Potential of surface-enhanced Raman spectroscopy for the rapid identification of Escherichia coli and Listeria monocytogenes cultures on silver colloidal nanoparticles

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Surface-enhanced Raman spectroscopy for comparison of biochemical profile of bacteriophage sensitive and resistant methicillin-resistant Staphylococcus aureus (MRSA) strains

Methicillin-resistant Staphylococcus aureus (MRSA) is gram positive bacteria and leading cause of a wide variety of diseases. It is a common cause of hospitalized and community-acquired infections. Development of increasing antibiotic-resistance by methicillin-resistant S. aureus (MRSA) strains demand to develop alternate novel therapies. Bacteriophages are now widely used as antibacterial therapies against antibiotic-resistant gram-positive pathogens. So, there is an urgent need to find fast detection techniques to point out phage susceptible and resistant strains of methicillin-resistant S. aureus (MRSA) bacteria. Samples of two separate strains of bacteria, S. aureus, in form of pellets and supernatant, were used for this purpose. Strain-I was resistant to phage, while the other (strain-II) was sensitive. Surface Enhanced Raman Spectroscopy (SERS) has detected significant biochemical changes in these bacterial strains of pellets and supernatants in the form of SERS spectral features. The protein portion of these two types of strains of methicillin-resistant S. aureus (MRSA) in their relevant pellets and supernatants is major distinguishing biomolecule as shown by their representative SERS spectral features. In addition, multivariate data analysis techniques such as principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were found to be helpful in identifying and characterizing various strains of S. aureus which are sensitive and resistant to bacteriophage with 100% specificity, 100% accuracy, and 99.8% sensitivity in case of SERS spectral data sets of bacterial cell pellets. Moreover, in case of supernatant samples, the results of PLS-DA model including 95.5% specificity, 96% sensitivity, and 96.5% accuracy are obtained.

Surface-enhanced Raman spectroscopy for studying the interaction of organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v) with the biofilm of Escherichia coli

Escherichia coli biofilms are a major cause of gastrointestinal tract diseases, such as esophageal, stomach and intestinal diseases. Nowadays, these are the most commonly occurring diseases caused by consuming contaminated food. In this study, we evaluated the efficacy of probiotics in controlling multidrug-resistant E. coli and reducing its ability to form biofilms. Our results substantiate the effective use of probiotics as antimicrobial alternatives and to eradicate biofilms formed by multidrug-resistant E. coli. In this research, surface enhanced Raman spectroscopy (SERS) was utilized to identify and evaluate Escherichia coli biofilms and their response to the varying concentrations of the organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v). Given the escalating challenge of antibiotic resistance in bacteria that form biofilms, understanding the impact of potential antibiotic agents is crucial for the healthcare sector. The combination of SERS with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) enabled the detection and characterization of the biofilm, providing insights into the biochemical changes induced by the antibiotic candidate. The identified SERS spectral features served as indicators for elucidating the mode of action of the potential drug on the biofilm. Through PCA and PLS-DA, metabolic variations allowing the differentiation and classification of unexposed biofilms and biofilms exposed to different concentrations of the synthesized antibiotic were successfully identified, with 95% specificity, 96% sensitivity, and a 0.75 area under the curve (AUC). This research underscores the efficiency of surface enhanced Raman spectroscopy in differentiating the impact of potential antibiotic agents on E. coli biofilms.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an effective tool for identifying biofilm forming bacterial strains. Biofilm forming bacteria are considered a major issue in the health sector because they have strong resistance against antibiotics. Staphylococcus epidermidis is commonly present on intravascular devices and prosthetic joints, catheters and wounds.

OBJECTIVES

To identify and characterize biofilm forming and non-biofilm forming bacterial strains, surface- enhanced Raman spectroscopy with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used.

METHODS

Surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles were employed for the analysis and characterization of biofilm forming bacterial strains. SERS is used to differentiate between non biofilm forming (five samples), medium biofilm forming (five samples) and strong biofilm forming (five samples) bacterial strains by applying silver nanoparticles (AgNPs) as SERS substrate. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) were used to discriminate between non, medium and strong biofilm ability of bacterial strains.

RESULTS

Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been used to identify the biochemical differences in the form of SERS features which can be used to differentiate between biofilm forming and non-biofilm forming bacterial strains. PLS-DA provides successful differentiation and classification of these different strains with 94.5% specificity, 96% sensitivity and 89% area under the curve (AUC).

