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

Rapid and accurate identification of foodborne bacteria: a combined approach using confocal Raman micro-spectroscopy and explainable machine learning

This study proposes a rapid identification method for foodborne pathogens by combining Raman spectroscopy with explainable machine learning. Spectral data of nine common foodborne pathogens are collected using a laser confocal Raman spectrometer, and their characteristic Raman peaks are identified and analyzed. Key spectral features are extracted using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA), while t-distributed stochastic neighbor embedding (t-SNE) is employed for visualization. Subsequently, classification models, including support vector machine (SVM) and random forest (RF), are developed, and the optimal model is selected based on classification accuracy (ACC), with the RF model achieving a test accuracy of 98.91%. To enhance the interpretability of the model, Shapley Additive exPlanations (SHAP) analysis is applied to evaluate the contribution of each spectral feature to the classification results, identifying critical Raman shifts significantly influencing pathogen classification. The results demonstrate that CARS-SPA feature selection not only improves the accuracy and efficiency of the classification model but also enhances its transparency and reliability. This study optimizes the workflow for food safety testing, reduces the risk of foodborne diseases, and provides robust technical support for public health and safety.

Making vancomycin a potent broad-spectrum antimicrobial agent using polyaziridine-stabilized gold nanoparticles as a delivery vehicle

The rise of antimicrobial drug resistance among microorganisms presents a global challenge to clinicians. Therefore, it is essential to investigate drug delivery systems to combat resistant bacteria and fungi. This study examined the potential and mode of action of vancomycin-conjugated gold nanoparticles (PEI-AuNP@Van) to enhance vancomycin's biocidal activity against C. tropicalis, C. albicans, E. coli, and P. aeruginosa. Drug conjugation and nanoparticle characterization were assessed using UV-Vis spectroscopy, X-ray diffraction, TEM, ATR-FTIR, and fluorescence spectroscopy. Effective vancomycin conjugation on polyethyleneimine-stabilized gold nanoparticles was achieved via electrostatic interactions or hydrogen bonding between the COO-/OH groups of vancomycin and the NH- groups of polyethyleneimine, yielding nanoparticles with a narrow size distribution and high zeta potential. The high luminescence of the nanoparticles facilitated their detection in microbial cells. PEI-AuNP@Van was internalized in C. albicans and C. tropicalis but showed surface adsorption in E. coli and P. aeruginosa. The in vitro results indicated that the nanodelivery system exhibited superior biocidal activity against the tested strains compared to free vancomycin and unconjugated AuNPs. The mode of action of PEI-AuNP@Van was cell-type-dependent, involving intracellular reactive oxygen species accumulation, cell membrane integrity loss, and apoptosis. The development of antimicrobial nanoformulations using AuNPs and efficient conjugation systems offers a promising approach to address antimicrobial drug resistance.

Recent Advances in Surface-Enhanced Raman Scattering for Pathogenic Bacteria Detection: A Review

Bacterial infection is one of the common infectious diseases in clinical practice, and the research on efficient detection of bacteria has attracted much attention in recent years. Currently, the traditional detection methods of bacteria are mainly based on cell culturing, microscopic examination, and molecular biology techniques, all of which have the disadvantages of complex operation and time-consuming. Surface-enhanced Raman spectroscopy (SERS) technology has shown prominent advantages in bacterial detection and identification because of the merit of high-sensitivity, fast detection and unique molecular fingerprint spectrum. This paper mainly investigates and discusses the application of SERS in bacterial detection, and systematically reviews the progress of SERS applications, including nano-enhanced dielectric materials of SERS, signal amplification of SERS labeled molecules, and the integration of SERS with microfluidic technology. Finally, the paper analyzes the challenges associated with the application of SERS in bacterial detection and offers insights into future development trends.

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.

Urchin-Shaped Au-Ag@Pt Sensor Integrated Lateral Flow Immunoassay for Multimodal Detection and Specific Discrimination of Clinical Multiple Bacterial Infections

