Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy

The interplay of Pseudomonas aeruginosa and Staphylococcus aureus in dual-species biofilms impacts development, antibiotic resistance and virulence of biofilms in in vitro wound infection models

Due to high tolerance to antibiotics and pronounced virulence, bacterial biofilms are considered a key factor and major clinical challenge in persistent wound infections. They are typically composed of multiple species, whose interactions determine the biofilm's structural development, functional properties and thus the progression of wound infections. However, most attempts to study bacterial biofilms in vitro solely rely on mono-species populations, since cultivating multi-species biofilms, especially for prolonged periods of time, poses significant challenges. To address this, the present study examined the influence of bacterial composition on structural biofilm development, morphology and spatial organization, as well as antibiotic tolerance and virulence on human skin cells in the context of persistent wound infections. By creating a wound-mimetic microenvironment, the successful cultivation of dual-species biofilms of two of the most prevalent wound pathogens, Pseudomonas aeruginosa and Staphylococcus aureus, was realized over a period of 72 h. Combining quantitative analysis with electron microscopy and label-free imaging enabled a comprehensive evaluation of the dynamics of biofilm formation and matrix secretion, revealing a twofold increased maturation of dual-species biofilms. Antibiotic tolerance was comparable for both mono-species cultures, however, dual-species communities showed a 50% increase in tolerance, mediated by a significantly reduced penetration of the applied antibiotic into the biofilm matrix. Further synergistic effects were observed, where dual-species biofilms exacerbated wound healing beyond the effects observed from either Pseudomonas or Staphylococcus. Consequently, predicting biofilm development, antimicrobial tolerance and virulence for multi-species biofilms based solely on the results from mono-species biofilms is unreliable. This study underscores the substantial impact of a multi-species composition on biofilm functional properties and emphasizes the need to tailor future studies reflecting the bacterial composition of the respective in vivo situation, leading to a more comprehensive understanding of microbial communities in the context of basic microbiology and the development of effective treatments.

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.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Identification of staphyloxanthin and derivates in yellow-pigmented Staphylococcus capitis subsp. capitis

Introduction

Staphylococcus capitis naturally colonizes the human skin but as an opportunistic pathogen, it can also cause biofilm-associated infections and bloodstream infections in newborns. Previously, we found that two strains from the subspecies S. capitis subsp. capitis produce yellow carotenoids despite the initial species description, reporting this subspecies as non-pigmented. In Staphylococcus aureus, the golden pigment staphyloxanthin is an important virulence factor, protecting cells against reactive oxygen species and modulating membrane fluidity.

Methods

In this study, we used two pigmented (DSM 111179 and DSM 113836) and two non-pigmented S. capitis subsp. capitis strains (DSM 20326T and DSM 31028) to identify the pigment, determine conditions under which pigment-production occurs and investigate whether pigmented strains show increased resistance to ROS and temperature stress.

Results

We found that the non-pigmented strains remained colorless regardless of the type of medium, whereas intensity of pigmentation in the two pigmented strains increased under low nutrient conditions and with longer incubation times. We were able to detect and identify staphyloxanthin and its derivates in the two pigmented strains but found that methanol cell extracts from all four strains showed ROS scavenging activity regardless of staphyloxanthin production. Increased survival to cold temperatures (-20°C) was detected in the two pigmented strains only after long-term storage compared to the non-pigmented strains.

Conclusion

The identification of staphyloxanthin in S. capitis is of clinical relevance and could be used, in the same way as in S. aureus, as a possible target for anti-virulence drug design.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

Antimicrobial Agent against Methicillin-Resistant Staphylococcus aureus Biofilm Monitored Using Raman Spectroscopy

The prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)-a leading cause of infections-forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with principal component analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of the incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.

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.

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.

Dynamic insights into increasing antibiotic resistance in Staphylococcus aureus by label-free SERS using a portable Raman spectrometer

