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.

Multi-wavelength Raman microscopy of nickel-based electron transport in cable bacteria

Cable bacteria embed a network of conductive protein fibers in their cell envelope that efficiently guides electron transport over distances spanning up to several centimeters. This form of long-distance electron transport is unique in biology and is mediated by a metalloprotein with a sulfur-coordinated nickel (Ni) cofactor. However, the molecular structure of this cofactor remains presently unknown. Here, we applied multi-wavelength Raman microscopy to identify cell compounds linked to the unique cable bacterium physiology, combined with stable isotope labeling, and orientation-dependent and ultralow-frequency Raman microscopy to gain insight into the structure and organization of this novel Ni-cofactor. Raman spectra of native cable bacterium filaments reveal vibrational modes originating from cytochromes, polyphosphate granules, proteins, as well as the Ni-cofactor. After selective extraction of the conductive fiber network from the cell envelope, the Raman spectrum becomes simpler, and primarily retains vibrational modes associated with the Ni-cofactor. These Ni-cofactor modes exhibit intense Raman scattering as well as a strong orientation-dependent response. The signal intensity is particularly elevated when the polarization of incident laser light is parallel to the direction of the conductive fibers. This orientation dependence allows to selectively identify the modes that are associated with the Ni-cofactor. We identified 13 such modes, some of which display strong Raman signals across the entire range of applied wavelengths (405-1,064 nm). Assignment of vibrational modes, supported by stable isotope labeling, suggest that the structure of the Ni-cofactor shares a resemblance with that of nickel bis(1,2-dithiolene) complexes. Overall, our results indicate that cable bacteria have evolved a unique cofactor structure that does not resemble any of the known Ni-cofactors in biology.

Highly sensitive SERS platform for pathogen analysis by cyclic DNA nanostructure@AuNP tags and cascade primer exchange reaction

The capacity to identify small amounts of pathogens in real samples is extremely useful. Herein, we proposed a sensitive platform for detecting pathogens using cyclic DNA nanostructure@AuNP tags (CDNA) and a cascade primer exchange reaction (cPER). This platform employs wheat germ agglutinin-modified Fe3O4@Au magnetic nanoparticles (WMRs) to bind the E. coli O157:H7, and then triggers the cPER to generate branched DNA products for CDNA tag hybridization with high stability and amplified SERS signals. It can identify target pathogens as low as 1.91 CFU/mL and discriminate E. coli O157:H7 in complex samples such as water, milk, and serum, demonstrating comparable or greater sensitivity and accuracy than traditional qPCR. Moreover, the developed platform can detect low levels of E. coli O157:H7 in mouse serum, allowing the discrimination of mice with early-stage infection. Thus, this platform holds promise for food analysis and early infection diagnosis.

Detection and discrimination of pathogenic bacteria with nanomaterials-based optical biosensors: A review

Pathogenic bacteria can pose a great threat to food safety and human health. It is therefore imperative to develop a rapid, portable, and sensitive determination and discrimination method for pathogenic bacteria. Over the past few years, various nanomaterials (NMs) have been employed as desirable nanoprobes because they possess extraordinary properties that can be used for optical signal enabled detection and identification of bacteria. By means of modification, NMs can, depending on different mechanisms, sense targets directly or indirectly, which then provides an essential support for the detection and differentiation of pathogenic bacteria. In this review, recent application of NMs-based optical biosensors for food safety bacterial detection and discrimination is performed, mainly in but not limited to noble metal NMs, fluorescent NMs, and point-of-care testing (POCT). This review also focuses on future trends in bacterial detection and discrimination, and machine learning in performing intelligent rapid detection and multiple accurate identification of bacteria.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

Optical Methods for Label-Free Detection of Bacteria

Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

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.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

Development of a Fast Raman-Assisted Antibiotic Susceptibility Test (FRAST) for the Antibiotic Resistance Analysis of Clinical Urine and Blood Samples