A new lateral flow immunoassay strip (LFIA) combining sensitive detection and identification of multiple bacteria remains a huge challenge. In this study, we first developed multifunctional urchin-shaped Au-Ag@Pt nanoparticles (UAA@P NPs) with a unique combination of colorimetric-SERS-photothermal-catalytic (CM/SERS/PT/CL) properties and integrated them with LFIA for multiplexed detection and specific discrimination of pathogenic bacteria in blood samples. Unlike the conventional LFIA that relied on antibody (Ab), this novel LFIA introduced 4-mercaptophenylboronic acid (4-MPBA) as an ideal Ab replacer that was functionalized on UAA@P NPs (UAA@P/M NPs) with outstanding binding and enrichment capacities toward bacteria. Taking Staphylococcus aureus (S. aureus) as model bacteria, the limit of detection (LOD) was 3 CFU/mL for SERS-LFIA, 27 CFU/mL for PT-LFIA, and 18 CFU/mL for CL-LFIA, three of which were over 330-fold, 37-fold, and 55-fold more sensitive than ordinary visual CM-LFIA, respectively. Besides, this SERS-LFIA is capable of identifying three types of bacterial spiked blood samples (E. coli, S. aureus, and P. aeruginosa) effectively according to specific bacterial Raman "fingerprints" by partial least-squares-discriminant analysis (PLS-DA). More importantly, this LFIA was successfully applied to blood samples with satisfactory recoveries from 90.3% to 108.8% and capable of identifying the infected patients (N = 4) from healthy subjects (N = 2) with great accuracy. Overall, the multimodal LFIA incorporates bacteria discrimination and quantitative detection, offering an avenue for early warning and diagnosis of bacterial infection.

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".

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

BACKGROUND

Sinusitis is defined as inflammation of the paranasal sinus mucous membrane lining caused by bacteria which usually invade the sinus by upper respiratory tract viral infections (UTI).

OBJECTIVES

In the present study, Surface-enhanced Raman spectroscopy (SERS) has been applied to differentiate and characterize supernatant samples, in triplicate, of three different types of bacteria which are considered leading cause of sinusitis disease.

METHODS

For this purpose, supernatant samples of three different strains of bacteria namely Staphylococcus aureus, Klebsiella pneumoniae and Enterococcus faecalis. The SERS has identified significant changes as a result of secretions of biomolecules by these bacteria in their supernatants which can be helpful to explore the potential of this technique for the identification and characterization of different strains of bacteria causing same disease.

RESULTS

These differentiating characteristic SERS spectral features including 552 cm^-1 (C-S-S-C bonds), 951 cm^-1 (CN stretching), 1008 cm^-1 (Phenylalanine), 1032 cm^-1 (In plane CH bending mode Phenylalanine), 1280 cm^-1, 1320 cm^-1, 1329 cm^-1 (Amide III band), 1368 cm^-1, 1400 cm^-1, 1420 cm^-1 (COO^-sym. stretching and CH bending), 1583 cm^-1 (Tyrosine) correspond to Proteins and 1051 cm^-1 (C-C, C-O, -C-OH def.) correspond to carbohydrates contents of these three different types of bacterial secretions in their respective supernatants. Furthermore, multivariate data analysis techniques like principal component analysis (PCA) and a supervised method partial least squares-discriminant analysis (PLS-DA) were found to be useful for the identification and characterization of different bacterial supernatants.

CONCLUSIONS

Surface-enhanced Raman spectroscopy is proven to be a helpful approach for the characterization and discrimination of three bacterial supernatants including S. aureus, K. pneumonia and E. faecalis.

Multifunctional Au nano-bridged nanogap probes as ICP-MS/SERS dual-signal tags and signal amplifiers for bacteria discriminating, quantitative detecting and photothermal bactericidal activity

Ultra-sensitive detection of pathogenic bacteria is of great significance in the early stage of bacterial infections and treatment. In this work, we report a novel strategy using multifunctional Au nano-bridged nanogap nanoparticles (Au NNPs)-based sandwich nanocomposites, that made of Concanavalin A-conjugated Fe₃O₄@SiO₂ NPs (ConA-Fe₃O₄@SiO₂ NPs)/bacteria/aptamer-modified Au NNPs (apt-Au NNPs), for bacteria discrimination and quantitative detection by surface-enhanced Raman scattering (SERS) and inductively coupled plasma mass spectrometry (ICP-MS), and subsequently photothermal antibacterial assay. The sandwich nanocomposite consists of ConA-Fe₃O₄@SiO₂ NPs to magnetically enrich and photothermal killing bacteria, and dual-signal tags of apt-Au NNPs for both SERS sensing and ICP-MS quantification. This strategy can specifically distinguish different kinds of pathogenic bacteria, and provided a good linear relationship of Staphylococcus aureus (S. aureus) in the range from 50 to 10⁴ CFU/mL with a detection limit of 11 CFU/mL, as well as realized ultralow amounts of bacterial detection in serum sample with high accuracy. Based on the quantitative detection, high antibacterial efficiency was monitored by ICP-MS. Overall, the established method combines bacteria discrimination, quantitative detection, and photothermal elimination with a simple and rapid process, which provides a novel way for the early diagnosis and treatment of bacterial infection.

