RS is more inclined to obtaining information in a clinical environment within minutes, with the advantages of being in situ, non-invasive, and accurate as RS is not interfered with by water, causes no damage to the sample, and reflects the chemical bond information clearly. To tap its potential in chairside applications, researchers have made many attempts in vitro, including identification of bacterial species (Kriem et al, 2020), detecting bacterial metabolic changes through D 2 O-labeled RS (Guo et al, 2019), quorumsensing molecules (Culhane et al, 2017), and structure of biofilms (Kriem et al, 2021). In addition, as RS can truthfully reflect information on the composition, structure, and concentration of all biological samples, it will provide a basis for obtaining more bacterial information chairside when changes in the flora and the expression of virulence factors can be obtained through RS.…”

Omics for deciphering oral microecology

“…The method demonstrated the ability to analyze and predict the identity of both planktonic and mono-species biofilm species, indicating its potential as a technique for mapping oral multi-species biofilm models. 87 , 88 Based on previous techniques, a standard Raman spectral detection process was developed to distinguish between various serotypes of P. gingivalis , Aggregatibacter actinomycetemcomitans , and Streptococcus spp. 89 …”