Human health is at great risk due to the spreading of antimicrobial resistance (AMR). The lengthy procedure of conventional antimicrobial susceptibility testing (AST) usually requires a few days. We developed a fast Raman-assisted antibiotic susceptibility test (FRAST), which detects single bacterial metabolic activity in the presence of antibiotics, using Raman single-cell spectroscopy. It was found that single-cell Raman spectra (SCRS) would show a clear and distinguishable Raman band at the "silent zone" (2000-2300 cm^-1), due to the active incorporation of deuterium from heavy water (D₂O) by antibiotic-resistant bacteria. This pilot study has compared the FRAST and the conventional AST for six clinical standard quality controls (four Gram-negative and two Gram-positive bacteria strains) in response to 38 antibiotics. In total, 3200 treatments have been carried out and approximately 64 000 SCRS have been acquired for FRAST analysis. The result showed an overall agreement of 88.0% between the FRAST and the conventional AST assay. The gram-staining classification based on the linear discriminant analysis (LDA) model of SCRS was developed, seamlessly coupling with the FRAST to further reduce the turnaround time. We applied the FRAST to real clinical analysis for nine urinary infectious samples and three sepsis samples. The results were consistent with MALDI-TOF identification and the conventional AST. Under the optimal conditions, the "sample to report" of the FRAST could be reduced to 3 h for urine samples and 21 h for sepsis samples. The FRAST provides fast and reliable susceptibility tests, which could speed up microbiological analysis for clinical practice and facilitate antibiotic stewardship.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Klebsiella pneumoniae: Prevalence, Reservoirs, Antimicrobial Resistance, Pathogenicity, and Infection: A Hitherto Unrecognized Zoonotic Bacterium

Klebsiella pneumoniae is considered an opportunistic pathogen, constituting an ongoing health concern for immunocompromised patients, the elderly, and neonates. Reports on the isolation of K. pneumoniae from other sources are increasing, many of which express multidrug-resistant (MDR) phenotypes. Three phylogroups were identified based on nucleotide differences. Niche environments, including plants, animals, and humans appear to be colonized by different phylogroups, among which KpI (K. pneumoniae) is commonly associated with human infection. Infections with K. pneumoniae can be transmitted through contaminated food or water and can be associated with community-acquired infections or between persons and animals involved in hospital-acquired infections. Increasing reports are describing detections along the food chain, suggesting the possibility exists that this could be a hitherto unexplored reservoir for this opportunistic bacterial pathogen. Expression of MDR phenotypes elaborated by these bacteria is due to the nature of various plasmids carrying antimicrobial resistance (AMR)-encoding genes, and is a challenge to animal, environmental, and human health alike. Raman spectroscopy has the potential to provide for the rapid identification and screening of antimicrobial susceptibility of Klebsiella isolates. Moreover, hypervirulent isolates linked with extraintestinal infections express phenotypes that may support their niche adaptation. In this review, the prevalence, reservoirs, AMR, Raman spectroscopy detection, and pathogenicity of K. pneumoniae are summarized and various extraintestinal infection pathways are further narrated to extend our understanding of its adaptation and survival ability in reservoirs, and associated disease risks.

Microfluidic device for concentration and SERS-based detection of bacteria in drinking water

There is a constant need for the development of easy-to-operate systems for the rapid and unambiguous identification of bacterial pathogens in drinking water without the requirement for time-consuming culture processes. In this study, we present a disposable and low-cost lab-on-a-chip device utilizing a nanoporous membrane, which connects two stacked perpendicular microfluidic channels. Whereas one of the channels supplies the sample, the second one attracts it by potential-driven forces. Surface-enhanced Raman spectrometry (SERS) is employed as a reliable detection method for bacteria identification. To gain the effect of surface enhancement, silver nanoparticles were added to the sample. The pores of the membrane act as a filter trapping the bodies of microorganisms as well as clusters of nanoparticles creating suitable conditions for sensitive SERS detection. Therein, we focused on the construction and characterization of the device performance. To demonstrate the functionality of the microfluidic chip, we analyzed common pathogens (Escherichia coli DH5α and Pseudomonas taiwanensis VLB120) from spiked tap water using the optimized experimental parameters. The obtained results confirmed our system to be promising for the construction of a disposable optical platform for reliable and rapid pathogen detection which couples their electrokinetic concentration on the integrated nanoporous membrane with SERS detection.

