Image_1_Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy.TIF (6.03 MB)
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Image_1_Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy.TIF

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posted on 2021-10-05, 04:50 authored by Lukas Simon Kriem, Kevin Wright, Renzo Alberto Ccahuana-Vasquez, Steffen Rupp

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.