Surface-Enhanced Raman Spectroscopy of Bacterial Metabolites for Bacterial Growth Monitoring and Diagnosis of Viral Infection

Using surface-enhanced Raman scattering for simultaneous multiplex detection and quantification of thiols associated to axillary malodour

Axillary malodour is caused by the microbial conversion of human-derived precursors to volatile organic compounds. Thiols strongly contribute to this odour but are hard to detect as they are present at low concentrations. Additionally, thiols are highly volatile and small making sampling and quantification difficult, including by gas chromatography-mass spectrometry. In this study, surface-enhanced Raman scattering (SERS), combined with chemometrics, was utilised to simultaneously quantify four malodourous thiols associated with axillary odour, both in individual and multiplex solutions. Univariate and multivariate methods of partial least squares regression (PLS-R) were used to calculate the limit of detection (LoD) and results compared. Both methods yielded comparable LoD values, with LoDs using PLS-R ranging from 0.0227 ppm to 0.0153 ppm for the thiols studied. These thiols were then examined and quantified simultaneously in 120 mixtures using PLS-R. The resultant models showed high linearity (Q2 values between 0.9712 and 0.9827 for both PLS-1 and PLS-2) and low values of root mean squared error of predictions (0.0359 ppm and 0.0459 ppm for PLS-1 and PLS-2, respectively). To test this approach further, these models were challenged with 15 new blind test samples, collected independently from the initial samples. This test demonstrated that SERS combined with PLS-R could be used to predict the unknown concentrations of these thiols in a mixture. These results display the ability of SERS for the simultaneous multiplex detection and quantification of analytes and its potential for future development for detecting gaseous thiols produced from skin and other body sites.

Metabolic profiling of bacteria with the application of polypyrrole-MOF SPME fibers and plasmonic nanostructured LDI-MS substrates

Here we present application of innovative lab-made analytical devices such as plasmonic silver nanostructured substrates and polypyrrole-MOF solid-phase microextraction fibers for metabolic profiling of bacteria. For the first time, comprehensive metabolic profiling of both volatile and non-volatile low-molecular weight compounds in eight bacterial strains was carried out with utilization of lab-made devices. Profiles of low molecular weight metabolites were analyzed for similarities and differences using principal component analysis, hierarchical cluster analysis and random forest algorithm. The results showed clear differentiation between Gram positive (G+) and Gram negative (G-) species which were identified as distinct clusters according to their volatile metabolites. In case of non-volatile metabolites, differentiation between G+ and G- species and clustering for all eight species were observed for the chloroform fraction of the Bligh & Dyer extract, while methanolic fraction failed to recover specific ions in the profile. Furthermore, the results showed correlation between volatile and non-volatile metabolites, which suggests that lab-made devices presented in the current study might be complementary and therefore, useful for species differentiation and gaining insights into bacterial metabolic pathways.

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage

Fire blight is one of the most devastating plant diseases, causing severe social and economic problems. Herein, we report a novel method based on label-free surface-enhanced Raman scattering (SERS) combined with an Erwinia amylovora-specific bacteriophage that allows detecting efficiently fire blight bacteria E. amylovora for the first time. To achieve the highest SERS signals for E. amylovora, we synthesized and compared plasmonic nanoparticles (PNPs) with different sizes, i.e., bimetallic gold core-silver shell nanoparticles (Au@AgNPs) and monometallic gold nanoparticles (AuNPs) and utilized the coffee-ring effect for the self-assembly of PNPs and enrichment of fire blight bacteria. Furthermore, we investigated the changes in the SERS spectra of E. amylovora after incubation with an E. amylovora-specific bacteriophage, and we found considerable differences in the SERS signals as a function of the bacteriophage incubation time. The results indicate that our bacteriophage-based label-free SERS analysis can specifically detect E. amylovora without the need for peak assignment on the SERS spectra but simply by monitoring the changes in the SERS signals over time. Therefore, our facile method holds great potential for the label-free detection of pathogenic bacteria and the investigation of viral-bacterial interactions.

Automatically showing microbial growth kinetics with a high-performance microbial growth analyzer

It is difficult to show microbial growth kinetics online when they grow in complex matrices. We presented a novel strategy to address this challenge by developing a high-performance microbial growth analyzer (HPMGA), which employed a unique 32-channel capacitively coupled contactless conductivity detector as a sensing element and fixed with a CellStatz software. It was capable of online showing accurate and repeatable growth curves of well-dispersed and bad-dispersed microbes, whether they grew in homogeneous simple culture broth or heterogeneous complex matrices. Moreover, it could automatically report key growth kinetics parameters. In comparison to optical density (OD), plate counting and broth microdilution (BMD) methods, we demonstrated its practicability in five scenarios: 1) the illustration of the growth, growth rate, and acceleration curves of Escherichia coli (E. coli); 2) the antimicrobial susceptibility testing (AST) of Oxacillin against Staphylococcus aureus (S. aureus); 3) the determination of Ag nanoparticle toxicity on Providencia rettgeri (P. rettgeri); 4) the characterization of milk fermentation; and 5) the enumeration of viable pathogenic Vibrio in shrimp body. Results highlighted that the HPMGA method had the advantages of universality and effectivity. This technology would significantly facilitate the routine analysis of microbial growth in many fields (biology, medicine, clinic, life, food, environment, and ecology), paving an avenue for microbiologists to achieve research goals that have been inhibited for years due to a lack of practical analytical methods.