Rapid and quantitative detection of bacterial antibiotic resistance is of great significance for the prevention and treatment of infections and understanding drug-resistant mechanism. In this study, label-free surface-enhanced Raman spectroscopy (SERS) technology was applied to dynamically explore oxacillin/cefazolin-derived resistance in Staphylococcus aureus using a portable Raman spectrometer. The results showed that S. aureus rapidly responded to oxacillin/cefazolin stimulation and gradually developed different degrees of drug resistance during the 21 days of exposure. The molecular changes that accumulated in the drug-resistant strains were sensitively recorded by SERS in a whole-cell manner. Principal components-linear discriminant analysis correctly distinguished various degrees of drug-resistant strains. The typical Raman peak intensities of I₇₃₄/I₈₆₇ showed a negative and non-linear correlation with the minimum inhibitory concentration (MIC). The correlation coefficient reached above 0.9. The target sites of oxacillin/cefazolin on S. aureus clearly reflected on SERS profiles. The results collected by SERS were further verified by other biological methods including the antibiotic susceptibility test, MIC determination, and PCR results. This study indicates that SERS technology provides a rapid and flexible alternative to current drug susceptibility testing, laying a foundation for qualitative and quantitative evaluation of drug resistance in clinical detection.

In Vitro Fiber-Probe-Based Identification of Pathogens in Biofilms by Raman Spectroscopy

Biofilms are the preferred habitat of microorganisms on living and artificial surfaces. Biofilm-related infections, such as infections of medical implants, are difficult to treat, and due to a reduced cultivability of the included bacteria, difficult to diagnose. Therefore, it is highly important to rapidly identify and investigate biofilms on implant surfaces, e.g., during surgery. In this study, we present fiber-probe-based Raman spectroscopy with an excitation wavelength of 785 nm, which was applied to investigate six different pathogen species involved in biofilm-related infections. Biofilms were cultivated in a drip flow reactor, which can model a biofilm growth environment. The signals collected from a fiber probe allowed us to collect Raman spectra not only from the embedded bacterial and yeast cells but also the surrounding extracellular polymeric substance matrix. This information was used in a classification model. The model consists of a principal component analysis in combination with linear discriminant analysis and was examined by applying a leave-one-batch-out cross-validation. This model achieved a classification accuracy of 93.8%. In addition, the identification accuracy increased up to 97.5% when clinical strains were used for identification. A fiber-probe-based Raman spectroscopy method combined with a chemometric analysis might therefore serve as a fast, accurate, and portable strategy for the species identification of biofilm-related infections, e.g., during surgical procedures.

Fiber Probe-Based Raman Spectroscopic Identification of Pathogenic Infection Microorganisms on Agar Plates

Rapid identification of microorganisms is clinically meaningful, and it helps to decelerate the spread of drug resistance and improve patient treatment. In this study, we present a rapid fiber probe-based Raman technique with an excitation wavelength of 785 nm, which is applied to classify and identify nine different species of microorganisms. The cost-effective fiber probe compresses the dimension of the system and provides a more reliable and stable database. All microorganisms were simply cultivated on Luria-Bertani (LB) agar, and Raman spectra were obtained directly from the microbial colonies with the fiber probe within 30 s. The classification model consists of principal component analysis (PCA) in combination with linear discriminant analysis (LDA) and was examined by applying leave-one-batch-out cross-validation (LOBOCV). This model achieved an accuracy of 98.9%. In addition, the validation and identification processes based on independent replicates achieved accuracies of 99.8% and 100%, respectively. The results demonstrated that fiber probe Raman spectroscopy in combination with chemometric analysis allowed a rapid classification and identification of microorganisms only with a normal culture. Therefore, it is promising especially for medical applications and could moreover be helpful to investigate and identify microorganisms rapidly in further studies.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

Simultaneous Raman and infrared spectroscopy: a novel combination for studying bacterial infections at the single cell level

Sepsis is a life-threatening clinical condition responsible for approximately 11 million deaths worldwide. Rapid and accurate identification of pathogenic bacteria and its antimicrobial susceptibility play a critical role in reducing the morbidity and mortality rates related to sepsis. Raman and infrared spectroscopies have great potential to be used as diagnostic tools for rapid and culture-free detection of bacterial infections. Despite numerous reports using both methods to analyse bacterial samples, there is to date no study collecting both Raman and infrared signatures from clinical samples simultaneously due to instrument incompatibilities. Here, we report for the first time the use of an emerging technology that provides infrared signatures via optical photothermal infrared (O-PTIR) spectroscopy and Raman spectra simultaneously. We use this approach to analyse 12 bacterial clinical isolates including six isolates of Gram-negative and six Gram-positive bacteria commonly associated with bloodstream infection in humans. To benchmark the single cell spectra obtained by O-PTIR spectroscopy, infrared signatures were also collected from bulk samples via both FTIR and O-PTIR spectroscopies. Our findings showed significant similarity and high reproducibility in the infrared signatures obtained by all three approaches, including similar discrimination patterns when subjected to clustering algorithms. Principal component analysis (PCA) showed that O-PTIR and Raman data acquired simultaneously from bulk bacterial isolates displayed different clustering patterns due to the ability of both methods to probe metabolites produced by bacteria. By contrast, signatures of microbial pigments were identified in Raman spectra, providing complementary and orthogonal information compared to infrared, which may be advantageous as it has been demonstrated that certain pigments play an important role in bacterial virulence. We found that infrared spectroscopy showed higher sensitivity than Raman for the analysis of individual cells. Despite the different patterns obtained by using Raman and infrared spectral data as input for clustering algorithms, our findings showed high data reproducibility in both approaches as the biological replicates from each bacterial strain clustered together. Overall, we show that Raman and infrared spectroscopy offer both advantages and disadvantages and, therefore, having both techniques combined in one single technology is a powerful tool with promising applications in clinical microbiology.