Recent Advancement in the Surface-Enhanced Raman Spectroscopy-Based Biosensors for Infectious Disease Diagnosis

Diagnosis is the key component in disease elimination to improve global health. However, there is a tremendous need for diagnostic innovation for neglected tropical diseases that largely consist of mosquito-borne infections and bacterial infections. Early diagnosis of these infectious diseases is critical but challenging because the biomarkers are present at low concentrations, demanding bioanalytical techniques that can deliver high sensitivity with ensured specificity. Owing to the plasmonic nanomaterials-enabled high detection sensitivities, even up to single molecules, surface-enhanced Raman spectroscopy (SERS) has gained attention as an optical analytical tool for early disease biomarker detection. In this mini-review, we highlight the SERS-based assay development tailored to detect key types of biomarkers for mosquito-borne and bacterial infections. We discuss in detail the variations of SERS-based techniques that have developed to afford qualitative and quantitative disease biomarker detection in a more accurate, affordable, and field-transferable manner. Current and emerging challenges in the advancement of SERS-based technologies from the proof-of-concept phase to the point-of-care phase are also briefly discussed.

Surface-Enhanced Raman Scattering for Rapid Detection and Characterization of Antibiotic-Resistant Bacteria

As the prevalence of antibiotic-resistant bacteria continues to rise, biosensing technologies are needed to enable rapid diagnosis of bacterial infections. Furthermore, understanding the unique biochemistry of resistance mechanisms can facilitate the development of next generation therapeutics. Surface-enhanced Raman scattering (SERS) offers a potential solution to real-time diagnostic technologies, as well as a route to fundamental, mechanistic studies. In the current review, SERS-based approaches to the detection and characterization of antibiotic-resistant bacteria are covered. The commonly used nanomaterials (nanoparticles and nanostructured surfaces) and surface modifications (antibodies, aptamers, reporters, etc.) for SERS bacterial detection and differentiation are discussed first, and followed by a review of SERS-based detection of antibiotic-resistant bacteria from environmental/food processing and clinical sources. Antibiotic susceptibility testing and minimum inhibitory concentration testing with SERS are then summarized. Finally, recent developments of SERS-based chemical imaging/mapping of bacteria are reviewed.

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.

A Graphene-Silver Nanoparticle-Silicon Sandwich SERS Chip for Quantitative Detection of Molecules and Capture, Discrimination, and Inactivation of Bacteria

There currently exists increasing concerns on the development of a kind of high-performance SERS platform, which is suitable for sensing applications ranging from the molecular to cellular (e.g., bacteria) level. Herein, we develop a novel kind of universal SERS chip, made of graphene (G)-silver nanoparticle (AgNP)-silicon (Si) sandwich nanohybrids (G@AgNPs@Si), in which AgNPs are in situ grown on a silicon wafer through hydrofluoric acid-etching-assisted chemical reduction, followed by coating with single-layer graphene via a polymer-protective etching method. The resultant chip features a strong, stable, reproducible surface-enhanced Raman scattering (SERS) effect and reliable quantitative capability. By virtues of these merits, the G@AgNPs@Si platform is capable for not only molecular detection and quantification but also cellular analysis in real systems. As a proof-of-concept application, the chip allows ultrahigh sensitive and reliable detection of adenosine triphosphate (ATP), with a detection limit of ∼1 pM. In addition, the chip, serving as a novel multifunctional platform, enables simultaneous capture, discrimination, and inactivation of bacteria. Typically, the bacterial capture efficiency is 54% at 10⁸ CFU mL^-1 bacteria, and the antibacterial rate reaches 93% after 24 h of treatment. Of particular note, Escherichia coli and Staphylococcus aureus spiked into blood can be readily distinguished via the chip, suggesting its high potential for clinical applications.

