Label and label-free based surface-enhanced Raman scattering for pathogen bacteria detection: A review

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

The applicability of the SERS technique in food contamination testing - The detailed spectroscopic, chemometric, genetic, and comparative analysis of food-borne Cronobacter spp. strains

Microorganisms assigned as Cronobacter are Gram-negative, facultatively anaerobic, bacteria widely distributed in nature, home environments, and hospitals. They can also be detected in foods, milk powder, and powdered infant formula (PIF). Additionally, as an opportunistic pathogen, Cronobacter may cause serious infections, sometimes leading to the death of neonates and infants. Thus, it is essential to test food products for the presence of Cronobacter spp. The currently used standard described in ISO 22964:2017 is a laborious method that could be easily replaced by surface-enhanced Raman scattering (SERS). Here, we demonstrate that SERS allows the identification of food-borne bacteria belonging to Cronobacter spp. based on their SERS spectra. For this purpose, twenty-six Cronobacter strains from different food samples were analyzed. Additionally, it was shown that it is possible to differentiate them from other closely related pathogens such as Salmonella enterica subsp. enterica, Escherichia coli, or Enterobacter spp. The SERS results were supported by principal component analysis (PCA), as well as and sequencing of 16S rRNA, rpoB and fusA genes. Last but not least, it was demonstrated that the cells of Cronobacter sakazakii may be easily separated from PIF using an appropriate filter, microfluidic chip, and dielectrophoresis (DEP) technique.

A novel SERS method for detecting E. coli based on an aptamer-functionalized Au-Ag@Si triangular pyramid substrate

Escherichia coli contamination in food and pharmaceutical preparations needs to be strictly controlled according to the regulations in many countries. However, rapid and sensitive E. coli detection is still a challenge. In this study, an aptamer-based surface-enhanced Raman spectroscopy (SERS) sandwich method was developed for the rapid and sensitive detection of E. coli using an aptamer-functionalized Au-Ag@Si triangular pyramid (TP) substrate. The Au-Ag@Si TP substrate was functionalized with E. coli aptamer to work as both the capture probe and SERS tag by integrating with Raman reporter (6-carboxy-X-rhodamine). The fabrication of the capture probe, SH-apt@Au-Ag@Si TP, was simple and rapid (20.5 h). This method could selectively and rapidly detect E. coli with a limit of detection of 2.8 CFU/mL within approximately 3 h. It was successfully applied to a traditional Chinese medicine preparation, Xinhuang tablets, with recoveries ranging from 90.19 % to 104.17 %. The results indicated that the developed method was simple and rapid, and it could be a promising alternative for the on-site detection of E. coli in pharmaceutical and food samples.

Precise diagnosis of tumor cells and hemocytes using ultrasensitive, stable, selective cuprous oxide composite SERS bioprobes assisted with high-efficiency separation microfluidic chips

Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu2O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10-9 mol L-1). Finite-difference time-domain (FDTD) simulation of Cu2O, Ag and Cu2O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu2O@Ag, and surface defects of Cu2O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu2O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu2O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors.

SERS characterization of biochemical changes associated with biodesulfurization of dibenzothiophene using Gordonia sp. HS126-4N

In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.

Using gold-based nanomaterials for fighting pathogenic bacteria: from detection to therapy

Owing to the unique quantum size effect and surface effect, gold-based nanomaterials (GNMs) are promising for pathogen detection and broad-spectrum antimicrobial activity. This review summarizes recent research on GNMs as sensors for detecting pathogens and as tools for their elimination. Firstly, the need for pathogen detection is briefly introduced with an overview of the physicochemical properties of gold nanomaterials. And then strategies for the application of GNMs in pathogen detection are discussed. Colorimetric, fluorescence, surface-enhanced Raman scattering (SERS) techniques, dark-field microscopy detection and electrochemical methods can enable efficient, sensitive, and specific pathogen detection. The third section describes the antimicrobial applications of GNMs. They can be used for antimicrobial agent delivery and photothermal conversion and can act synergistically with photosensitizers to achieve the precise killing of pathogens. In addition, GNMs are promising for integrated pathogen detection and treatment; for example, combinations of colorimetric or SERS detection with photothermal sterilization have been demonstrated. Finally, future outlooks for the applications of GNMs in pathogen detection and treatment are summarized.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

Transcriptional switches in melanoma T Cells: Facilitating polarizing into regulatory T cells

