Exploiting Purine as an Internal Standard for SERS Quantification of Purine Derivative Molecules Released by Bacteria

Surface-enhanced Raman scattering (SERS) is a highly sensitive technique used in diverse biomedical applications including rapid antibiotic susceptibility testing (AST). However, signal fluctuation in SERS, particularly the widespread of signals measured from different batches of SERS substrates, compromises its reliability and introduces potential errors in SERS-AST. In this study, we investigate the use of purine as an internal standard (IS) to recalibrate SERS signals and quantify the concentrations of two important purine derivatives, adenine and hypoxanthine, which are the most important biomarkers used in SERS-AST. Our findings demonstrate that purine IS effectively mitigates SERS signal fluctuations and enables accurate prediction of adenine and hypoxanthine concentrations across a wide range (5 orders of magnitude). Calibrations with purine as an IS outperform those without, exhibiting a 10-fold increase in predictive accuracy. Additionally, the calibration curve obtained from the first batch of SERS substrates remains effective for 64 additional substrates fabricated over a half-year period. Measurements of adenine and hypoxanthine concentrations in bacterial supernatants using SERS with purine IS closely align with the liquid chromatography-mass spectrometry results. The use of purine as an IS offers a simple and robust platform to enhance the speed and accuracy of SERS-AST, while also paving the way for in situ SERS quantification of purine derivatives released by bacteria under various stress conditions.

SERS-based rapid susceptibility testing of commonly administered antibiotics on clinically important bacteria species directly from blood culture of bacteremia patients

Bloodstream infections are a growing public health concern due to emerging pathogens and increasing antimicrobial resistance. Rapid antibiotic susceptibility testing (AST) is urgently needed for timely and optimized choice of antibiotics, but current methods require days to obtain results. Here, we present a general AST protocol based on surface-enhanced Raman scattering (SERS-AST) for bacteremia caused by eight clinically relevant Gram-positive and Gram-negative pathogens treated with seven commonly administered antibiotics. Our results show that the SERS-AST protocol achieves a high level of agreement (96% for Gram-positive and 97% for Gram-negative bacteria) with the widely deployed VITEK 2 diagnostic system. The protocol requires only five hours to complete per blood-culture sample, making it a rapid and effective alternative to conventional methods. Our findings provide a solid foundation for the SERS-AST protocol as a promising approach to optimize the choice of antibiotics for specific bacteremia patients. This novel protocol has the potential to improve patient outcomes and reduce the spread of antibiotic resistance.

Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification

Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.

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.

Antibiotic Susceptibility Testing with Raman Biosensing

Antibiotics guard us against bacterial infections and are among the most commonly used medicines. The immediate consequence of their large-scale production and prescription is the development of antibiotic resistance. Therefore, rapid detection of antibiotic susceptibility is required for efficient antimicrobial therapy. One of the promising methods for rapid antibiotic susceptibility testing is Raman spectroscopy. Raman spectroscopy combines fast and contactless acquisition of spectra with good selectivity towards bacterial cells. The antibiotic-induced changes in bacterial cell physiology are detected as distinct features in Raman spectra and can be associated with antibiotic susceptibility. Therefore, the Raman-based approach may be beneficial in designing therapy against multidrug-resistant infections. The surface-enhanced Raman spectroscopy (SERS) and resonance Raman spectroscopy (RRS) additionally provide excellent sensitivity. In this review, we present an analysis of the Raman spectroscopy-based optical biosensing approaches aimed at antibiotic susceptibility testing.

An antibiotic concentration gradient microfluidic device integrating surface-enhanced Raman spectroscopy for multiplex antimicrobial susceptibility testing

Antimicrobial susceptibility testing (AST) is a key measure in clinical microbiology laboratories to enable appropriate antimicrobial administration. During an AST, the determination of the minimum inhibitory concentration (MIC) is an important step in which the bacterial responses to an antibiotic at a series of concentrations obtained in separate bacterial growth chambers or sites are compared. However, the preparation of different antibiotic concentrations is time-consuming and labor-intensive. In this paper, we present a microfluidic device that generates a concentration gradient for antibiotics that is produced by diffusion in the laminar flow regime along a series of lateral microwells to encapsulate bacteria for antibiotic treatment. All the AST preparation steps (including bacterium loading, antibiotic concentration generation, buffer washing, and isolated bacterial growth with an antibiotic) can be performed in a single chip. The viable bacterial cells in each microwell after the antibiotic treatment are then quantified by their surface-enhanced Raman scattering (SERS) signals that are acquired after placing a uniform SERS-active substrate in contact with all the microwells. For proof-of-concept, we demonstrated the AST performance of this system on ampicillin (AMP)-susceptible and -resistant E. coli strains. Compared with the parameters for conventional AST methods, the AST procedure based on this chip requires only 20 μL of bacteria solution and 5 h of operation time. This result indicates that this integrated system can greatly shorten and simplify the tedious and labor-intensive procedures required for current standard AST methods.

Spectroscopic investigation of faeces with surface-enhanced Raman scattering: a case study with coeliac patients on gluten-free diet

Surface-enhanced Raman scattering (SERS) spectra of faecal samples can be obtained by adding AuNP to their methanol extracts according to the reported protocol, and display bands that are due to bilirubin-like species but also to xanthine and hypoxanthine, two metabolic products secreted by gut bacteria. A total of 27 faecal samples from three different groups, i.e. coeliac patients (n = 9), coeliac patients on gluten-free diet (n = 10) and a control group (n = 8), were characterized with both SERS spectroscopy and 16S rRNA sequencing analysis. Significant differences are present between SERS spectra of coeliac patients and those on gluten-free diet, with a marked increase in the relative intensity of both xanthine and hypoxanthine for the latter. Interestingly, these differences do not correlate with bacterial composition as derived from 16S rRNA sequencing.