A novel surface-enhanced Raman scattering (SERS) detection for natural gas exploration using methane-oxidizing bacteria

Methane-oxidizing bacteria (MOB), a unique group of Gram-negative bacteria utilizing methane as a sole source of carbon and energy, have been proved to be a biological indicator for gas prospecting. Field and cultivation-free detection of MOB is important but still challenging in current microbial prospecting of oil and gas (MPOG) system. Herein, SERS was used for the first time to our knowledge to investigate two species of methanotrophs and four closely relevant bacteria that universally coexisted in the upper soil of natural gas. A special but very simple approach was utilized to make silver nanoparticles (Ag NPs) sufficiently contact with every single bacterial cell, and highly strong and distinct Raman signals free from any native fluorescence have been obtained, and successfully utilized for distinguishing MOB from other species. A more convincing multi-Raman criterion based on single Raman bands, and further the entire Raman spectrum in combination with statistical analysis (e.g., principal component analysis (PCA)), which were found capable of classifying MOB related bacterial cells in soil with an accuracy of 100%. This study therefore demonstrated sensitive and rapid SERS measurement technique accompanied by complete Raman database of various gas reservoirs related bacteria could aid field exploration of natural gas reservoir.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.

Chemically imaging bacteria with super-resolution SERS on ultra-thin silver substrates

Plasmonic hotspots generate a blinking Surface Enhanced Raman Spectroscopy (SERS) effect that can be processed using Stochastic Optical Reconstruction Microscopy (STORM) algorithms for super-resolved imaging. Furthermore, by imaging through a diffraction grating, STORM algorithms can be modified to extract a full SERS spectrum, thereby capturing spectral as well as spatial content simultaneously. Here we demonstrate SERS and STORM combined in this way for super-resolved chemical imaging using an ultra-thin silver substrate. Images of gram-positive and gram-negative bacteria taken with this technique show excellent agreement with scanning electron microscope images, high spatial resolution at <50 nm, and spectral SERS content that can be correlated to different regions. This may be used to identify unique chemical signatures of various cells. Finally, because we image through as-deposited, ultra-thin silver films, this technique requires no nanofabrication beyond a single deposition and looks at the cell samples from below. This allows direct imaging of the cell/substrate interface of thick specimens or imaging samples in turbid or opaque liquids since the optical path doesn’t pass through the sample. These results show promise that super-resolution chemical imaging may be used to differentiate chemical signatures from cells and could be applied to other biological structures of interest.

Replacing a Century Old Technique - Modern Spectroscopy Can Supplant Gram Staining

Rapid and accurate Gram differentiation is paramount as the first step of pathogen identification and antibiotics administration. However, the current method requires additional reagents, is time-consuming, and is operator dependent. Here we show the principle of tip enhanced Raman spectroscopy (TERS) can differentiate between Gram negative and positive species, by detecting the changes in tip-enhancement in the Raman scattering from the bacteria's lipid-bilayer membrane, which specifically enhances Gram negative bacteria.

Accurate and Rapid Differentiation of Acinetobacter baumannii Strains by Raman Spectroscopy: a Comparative Study

In recent years, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has become the standard for routine bacterial species identification due to its rapidity and low costs for consumables compared to those of traditional DNA-based methods. However, it has been observed that strains of some bacterial species, such as Acinetobacter baumannii strains, cannot be reliably identified using mass spectrometry (MS). Raman spectroscopy is a rapid technique, as fast as MALDI-TOF, and has been shown to accurately identify bacterial strains and species. In this study, we compared hierarchical clustering results for MS, genomic, and antimicrobial susceptibility test data to hierarchical clustering results from Raman spectroscopic data for 31 A. baumannii clinical isolates labeled according to their pulsed-field gel electrophoresis data for strain differentiation. In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of analysis, including principal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS and Raman spectral data, along with a variety of spectral preprocessing techniques for best discriminative results. Finally, simple HCA algorithms were performed on all of the data sets to explore the relationships between, and natural groupings of, the strains and to compare results for the four data sets. To obtain numerical comparison values of the clustering results, the external cluster evaluation criteria of the Rand index of the HCA dendrograms were calculated. With a Rand index value of 0.88, Raman spectroscopy outperformed the other techniques, including MS (with a Rand index value of 0.58).