Melanoma is a malignant skin tumor with a high mortality rate. Regulatory T cells (Tregs) are immune cells with immunosuppressive roles, however, the precise mechanisms governing Treg involvement in melanoma remain enigmatic. Experimental findings unveiled different transcription factor switches between normal and tumor T cell, with heightened FOXP3 and BATF in the latter. These factors induced immunosuppressive molecules and Treg maintenance genes, polarizing tumor T cells into Tregs. Spatial transcriptomics illuminated the preferential settlement of Tregs at the melanoma periphery. Within this context, FOXP3 in Tregs facilitated direct enhancement of specific ligand gene expression, fostering communication with neighboring cells. Novel functional molecules bound to FOXP3 or BATF in Tregs, such as SPOCK2, SH2D2A, and ligand molecules ITGB2, LTA, CLEC2C, CLEC2D, were discovered, which had not been previously reported in melanoma Treg studies. Furthermore, we validated our findings in a large number of clinical samples and identified the Melanoma Treg-Specific Regulatory Tag Set (Mel TregS). ELISA analysis showed that the protein levels of Mel TregS in melanoma Tregs were higher than in normal Tregs. We then utilized SERS technology to measure the signal values of Mel TregS in exosome, and successfully discriminated between healthy individuals and melanoma patients, as well as early and late-stage patients. This approach significantly enhanced detection sensitivity. In sum, our research elucidated fresh insights into the mechanisms governing Treg self-maintenance and communication with surrounding cells in melanoma. We also introduced an innovative method for clinical disease monitoring through SERS technology.

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Primer Exchange Reaction (PER)-Based Construction of Scaffold for Low-Speed Centrifugation-Based Isolation and Quantitative Analysis of P. aeruginosa and its application in analyzing uterine secretions with intrauterine adhesion

Efficient isolation and sensitive quantification of Pseudomonas aeruginosa (P. aeruginosa) are crucial for identifying intrauterine infections and preventing the occurrence of intrauterine adhesion (IUA). However, traditional approaches, such as culture-based approach, are time-consuming. Herein, we constructed a detection scaffold by using primer exchange reaction (PER) that integrated the low-speed centrifugation-based isolation and sensitive quantification of target pathogenic bacteria. The established approach possesses several advantages, including (i) the approach is capable of simultaneous isolation and sensitive quantification of target bacteria; (ii) low-speed centrifugation or even manual equipment could be used to isolate target bacteria; and (iii) a low limit of detection was obtained as 54 cfu/mL. Based on this, the approach is a promising approach in analyzing P. aeruginosa from uterine secretions with IUA.

Simultaneous SERS-decoding detection of multiple pathogens in drinking water with home-made portable double-layer filtration and concentration device

The engineering of a home-made portable double-layer filtration and concentration device with the common syringe for rapid analysis of water samples is reported. The core elements of the device were two installed filtration membranes with different pore sizes for respective functions. The upper filtration membrane was used for preliminary intercepting large interfering impurities (interception membrane), while the lower filtration membrane was used for collecting multiple target pathogens (enrichment membrane) for determination. This combination can make the contaminated environmental water, exemplified by surface water, filtrated quickly through the device and just retained the target bacteria of Escherichia coli O157:H7, Staphylococcus aureus, and Listeria monocytogenes on the lower enrichment membrane. Integrating with surface-enhanced Raman spectra (SERS) platform to decode the SERS-Tags (SERS-TagCVa, SERS-TagR6G, and SERS-TagMB) already labeled on each of the enriched bacteria based the antibody-mediated immuno-recognition effect, fast separation, concentration, and detection of multiple pathogenic bacteria from the bulk of contaminated environmental water were realized. Results show that within 30 min, all target bacteria in the lake water can be simultaneously and accurately measured in the range from 101 to 106 CFU mL-1 with detection limit of 10.0 CFU mL-1 without any pre-culture procedures. This work highlights the simplicity, rapidness, cheapness, selectivity, and the robustness of the constructed method for simultaneous detecting multiple pathogens in aqueous samples. This protocol opens a new avenue for facilitating the development of versatile analytical tools for drinking water and food safety monitoring in underdeveloped or developing countries.

Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate

BACKGROUND

Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety.

RESULTS

In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 105). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca2⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL.

SIGNIFICANCE

It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

SERS-Based Microneedle Biosensor for In Situ and Sensitive Detection of Tyrosinase

