Recent development of surface-enhanced Raman scattering for biosensing.

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Multiplex Lithographic SERS Aptasensor for Detection of Several Respiratory Viruses in One Pot.

Rapid and reliable techniques for virus identification are required in light of recurring epidemics and pandemics throughout the world. Several techniques have been distributed for testing the flow of patients. Polymerase chain reaction with reverse transcription is a reliable and sensitive, though not rapid, tool. The antibody-based strip is a rapid, though not reliable, and sensitive tool. A set of alternative tools is being developed to meet all the needs of the customer. Surface-enhanced Raman spectroscopy (SERS) provides the possibility of single molecule detection taking several minutes. Here, a multiplex lithographic SERS aptasensor was developed aiming at the detection of several respiratory viruses in one pot within 17 min. The four labeled aptamers were anchored onto the metal surface of four SERS zones; the caught viruses affect the SERS signals of the labels, providing changes in the analytical signals. The sensor was able to decode mixes of SARS-CoV-2 (severe acute respiratory syndrome coronavirus two), influenza A virus, respiratory syncytial virus, and adenovirus within a single experiment through a one-stage recognition process.

Gold-silver alloy hollow nanoshells-based lateral flow immunoassay for colorimetric, photothermal, and SERS tri-mode detection of SARS-CoV-2 neutralizing antibody.

Although many approaches have been developed for the quick assessment of SARS-CoV-2 infection, few of them are devoted to the detection of the neutralizing antibody, which is essential for assessing the effectiveness of vaccines. Herein, we developed a tri-mode lateral flow immunoassay (LFIA) platform based on gold-silver alloy hollow nanoshells (Au-Ag HNSs) for the sensitive and accurate quantification of neutralizing antibodies. By tuning the shell-to-core ratio, the surface plasmon resonance (SPR) absorption band of the Au-Ag HNSs is located within the near infrared (NIR) region, endowing them with an excellent photothermal effect under the irradiation of optical maser at 808 nm. Further, the Raman reporter molecule 4-mercaptobenzoic acid (MBA) was immobilized on the gold-silver alloy nanoshell to obtain an enhanced SERS signal. Thus, these Au-Ag HNSs could provide colorimetric, photothermal and SERS signals, with which, tri-mode strips for SARS-CoV-2 neutralizing antibody detection were constructed by competitive immunoassay. Since these three kinds of signals could complement one another, a more accurate detection was achieved. The tri-mode LFIA achieved a quantitative detection with detection limit of 20 ng/mL. Moreover, it also successfully detected the serum samples from 98 vaccinated volunteers with 79 positive results, exhibiting great application value in neutralizing antibody detection.

Ultrasensitive detection of SARS-CoV-2 antigen using surface-enhanced Raman spectroscopy-based lateral flow immunosensor.

The fast spread and transmission of the coronavirus 2019 (COVID-19) has become one of serious global public health problems. Herein, a surface enhanced Raman spectroscopy-based lateral flow immunoassay (LFA) was developed for the detection of SARS-CoV-2 antigen. Using uniquely designed core-shell nanoparticle with embedded Raman probe molecules as the indicator to reveal the concentration of target protein, excellent quantitative performance with a limit of detection (LOD) of 0.03 ng/mL and detection range of 10-1000 ng/mL can be achieved within 15 min. Besides, the detection of spiked virus protein in human saliva was also performed with a portable Raman spectrometer, proposing the feasibility of the method in practical applications. This easy-to-use, rapid and accurate method would provide a point-of-care testing way as the ideal alternative for current detection requirement of virus-related biomarkers.

Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults.

COVID-19 represents a multi-system infectious disease with broad-spectrum manifestations, including changes in host metabolic processes connected to the disease pathogenesis. Understanding biochemical dysregulation patterns as a consequence of COVID-19 illness promises to be crucial for tracking disease course and clinical outcomes. Surface-enhanced Raman scattering (SERS) has attracted considerable interest in biomedical diagnostics for the sensitive detection of intrinsic profiles of unique fingerprints of serum biomolecules indicative of SARS-CoV-2 infection in a label-free format. Here, we applied label-free SERS and chemometrics for rapid interrogation of temporal metabolic dynamics in longitudinal sera of mildly infected non-hospitalized patients (n = 22), at 4 and 16 weeks post PCR-positive diagnosis, and compared them with negative controls (n = 8). SERS spectral markers revealed distinct metabolic profiles in patient sera that significantly deviated from the healthy metabolic state at the two sampling time intervals. Multivariate and univariate analyses of the spectral data identified abundance dynamics in amino acids, lipids, and protein vibrations as the key spectral features underlying the metabolic differences detected in convalescent samples and perhaps associated with patient recovery progression. A validation study performed using spontaneous Raman spectroscopy yielded spectral data results that corroborated SERS spectral findings and confirmed the detected disease-specific molecular phenotypes in clinical samples. Label-free SERS promises to be a valuable analytical technique for rapid screening of the metabolic phenotype induced by SARS-CoV-2 infection to allow appropriate healthcare intervention.

Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses.

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO2 (Au NPs/pZrO2), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1-1.0% (or 104-5 copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis.

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis.

This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93-94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.

Research Progress of Raman Spectroscopy and Raman Imaging in Pharmaceutical Analysis.

The analytical investigation of the pharmaceutical process monitors the critical process parameters of the drug, beginning from its development until marketing and post-marketing, and appropriate corrective action can be taken to change the pharmaceutical design at any stage of the process. Advanced analytical methods, such as Raman spectroscopy, are particularly suitable for use in the field of drug analysis, especially for qualitative and quantitative work, due to the advantages of simple sample preparation, fast, non-destructive analysis speed and effective avoidance of moisture interference. Advanced Raman imaging techniques have gradually become a powerful alternative method for monitoring changes in polymorph distribution and active pharmaceutical ingredient distribution in drug processing and pharmacokinetics. Surface-enhanced Raman spectroscopy (SERS) has also solved the inherent insensitivity and fluorescence problems of Raman, which has made good progress in the field of illegal drug analysis. This review summarizes the application of Raman spectroscopy and imaging technology, which are used in the qualitative and quantitative analysis of solid tablets, quality control of the production process, drug crystal analysis, illegal drug analysis, and monitoring of drug dissolution and release in the field of drug analysis in recent years.

Ultrasensitive detection of SARS-CoV-2 S protein with aptamers biosensor based on surface-enhanced Raman scattering.

A rapid and accurate diagnostic modality is essential to prevent the spread of SARS-CoV-2. In this study, we proposed a SARS-CoV-2 detection sensor based on surface-enhanced Raman scattering (SERS) to achieve rapid and ultrasensitive detection. The sensor utilized spike protein deoxyribonucleic acid aptamers with strong affinity as the recognition entity to achieve high specificity. The spherical cocktail aptamers-gold nanoparticles (SCAP) SERS substrate was used as the base and Au nanoparticles modified with the Raman reporter molecule that resonates with the excitation light and spike protein aptamers were used as the SERS nanoprobe. The SCAP substrate and SERS nanoprobes were used to target and capture the SARS-CoV-2 S protein to form a sandwich structure on the Au film substrate, which can generate ultra-strong "hot spots" to achieve ultrasensitive detection. Analysis of SARS-CoV-2 S protein was performed by monitoring changes in SERS peak intensity on a SCAP SERS substrate-based detection platform. This assay detects S protein with a LOD of less than 0.7 fg mL-1 and pseudovirus as low as 0.8 TU mL-1 in about 12 min. The results of the simulated oropharyngeal swab system in this study indicated the possibility of it being used for clinical detection, providing a potential option for rapid and accurate diagnosis and more effective control of SARS-CoV-2 transmission.

SARS-CoV-2 proteins monitored by long-range surface plasmon field-enhanced Raman scattering with hybrid bowtie nanoaperture arrays and nanocavities.

The identification of biomacromolecules by using surface-enhanced Raman scattering (SERS) remains a challenge because of the near-field effect of traditional substrates. Long-range surface plasmon resonance (LRSPR) is a special type of surface optical phenomenon that provides higher electromagnetic field enhancement and longer penetration depth than conventional surface plasmon resonance. To break the limit of SERS detection distance and obtain a SERS substrate with increased enhancement ability, a bowtie nanoaperture array was sandwiched between two symmetric dielectric environments. Then, an Au mirror was inserted to form a metal-insulator-metal configuration. Finite-difference time-domain simulations revealed that numerous hybrid modes can be provided by this novel configuration (denoted as long-range SERS [LR-SERS] substrate). In particular, the LRSPR mode can be excited and reach the maximum value through the regulation of the polarizations of the incident light and the geometrical parameters of the LR-SERS substrate. The optimized LR-SERS substrate was then applied to detect SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. This substrate displayed ultralow detection limits of ∼9.2 and ∼11.3 pg mL-1 for the S and N proteins, respectively. Moreover, with the help of principal component analysis and receiver operating characteristic methods, our fabricated sensors exhibited excellent selectivity and hold great potential for the diagnosis of SARS-CoV-2 proteins in real samples.

Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

Long-Range SERS Detection of the SARS-CoV-2 Antigen on a Well-Ordered Gold Hexagonal Nanoplate Film.

The development of an effective method for identifying severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) via direct viral protein detection is significant but challenging in combatting the COVID-19 epidemic. As a promising approach for direct detection, viral protein detection using surface-enhanced Raman scattering (SERS) is limited by the larger viral protein size compared to the effective electromagnetic field (E-field) range because only the analyte remaining within the E-field can achieve high detection sensitivity. In this study, we designed and fabricated a novel long-range SERS (LR-SERS) substrate with an Au nanoplate film/MgF2/Au mirror/glass configuration to boost the LR-SERS resulting from the extended E-field. On applying the LR-SERS to detect the SARS-CoV-2 spike protein (S protein), reagent-free detection achieved a low detection limit of 9.8 × 10-11 g mL-1 and clear discrimination from the SARS-CoV S protein. The developed technique also allows testing of the S protein in saliva with 98% sensitivity and 100% specificity.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.

BACKGROUND AND OBJECTIVE: Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS: Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS: For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS: Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

Plasmonic nanostructure-enhanced Raman scattering for detection of SARS-CoV-2 nucleocapsid protein and spike protein variants.

Epidemiological control and public health monitoring during the outbreaks of infectious viral diseases rely on the ability to detect viral pathogens. Here we demonstrate a rapid, sensitive, and selective nanotechnology-enhanced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection based on the surface-enhanced Raman scattering (SERS) responses from the plasma-engineered, variant-specific antibody-functionalized silver microplasma-engineered nanoassemblies (AgMEN) interacting with the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. The three-dimensional (3D) porous AgMEN with plasmonic-active nanostructures provide a high sensitivity to virus detection via the remarkable SERS signal collection. Moreover, the variant-specific antibody-functionalization on the SERS-active AgMEN enabled the high selectivity of the SARS-CoV-2 S variants, including wild-type, Alpha, Delta, and Omicron, under the simulated human saliva conditions. The exceptional ultrahigh sensitivity of our SERS biosensor was demonstrated via SARS-CoV-2 S and N proteins at the detection limit of 1 fg mL-1 and 0.1 pg mL-1, respectively. Our work demonstrates a versatile SERS-based detection platform can be applied for the ultrasensitive detection of virus variants, infectious diseases, and cancer biomarkers.

Rapid and Accurate On-Site Immunodiagnostics of Highly Contagious Severe Acute Respiratory Syndrome Coronavirus 2 Using Portable Surface-Enhanced Raman Scattering-Lateral Flow Assay Reader.

In early 2022, the number of people infected with the highly contagious mutant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), called Omicron, was increasing worldwide. Therefore, several countries approved the lateral flow assay (LFA) strip as a diagnostic method for confirming SARS-CoV-2 instead of reverse transcription-polymerase chain reaction (RT-PCR), which takes a long time to generate the results. However, owing to the limitation of detection sensitivity, commercial LFA strips have high false-negative diagnosis rates for patients with low virus concentrations. Therefore, in this study, we developed a portable surface-enhanced Raman scattering (SERS)-LFA reader based on localized surface plasmon effects to solve the sensitivity problem of the commercial LFA strip. We tested 54 clinical samples using this portable SERS-LFA reader, which generated 49 positive and 5 negative results. Out of the 49 positive results, SERS-LFA classified only 2 as false negative, while the commercial LFA classified 21 as false negative. This confirmed that the false-negative rate had significantly improved compared to that of commercial LFA strips. We believe that the proposed SERS-LFA system can be utilized as a point-of-care diagnostic system to quickly and accurately determine a virus infection that could spread significantly within a short period.

A flap endonuclease 1-assisted universal viral nucleic acid sensing system using surface-enhanced Raman scattering.