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

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

Single Cell Raman Spectroscopy Deuterium Isotope Probing for Rapid Antimicrobial Susceptibility Test of Elizabethkingia spp

Nosocomial infection by multi-drug resistance Elizabethkingia spp. is an emerging concern with severe clinical consequences, particularly in immunocompromised individuals and infants. Efficient control of this infection requires quick and reliable methods to determine the appropriate drugs for treatment. In this study, a total of 31 Elizabethkingia spp., including two standard strains (ATCC 13253 and FMS-007) and 29 clinical isolates obtained from hospitals in China were subjected to single cell Raman spectroscopy analysis coupled with deuterium probing (single cell Raman-DIP). The results demonstrated that single cell Raman-DIP could determine antimicrobial susceptibility of Elizabethkingia spp. in 4 h, only one third of the time required by standard broth microdilution method. The method could be integrated into current clinical protocol for sepsis and halve the report time. The study also confirmed that minocycline and levofloxacin are the first-line antimicrobials for Elizabethkingia spp. infection.

SERS-based Au@Ag NPs Solid-phase substrate combined with chemometrics for rapid discrimination of multiple foodborne pathogens

In this study, a surface enhanced Raman scattering (SERS) sensor based on Au@Ag NPs solid-phase substrate combined with chemometrics was constructed for the discrimination of three pathogenic bacteria (Staphylococcus aureus, Escherichia coli and Listeria monocytogenes). The Au@Ag NPs were synthesized and self-assembled on filter paper using the dip-coating method. The good absorbency of the filter paper immobilized the bacteria on the substrate, increased the interaction between the bacteria and the substrate, and enhanced the SERS signal of the bacteria. The main peaks of the bacterial spectra were close to each other, but the relative intensities of the vibrational peaks were significantly different, and each strain exhibited unique Raman peaks. The combination of partial least squared discriminant analysis (PLS-DA) method with bacterial SERS allowed the effective identification of the three bacteria. Moreover, the method was applied for the quantitative detection of Staphylococcus aureus with a minimum detection concentration of 10⁴ cfu/mL.

Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria

Raman spectroscopy combined with artificial intelligence algorithms have been widely explored and focused on in recent years for food safety testing. It is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy. In this paper, we propose a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine to classify foodborne pathogenic bacteria. 30,000 iterations of generative adversarial network are trained for three strains of bacteria, generative model G generates data similar to the actual samples, discriminant model D verifies the accuracy of the generated data, and 19 feature variables are obtained by selecting the feature bands according to the Raman spectroscopy pattern. Better classification results are obtained by optimising the parameters of the multi-class support vector machine, etc. Our detection and classification method not only solves the problem of needing a large number of samples as training set, but also improves the accuracy of the classification model. Therefore, this GAN-SVM classification model provides a new idea for the detection of bacteria based on Raman spectroscopy technology combined with artificial intelligence algorithms.

Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy

Techniques for continuously monitoring the formation of subgingival biofilm, in relation to the determination of species and their accumulation over time in gingivitis and periodontitis, are limited. In recent years, advancements in the field of optical spectroscopic techniques have provided an alternative for analyzing three-dimensional microbiological structures, replacing the traditional destructive or biofilm staining techniques. In this work, we have demonstrated that the use of confocal Raman spectroscopy coupled with multivariate analysis provides an approach to spatially differentiate bacteria in an in vitro model simulating a subgingival dual-species biofilm. The present study establishes a workflow to evaluate and differentiate bacterial species in a dual-species in vitro biofilm model, using confocal Raman microscopy (CRM). Biofilm models of Actinomyces denticolens and Streptococcus oralis were cultured using the “Zürich in vitro model” and were analyzed using CRM. Cluster analysis was used to spatially differentiate and map the biofilm model over a specified area. To confirm the clustering of species in the cultured biofilm, confocal laser scanning microscopy (CLSM) was coupled with fluorescent in vitro hybridization (FISH). Additionally, dense bacteria interface area (DBIA) samples, as an imitation of the clusters in a biofilm, were used to test the developed multivariate differentiation model. This confirmed model was successfully used to differentiate species in a dual-species biofilm and is comparable to morphology. The results show that the developed workflow was able to identify main clusters of bacteria based on spectral “fingerprint region” information from CRM. Using this workflow, we have demonstrated that CRM can spatially analyze two-species in vitro biofilms, therefore providing an alternative technique to map oral multi-species biofilm models.