Tyrosinase (TYR) emerges as a key enzyme that exerts a regulatory influence on the synthesis of melanin, thereby assuming the role of a critical biomarker for the detection of melanoma. Detecting the authentic concentration of TYR in the skin remains a primary challenge. Distinguished from ex vivo detection methods, this study introduces a novel sensor platform that integrates a microneedle (MN) biosensor with surface-enhanced Raman spectroscopy (SERS) technology for the in situ detection of TYR in human skin. The platform utilized dopamine (DA)-functionalized gold nanoparticles (Au NPs) as the capturing substrate and 4-mercaptophenylboronic acid (4-MPBA)-modified silver nanoparticles (Ag NPs) acting as the SERS probe. Here, the Au NPs were functionalized with mercaptosuccinic acid (MSA) for DA capture. In the presence of TYR, DA immobilized on the MN is preferentially oxidized to dopamine quinone (DQ), a process that results in a decreased density of SERS probes on the platform. TYR concentration was detected through variations in the signal intensity emitted by the phenylboronic acid. The detection system was able to evaluate TYR concentrations within a linear range of 0.05 U/mL to 200 U/mL and showed robust anti-interference capabilities. The proposed platform, integrating MN-based in situ sensing, SERS technology, and TYR responsiveness, holds significant importance for diagnosing cutaneous melanoma.

Plasmonic Au@Ag Core-Shell Nanoisland Film for Photothermal Inactivation and Surface-Enhanced Raman Scattering Detection of Bacteria

Plasmonic metal nanomaterials have been extensively investigated for their utilizations in biomedical sensing and treatment. In this study, plasmonic Au@Ag core-shell nanoisland films (Au@AgNIFs) were successfully grown onto a glass substrate using a seed-mediated growth procedure. The nanostructure of the Au@AgNIFs was confirmed through scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and atomic force microscopy (AFM). The UV-Vis spectra of the Au@AgNIFs exhibited a broad absorption in the visible range from 300 to 800 nm because of the surface plasmon absorption. Under simulated sunlight exposure, the temperature of optimal Au@AgNIF was increased to be 66.9 °C to meet the requirement for photothermal bacterial eradication. Furthermore, the Au@AgNIFs demonstrated a consistent photothermal effect during the cyclic on/off exposure to light. For photothermal therapy, the Au@AgNIFs revealed superior efficiency in the photothermal eradication of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). With their unique nanoisland nanostructure, the Au@AgNIFs exhibited excellent growth efficiency of bacteria in comparison with that of the bare glass substrate. The Au@AgNIFs were also validated as a surface-enhanced Raman scattering (SERS) substrate to amplify the Raman signals of E. coli and S. aureus. By integrating photothermal therapy and SERS detection, the Au@AgNIFs were revealed to be a potential platform for bacterial theranostics.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

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.

A novel multifunctional SERS microfluidic sensor based on ZnO/Ag nanoflower arrays for label-free ultrasensitive detection of bacteria

This study proposes a microfluidic platform for rapid enrichment and ultrasensitive SERS detection of bacteria. The platform comprises ZnO nanoflower arrays decorated with silver nanoparticles to enhance the SERS sensitivity. The ZnO nanoflower array substrate with a 3D reticular columnar structure is prepared using the hydrothermal method. SEM analysis depicts the 3.05 μm gap distribution of the substrate array to intercept the most bacteria in the particle sizes range of 0.5 to 3 μm. Then, silver nanoparticles are deposited on the ZnO nano-array surface by liquid evaporation self-assembly. TEM and SEM analysis indicate nanosize of Ag particles, evenly distributed on the substrate, enhancing the SERS efficiency and improving sensing reproducibility. The probe molecules (R6G) are tested to demonstrate the high SERS activity of the proposed microfluidic sensor. Then, Escherichia coli, Staphylococcus aureus, Enterococcus faecalis, and Bacillus subtilis are selected, demonstrating the sensor's excellent bacterial capture and sensitive recognition capabilities, with a detection limit as low as 102 CFU mL-1. Additionally, the antibacterial properties of ZnO/Ag heterojunction nanostructures are studied, suggesting their ability to inactivate bacteria. Compared with the traditional Au-enhanced chip, the sensor preparation is easy, safe, reliable, and low-cost. Moreover, the ZnO nano-array exhibits a large specific surface area, high interception ability, stronger and uniform SERS performance, and effective and reliable detection of trace pathogens. This work provides potential future ZnO/Ag microfluidic SERS sensor applications for rapid, unlabeled, and trace pathogens detection in clinical and environmental applications, potentially achieving breakthroughs in early detection, prevention, and treatment.

