Research Article: Raman microspectroscopy for species identification and mapping within bacterial biofilms

Date Published: July 13, 2012

Publisher: Springer

Author(s): Brooke D Beier, Robert G Quivey, Andrew J Berger.

http://doi.org/10.1186/2191-0855-2-35

Abstract

A new method of mapping multiple species of oral bacteria in intact biofilms has been developed, using the optical technique of confocal Raman microscopy. A species classification algorithm, developed on dried biofilms, was used to analyze spectra of hydrated biofilms containing two microbial species central to dental health: Streptococcus sanguinis and Streptococcus mutans. The algorithm transferred successfully to the hydrated environment, correctly identifying the species of origin of single-species biofilms. We then used the algorithm successfully both to detect the presence of two species in mixed biofilms and to create spatial maps within these biofilms.

Partial Text

The identification of microbial species within sample specimens is relevant to both the microbiological research laboratory and the clinical setting. A number of standard methods of species identification are currently used. Selective plating may be used to identify the constituent species and to obtain an order-of-magnitude estimate of the initial concentrations of bacteria present in a sample. Since this method involves serial dilutions, it can take up to several days and is insensitive to cells that are dead or incapable of reproduction by the time the sample is procured. Quantitative polymerase chain reaction (qPCR) is highly sensitive to the initial concentrations of known species, but it requires prior knowledge of their genomes in order to provide specificity. Both qPCR and selective plating disrupt the initial sample’s architecture, and are thus ill-suited for spatial mapping studies of specimens such as biofilms. Fluorescence in situ hybridization (FISH) can provide spatial resolution, but requires sample fixation (eliminating the potential to study a sample over time), extensive preparation steps, and a genetically-targeted exogenous marker. A new measurement method that could map the spatial distribution of multiple species in intact, unfixed specimens in a label-free, non-contact manner would therefore be valuable. Such a technique could also make longitudinal study of samples like biofilms a possibility.

As mentioned above, the species prediction model was originally constructed from dehydrated biofilm samples and was here applied to the study of hydrated biofilms, in situ. For the transfer of this model to a new sample preparation, a test set of single-species biofilms was first examined. The mean spectra of the newly-measured S. sanguinis and S. mutans biofilms, shown in Figure
2, appear nearly identical. In such cases, ad hoc classification models based upon a few visibly different peaks (e.g. near 920 and 1100 cm−1, marked with ‘*’) perform poorly at the single-spectrum level, where noise is higher. Figure
2 emphasizes the importance of using a multivariate model (the PC-based technique referenced in the previous section) to utilize information throughout the full spectrum in constructing the classification formula.

Examination of the test set of hydrated biofilm samples showed that overall, 93% of voxels were properly identified using the transferred model. Our previous work saw 96% correct identification of species (Beier et al.
2010). The move to the new sample type involved several changes relative to the original training set, including a replacement of the laser with a corresponding 2 nm wavelength difference in excitation, the use of a water-immersion objective rather than air-immersion, the addition of depth scanning, and a decrease in signal level associated with decreased concentrations within hydrated biofilms. The high performance on this single species confirmation set given these system changes indicates the robustness of the prediction model.

The authors declare that they have no competing interests.

 

Source:

http://doi.org/10.1186/2191-0855-2-35

 

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