O-PTIR was used for simultaneous collection of infrared and Raman spectra from clinical pathogens associated with bloodstream infections.

ISMN-loaded PLGA-PEG nanoparticles conjugated with anti-Staphylococcus aureus α-toxin inhibit Staphylococcus aureus biofilms in chronic rhinosinusitis

Background: Staphylococcus aureus biofilms were linked to negative postsurgical outcomes of chronic rhinosinusitis (CRS). This study aims to develop a targeted nanoparticle and characterize its bactericidal effects. Methods: The authors prepared ISMN-loaded poly-lactide-co-glycolide acid (PLGA) and polyethylene glycol (PEG) nanoparticles conjugated with anti-S. aureus α-toxin (AA; ISMN-PLGA-PEG-AA), and determined its bactericidal and toxic effects. The antibiofilm propriety of ISMN-PLGA-PEG-AA was further investigated in a sheep CRS model. Results: ISMN-PLGA-PEG-AA had no toxic effect, while ISMN, ISMN-PLGA-PEG and ISMN-PLGA-PEG-AA had significantly anti-S. aureus effects. The blood concentrations and mRNA levels in sinus tissues of IL-4, IL-8 and IFN-γ in the sheep CRS model were significantly low. Conclusion: ISMN-PLGA-PEG-AA can effectively inhibit S. aureus biofilm, and is a promising drug for CRS treatment.

Visualizing and quantifying antimicrobial drug distribution in tissue

The biodistribution and pharmacokinetics of drugs are vital to the mechanistic understanding of their efficacy. Measuring antimicrobial drug efficacy has been challenging as plasma drug concentration is used as a surrogate for tissue drug concentration, yet typically does not reflect that at the intended site(s) of action. Utilizing an image-guided approach, it is feasible to accurately quantify the biodistribution and pharmacokinetics within the desired site(s) of action. We outline imaging modalities used in visualizing drug distribution with examples ranging from in vitro cellular drug uptake to clinical treatment of microbial infections. The imaging modalities of interest are: radio-labeling, magnetic resonance, mass spectrometry imaging, computed tomography, fluorescence, and Raman spectroscopy. We outline the progress, limitations, and future outlook for each methodology. Further advances in these optical approaches would benefit patients and researchers alike, as non-invasive imaging could yield more profound insights with a lower clinical burden than invasive measurement approaches used today.

Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets

Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. This paper considers fast Raman spectroscopy data and demonstrates that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.

Campylobacter jejuni Antimicrobial Resistance Profiles and Mechanisms Determined Using a Raman Spectroscopy-Based Metabolomic Approach