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 bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

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.

Polydimethylsiloxane-graphene oxide nanocomposite coatings with improved anti-corrosion and anti-biofouling properties

We demonstrate enhanced anti-corrosion and anti-biofouling properties of graphene oxide-silica-polydimethylsiloxane (GSP) coating on carbon steel (CS). Electrochemical analyses of GSP-coated carbon steel exposed to Gram-positive Bacillus sp., Gram-negative Pseudomonas sp., and freshwater bacterial cultures for 72 h showed a 3-5 orders of magnitude reduction in i_corr values and high impedance values (10⁷ Ω) as compared with polished specimens. The corrosion protection efficiency of GSP-coated specimens was 99.9% against Bacillus sp. and freshwater culture and it was 89.6% against Pseudomonas sp. Evaluation of anti-biofouling property of GSP coating using microbiological and epifluorescence microscopic techniques showed three order reductions in total viable cells on GSP-coated specimens exposed to bacterial cultures. Confocal laser scanning microscopic analysis of biofilm architecture confirmed a significant reduction of biomass and biofilm thickness on GSP-coated CS demonstrating an excellent anti-biofouling activity of GSP.

Combining vancomycin-modified gold nanorod arrays and colloidal nanoparticles as a sandwich model for the discrimination of Gram-positive bacteria and their detection via surface-enhanced Raman spectroscopy (SERS)

This study reports the development of a highly sensitive antibiotic-based discrimination and sensor platform for the detection of Gram-positive bacteria through surface-enhanced Raman spectroscopy (SERS).

Bacteria Detection: From Powerful SERS to Its Advanced Compatible Techniques

The rapid, highly sensitive, and accurate detection of bacteria is the focus of various fields, especially food safety and public health. Surface-enhanced Raman spectroscopy (SERS), with the advantages of being fast, sensitive, and nondestructive, can be used to directly obtain molecular fingerprint information, as well as for the on-line qualitative analysis of multicomponent samples. It has therefore become an effective technique for bacterial detection. Within this progress report, advances in the detection of bacteria using SERS and other compatible techniques are discussed in order to summarize its development in recent years. First, the enhancement principle and mechanism of SERS technology are briefly overviewed. The second part is devoted to a label-free strategy for the detection of bacterial cells and bacterial metabolites. In this section, important considerations that must be made to improve bacterial SERS signals are discussed. Then, the label-based SERS strategy involves the design strategy of SERS tags, the immunomagnetic separation of SERS tags, and the capture of bacteria from solution and dye-labeled SERS primers. In the third part, several novel SERS compatible technologies and applications in clinical and food safety are introduced. In the final part, the results achieved are summarized and future perspectives are proposed.

Understanding Escherichia coli damages after chlorophyllin-based photosensitization

Pathogenic strains of bacteria are causing various illnesses all around the world and have a major socio-economic impact. Thus, fast- and low-cost methods for the microbial control of foods are needed. One of them might be photosensitization. This study looks deeper into the mechanism of Escherichia coli damage by chlorophyllin-based photosensitization. Fluorimetric data indicate that after 15 minute incubation with chlorophyllin (Chl) (1.5 × 10^-5 M Chl) 0.73 ± 0.03 μM of this compound was associated with E. coli cell surface. After photoactivation (405 nm, 6-30 J/cm² ) significant reduction (88.2%) of bacterial viability was observed. Higher concentration of Chl (5 × 10^-4 M Chl) reduced viability of bacteria more than by 98%. Results indicated that reactive oxygen species (ROS) took place in this inactivation. Colloidal surface enhanced Raman scattering (SERS) spectroscopy was employed to detect the molecular changes in the treated bacteria. It was found that Chl-based based photosensitization triggers multiple surface structure changes in E. coli what induce lethal unrepairable damages and inactivation of pathogen.

