Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

Characterization of Bacteria Using Surface-Enhanced Raman Spectroscopy (SERS): Influence of Microbiological Factors on the SERS Spectra

SERS is currently being explored as a rapid method for identification of bacteria but variation in the experimental procedures has resulted in considerable variation in the spectra reported for a range of bacterial species. Here, we show that mixing bacteria with a conventional citrate-reduced silver colloid (CRSC) and drying the resulting suspension yield highly reproducible spectra. These signals were due to intracellular components released when the structure of the bacteria was disrupted during sample preparation. This reproducibility allowed us to examine the effects of variables that do not arise in SERS of simple solutions but are relevant in studies of bacteria. These included growth phase and biological variation, which occurred when the same bacterial isolates were cultured under nominally identical conditions on different days. It was found that even under optimal standardized conditions the effect of differences in experimental parameters such as growth phase was very large in some bacterial species but insignificant in others. This suggests that it is important to avoid drawing general conclusions about bacterial SERS based on studies using small numbers of samples. Similarly, discrimination between bacterial species was straightforward when a small number of isolates with distinct spectral features were investigated; however, this became more challenging when more bacterial species were included, as this increased the possibility of finding different species of bacteria with similar spectra. These observations are important because they clearly delineate the challenges that will need to be addressed if SERS is to be used for clinical applications.

Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thaliana

Chemical imaging of plant-bacteria co-cultures makes it possible to characterize bacterial populations and behaviors and their interactions with proximal organisms, under conditions closest to the environment in the rhizosphere. Here Raman micro-spectroscopy and confocal Raman imaging are used as minimally invasive probes to study the rhizosphere bacterial isolate, Pantoea sp. YR343, and its co-culture with model plant Arabidopsis thaliana by combining enhanced Raman spectroscopies with electron microscopy and principal component analysis (PCA). The presence of carotenoid pigments in the wild type Pantoea sp. YR343 was characterized using resonance Raman scattering, which was also used to confirm successful disruption of the crtB gene in an engineered carotenoid mutant strain. Other components of the Pantoea sp. YR343 cells were imaged in the presence of resonantly enhanced pigments using a combination of surface enhanced Raman imaging and PCA. Pantoea sp. YR343 cells decorated with Ag colloid synthesized ex situ gave spectra dominated by carotenoid scattering, whereas colloids synthesized in situ produced spectral signatures characteristic of flavins in the cell membrane. Scanning electron microscopy (SEM) of whole cells and transmission electron microscopy (TEM) images of thinly sliced cross-sections were used to assess structural integrity of the coated cells and to establish the origin of spectral signatures based on the position of Ag nanoparticles in the cells. Raman imaging was also used to characterize senescent green Arabidopsis thaliana plant roots inoculated with Pantoea sp. YR343, and PCA was used to distinguish spectral contributions from plant and bacterial cells, thereby establishing the potential of Raman imaging to visualize the distribution of rhizobacteria on plant roots.

Discrimination of urinary tract infection pathogens by means of their growth profiles using surface enhanced Raman scattering

Urinary tract infection (UTI) is a widespread infection and affects millions of people around the globe. The gold standard for identification of microorganisms causing infection is urine culture. However, current methods require at least 24 h for the results. In clinical settings, identification and discrimination of bacteria with less time-consuming and cheaper methods are highly desired. In recent years, the power of surface-enhanced Raman scattering (SERS) for fast identification of bacteria and biomolecules has been demonstrated. In this study, we show discrimination of urinary tract infection causative pathogens within 1 h of incubation using principal component analysis (PCA) of SERS spectra of seven different UTI causative bacterial species. In addition, we showed differentiation of them at their different growth phases. We also analyzed origins of bacterial SERS spectra and demonstrated the highly dynamic structure of the bacteria cell wall during their growth. Graphical Abstract Collection of bacteria from urine sample, and their discrimination using their SERS spectra and multivariate analysis.

Shining light on the microbial world the application of Raman microspectroscopy

Raman microspectroscopy is a noninvasive, label-free, and single-cell technology for biochemical analysis of individual mammalian cells, organelles, bacteria, viruses, and nanoparticles. Chemical information derived from a Raman spectrum provides comprehensive and intrinsic information (e.g., nucleic acids, protein, carbohydrates, and lipids) of single cells without the need of any external labeling. A Raman spectrum functions as a molecular "fingerprint" of single cells, which enables the differentiation of cell types, physiological states, nutrient condition, and variable phenotypes. Raman microspectroscopy combined with stable isotope probing, fluorescent in situ hybridization, and optical tweezers offers a culture-independent approach to study the functions and physiology of unculturable microorganisms in the ecosystem. Here, we review the application of Raman microspectroscopy to microbiology research with particular emphasis on single bacterial cells.