CONCLUSIONS

Surface-enhanced Raman spectroscopy can be utilized to differentiate between non, medium and strong biofilm forming bacterial strains.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.

Endophytes from blueberry (Vaccinium sp.) fruit: Characterization of yeast and bacteria via label-free surface-enhanced Raman spectroscopy (SERS)

Blueberries (Vaccinium sp.) are consumed all around the globe, however, their endophytic community has not been thoroughly researched, specifically their fruit endophytes. We aimed to isolate and analyze easily cultivable blueberry fruit endophytes to help in future research, concerning probiotic microorganisms. Twelve strains were isolated in this pilot study, genetically homologous with Staphylococcus hominis, Staphylococcus cohnii, Salmonella enterica, Leuconostoc mesenteroides, and [Candida] santamariae. To determine the molecular composition of these isolates we used label-free surface-enhanced Raman spectroscopy (SERS). To our knowledge, this is the first time that SERS spectra for L. mesenteroides and C. santamariae are presented, as well as the first report of Candida yeast, isolated specifically from blueberry fruits. Our findings suggest that the differences in tested yeast and bacteria SERS spectra and subsequent differentiation are facilitated by minor shifts in spectral peak positions as well as their intensities. Moreover, we used principal component and discriminant function analyses to differentiate chemotypes within our isolate group, proving the sensitivity of the technique and its usefulness to recognize different strains in plant-associated microbe samples, which will aid to streamline future studies in biofertilizers and biocontrol agents.

Assessing the effect of different pH maintenance situations on bacterial SERS spectra

Phenotyping of bacteria with vibrational spectroscopy has caught much attention in bacteria-related research. It is known that many factors could affect this process. Among them, solution pH maintenance is crucial, yet its impact on the bacterial SERS spectra is surprisingly neglected. In this work, we focused on two situations related to pH maintenance: the effect of the same buffer on the SERS spectra of bacteria under different pH values, and the influence of different buffers on the SERS spectra of bacteria under the same pH value. Specifically, Britton-Robison (BR) buffer was used to evaluate the effect of pH value on bacteria SERS spectra thanks to its wide pH range. Four different buffers, namely BR buffer, acetate buffer, phosphate buffer, and carbonate buffer, were used to illustrate the impact of buffer types on SERS spectra of bacteria. The results showed that the intensity and number of characteristic peaks of the SERS spectra of Gram-negative (G -) bacteria changed more significantly than Gram-positive (G +) bacteria with the change of pH value. Furthermore, compared with phosphate buffer and carbonate buffer, BR buffer could bring more characteristic SERS bands with better reproducibility, but slightly inferior to acetate buffer. In conclusion, the influence of the pH and types of the buffer on the SERS spectra of bacteria are worthy of further discussion.

Boosting bacteria differentiation efficiency with multidimensional surface-enhanced Raman scattering: the example of Bacillus cereus

Surface-enhanced Raman scattering (SERS) is a powerful tool for constructing biomolecular fingerprints, which play a vital role in differentiation of bacteria. Due to the rather subtle differences in the SERS spectra among different bacteria, artificial intelligence is usually adopted and enormous amounts of spectral data are required to improve the differentiation efficiency. However, in many cases, large volume data acquisition on bacteria is not only technical difficult but labour intensive. It is known that surface modification of SERS nanomaterials can bring additional dimensionality (difference) of the SERS fingerprints. Here in this work, we show that the concept could be used to improve the bacteria differentiation efficiency. Ag NPs were modified with 11-mercaptoundecanoic acid, 11-mercapto-1-undecanol, and 1-dodecanethiol to provide additional dimensionality. The modified NPs then were mixed with cell lysate from different strains of Bacillus cereus (B. cereus). Even by applying a simple PCA process to the resulting SERS spectra data, all the three modified Ag NPs showed superior differentiation results compared with bare Ag NPs, which could only separate Staphylococcus aureus (S. aureus) and B. cereus. It is believed that the multidimensional SERS could find great potential in bacteria differentiation.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.

Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS)

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

Review of microchip analytical methods for the determination of pathogenic Escherichia coli

Bacterial infections remain the principal cause of mortality worldwide, making the detection of pathogenic bacteria highly important, especially Escherichia coli (E. coli). Current E. coli detection methods are labour-intensive, time-consuming, or require expensive instrumentation, making it critical to develop new strategies that are sensitive and specific. Microchips are an automated analytical technique used to analyse food based on their separation efficiency and low analyte consumption, which make them the preferred method to detect pathogenic bacteria. This review presents an overview of microchip-based analytical methods for analysing E. coli, which were published in recent years. Specifically, this review focuses on current research based on microchips for the detection of E. coli and reviews the limitations of microchip-based methods and future perspectives for the analysis of pathogenic bacteria.

Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools

BACKGROUND

Raman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria.

OBJECTIVES

To develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains.

METHODS

SERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400-1800 cm^-1 (fingerprint region) for the identification and differentiation of different E. coli strains.

RESULTS

The SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

CONCLUSION

SERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.

In situ molecular vibration insights into the antibacterial behavior of silicon nitride bioceramic versus gram-negative Escherichia coli

Gram-negative bacteria represent a substantial fraction of pathogens responsible for periprosthetic infections. Given the increasing resistance of such bacteria to antibiotics, significant efforts are nowadays paid in developing new biomaterial surfaces, which offer resistance against bacterial adhesion and/or possess inherent antibacterial effects. Non-oxide silicon nitride (Si₃N₄) bioceramic in its polycrystalline form is a biomaterial with inherent antibacterial properties. Building upon previous phenomenological findings, the present study focuses on vibrational analyses of the metabolic response of Escherichia coli at the molecular level. A time-lapse study is conducted upon exposing the bacteria in vitro to Si₃N₄ bioceramic surfaces. A comparison is carried out with the as-cultured bacterial strain and with bacteria exposed to other commercially available biomaterials, namely, Ti-alloy (Ti6Al4V-ELI) and zirconia-toughened alumina (ZTA) oxide bioceramic tested under exactly the same experimental conditions. The metabolic pathways before and after exposure to different substrates were monitored by means of Raman and FTIR spectroscopies. Results indicated the development of significant osmotic stress in the bacterial strain and constant concentration decreases of its cellular compounds markers over time upon exposure to Si₃N₄. This ultimately led to bacterial lysis (also confirmed by conventional fluorescence microscopy assays). The main antibacterial effect was of chemical origin and driven by the elution of nitrogen ions from the Si₃N₄ surface, successively converted into ammonia (NH₃) or ammonium (NH₄)⁺ in aqueous solution, depending on environmental pH. The presence of these nitrogen species created osmotic stress in the cytoplasmic space. In answer to the osmotic stress, metabolic rates changed rapidly, the bacterial membrane was damaged, and lysis occurred almost completely within 48 h exposure. The antibacterial behavior exerted by the Si₃N₄ substrate on E. coli was more effective than that observed on the biomedical Ti6Al4V alloy. Conversely, no lysis but bacterial proliferation was recorded for E. coli exposed to ZTA bioceramic oxide substrates.

Silicon Nitride: A Bioceramic with a Gift

In the closing decades of the 20th century, silicon nitride (Si₃N₄) was extensively developed for high-temperature gas turbine applications. Technologists attempted to take advantage of its superior thermal and mechanical properties to improve engine reliability and fuel economy. Yet, this promise was never realized in spite of the worldwide research, which was conducted at that time. Notwithstanding this disappointment, its use in medical applications in the early 21st century has been an unexpected gift. While retaining all of its engineered mechanical properties, it is now recognized for its peculiar surface chemistry. When immersed in an aqueous environment, the slow elution of silicon and nitrogen from its surface enhances healing of soft and osseous tissue, inhibits bacterial proliferation, and eradicates viruses. These benefits permit it to be used in a wide array of different disciplines inside and outside of the human body including orthopedics, dentistry, virology, agronomy, and environmental remediation. Given the global public health threat posed by mutating viruses and bacteria, silicon nitride offers a valid and straightforward alternative approach to fighting these pathogens. However, there is a conundrum behind these recent discoveries: How can this unique bioceramic be both friendly to mammalian cells while concurrently lysing invasive pathogens? This unparalleled characteristic can be explained by the pH-dependent kinetics of two ammonia species-NH₄⁺ and NH₃-both of which are leached from the wet Si₃N₄ surface.

Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level

Detection and characterization of microorganisms is essential for both clinical diagnostics and environmental studies. An emerging technique to analyse microbes at single-cell resolution is surface-enhanced Raman spectroscopy (surface-enhanced Raman scattering: SERS). Optimised SERS procedures enable fast analytical read-outs with specific molecular information, providing insight into the chemical composition of microbiological samples. Knowledge about the origin of microbial SERS signals and parameter(s) affecting their occurrence, intensity and/or reproducibility is crucial for reliable SERS-based analyses. In this work, we explore the feasibility and limitations of the SERS approach for characterizing microbial cells and investigate the applicability of SERS for single-cell sorting as well as for three-dimensional visualization of microbial communities. Analyses of six different microbial species (an archaeon, two Gram-positive bacteria, three Gram-negative bacteria) showed that for several of these organisms distinct features in their SERS spectra were lacking. As additional confounding factor, the physiological conditions of the cells (as influenced by e.g., storage conditions or deuterium-labelling) were systematically addressed, for which we conclude that the respective SERS signal at the single-cell level is strongly influenced by the metabolic activity of the analysed cells. While this finding complicates the interpretation of SERS data, it may on the other hand enable probing of the metabolic state of individual cells within microbial populations of interest.

Strain-level typing and identification of bacteria - a novel approach for SERS active plasmonic nanostructures

One of the potential applications of surface-enhanced Raman spectroscopy (SERS) is the detection of biological compounds and microorganisms. Here we demonstrate that SERS coupled with principal component analysis (PCA) serves as a perfect method for determining the taxonomic affiliation of bacteria at the strain level. We demonstrate for the first time that it is possible to distinguish different genoserogroups within a single species, Listeria monocytogenes, which is one of the most virulent foodborne pathogens and in some cases contact with which may be fatal. We also postulate that it is possible to detect additional proteins in the L. monocytogenes cell envelope, which provide resistance to benzalkonium chloride and cadmium. A better understanding of this infectious agent could help in selecting the appropriate pharmaceutical product for enhanced treatment. Graphical abstract ᅟ.

Bare laser-synthesized Au-based nanoparticles as nondisturbing surface-enhanced Raman scattering probes for bacteria identification

The ability of noble metal-based nanoparticles (NPs) (Au, Ag) to drastically enhance Raman scattering from molecules placed near metal surface, termed as surface-enhanced Raman scattering (SERS), is widely used for identification of trace amounts of biological materials in biomedical, food safety and security applications. However, conventional NPs synthesized by colloidal chemistry are typically contaminated by nonbiocompatible by-products (surfactants, anions), which can have negative impacts on many live objects under examination (cells, bacteria) and thus decrease the precision of bioidentification. In this article, we explore novel ultrapure laser-synthesized Au-based nanomaterials, including Au NPs and AuSi hybrid nanostructures, as mobile SERS probes in tasks of bacteria detection. We show that these Au-based nanomaterials can efficiently enhance Raman signals from model R6G molecules, while the enhancement factor depends on the content of Au in NP composition. Profiting from the observed enhancement and purity of laser-synthesized nanomaterials, we demonstrate successful identification of 2 types of bacteria (Listeria innocua and Escherichia coli). The obtained results promise less disturbing studies of biological systems based on good biocompatibility of contamination-free laser-synthesized nanomaterials.

Alkaline phosphatase labeled SERS active sandwich immunoassay for detection of Escherichia coli

In this study, a sandwich immunoassay method utilizing enzymatic activity of alkaline phosphatase (ALP) on 5-bromo-4-chloro-3-indolyl phosphate (BCIP) for Escherichia coli (E. coli) detection was developed using surface enhanced Raman spectroscopy (SERS). For this purpose, spherical magnetic gold coated core-shell nanoparticles (MNPs-Au) and rod shape gold nanoparticles (Au-NRs) were synthesized and modified for immunomagnetic separation (IMS) of E. coli from the solution. In order to specify the developed method to ALP activity, Au-NRs were labeled with this enzyme. After successful construction of the immunoassay, BCIP substrate was added to produce the SERS-active product; 5-bromo-4-chloro-3-indole (BCI). A good linearity (R²=0.992) was established between the specific SERS intensity of BCI at 600cm^-1 and logarithmic E. coli concentration in the range of 1.7×10¹-1.7×10⁶cfumL^-1. LOD and LOQ values were also calculated and found to be 10cfumL^-1 and 30cfumL^-1, respectively.

