Unveiling Microbial Diversity: Raman Spectroscopy's Discrimination of Clostridium and Related Genera.

In the clinical environment, the identification of phylogenetic closely related genera and species like Clostridium and Bacillus spp. is challenging. Both genera contain representatives of pathogenic and nonpathogenic species that need to be distinguished for a proper diagnostic read-out. Therefore, reliable and accurate detection methods must be employed for the correct identification of these genera and species. Despite their high pathogenicity, clostridial infections and food contaminations present significant challenges due to their unique cultivation conditions and developmental needs. Therefore, in many diagnostic protocols, the toxins are used for microbiological documentation. However, the applied laboratory methods suffer in accuracy, sometimes require large bacterial loads to provide reliable results, and cannot differentiate pathogenic from nonpathogenic strains. Here, Raman spectroscopy was employed to create an extensive Raman database consisting of pathogenic and nonpathogenic Bacillus and Clostridium species. These genera, as well as representatives of Paraclostridium and Clostridioides were specifically selected for their phylogenetic relation, cultivation conditions, and metabolic activity. A chemometric evaluation of the Raman spectra of single vegetative cells revealed a high discriminating power at the genus level. However, bacilli are considerably easier to classify at the species level than clostridia. The discrimination between the genera and species was based on their phylogeny and not their aerobic and anaerobic cultivation conditions. These encouraging results demonstrated that Raman spectroscopy coupled with chemometrics is a robust and helpful method for differentiating Clostridium species from Bacillus, Clostridioides, and Paraclostridium species. This approach has the potential to be a valuable tool in clinical diagnostics.

Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria.

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.

Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods.

Enterobacteriaceae are the leading cause of urinary tract infections, and include pathogens such as E. coli, K. pneumoniae and P. mirabilis. Due to their similarity, the correct identification of these pathogens is difficult and time-consuming. Raman spectroscopy has been demonstrated extensively as a tool for rapid microbiological differentiation. However, for pathogenic Enterobacteriaceae the application of Raman spectroscopy has been particularly challenging. In this study, two promising methods for Raman-based microbiological diagnostics were compared for differentiating Enterobacteriaceae. Spectra were collected from single-cells with Raman microspectroscopy and from colonies on agar with an NIR Raman fiber-probe. A comprehensive dataset of spectra from 8 different, clinically relevant, genera was collected. Visually, the spectra obtained from both methods presented little difference between the genera. For classification, single cell analysis yielded limited results, while the fiber-probe spectra enabled perfect classification of all 16 isolates. Moreover, the model was validated on new replicates and 15/16 strains were correctly identified (94% overall accuracy). This is the first study to focus on the closely related Enterobacteriaceae, who have previously been avoided or differentiated poorly. It shows how, with the correct spectroscopic setup, even challenging questions in clinical microbiology can be resolved with Raman spectroscopy, highlighting the method's potential for improving patient care.