Rapid identification of antimicrobial resistance (AMR) profiles and mechanisms is critical for clinical management and drug development. However, the current AMR detection approaches take up to 48 h to obtain a result. Here, we demonstrate a Raman spectroscopy-based metabolomic approach to rapidly determine the AMR profile of Campylobacter jejuni, a major cause of foodborne gastroenteritis worldwide. C. jejuni isolates with susceptible and resistant traits to ampicillin and tetracycline were subjected to different antibiotic concentrations for 5 h, followed by Raman spectral collection and chemometric analysis (i.e., second-derivative transformation analysis, hierarchical clustering analysis [HCA], and principal-component analysis [PCA]). The MICs obtained by Raman-2nd derivative transformation agreed with the reference agar dilution method for all isolates. The AMR profile of C. jejuni was accurately classified by Raman-HCA after treating bacteria with antibiotics at clinical susceptible and resistant breakpoints. According to PCA loading plots, susceptible and resistant strains showed different Raman metabolomic patterns for antibiotics. Ampicillin-resistant isolates had distinctive Raman signatures of peptidoglycan, which is related to cell wall synthesis. The ratio of saturated to unsaturated fatty acids in the lipid membrane layer of ampicillin-resistant isolates was higher than in susceptible ones, indicating more rigid envelope structure under ampicillin treatment. In comparison, tetracycline-resistant isolates exhibited prominent Raman spectral features associated with proteins and nucleic acids, demonstrating more active protein synthesis than susceptible strains with the presence of tetracycline. Taken together, Raman spectroscopy is a powerful metabolic fingerprinting technique for simultaneously revealing the AMR profiles and mechanisms of foodborne pathogens. IMPORTANCE Metabolism plays the central role in bacteria to mediate the early response against antibiotics and demonstrate antimicrobial resistance (AMR). Understanding the whole-cell metabolite profiles gives rise to a more complete AMR mechanism insight. In this study, we have applied Raman spectroscopy and chemometrics to achieve a rapid, accurate, and easy-to-operate investigation of bacterial AMR profiles and mechanisms. Raman spectroscopy reduced the analysis time by an order of magnitude to obtain the same results achieved through traditional culture-based antimicrobial susceptibility approaches. It offers great benefits as a high-throughput screening method in food chain surveillance and clinical diagnostics. Meanwhile, the AMR mechanisms toward two representative antibiotic classes, namely, ampicillin and tetracycline, were revealed by Raman spectroscopy at the metabolome level. This approach is based on bacterial phenotypic responses to antibiotics, providing information complementary to that obtained by conventional genetic methods such as genome sequencing. The knowledge obtained from Raman metabolomic data can be used in drug discovery and pathogen intervention.

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.

Haem toxicity provides a competitive advantage to the clinically relevant Staphylococcus aureus small colony variant phenotype

Microorganisms encounter toxicities inside the host. Many pathogens exist as subpopulations to maximize survivability. Subpopulations of Staphylococcus aureus include antibiotic-tolerant small colony variants (SCVs). These mutants often emerge following antibiotic treatment but can be present in infections prior to antibiotic exposure. We hypothesize that haem toxicity in the host selects for respiration-deficient S. aureus SCVs in the absence of antibiotics. We demonstrate that some but not all respiration-deficient SCV phenotypes are more protective than the haem detoxification system against transient haem exposure, indicating that haem toxicity in the host may contribute to the dominance of menaquinone-deficient and haem-deficient SCVs prior to antibiotic treatment.

Photo-responsive functional gold nanocapsules for inactivation of community-acquired, highly virulent, multidrug-resistant MRSA

The indiscriminate and sporadic use of antibiotics has contributed to the emergence of drug resistance phenomenon in bacteria including but not limited to Staphylococcus aureus. These drug-resistant bacteria have been threatening safety in hospitals and adversely affecting human health. Here we report a strategy to design photo-stimulated theranostic nanoprobes against methicillin-resistant Staphylococcus aureus (MRSA) "superbug" USA300. The nanocapsule probe is based on gold nanorods (GNRs) coated with pegylated thiol, mPEG-SH, which has been further modified by adding successively a natural antibacterial compound such as curcumin, and a cell targeting deoxyribonucleic acid (DNA) aptamer. We have used this novel gold nanocapsules for near-infrared (NIR) photophysical stimulation against pathogenic bacteria. We have found that the novel nanocapsule blocks biofilm formation and kills bacteria by photothermal action that causes disruption of the bacterial cell wall and membrane. In this approach, multiple drug-resistant Staphylococcus aureus has been captured by these nanocapsules through DNA aptamer targeting. All of the trapped bacteria could be killed in 30 minutes during the NIR stimulation due to the combination of photothermal effect, the generation of reactive oxygen species (ROS) and a loss of transmembrane potential (Δψ). Importantly we did not notice any resistance developed against the photothermal treatment. This is remarkable from an anti-biofilm activity point of view. Importantly, these multifunctional nanocapsules have also shown a surface enhanced Raman spectroscopy (SERS) effect, which could be used to evaluate the success of the inactivation effect during treatment. These results indicate that nanocapsule-based photo treatment can be an alternative antibacterial strategy without contributing to antibiotic resistance, and thus can be used for both environmental and therapeutic applications.

