Surface-Enhanced Raman Spectroscopy (SERS) for characterization SARS-CoV-2

Fast screening of COVID-19 inpatient samples by integrating machine learning and label-free SERS methods

BACKGROUND

Advances in bio-analyte detection demonstrate the need for innovation to overcome the limitations of traditional methods. Emerging viruses evolve into variants, driving the need for fast screening to minimize the time required for positive detection and establish standardized detection. In this study, a SERS-active substrate with Au NPs on a regularly arranged ZrO2 nanoporous structure was utilized to obtain the SERS spectrum of inpatient samples from COVID-19 patients. Two analytical approaches were applied to classify clinical samples - empirical method to identify peak assignments corresponding to the target SARS-CoV-2 BA.2 variant, and machine learning (ML) method to build classifier models.

RESULTS

Comparison of spectral profiles of pure BA.2 variant and inpatient samples showed significant differences in the occurrence of SERS peaks, requiring the selection of regions of interest for further analysis through the empirical method. SERS spectra are classified into CoV (+) and CoV (-) using both empirical and machine learning methods, each demonstrating a sensitivity of 85.7 % and a specificity of 60 %. The former method relies on peak assignment, which is performed manually relying on established and results in a longer turnaround time. In contrast, the latter method is based on the mathematical correlations between variables across the entire spectrum. The machine must continuously learn from larger datasets, and the response time for interpretation is short. Nonetheless, both methods demonstrated their suitability in classifying clinical samples.

SIGNIFICANCE

This study demonstrated that a more comprehensive discussion can be formed with the integration of machine learning classification with biochemical profiling with the empirical analysis approach. Further improvement is expected by combining these two methods by utilizing only the regions of interest instead of the entire spectrum as input for machine learning, as a feature extraction technique.

SERS-assisted characterization of cell biomass from biofilm-forming Acinetobacter baumannii strains using chemometric tools

Acinetobacter baumannii (A. baumannii) is an emerging Gram-negative nosocomial pathogen responsible for infection on a global scale. It has the ability to develop biofilms on different surfaces, especially abiotic surfaces, which is considered a major contributor of its pathogenicity. Surface-enhanced Raman spectroscopy (SERS) holds great potential as an effective method for identifying and characterizing the biochemical composition of biofilm-forming species. In this study, cell mass samples from different strains of A. baumannii, categorized based on their biofilm-forming ability (strong, medium and non-biofilm forming) using a 96-well microtiter plate assay (MTP), were analyzed by SERS. The identified spectral features of the SERS spectra were used to characterize bacterial strains capable of producing biofilms. Silver nanoparticles (Ag-NPs) served as the SERS substrate to differentiate biofilm-forming strains of A. baumannii. Chemometric tools, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were employed for the classification and differentiation of SERS spectra from bacterial strains with varying biofilm-producing capacities, achieving 100% sensitivity, 94.3% specificity, and an area under the curve (AUC) value of 0.81 through Monte Carlo cross-validation. Furthermore, K-fold (Leave-K-out cross-validation (LKOCV)) was applied to verify the robustness of the PLS-DA model, and the AUC value was found to be 0.90, with a sensitivity of 100% and specificity of 98%. These results demonstrate that the PLS-DA model is highly effective for the differentiation and classification of bacterial strains with varying capacities for biofilm production.

Unraveling the Nanomechanical and Vibrational Properties of the Mayaro Virus

Mayaro virus (MAYV) is an emerging mosquito-borne viral pathogen whose infection results in arthritogenic disease. Despite ongoing research efforts, MAYV biology is largely unknown. Physical virology can assess MAYV nanoparticle metastability, assembly/disassembly, and polymorphism, allowing us to understand virion architecture and dynamics. Here, we employ atomic force microscopy (AFM) and surface enhancement Raman spectroscopy (SERS) to assess MAYV nanomechanical properties, including maps of adhesion force and Young's modulus on individual viral particles. We established topographic maps of MAYV in two and three dimensions, revealing the three-dimensional arrangement and distribution of charges on viral spikes at the virus surface. Furthermore, the organization of the densely packaged RNA, which affords the viral particle exceptional mechanical resistance compared to chikungunya (CHIKV), was observed using MAYV adsorption patterns. The vibrational signature of MAYV particles differs from CHIKV, with more intense protein modes matching the distribution of E1/E2 dimers and the nucleocapsid, which are well structured and suggestive of mechanical strength.