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

A novel strategy for specific sensing and inactivation of Escherichia coli: Constructing a targeted sandwich-type biosensor with multiple SERS hotspots to enhance SERS detection sensitivity and near-infrared light-triggered photothermal sterilization performance

Human health is greatly threatened by bacterial infection, which raises the risk of serious illness and death in humans. For early screening and accurate treatment of bacterial infection, there is a strong desire to undertake ultrasensitive detection and effective killing of pathogenic bacteria. Herein, a novel surface-enhanced Raman scattering (SERS) biosensor based on sandwich structure consisting of capture probes/bacteria/SERS tags was established for specific identification, capture and photothermal killing of Escherichia coli (E. coli). Finite-difference time-domain (FDTD) technique was used to simulate the electromagnetic field distribution of capture probes, SERS tags and sandwich-type SERS substrate, and a possible SERS enhancement mechanism based on sandwich structure was presented and discussed. Sandwich-type SERS biosensor successfully achieved distinctive identification and magnetic beneficiation of E. coli. In addition, a single SERS substrate, including capture probes and SERS tags, could also achieve outstanding photothermal effects as a consequence of localized surface plasmon resonance (LSPR) effect. Intriguingly, sandwich-type SERS biosensor demonstrated a higher photothermal conversion efficiency (50.03 %) than the single substrate, which might be attributed to the formation of target bacterial clusters. The superior biocompatibility and the low toxicity of the sandwich-type biosensor were confirmed. Our approach offers a fresh method for constructing sandwich-type biosensor with multiple SERS hotspots based on extremely effective hybrid plasmonic nanoparticles, and has a wide range of potential applications in the recognition and treatment of bacteria.

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

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

Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

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.

Label-free SERS for rapid identification of interleukin 6 based on intrinsic SERS fingerprint of antibody‑gold nanoparticles conjugate

A label-free surface-enhanced Raman scattering (SERS) was designed for sensitive detection of interleukin-6 (IL-6). The sensing element composed of anti-IL-6 antibodies adsorbed on the surface of spherical gold nanoparticles (AuNPs) as SERS-active surface. The principle of detection was probing antibody conformational changes using its intrinsic SERS fingerprint after binding to IL-6. Comparison of SERS spectra of antibody before and after binding to IL-6 showed that secondary structure of antibody does not change upon binding to IL-6. Vibrational information from disulfide bonds ν(SS) in antibody structure indicated some changes of geometry around SS bridges as a consequence of the immunocomplex formation. Transmission electron microscopy (TEM) and UV-Vis spectroscopy were used to confirm AuNPs conjugation with antibody as well as IL-6 binding to antibody on the surface of AuNPs. The SERS-based immunoassay showed a wide linear range (2.0-1000 pg mL-1) and a high sensitivity with a limit of detection (LOD) as low as 0.91 pg mL-1 (0.04 pM) without using any extrinsic Raman label. UV-Vis spectroscopy was employed as a conventional method for IL-6 detection based on observation of any change in the position of localized surface plasmon resonance (LSPR) band of AuNPs-antibody conjugates with LOD of 10 ng mL-1.

Gold Nanoparticle-Based Colorimetric Biosensing for Foodborne Pathogen Detection

Ensuring safe high-quality food is an ongoing priority, yet consumers face heightened risk from foodborne pathogens due to extended supply chains and climate change in the food industry. Nanomaterial-based assays are popular and have recently been developed to ensure food safety and high quality. This review discusses strategies for utilizing gold nanoparticles in colorimetric biosensors. The visible-signal biosensor proves to be a potent sensing technique for directly measuring targets related to foodborne pathogens in the field of food analysis. Among visible-signal biosensors, the localized surface plasmon resonance (LSPR) biosensor has garnered increasing attention and experienced rapid development in recent years. This review succinctly introduces the origin of LSPR theory, providing detailed insights into its fundamental principles. Additionally, this review delves into the application of nanotechnology for the implementation of the LSPR biosensor, exploring methods for utilizing gold nanoparticles and elucidating the factors that influence the generation of visible signals. Several emerging technologies aimed at simple and rapid immunoassays for onsite applications have been introduced in the food industry. In the foreseeable future, field-friendly colorimetric biosensors could be adopted in food monitoring systems. The onsite and real-time detection of possible contaminants and biological substances in food and water is essential to ensure human health and safety.

Design and performance of pH-responsive cyano-Raman label SERS probes based on single urchin Au nanoparticles

A cyano-Raman label pH-responsive SERS probe was constructed by immobilizing 6-MPN molecules onto the surface of a single urchin Au nanoparticle (AuNP). The effects of different conditions on the synthetic materials were investigated and the optical properties of the single nanoparticles were evaluated. The peak-strength ratio of SERS probes at 1589 cm-1 and 2240 cm-1 exhibited a linear relationship in the pH range 4-7. The properties and stability of the probe were also verified by the acid-base cycle and ion interference tests.

Optical Sensors for Bacterial Detection

Analytical devices for bacterial detection are an integral part of modern laboratory medicine, as they permit the early diagnosis of diseases and their timely treatment. Therefore, special attention is directed to the development of and improvements in monitoring and diagnostic methods, including biosensor-based ones. A promising direction in the development of bacterial detection methods is optical sensor systems based on colorimetric and fluorescence techniques, the surface plasmon resonance, and the measurement of orientational effects. This review shows the detecting capabilities of these systems and the promise of electro-optical analysis for bacterial detection. It also discusses the advantages and disadvantages of optical sensor systems and the prospects for their further improvement.