Highly efficient silver particle layers on glass substrate synthesized by the sonochemical method for surface enhanced Raman spectroscopy purposes

A fast method for preparing of silver particle layers on glass substrates with high application potential for using in surface enhanced Raman spectroscopy (SERS) is introduced. Silver particle layers deposited on glass cover slips were generated in one-step process by reduction of silver nitrate using several reducing agents (ethylene glycol, glycerol, maltose, lactose and glucose) under ultrasonic irradiation. This technique allows the formation of homogeneous layers of silver particles with sizes from 80nm up to several hundred nanometers depending on the nature of the used reducing agent. Additionally, the presented method is not susceptible to impurities on the substrate surface and it does not need any additives to capture or stabilize the silver particles on the glass surface. The characteristics of prepared silver layers on glass substrate by the above mentioned sonochemical approach was compared with chemically prepared ones. The prepared layers were tested as substrates for SERS using adenine as a model analyte. The factor of Raman signal enhancement reached up to 5·10(5). On the contrary, the chemically prepared silver layers does not exhibit almost any pronounced Raman signal enhancement. Presented sonochemical approach for preparation of silver particle layers is fast, simple, robust, and is better suited for reproducible fabrication functional SERS substrates than chemical one.

A rapid SERS method for label-free bacteria detection using polyethylenimine-modified Au-coated magnetic microspheres and Au@Ag nanoparticles

A rapid and efficient method for label-free SERS detection of bacteria in solution.

Isolation and identification of bacteria by means of Raman spectroscopy

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Label-free NIR-SERS discrimination and detection of foodborne bacteria by in situ synthesis of Ag colloids

Background

Rapid detection and discrimination of bacteria for biomedical and food safety applications remain a considerable challenge. We report a label-free near infrared surface-enhanced Raman scattering (NIR-SERS) method for the discrimination of pathogenic bacteria from drinking water. The approach relies on the in situ synthesis of silver nanoparticles (Ag NPs) within the bacterial cell suspensions.

Results

Pre-treatment of cells with Triton X-100 significantly improved the sensitivity of the assay. Using this method, we were able to discriminate several common pathogenic bacteria such as Escherichia coli, Pseudomonas aeruginosa, Methicillin-resistant Staphylococcus aureus (MRSA) and Listeria spp. A comparison of the SERS spectra allowed for the discrimination of two Listeria species, namely L. monocytogenes and L. innocua. We further report the application of the method to discriminate two MRSA strains from clinical isolates. The complete assay was completed in a span of 5 min.

Conclusions

The proposed analytical method proves to be a rapid tool for selective and label-free identification of pathogenic bacterium. Pre-treatment of bacterial cells with Triton X-100 resulted in new features on the SERS spectra, allowing for a successful discrimination of common disease related bacteria including E. coli, P. aeruginosa, Listeria and MRSA. We also demonstrate that the spectral features obtained using in situ synthesis of nanoparticles could be could be used to differentiate two species of listeria. By using L.innocua as a model sample, we found the limit of detection of our assay to be 10³ CFU/mL. The method can selectively discriminate different bacterial species, and has a potential to be used in the development of point-of-care diagnostics with biomedical and food safety applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12951-015-0106-4) contains supplementary material, which is available to authorized users.

Rapid identification by surface-enhanced Raman spectroscopy of cancer cells at low concentrations flowing in a microfluidic channel

Reliable identification and collection of cells from bodily fluids is of growing interest for monitoring patient response to therapy and for early detection of disease or its recurrence. We describe a detection platform that combines microfluidics with surface-enhanced Raman spectroscopy (SERS) for the identification of individual mammalian cells continuously flowing in a microfluidics channel. A mixture of cancerous and noncancerous prostate cells was incubated with SERS biotags (SBTs) developed and synthesized by us, then injected into a flow-focused microfluidic channel, which forces the cells into a single file. The spectrally rich SBTs are based on a silver nanoparticle dimer core labeled with a Raman-active small reporter molecule paired with an affinity biomolecule, providing a unique barcode whose presence in a composite SERS spectrum can be deconvoluted. Individual cancer cells passing through the focused laser beam were correctly identified among a proportionally larger number of other cells by their Raman signatures. We examine two deconvolution strategies: principal component analysis and classical least-squares. The deconvolution strategies are used to unmix the overall spectrum to determine the relative contributions between two SBT barcodes, where one SBT barcode indicates neuropilin-1 overexpression, while a second SBT barcode is more universal and indicates unspecific binding to a cell's membrane. Highly reliable results were obtained for all of the cell mixture ratios tested, the lowest being 1 in 100 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.

