Surface-enhanced Raman spectroscopy (SERS) for the characterization of supernatants of bacterial cultures of bacterial strains causing sinusitis

SERS-assisted characterization of cell biomass from biofilm-forming Acinetobacter baumannii strains using chemometric tools

Acinetobacter baumannii (A. baumannii) is an emerging Gram-negative nosocomial pathogen responsible for infection on a global scale. It has the ability to develop biofilms on different surfaces, especially abiotic surfaces, which is considered a major contributor of its pathogenicity. Surface-enhanced Raman spectroscopy (SERS) holds great potential as an effective method for identifying and characterizing the biochemical composition of biofilm-forming species. In this study, cell mass samples from different strains of A. baumannii, categorized based on their biofilm-forming ability (strong, medium and non-biofilm forming) using a 96-well microtiter plate assay (MTP), were analyzed by SERS. The identified spectral features of the SERS spectra were used to characterize bacterial strains capable of producing biofilms. Silver nanoparticles (Ag-NPs) served as the SERS substrate to differentiate biofilm-forming strains of A. baumannii. Chemometric tools, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were employed for the classification and differentiation of SERS spectra from bacterial strains with varying biofilm-producing capacities, achieving 100% sensitivity, 94.3% specificity, and an area under the curve (AUC) value of 0.81 through Monte Carlo cross-validation. Furthermore, K-fold (Leave-K-out cross-validation (LKOCV)) was applied to verify the robustness of the PLS-DA model, and the AUC value was found to be 0.90, with a sensitivity of 100% and specificity of 98%. These results demonstrate that the PLS-DA model is highly effective for the differentiation and classification of bacterial strains with varying capacities for biofilm production.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Production and SERS characterization of bacteriocin-like inhibitory substances by latilactobacillus sakei in whey permeate powder: exploring natural antibacterial potential

Bacteriocins are antimicrobial compounds that have awakened interest across several industries due to their effectiveness. However, their large-scale production often becomes unfeasible on an industrial scale, primarily because of high process costs. Addressing this challenge, this work analyzes the potential of using low-cost whey permeate powder, without any supplementation, to produce bacteriocin-like inhibitory substances (BLIS) through the fermentation of Latilactobacillus sakei. For this purpose, different concentrations of whey permeate powder (55.15 gL-1, 41.3 gL-1 and 27.5 gL-1) were used. The ability of L. sakei to produce BLIS was evaluated, as well as the potential of crude cell-free supernatant to act as a preservative. Raman spectroscopy and surface-enhanced Raman scattering (SERS) provided detailed insights into the composition and changes occurring during fermentation. SERS, in particular, enhanced peak definition significantly, allowing for the identification of key components, such as lactose, proteins, and phenylalanine, which are crucial in understanding the fermentation process and BLIS characteristics. The results revealed that the concentration of 55.15 gL-1 of whey permeate powder, in flasks without agitation and a culture temperature of 32.5 °C, presented the highest biological activity of BLIS, reaching 99% of inhibition of Escherichia coli and Staphylococcus aureus with minimum inhibitory concentration of 36-45%, respectively. BLIS production began within 60 h of cultivation and was associated with class II bacteriocins. The results demonstrate a promising approach for producing BLIS in an economical and environmentally sustainable manner, with potential implications for various industries.

Differentiation of different dibenzothiophene (DBT) desulfurizing bacteria via surface-enhanced Raman spectroscopy (SERS)

Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.

Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.