Construction of Graphene@Ag-MLF composite structure SERS platform and its differentiating performance for different foodborne bacterial spores

A new surface-enhanced Raman spectroscopy (SERS) biosensor of Graphene@Ag-MLF composite structure has been fabricated by loading AgNPs on graphene films. The response of the biosensor is  based on plasmonic sensing. The results showed that the enhancement factor of three different spores reached 107 based on the Graphene@Ag-MLF substrate. In addition, the SERS performance was stable, with good reproducibility (RSD<3%). Multivariate statistical analysis and chemometrics were used to distinguish different spores. The accumulated variance contribution rate was up to 96.35% for the top three PCs, while HCA results revealed that the spectra were differentiated completely. Based on optimal principal components, chemometrics of KNN and LS-SVM were applied to construct a model for rapid qualitative identification of different spores, of which the prediction set and training set of LS-SVM achieved 100%. Finally, based on the Graphene@Ag-MLF substrate, the LOD of three different spores was lower than 102 CFU/mL. Hence, this novel Graphene@Ag-MLF SERS substrate sensor was rapid, sensitive, and stable in detecting spores, providing strong technical support for the application of SERS technology in food safety.

Research progress of sensors based on molecularly imprinted polymers in analytical and biomedical analysis

Molecularly imprinted polymers (MIPs) have had tremendous impact on biomimetic recognition due to their precise specificity and high affinity comparable to that of antibodies, which has shown the great advantages of easy preparation, good stability and low cost. The combination of MIPs with other analytical technologies can not only achieve rapid extraction and sensitive detection of target compounds, improving the level of analysis, but also achieve precise targeted delivery, in-vivo imaging and other applications. Among them, the recognition mechanism plays a vital role in chemical and biological sensing, while the improvement of the recognition element, such as the addition of new nanomaterials, can greatly improve the analytical performance of the sensor, especially in terms of selectivity. Currently, due to the need for rapid diagnosis and improved sensing properties (such as selectivity, stability, and cost-effectiveness), researchers are investigating new recognition elements and their combinations to improve the recognition capabilities of chemical sensing and bio-sensing. Therefore, this review mainly discusses the design strategies of optical sensors, electrochemical sensors and photoelectric sensors with molecular imprinting technology and their applications in environmental systems, food fields, drug detection and biology including bacteria and viruses.

Recent advancements in nanomaterial based optical detection of food additives: a review

Food additives have become a critical component in the food industry. They are employed as preservatives to decelerate the negative effects of environmental and microbial factors on food quality. Currently, food additives are used for a variety of purposes, including colorants, flavor enhancers, nutritional supplements, etc., owing to improvements in the food industry. Since the usage of food additives has increased dramatically, the efficient monitoring of their acceptable levels in food products is quite necessary to mitigate the problems associated with their inappropriate use. The traditional methods used for detecting food additives are generally based on standard spectroscopic and chromatographic techniques. However, these analytical techniques are limited by their high instrumentation cost and time-consuming procedures. The emerging field of nanotechnology has enabled the development of highly sensitive and specific sensors to analyze food additives in a rapid manner. The current article emphasizes the need to detect various food additives owing to their potential negative effects on humans, animals, and the environment. In this article, the role of nanomaterials in the optical sensing of food additives has been discussed owing to their high accuracy, ease-of-use, and excellent sensitivity. The applications of nanosensors for the detection of various food additives have been elaborated with examples. The current article will assist policymakers in developing new rules and regulations to mitigate the adverse effects of toxic food additives on humans and the environment. In addition, the prospects of nanosensors for the optical detection of food additives at a commercial scale have been discussed to combat their irrational use in the food industry.

Dual-channel biosensor for simultaneous detection of S. typhimurium and L. monocytogenes using nanotags of gold nanoparticles loaded metal-organic frameworks