Confocal Raman microscopy to identify bacteria in oral subgingival biofilm models

The study of oral disease progression, in relation to the accumulation of subgingival biofilm in gingivitis and periodontitis is limited, due to either the ability to monitor plaque in vitro. When compared, optical spectroscopic techniques offer advantages over traditional destructive or biofilm staining approaches, making it a suitable alternative for the analysis and continued development of three-dimensional structures. In this work, we have developed a confocal Raman spectroscopy analysis approach towards in vitro subgingival plaque models. The main objective of this study was to develop a method for differentiating multiple oral subgingival bacterial species in planktonic and biofilm conditions, using confocal Raman microscopy. Five common subgingival bacteria (Fusobacterium nucleatum, Streptococcus mutans, Veillonella dispar, Actinomyces naeslundii and Prevotella nigrescens) were used and differentiated using a 2-way orthogonal Partial Least Square with Discriminant Analysis (O2PLS-DA) for the collected spectral data. In addition to planktonic growth, mono-species biofilms cultured using the 'Zürich Model' were also analyzed. The developed method was successfully used to predict planktonic and mono-species biofilm species in a cross validation setup. The results show differences in the presence and absence of chemical bands within the Raman spectra. The O2PLS-DA model was able to successfully predict 100% of all tested planktonic samples and 90% of all mono-species biofilm samples. Using this approach we have shown that Confocal Raman microscopy can analyse and predict the identity of planktonic and mono-species biofilm species, thus enabling its potential as a technique to map oral multi-species biofilm models.

Yeast cell wall - Silver nanoparticles interaction: A synergistic approach between surface-enhanced Raman scattering and computational spectroscopy tools

Candida species are becoming one of the pathogens developing antifungal resistance due to inappropriate treatment and overuse of antimycotic drugs in building construction and agriculture. Further, fungal infections are often difficult to detect, also due to slow in vitro growth of the organisms from clinical specimens. Thus, fast detection and discrimination of yeast cells in direct patient materials is essential for an adequate treatment and success rate. In this work, we investigated Candida species isolated from patients, by using surface-enhanced Raman scattering (SERS) combined with computational spectroscopy tools, aiming to detect and discriminate between the three considered species, Candida albicans, Candida glabrata, and Candida parapsilosis. Density functional theory (DFT) was used to calculate Raman spectra of yeasts' main cell wall components for elucidating the origin of the observed bands. Accurate assignments of normal modes helped for a better understanding of the interaction between silver nanoparticles with yeasts' cell wall. Further, SERS spectra were used as samples in a database on which we performed multivariate analyses. By Principal component analysis (PCA), we obtained a maximum variation of 79% between the three samples. Linear discriminant analysis (LDA) was successfully used to discriminate between the three species.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

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.

Portable bacteria-capturing chip for direct surface-enhanced Raman scattering identification of urinary tract infection pathogens

Acute urinary tract infections (UTIs) are one of the most common nosocomial bacterial infections, which affect almost 50% of the population at least once in their lifetime. UTIs may lead to lethal consequences if they are left undiagnosed and not properly treated. Early, rapid and accurate uropathogens detection methods play a pivotal role in clinical process. In this work, a portable bacteria-grasping surface-enhanced Raman scattering (SERS) chip for identification of three species of uropathogens (Escherichia coli CFT 073, Pseudomonas aeruginosa PAO1 and Proteus mirabilis PRM1) directly from culture matrix was reported. The chip was firstly modified with a positively charged NH3 + group, which enables itself grasp the negatively charged bacterial cells through the electrostatic adsorption principle. After the bacterial cells were captured by the chip, concentrated Ag nanoparticles (NPs) were used to obtain their Raman fingerprint spectra with recognizable characteristic peaks and good reproducibility. With the help of chemometric method such as discriminant analysis (DA), the SERS-based chip allows a rapid, successful identification of three species of UTI bacteria with a minimal bacterial concentration (105 cells ml-1) required for clinical diagnostics. In addition, this chip could spot the bacterial SERS fingerprints information directly from LB culture medium and artificial urine without sample pre-treatment. The portable bacteria-grasping SERS-based chip provides a possibility for fast and easy detection of uropathogens, and viability of future development in healthcare applications.

Review on SERS of Bacteria

Surface enhanced Raman spectroscopy (SERS) has been widely used for chemical detection. Moreover, the inherent richness of the spectral data has made SERS attractive for use in detecting biological materials, including bacteria. This review discusses methods that have been used to obtain SERS spectra of bacteria. The kinds of SERS substrates employed to obtain SERS spectra are discussed as well as how bacteria interact with silver and gold nanoparticles. The roll of capping agents on Ag/Au NPs in obtaining SERS spectra is examined as well as the interpretation of the spectral data.