The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS

The dominant molecular species contributing to the surface-enhanced Raman spectroscopy (SERS) spectra of bacteria excited at 785 nm are the metabolites of purine degradation: adenine, hypoxanthine, xanthine, guanine, uric acid, and adenosine monophosphate. These molecules result from the starvation response of the bacterial cells in pure water washes following enrichment from nutrient-rich environments. Vibrational shifts due to isotopic labeling, bacterial SERS spectral fitting, SERS and mass spectrometry analysis of bacterial supernatant, SERS spectra of defined bacterial mutants, and the enzymatic substrate dependence of SERS spectra are used to identify these molecular components. The absence or presence of different degradation/salvage enzymes in the known purine metabolism pathways of these organisms plays a central role in determining the bacterial specificity of these purine-base SERS signatures. These results provide the biochemical basis for the development of SERS as a rapid bacterial diagnostic and illustrate how SERS can be applied more generally for metabolic profiling as a probe of cellular activity. Graphical Abstract Bacterial typing by metabolites released under stress.

The origin of the band at around 730 cm−1 in the SERS spectra of bacteria: a stable isotope approach

A stable isotope approach combined with SERS analysis of bacteria allows clarification of the origin of a pronounced band at 730 cm⁻¹.

Detection of Listeria innocua on roll-to-roll produced SERS substrates with gold nanoparticles

The rapid and accurate detection of food pathogens plays a critical role in the early prevention of foodborne epidemics. Combination of low cost sensing platforms and SERS detection can offer a solution for the pathogen detection.

New post-processing method of preparing nanofibrous SERS substrates with a high density of silver nanoparticles

The protocol to control density of AgNP on surfaces of nanofibers, and thus electromagnetic hotspots by variation of Tollens' reagent is established. Nanofiber films enable SERS either of solutes or macromolecular structures such as bacterial cells.

Differentiation and classification of bacteria using vancomycin functionalized silver nanorods array based surface-enhanced Raman spectroscopy and chemometric analysis

Twenty seven different bacteria isolates from 12 species were analyzed using intrinsic surface-enhanced Raman scattering (SERS) spectra with recently developed vancomycin coated silver nanorod (VAN AgNR) substrates. The VAN AgNR substrates could generate reproducible SERS spectra of the bacteria with little to no interference from the environment or bacterial by-products as compared to the pristine substrates. By taking advantage of the structural composition of the cellular wall which varies from species to species, the differentiation of bacterial species is demonstrated by using chemometric analyses on those spectra. A second chemometric analysis step within the species cluster is able to differentiate serotypes and strains. The spectral features used for serotype differentiation arises from the surface proteins, while Raman peaks from adenine dominate the differentiation of strains. In addition, due to the intrinsic structural differences in the cell walls, the SERS spectra can distinguish Gram-positive from Gram-negative bacteria with high sensitivity and specificity, as well as 100% accuracy on predicting test samples. Our results provide important insights for using SERS as a bacterial diagnostic tool and further guide the design of a SERS-based detection platform.

Influence of various chloride ion concentrations on silver nanoparticle transformations and effectiveness in surface enhanced Raman scattering for different excitation wavelengths

This study reports the effect of six various concentrations of chlorides on the surface enhanced Raman scattering activity of silver nanoparticles.

Detecting and tracking nosocomial methicillin-resistant Staphylococcus aureus using a microfluidic SERS biosensor

Rapid detection and differentiation of methicillin-resistant Staphylococcus aureus (MRSA) are critical for the early diagnosis of difficult-to-treat nosocomial and community acquired clinical infections and improved epidemiological surveillance. We developed a microfluidics chip coupled with surface enhanced Raman scattering (SERS) spectroscopy (532 nm) "lab-on-a-chip" system to rapidly detect and differentiate methicillin-sensitive S. aureus (MSSA) and MRSA using clinical isolates from China and the United States. A total of 21 MSSA isolates and 37 MRSA isolates recovered from infected humans were first analyzed by using polymerase chain reaction (PCR) and multilocus sequence typing (MLST). The mecA gene, which refers resistant to methicillin, was detected in all the MRSA isolates, and different allelic profiles were identified assigning isolates as either previously identified or novel clones. A total of 17 400 SERS spectra of the 58 S. aureus isolates were collected within 3.5 h using this optofluidic platform. Intra- and interlaboratory spectral reproducibility yielded a differentiation index value of 3.43-4.06 and demonstrated the feasibility of using this optofluidic system at different laboratories for bacterial identification. A global SERS-based dendrogram model for MRSA and MSSA identification and differentiation to the strain level was established and cross-validated (Simpson index of diversity of 0.989) and had an average recognition rate of 95% for S. aureus isolates associated with a recent outbreak in China. SERS typing correlated well with MLST indicating that it has high sensitivity and selectivity and would be suitable for determining the origin and possible spread of MRSA. A SERS-based partial least-squares regression model could quantify the actual concentration of a specific MRSA isolate in a bacterial mixture at levels from 5% to 100% (regression coefficient, >0.98; residual prediction deviation, >10.05). This optofluidic platform has advantages over traditional genotyping for ultrafast, automated, and reliable detection and epidemiological surveillance of bacterial infections.