Simultaneous detection of multiple foodborne pathogens is of great importance for ensuring food safety. Herein, we present a sensitive dual-channel electrochemical biosensor based on copper metal organic frameworks (CuMOF) and lead metal organic framework (PbMOF) for simultaneous detection of Salmonella typhimurium (S. typhimurium) and Listeria monocytogenes (L. monocytogenes). The MOF-based nanotags were prepared by functionalizing gold nanoparticles loaded CuMOF (Au@CuMOF) and PbMOF (Au@PbMOF) with signal DNA sequences 1 (sDNA1) and sDNA2, respectively. By selecting invA of S. typhimurium and inlA gene of L. monocytogenes as targe sequences, a sandwich-typed dual-channel biosensor was developed on glassy carbon electrodes (GCE) through hybridization reactions. The sensitive detection of S. typhimurium and L. monocytogenes was achieved by the direct differential pulse voltametric (DPV) signals of Cu2+ and Pb2+. Under optimal conditions, channel 1 of the biosensor showed linear range for invA gene of S. typhimurium in 1 × 10-14-1 × 10-8 M with low detection limit (LOD) of 3.42 × 10-16 M (S/N = 3), and channel 2 of the biosensor showed linear range for inlA gene of L. monocytogenes in 1 × 10-13-1 × 10-8 M with LOD of 6.11 × 10-15 M (S/N = 3). The dual-channel biosensor showed good selectivity which were used to detect S. typhimurium with linear range of 5-1.0 × 104 CFU mL-1 (LOD of 2.33 CFU mL-1), and L. monocytogenes with linear range of 10 - 1.0 × 104 CFU mL-1 (LOD of 6.61 CFU mL-1).

Rapid identification of bacteria by the pattern of redox reactions rate using 2',7'-dichlorodihydrofluorescein diacetate

Bacterial infection is a life-threatening situation, and its rapid diagnosis is essential for treatment. Apart from medical applications, rapid identification of bacteria is vital in the food industry or the public health system. There are various bacterial identification techniques, including molecular-based methods, immunological approaches, and biosensor-based procedures. The most commonly used methods are culture-based methods, which are time-consuming. The objective of this study is to find a fingerprint of bacteria to identify them. Three strains of bacteria were selected, and seven different concentrations of each bacterium were prepared. The bacteria were then treated with two different molar concentrations of the fluorescent fluorophore, dichlorodihydrofluorescein diacetate for 30 minutes. Then, using the fluorescence mode of a multimode reader, the fluorescence emission of each bacterium is scanned twice during 60 minutes. Plotting the difference between two scans versus the bacteria concentration results in a unique fluorescence pattern for each bacterium. Observation of the redox state of bacteria, during 90 minutes, results in a fluorescence pattern that is clearly a fingerprint of different bacteria. This pattern is independent of fluorophore concentration. Mean Squares Errors (MSE) between the fluorescence patterns of similar bacteria is less than that of different bacteria, which shows the method can properly identify the bacteria. In this study, a new label-free method is developed to detect and identify different species of bacteria by measuring the redox activity and using the fluorescence fluorophore, dichlorodihydrofluorescein diacetate. This robust and low-cost method can properly identify the bacteria, uses only one excitation and emission wavelength, and can be simply implemented with current multimode plate readers.

Exo-III Enzyme-Assisted Triple Cycle Signal Amplifications for Sensitive and Accurate Identification of Pathogenic Bacteria

Early determination of infectious pathogens is vitally important to select appropriate antibiotics, and to manage nosocomial infection. Herein, we propose a target recognition triggered triple signal amplification-based approach for sensitive pathogenic bacteria detection. In the proposed approach, a double-strand DNA probe (capture probe) that is composed of an aptamer sequence and a primer sequence is designed for specific identification of target bacteria and initiation of following triple signal amplification. After recognition of target bacteria, primer sequence is released from capture probe to bind with the designed H1 probe, forming a blunt terminal in the H1 probe. Exonuclease-III (Exo-III enzyme) specifically recognizes the blunt terminal in H1 probe and degrades the sequence from 3' terminal, resulting a single-strand DNA to induce the following signal amplification. Eventually, the approach exhibits a low detection limit of 36 cfu/mL with a broad dynamic range. The high selectivity endows the method a promising prospective for clinical sample analysis.

Recent Progresses in Optical Biosensors for Interleukin 6 Detection

Interleukin 6 (IL-6) is pleiotropic cytokine with pathological pro-inflammatory effects in various acute, chronic and infectious diseases. It is involved in a variety of biological processes including immune regulation, hematopoiesis, tissue repair, inflammation, oncogenesis, metabolic control, and sleep. Due to its important role as a biomarker of many types of diseases, its detection in small amounts and with high selectivity is of particular importance in medical and biological fields. Laboratory methods including enzyme-linked immunoassays (ELISAs) and chemiluminescent immunoassays (CLIAs) are the most common conventional methods for IL-6 detection. However, these techniques suffer from the complexity of the method, the expensiveness, and the time-consuming process of obtaining the results. In recent years, too many attempts have been conducted to provide simple, rapid, economical, and user-friendly analytical approaches to monitor IL-6. In this regard, biosensors are considered desirable tools for IL-6 detection because of their special features such as high sensitivity, rapid detection time, ease of use, and ease of miniaturization. In this review, current progresses in different types of optical biosensors as the most favorable types of biosensors for the detection of IL-6 are discussed, evaluated, and compared.

