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

Abstract

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.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

“…Consequently, the Raman signal is significantly amplified when the sample is near to roughened gold or silver metal surfaces or nanoparticles (NPs). Two primary SERS methods may be employed in biomedical applications: label-free [23,24] or labeled, both of which possess inherent advantages and disadvantages. The label-free method involves extracting chemical bond vibration information from biomolecules through direct interaction with the substrate nanostructure, thereby revealing the biomolecular composition of samples.…”

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

“…SARS-CoV-2 has been analyzed in saliva 11,12,76 and serum. 77 Paria et al 76 used a field-enhancing metal insulator antenna architecture featuring multiple alternate stacks of Ag and Si substrates to detect purified spike protein from SARS-CoV-2 and the hemagglutinin protein from the influenza H1N1 A virus. The spike protein comprising 1288 amino acids compared to 565 amino acids of the hemagglutinin protein exhibited a stronger Raman signature at the same concentration.…”

“…Combining ML improved diagnostic accuracy. 11,12,76,77 The random forest algorithm accurately classified SARS-CoV-2 against other samples (control, H1N1 A, Marburg, and Zika) with an accuracy ranging from 92% to 94% and an AUC of 0.98. 76 process of feature extraction and selection is automated, allowing the model to learn complex patterns of the data on its own.…”

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

“…SERS has also shown the potential of simultaneous detection of influenza virus (H1N1), SARS-CoV-2, and respiratory syncytial virus by using magnetic-tags-based SERS substrates with extended studies in throat swabs [ 272 ]. Label-free SERS was performed on serum samples of patients after 4 to 16 days of testing positive for COVID-19, and chemometric techniques were used to find significant difference in the SERS spectral features [ 273 ]. Figure 5 summarizes different techniques that are used for the SERS-based detection of SARS-CoV-2.…”