Effect of toxicity of Ag nanoparticles on SERS spectral variance of bacteria

Ag nanoparticles (NPs) have been extensively utilized in surface-enhanced Raman scattering (SERS) spectroscopy for bacterial identification. However, Ag NPs are toxic to bacteria. Whether such toxicity can affect SERS features of bacteria and interfere with bacterial identification is still unknown and needed to explore. Here, by carrying out a comparative study on non-toxic Au NPs with that on toxic Ag NPs, we investigated the influence of nanoparticle concentration and incubation time on bacterial SERS spectral variance, both of which were demonstrated to be closely related to the toxicity of Ag NPs. Sensitive spectral alterations were observed on Ag NPs with increase of NPs concentration or incubation time, accompanied with an obvious decrease in number of viable bacteria. In contrast, SERS spectra and viable bacterial number on Au NPs were rather constant under the same conditions. A further analysis on spectral changes demonstrated that it was cell response (i.e. metabolic activity or death) to the toxicity of Ag NPs causing spectral variance. However, biochemical responses to the toxicity of Ag were very different in different bacteria, indicating the complex toxic mechanism of Ag NPs. Ag NPs are toxic to a great variety of organisms, including bacteria, fungi, algae, protozoa etc., therefore, this work will be helpful in guiding the future application of SERS technique in various complex biological systems.

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.

Feasibility Study for Detection of Turnip yellow mosaic virus (TYMV) Infection of Chinese Cabbage Plants Using Raman Spectroscopy

Raman spectroscopy provides many advantages compared to other common analytical techniques due to its ability of rapid and accurate identification of unknown specimens as well as simple sample preparation. Here, we described potential of Raman spectroscopic technique as an efficient and high throughput method to detect plants infected by economically important viruses. To enhance the detection sensitivity of Raman measurement, surface enhanced Raman scattering (SERS) was employed. Spectra of extracts from healthy and Turnip yellow mosaic virus (TYMV) infected Chinese cabbage leaves were collected by mixing with gold (Au) nanoparticles. Our result showed that TYMV infected plants could be discriminated from non-infected healthy plants, suggesting the current method described here would be an alternative potential tool to screen virus-infection of plants in fields although it needs more studies to generalize the technique.

Evidence for phenotypic plasticity among multihost Campylobacter jejuni and C. coli lineages, obtained using ribosomal multilocus sequence typing and Raman spectroscopy

Closely related bacterial isolates can display divergent phenotypes. This can limit the usefulness of phylogenetic studies for understanding bacterial ecology and evolution. Here, we compare phenotyping based on Raman spectrometric analysis of cellular composition to phylogenetic classification by ribosomal multilocus sequence typing (rMLST) in 108 isolates of the zoonotic pathogens Campylobacter jejuni and C. coli. Automatic relevance determination (ARD) was used to identify informative peaks in the Raman spectra that could be used to distinguish strains in taxonomic and host source groups (species, clade, clonal complex, and isolate source/host). Phenotypic characterization based on Raman spectra showed a degree of agreement with genotypic classification using rMLST, with segregation accuracy between species (83.95%), clade (in C. coli, 98.41%), and, to some extent, clonal complex (86.89% C. jejuni ST-21 and ST-45 complexes) being achieved. This confirmed the utility of Raman spectroscopy for lineage classification and the correlation between genotypic and phenotypic classification. In parallel analysis, relatively distantly related isolates (different clonal complexes) were assigned the correct host origin irrespective of the clonal origin (74.07 to 96.97% accuracy) based upon different Raman peaks. This suggests that the phenotypic characteristics, from which the phenotypic signal is derived, are not fixed by clonal descent but are influenced by the host environment and change as strains move between hosts.