Development of a Low-Cost Paper-Based Platform for Coffee Ring-Assisted SERS

The need for highly sensitive, low-cost, and timely diagnostic technologies at the point of care is increasing. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is an advantageous technique to address this need, as it can rapidly detect analytes in small or dilute samples with improved sensitivity compared to conventional Raman spectroscopy. Despite the many advantages of SERS, one drawback of the technique is poor reproducibility due to variable interactions between nanoparticles and target analytes. To overcome this limitation, coupling SERS with the coffee ring effect has been implemented to concentrate and localize analyte-nanoparticle conjugates for improved signal reproducibility. However, current coffee ring platforms require laborious fabrication steps. Herein, we present a low-cost, two-step fabrication process for coffee ring-assisted SERS, utilizing wax-printed nitrocellulose paper. The platform was designed to produce a highly hydrophobic paper substrate that supports the coffee ring effect and tested using gold nanoparticles for SERS sensing. The nanoparticle concentration and solvent were varied to determine the effect of solution composition on ring formation and center clearance. The SERS signal was validated using 4-mercaptobenzoic acid (MBA) and tested with Moraxella catarrhalis bacteria to ensure functionality for chemical and biological applications. The limit of detection using MBA is 41.56 nM, and the biochemical components of the bacterial cell wall were enhanced with low spectral variability. The developed platform is advantageous due to ease of fabrication and use, representing the next step toward implementing low-cost coffee ring-assisted SERS for point-of-care sensing.

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

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

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

A characteristic bacterial SERS marker for direct identification of Salmonella in real samples assisted by a high-performance SERS chip and a selective culture medium

Salmonella should be absent in pharmaceutical preparations and foods according to the regulations. However, up to now, rapid and convenient identification of Salmonella is still full of challenge. Herein, we reported a label-free surface-enhanced Raman scattering (SERS) method for direct identification of Salmonella spiked in drug samples based on a characteristic bacterial SERS marker assisted by a high-performance SERS chip and a selective culture medium. The SERS chip being fabricated through in situ growth of bimetallic Au-Ag nanocomposites on silicon wafer within 2 h, featured a high SERS activity (EF > 10⁷), good uniformity and batch-to-batch consistency (RSD < 10 %), and satisfactory chemical stability. The directly-visualized SERS marker at 1222 cm^-1 originated from bacterial metabolite hypoxanthine was robust and exclusive for discrimination of Salmonella with other bacterial species. Moreover, the method was successfully used for direct discrimination of Salmonella in mixed pathogens by using a selective culture medium, and could identify Salmonella contaminant at ∼1 CFU spiked level in a real sample (Wenxin granule, a botanical drug) after 12 h of enrichment. The combined results showed that developed SERS method is practical and reliable, and could be a promising alternative for rapid identification of Salmonella contamination in pharmaceutical and foods industries.

SERS Immunosensors for Cancer Markers Detection

Early diagnosis and monitoring are essential for the effective treatment and survival of patients with different types of malignancy. To this end, the accurate and sensitive determination of substances in human biological fluids related to cancer diagnosis and/or prognosis, i.e., cancer biomarkers, is of ultimate importance. Advancements in the field of immunodetection and nanomaterials have enabled the application of new transduction approaches for the sensitive detection of single or multiple cancer biomarkers in biological fluids. Immunosensors based on surface-enhanced Raman spectroscopy (SERS) are examples where the special properties of nanostructured materials and immunoreagents are combined to develop analytical tools that hold promise for point-of-care applications. In this frame, the subject of this review article is to present the advancements made so far regarding the immunochemical determination of cancer biomarkers by SERS. Thus, after a short introduction about the principles of both immunoassays and SERS, an extended presentation of up-to-date works regarding both single and multi-analyte determination of cancer biomarkers is presented. Finally, future perspectives on the field of SERS immunosensors for cancer markers detection are briefly discussed.

A SERS bioassay based on vancomycin-modified PEI-interlayered nanocomposite and aptamer-functionalized SERS tags for synchronous detection of Acinetobacter baumannii and Klebsiella pneumoniae

Klebsiella pneumoniae (KP) and Acinetobacter baumannii (AB) are two important gram-negative bacteria that cause pneumonia and have been recently known to be associated with food. The rapid detection of these pathogens in food is important to minimize their colonization of the gut and stop new threats of the disease from spreading across the food chain. Herein, a double-edged sword aptasensor was developed for the synchronous detection of KP and AB in food and clinical samples. A highly sensitive, selective, specific, and synchronous detection of the target bacteria was achieved, and the limit of detection (LOD) was 10 cells/mL with a liner range of 50 to 10⁵ cells/mL. The total assay time was 1.5 h. This study does not only provide a new tool for the detection of the target bacteria, but also serves as a promising tool for food safety and pneumonia diagnosis.