Biopolymer Patterning-Directed Secretion in Mucoid and Nonmucoid Strains of Pseudomonas aeruginosa Revealed by Multimodal Chemical Imaging

Quinolone, pyocyanin, and rhamnolipid production were studied in Pseudomonas aeruginosa by spatially patterning mucin, a glycoprotein important to infection of lung epithelia. Mass spectrometric imaging and confocal Raman microscopy are combined to probe P. aeruginosa biofilms from mucoid and nonmucoid strains grown on lithographically defined patterns. Quinolone signatures from biofilms on patterned vs unpatterned and mucin vs mercaptoundecanoic acid (MUA) surfaces were compared. Microbial attachment is accompanied by secretion of 2-alkyl-4-quinolones as well as rhamnolipids from the mucoid and nonmucoid strains. Pyocyanin was also detected both in the biofilm and in the supernatant in the mucoid strain only. Significant differences in the spatiotemporal distributions of secreted factors are observed between strains and among different surface patterning conditions. The mucoid strain is sensitive to composition and patterning while the nonmucoid strain is not, and in promoting community development in the mucoid strain, nonpatterned surfaces are better than patterned, and mucin is better than MUA. Also, the mucoid strain secretes the virulence factor pyocyanin in a way that correlates with distress. A change in the relative abundance for two rhamnolipids is observed in the mucoid strain during exposure to mucin, whereas minimal variation is observed in the nonmucoid strain. Differences between mucoid and nonmucoid strains are consistent with their strain-specific phenology, in which the mucoid strain develops highly protected and withdrawn biofilms that achieve Pseudomonas quinolone signal production under limited conditions.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Optical Identification of Middle Ear Infection

Ear infection is one of the most commonly occurring inflammation diseases in the world, especially for children. Almost every child encounters at least one episode of ear infection before he/she reaches the age of seven. The typical treatment currently followed by physicians is visual inspection and antibiotic prescription. In most cases, a lack of improper treatment results in severe bacterial infection. Therefore, it is necessary to design and explore advanced practices for effective diagnosis. In this review paper, we present the various types of ear infection and the related pathogens responsible for middle ear infection. We outline the conventional techniques along with clinical trials using those techniques to detect ear infections. Further, we highlight the need for emerging techniques to reduce ear infection complications. Finally, we emphasize the utility of Raman spectroscopy as a prospective non-invasive technique for the identification of middle ear infection.

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.

The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra.

"Three-in-One" SERS Adhesive Tape for Rapid Sampling, Release, and Detection of Wound Infectious Pathogens

The traditional colony culture method for detection of pathogens is subjected to the laborious and tedious experimental procedure, which limits its application in point-of-care (POC) testing and quick diagnosis. This work designs an intelligent adhesive tape as a "three-in-one" platform for rapid sampling, photocontrolled release, and surface-enhanced Raman scattering (SERS) detection of pathogens from infected wounds. This tape is constructed by encapsulating densely packed gold nanostars as SERS substrates between two pieces of graphene and modified with a synthetic o-nitrobenzyl derivative molecule to form an artificial biointerface for highly efficient pathogen capture via electrostatic interaction. The captured targets can be conveniently released onto a solid culture medium by UV cleavage of o-nitrobenzyl moiety for pathogen growth and in situ SERS detection. As a proof of strategy, this "three-in-one" platform has been used for detecting the concurrent infection of Pseudomonas aeruginosa and Staphylococcus aureus by pasting the tape on a skin burn wound. The impressive detection performance with an analytical time of only several hours for these pathogens at an early growth stage demonstrates its great potential as a POC testing device for health care.

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.

Identification of ability to form biofilm in Candida parapsilosis and Staphylococcus epidermidis by Raman spectroscopy

Aim: Finding rapid, reliable diagnostic methods is a big challenge in clinical microbiology. Raman spectroscopy is an optical method used for multiple applications in scientific fields including microbiology. This work reports its potential in identifying biofilm positive strains of Candida parapsilosis and Staphylococcus epidermidis. Materials & methods: We tested 54 S. epidermidis strains (23 biofilm positive, 31 negative) and 51 C. parapsilosis strains (27 biofilm positive, 24 negative) from colonies on Mueller-Hinton agar plates, using Raman spectroscopy. Results: The accuracy was 98.9% for C. parapsilosis and 96.1% for S. epidermidis. Conclusion: The method showed great potential for identifying biofilm positive bacterial and yeast strains. We suggest that Raman spectroscopy might become a useful aid in clinical diagnostics.