Design and Implementation of Noble Metal Nanoparticle Cluster Arrays for Plasmon Enhanced Biosensing

Nanoparticle Cluster Arrays (NCAs) are a class of electromagnetic materials that comprise chemically defined nanoparticles assembled into clusters of defined size in an extended deterministic arrangement. NCAs are fabricated through integration of chemically synthesized building blocks into predefined patterns using a hybrid top-down/bottom-up fabrication approach that overcomes some of the limitations of conventional top-down fabrication methods with regard to minimum available feature size and structural complexity. NCAs can sustain near-field interactions between nanoparticles within individual clusters as well as between entire neighboring clusters. The availability of near-field interactions on multiple length scales - together with the ability to further enhance the coupled plasmon modes through photonic modes in carefully designed array morphologies - leads to a multiscale cascade electromagnetic field enhancement throughout the array. This feature article introduces the design and fabrication fundamentals of NCAs and characterizes the electromagnetic coupling mechanisms in the arrays. Furthermore, it reviews how the optical properties of NCAs can be tuned through the size and shape of the nanoparticle building blocks and the geometry, size, and separation of the assembled clusters. NCAs have potential applications in many different areas; this feature article focuses on plasmon enhanced biosensing and surface enhanced Raman spectroscopy (SERS), in particular.

Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device

The interest in a fast, high specific and reliable detection method for bacteria identification is increasing. We will show that the application of vibrational spectroscopy is feasible for the validation of bacteria in microfluidic devices. For this purpose, reproducible and specific spectral pattern as well as the establishment of large databases are essential for statistical analysis. Therefore, short recording times are beneficial concerning the time aspect of fast identification. We will demonstrate that the requirements can be fulfilled by measuring ultrasonic busted bacteria by means of microfluidic lab-on-a-chip based SERS. With the applied sample preparation, high specificity and reproducibility of the spectra are achieved. Taking advantage of the SERS enhancement, the spectral recording time is reduced to 1 s and a database of 11,200 spectra is established for a model system E. coli including nine different strains. The validation of the bacteria on strain level is achieved accomplishing SVM accuracies of 92%. Within this contribution the potential of our approach of bacterial identification for future application is discussed, focusing on the time-benefit and the combination with other microfluidic applications.

Surface-enhanced Raman scattering of bacterial cell culture growth media

The application of surface-enhanced Raman spectroscopy (SERS) to characterizing bacteria is an active area of investigation. Micro- and nano-structured SERS substrates have enabled detection of pathogens present in biofluids. Several publications have focused on determining the spectral bands characteristic of bacteria from different species and cell lines. In this report, the spectra of fifteen commonly used bacterial growth media are presented. In many instances, these spectra are similar to published spectra purportedly characteristic of specific bacterial species. The findings presented herein suggest that bacterial fingerprinting by SERS requires further examination.

Vibrational spectroscopy--a powerful tool for the rapid identification of microbial cells at the single-cell level

Rapid microbial detection and identification with a high grade of sensitivity and selectivity is a great and challenging issue in many fields, primarily in clinical diagnosis, pharmaceutical, or food processing technology. The tedious and time-consuming processes of current microbiological approaches call for faster ideally on-line identification techniques. The vibrational spectroscopic techniques IR absorption and Raman spectroscopy are noninvasive methods yielding molecular fingerprint information; thus, allowing for a fast and reliable analysis of complex biological systems such as bacterial or yeast cells. In this short review, we discuss recent vibrational spectroscopic advances in microbial identification of yeast and bacterial cells for bulk environment and single-cell analysis. IR absorption spectroscopy enables a bulk analysis whereas micro-Raman-spectroscopy with excitation in the near infrared or visible range has the potential for the analysis of single bacterial and yeast cells. The inherently weak Raman signal can be increased up to several orders of magnitude by applying Raman signal enhancement methods such as UV-resonance Raman spectroscopy with excitation in the deep UV region, surface enhanced Raman scattering, or tip-enhanced Raman scattering.