Metabolite-Mediated Bacterial Antibiotic Resistance Revealed by Surface-Enhanced Raman Spectroscopy

A prompt on-site, real-time method to detect bacterial antibiotic resistance is crucial for controlling the spread of resistance. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for the monitoring of bioactive metabolites produced by ampicillin-resistant Pseudomonas aeruginosa strains and identification of mechanisms underlying antibiotic resistance. The results indicate that the blue-green pigment pyocyanin (PYO) dominates the metabolite signals and is significantly enhanced upon exposure to subminimal inhibitory concentrations of ampicillin. PYO accumulates during exponential growth and subsequently either diffuses into the culture medium or is consumed in response to nutrient deprivation. The SERS spectra further reveal that the production of some intermediate substances such as polysaccharides and amino acids is minimally impacted and that nutrient consumption remains consistent. Moreover, the intensity changes and peak shifts observed in the SERS spectra of non-PYO-producing ampicillin-susceptible Escherichia coli demonstrate that exogenously added PYO enhances E. coli tolerance to ampicillin to some extent. These results indicate that PYO mediates antibiotic resistance not only in the parent species but also in cocultured bacterial strains. The metabolic SERS signal provides new insight regarding antibiotic resistance with promising applications for both environmental monitoring and rapid clinical detection.

In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

Analytical methods for assessing antimicrobial activity of nanomaterials in complex media: advances, challenges, and perspectives

Assessing the antimicrobial activity of engineered nanomaterials (ENMs), especially in realistic scenarios, is of great significance for both basic research and applications. Multiple analytical methods are available for analysis via off-line or on-line measurements. Real-world samples are often complex with inorganic and organic components, which complicates the measurements of microbial viability and/or metabolic activity. This article highlights the recent advances achieved in analytical methods including typical applications and specifics regarding their accuracy, cost, efficiency, and user-friendliness. Methodological drawbacks, technique gaps, and future perspectives are also discussed. This review aims to help researchers select suitable methods for gaining insight into antimicrobial activities of targeted ENMs in artificial and natural complex matrices.

Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra

Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 10⁴-10¹⁰ PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.

Fast Track Diagnostic Tools for Clinical Management of Sepsis: Paradigm Shift from Conventional to Advanced Methods

Sepsis is one of the deadliest disorders in the new century due to specific limitations in early and differential diagnosis. Moreover, antimicrobial resistance (AMR) is becoming the dominant threat to human health globally. The only way to encounter the spread and emergence of AMR is through the active detection and identification of the pathogen along with the quantification of resistance. For better management of such disease, there is an essential requirement to approach many suitable diagnostic techniques for the proper administration of antibiotics and elimination of these infectious diseases. The current method employed for the diagnosis of sepsis relies on the conventional culture of blood suspected infection. However, this method is more time consuming and generates results that are false negative in the case of antibiotic pretreated samples as well as slow-growing microbes. In comparison to the conventional method, modern methods are capable of analyzing blood samples, obtaining accurate results from the suspicious patient of sepsis, and giving all the necessary information to identify the pathogens as well as AMR in a short period. The present review is intended to highlight the culture shift from conventional to modern and advanced technologies including their limitations for the proper and prompt diagnosing of bloodstream infections and AMR detection.

Biomimetic Transparent Nanoplasmonic Meshes by Reverse-Nanoimprinting for Bio-Interfaced Spatiotemporal Multimodal SERS Bioanalysis

Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo.

Surface-enhanced Raman spectroscopy enabled evaluation of bacterial inactivation

An improved understanding of bacterial inactivation mechanisms will provide useful insights for infectious disease control and prevention. We evaluated bacterial response to several inactivation methods using surface-enhanced Raman spectroscopy (SERS). The results indicate that changes in the SERS signal are highly related to cellular disruption and that cellular changes arising after cell inactivation cannot be ignored. The membrane integrity of heat and the combination of UV₂₅₄ and free chlorine (UV₂₅₄/chlorine) treated Pseudomonas syringae (P. syringae) cells were severely disrupted, leading to significantly increased peak intensities. Conversely, ethanol treated bacteria exhibited intact cell morphologies and the SERS spectra remained virtually unchanged. On the basis of time dependent SERS signals, we extracted dominant SERS patterns. Peaks related to nucleic acids accounted for the main changes observed during heat, UV₂₅₄, and UV₂₅₄/chlorine treatment, likely due to their outward diffusion from the cell cytoplasm. For free chlorine treated P. syringae, carbohydrates and proteins on the cell membrane were denatured or lost, resulting in a decrease in related peak intensities. The nucleobases were likely oxidized when treated with UV₂₅₄ and chlorine, thus leading to shifts in the related peaks. The generality of the method was verified using two additional bacterial strains: Escherichia coli and Bacillus subtilis as well as in different water matrices. The results suggest that SERS spectral analysis is a promising means to examine bacterial stress response at the molecular level and has applicability in diverse environmental implementations.

Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics

Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.