Label-free and liquid state SERS detection of multi-scaled bioanalytes via light-induced pinpoint colloidal assembly

Surface-enhanced Raman scattering (SERS) has been extensively applied to detect complex analytes due to its ability to enhance the fingerprint signals of molecules around nanostructured metallic surfaces. Thus, it is essential to design SERS-active nanostructures with abundant electromagnetic hotspots in a probed volume according to the dimensions of the analytes, as the analytes must be located in their hotspots for maximum signal enhancement. Herein, we demonstrate a simple method for detecting robust SERS signals from multi-scaled bioanalytes, regardless of their dimensions in the liquid state, through a photothermally driven co-assembly with colloidal plasmonic nanoparticles as signal enhancers. Under resonant light illumination, plasmonic nanoparticles and analytes in the solution quickly assemble at the focused surface area by convective movements induced by the photothermal heating of the plasmonic nanoparticles without any surface modification. Such collective assemblies of plasmonic nanoparticles and analytes were optimized by varying the optical density and surface charge of the nanoparticles, the viscosity of the solvent, and the light illumination time to maximize the SERS signals. Using these light-induced co-assemblies, the intrinsic SERS signals of small biomolecules can be detected down to nanomolar concentrations based on their fingerprint spectra. Furthermore, large-sized biomarkers, such as viruses and exosomes, were successfully detected without labels, and the complexity of the collected spectra was statistically analyzed using t-distributed stochastic neighbor embedding combined with support vector machine (t-SNE + SVM). The proposed method is expected to provide a robust and convenient method to sensitively detect biologically and environmentally relevant analytes at multiple scales in liquid samples.

Enhancing the understanding of SARS-CoV-2 protein with structure and detection methods: An integrative review

As the rapid and accurate screening of infectious diseases can provide meaningful information for outbreak prevention and control, as well as owing to the existing limitations of the polymerase chain reaction (PCR), it is imperative to have new and validated detection techniques for SARS-CoV-2. Therefore, the rationale for outlining the techniques used to detect SARS-CoV-2 proteins and performing a comprehensive comparison to serve as a practical benchmark for future identification of similar viral proteins is clear. This review highlights the urgent need to strengthen pandemic preparedness by emphasizing the importance of integrated measures. These include improved tools for pathogen characterization, optimized societal precautions, the establishment of early warning systems, and the deployment of highly sensitive diagnostics for effective surveillance, triage, and resource management. Additionally, with an improved understanding of the virus' protein structure, considerable advances in targeted detection, treatment, and prevention strategies are expected to greatly improve our ability to respond to future outbreaks.

Thermoelectrically Driven Dual-Mechanism Regulation on SERS and Application Potential for Rapid Detection of SARS-CoV-2 Viruses and Microplastics

Electric-induced surface-enhanced Raman scattering (E-SERS) has been widely studied for its flexible regulation of SERS after the substrate is prepared. However, the enhancement effect is not sufficiently high in the E-SERS technology reported thus far, and no suitable field of application exists. In this study, a highly sensitive thermoelectrically induced SERS substrate, Ag/graphene/ZnO (AGZ), was fabricated using ZnO nanoarrays (NRs), graphene, and Ag nanoparticles (NPs). Applying a temperature gradient to the ZnO NRs enhanced the SERS signals of the probe molecules by a factor of approximately 20. Theoretical and experimental results showed that the thermoelectric potential enables the simultaneous modulation of the Fermi energy level of graphene and the plasma resonance peak of Ag NPs, resulting in a double enhancement in terms of physical and chemical mechanisms. The AGZ substrate was then combined with a mask to create a wearable thermoelectrically enhanced SERS mask for collecting SARS-CoV-2 viruses and microplastics. Its SERS signal can be enhanced by the temperature gradient created between a body heat source (∼37 °C) and a cold environment. The suitability of this method for virus detection was also examined using a reverse transcription-polymerase chain reaction and SARS-CoV-2 virus antigen detection. This work offers new horizons for research of E-SERS, and its application potential for rapid detection of the SARS-CoV-2 virus and microplastics was also studied.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Can DFT Calculations Provide Useful Information for SERS Applications?

Density functional theory (DFT) calculations allow us to reproduce the SERS (surface-enhanced Raman scattering) spectra of molecules adsorbed on nanostructured metal surfaces and extract the most information this spectroscopy is potentially able to provide. The latter point mainly concerns the anchoring mechanism and the bond strength between molecule and metal as well as the structural and electronic modifications of the adsorbed molecule. These findings are of fundamental importance for the application of this spectroscopic technique. This review presents and discusses some SERS-DFT studies carried out in Italy as a collaboration between the universities of Modena and Reggio-Emilia and of Florence, giving an overview of the information that we can extract with a combination of experimental SERS spectra and DFT modeling. In addition, a selection of the most recent studies and advancements on the DFT approach to SERS spectroscopy is reported with commentary.