The continued uncertainty of emerging infectious viral diseases has led to an extraordinary urgency to develop advanced molecular diagnostic tests that are faster, more reliable, simpler to use, and readily available than traditional methods. This study presents a system that can accurately and rapidly trace viral nucleic acids by employing flap endonuclease 1 (FEN1)-assisted specific DNA cleavage reactions and surface-enhanced Raman scattering (SERS)-based analysis. The designed Raman tag-labeled 5'- and 3'-flap provider DNA yielded structurally defined DNA substrates on magnetic nanoparticle surfaces when a target was present. The FEN1 enzyme subsequently processes the substrates formed via an invasive cleavage reaction, producing 5'-flap DNA products. Magnetic separation allows efficient purification of flap products from reaction mixtures. The isolated solution was directly applied onto high aspect-ratio plasmonic silver nanopillars serving as SERS-active substrates to induce amplified SERS signals. We verified the developed SERS-based sensing system using a synthetic target complementary to an avian influenza A (H9N2) virus gene and examined the detection performance of the system using complementary DNA (cDNA) derived from H9N2 viral RNA. As a result, we could detect a synthetic target with a detection limit of 41.1 fM with a single base-pair discrimination ability and achieved multiplexed detection capability for two targets. Using cDNA samples from H9N2 viruses, we observed a high concordance of R2 = 0.917 between the data obtained from SERS and the quantitative polymerase chain reaction. We anticipate that this enzyme-assisted SERS sensor may provide insights into the development of high-performance molecular diagnostic tools that can respond rapidly to viral pathogens.

One-click investigation of shape influence of silver nanostructures on SERS performance for sensitive detection of COVID-19.

Sensitive and accurate detection of SARS-CoV-2 methods is meaningful for preventing and controlling the novel coronavirus. The detection techniques supporting portable, onsite, in-time, and online data transfer are urgently needed. Here, we one-click investigated the shape influence of silver nanostructures on SERS performance and their applications in the sensitive detection of SARS-CoV-2. Such investigation is achieved by adjusting multiple parameters (concentration, potential, and time) on the integrated electrochemical array, thus various morphologies (e.g., bulk, dendritic, globular, and spiky) can be one-click synthesized. The SERS performance results indicated that dendritic nanostructures are superior to the other three with an order of magnitude signal enhancement. Such on-electrode dendritic silver substrate also represents high sensitivity (LOD = 7.42 × 10-14 M) and high reproducibility (RSD = 3.67%) toward the SARS-CoV-2 RNA sequence detection. Such approach provides great potentials for rapid diagnosis and prevention of diverse infectious diseases.

Model of the SARS-CoV-2 Virus for Development of a DNA-Modified, Surface-Enhanced Raman Spectroscopy Sensor with a Novel Hybrid Plasmonic Platform in Sandwich Mode.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed a great challenge for the development of ultra-fast methods for virus identification based on sensor principles. We created a structure modeling surface and size of the SARS-CoV-2 virus and used it in comparison with the standard antigen SARS-CoV-2-the receptor-binding domain (RBD) of the S-protein of the envelope of the SARS-CoV-2 virus from the Wuhan strain-for the development of detection of coronaviruses using a DNA-modified, surface-enhanced Raman scattering (SERS)-based aptasensor in sandwich mode: a primary aptamer attached to the plasmonic surface-RBD-covered Ag nanoparticle-the Cy3-labeled secondary aptamer. Fabricated novel hybrid plasmonic structures based on "Ag mirror-SiO2-nanostructured Ag" demonstrate sensitivity for the detection of investigated analytes due to the combination of localized surface plasmons in nanostructured silver surface and the gap surface plasmons in a thin dielectric layer of SiO2 between silver layers. A specific SERS signal has been obtained from SERS-active compounds with RBD-specific DNA aptamers that selectively bind to the S protein of synthetic virion (dissociation constants of DNA-aptamer complexes with protein in the range of 10 nM). The purpose of the study is to systematically analyze the combination of components in an aptamer-based sandwich system. A developed virus size simulating silver particles adsorbed on an aptamer-coated sensor provided a signal different from free RBD. The data obtained are consistent with the theory of signal amplification depending on the distance of the active compound from the amplifying surface and the nature of such a compound. The ability to detect the target virus due to specific interaction with such DNA is quantitatively controlled by the degree of the quenching SERS signal from the labeled compound. Developed indicator sandwich-type systems demonstrate high stability. Such a platform does not require special permissions to work with viruses. Therefore, our approach creates the promising basis for fostering the practical application of ultra-fast, amplification-free methods for detecting coronaviruses based on SARS-CoV-2.