Modern Analytical Techniques for Detection of Bacteria in Surface and Wastewaters

Contamination of surface waters with pathogens as well as all diseases associated with such events are a significant concern worldwide. In recent decades, there has been a growing interest in developing analytical methods with good performance for the detection of this category of contaminants. The most important analytical methods applied for the determination of bacteria in waters are traditional ones (such as bacterial culturing methods, enzyme-linked immunoassay, polymerase chain reaction, and loop-mediated isothermal amplification) and advanced alternative methods (such as spectrometry, chromatography, capillary electrophoresis, surface-enhanced Raman scattering, and magnetic field-assisted and hyphenated techniques). In addition, optical and electrochemical sensors have gained much attention as essential alternatives for the conventional detection of bacteria. The large number of available methods have been materialized by many publications in this field aimed to ensure the control of water quality in water resources. This study represents a critical synthesis of the literature regarding the latest analytical methods covering comparative aspects of pathogen contamination of water resources. All these aspects are presented as representative examples, focusing on two important bacteria with essential implications on the health of the population, namely Pseudomonas aeruginosa and Escherichia coli.

Sensible Functional Linear Discriminant Analysis Effectively Discriminates Enhanced Raman Spectra of Mycobacterium Species

Tuberculosis caused by Mycobacterium tuberculosis complex (MTBC) is one of the major infectious diseases in the world. Identification of MTBC and differential diagnosis of nontuberculous mycobacteria (NTM) species impose challenges because of their taxonomic similarity. This study describes a differential diagnosis method using the surface-enhanced Raman scattering (SERS) measurement of molecules released by Mycobacterium species. Conventional principal component analysis and linear discriminant analysis methods successfully separated the acquired spectrum of MTBC from those of NTM species but failed to distinguish between the spectra of different NTM species. A novel sensible functional linear discriminant analysis (SLDA), projecting the averaged spectrum of a bacterial specie to the subspace orthogonal to the within-species random variation, thereby eliminating its influence in applying linear discriminant analysis, was employed to effectively discriminate not only MTBC but also species of NTM. The successful demonstration of this SERS-SLDA method opens up new opportunities for the rapid differentiation of Mycobacterium species.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Rapid antibiotic susceptibility testing of bacteria from patients' blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy

Blood stream infection is one of the major public health issues characterized with high cost and high mortality. Timely effective antibiotics usage to control infection is crucial for patients’ survival. The standard microbiological diagnosis of infection however can last days. The delay in accurate antibiotic therapy would lead to not only poor clinical outcomes, but also to a rise in antibiotic resistance due to widespread use of empirical broad-spectrum antibiotics. An important measure to tackle this problem is fast determination of bacterial antibiotic susceptibility to optimize antibiotic treatment. We show that a protocol based on surface-enhanced Raman spectroscopy can obtain consistent antibiotic susceptibility test results from clinical blood-culture samples within four hours. The characteristic spectral signatures of the obtained spectra of Staphylococcus aureus and Escherichia coli—prototypic Gram-positive and Gram-negative bacteria—became prominent after an effective pretreatment procedure removed strong interferences from blood constituents. Using them as the biomarkers of bacterial metabolic responses to antibiotics, the protocol reported the susceptibility profiles of tested drugs against these two bacteria acquired from patients’ blood with high specificity, sensitivity and speed.

Noble Metal Nanostructured Materials for Chemical and Biosensing Systems

Nanomaterials with unique physical and chemical properties have attracted extensive attention of scientific research and will play an increasingly important role in the future development of science and technology. With the gradual deepening of research, noble metal nanomaterials have been applied in the fields of new energy materials, photoelectric information storage, and nano-enhanced catalysis due to their unique optical, electrical and catalytic properties. Nanostructured materials formed by noble metal elements (Au, Ag, etc.) exhibit remarkable photoelectric properties, good stability and low biotoxicity, which received extensive attention in chemical and biological sensing field and achieved significant research progress. In this paper, the research on the synthesis, modification and sensing application of the existing noble metal nanomaterials is reviewed in detail, which provides a theoretical guidance for further research on the functional properties of such nanostructured materials and their applications of other nanofields.

Antibiotic Susceptibility Test with Surface-Enhanced Raman Scattering in a Microfluidic System

Antibiotic susceptibility test (AST) is essential in clinical diagnosis of serious bacterial infection, such as sepsis, while it typically takes 2-5 days for sample culture, antibiotic treatment, and reading result. Detecting metabolites secreted from bacteria with surface-enhanced Raman scattering (SERS) enables rapid determination of antibiotic susceptibility, reducing the AST time to 1-2 days. However, it still requires 1 day of culture time to obtain sufficient quantity of bacteria for sample washing, bacterial extraction, and antibiotic treatment. Additionally, the whole procedure, manually performed in open environment, often suffers from contamination and human error. To address the above problems, a microfluidic system integrating membrane filtration and the SERS-active substrate (MF-SERS) was developed to perform on-chip bacterial enrichment, metabolite collection, and in situ SERS measurements for antibiotic susceptibility test. Using Escherichia coli as the prototype bacterium, the lowest SERS detection limit of bacterial concentration of the MF-SERS system is 10³ CFU/mL, which is 4 orders of magnitude lower than that using centrifugation-purification procedure, significantly shortening the bacterial culture time. The bacteria and secreted metabolites are enclosed during bacterial trapping, metabolite filtration, and SERS detection, thus minimizing possible contamination and human errors. Finally, the successful demonstration of AST on E. coli with a concentration of 10³ CFU/mL is presented. Overall, the MF-SERS system with a miniature size and well-confined microenvironment allows the integration of multiple bacteria processes for bacterial enrichment, culture, and determination of AST.

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.