Intelligent convolution neural network-assisted SERS to realize highly accurate identification of six pathogenic Vibrio

Based on label-free SERS technology, the relationship between the Raman signals of pathogenic Vibrio microorganisms and purine metabolites was analyzed in detail. A deep learning CNN model was successfully developed, achieving a high accuracy rate of 99.7% in the identification of six typical pathogenic Vibrio species within 15 minutes, providing a new method for pathogen identification.

Ultrasensitive Detection of Multidrug-Resistant Bacteria Based on Boric Acid-Functionalized Fluorescent MOF@COF

The widespread use of antibiotics has made multidrug-resistant bacteria (MDRB) one of the greatest threats toward global health. Current conventional microbial detection methods are usually time-consuming, labor-intensive, expensive, and unable to detect low concentrations of bacteria, which cause great difficulties in clinical diagnosis and treatment. Herein, we constructed a versatile biosensing platform on the basis of boric acid-functionalized porous framework composites (MOF@COF-BA), which were able to realize highly efficient and sensitive label-free MDRB detection via fluorescence. In this design, MDRB were captured using aptamer-coated nanoparticles and the fluorescent probe MOF@COF-BA was tightly anchored onto the surface of MDRB due to interactions between boric acid groups and glycolipids on bacteria cells. Benefitting from the remarkable fluorescence performance of MOF@COF-BA, rapid and specific detection of MDRB, such as methicillin-resistant Staphylococcus aureus (MRSA) and Acinetobacter baumannii (AB), was realized with a detection range of 20-10⁸ CFU/mL (for both) and limits of detection of 7 CFU/mL (MRSA) and 5 CFU/mL (AB). The feasibility of using the developed platform to selectively detect MRSA and AB from complex urine, human serum, and cerebrospinal fluid samples was also demonstrated. This work provides a promising strategy for accurate MDRB diagnosis, avoiding serious infection using rational antibiotic therapy.

Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuxO Nanostructures

Bacteria cause many common infections and are the culprit of many outbreaks throughout history that have led to the loss of millions of lives. Contamination of inanimate surfaces in clinics, the food chain, and the environment poses a significant threat to humanity, with the increase in antimicrobial resistance exacerbating the issue. Two key strategies to address this issue are antibacterial coatings and effective detection of bacterial contamination. In this study, we present the formation of antimicrobial and plasmonic surfaces based on Ag-CuxO nanostructures using green synthesis methods and low-cost paper substrates. The fabricated nanostructured surfaces exhibit excellent bactericidal efficiency and high surface-enhanced Raman scattering (SERS) activity. The CuxO ensures outstanding and rapid antibacterial activity within 30 min, with a rate of >99.99% against typical Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria. The plasmonic Ag nanoparticles facilitate the electromagnetic enhancement of Raman scattering and enables rapid, label-free, and sensitive identification of bacteria at a concentration as low as 103 cfu/mL. The detection of different strains at this low concentration is attributed to the leaching of the intracellular components of the bacteria caused by the nanostructures. Additionally, SERS is coupled with machine learning algorithms for the automated identification of bacteria with an accuracy that exceeds 96%. The proposed strategy achieves effective prevention of bacterial contamination and accurate identification of the bacteria on the same material platform by using sustainable and low-cost materials.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Recent Progress of Surface-Enhanced Raman Spectroscopy for Bacteria Detection

There are various pathogenic bacteria in the surrounding living environment, which not only pose a great threat to human health but also bring huge losses to economic development. Conventional methods for bacteria detection are usually time-consuming, complicated and labor-intensive, and cannot meet the growing demands for on-site and rapid analyses. Sensitive, rapid and effective methods for pathogenic bacteria detection are necessary for environmental monitoring, food safety and infectious bacteria diagnosis. Recently, benefiting from its advantages of rapidity and high sensitivity, surface-enhanced Raman spectroscopy (SERS) has attracted significant attention in the field of bacteria detection and identification as well as drug susceptibility testing. Here, we comprehensively reviewed the latest advances in SERS technology in the field of bacteria analysis. Firstly, the mechanism of SERS detection and the fabrication of the SERS substrate were briefly introduced. Secondly, the label-free SERS applied for the identification of bacteria species was summarized in detail. Thirdly, various SERS tags for the high-sensitivity detection of bacteria were also discussed. Moreover, we emphasized the application prospects of microfluidic SERS chips in antimicrobial susceptibility testing (AST). In the end, we gave an outlook on the future development and trends of SERS in point-of-care diagnoses of bacterial infections.

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.