Specific intracellular signature of SARS-CoV-2 infection using confocal Raman microscopy

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, necessitates a deeper understanding of virus-host interactions to develop effective diagnostics and treatments. Current methods like ELISA, PCR, and next-generation sequencing are sensitive but require prior knowledge of the infectious agent or face challenges with low virus quantities. The heterogeneity of COVID-19 disease profiles underscores the urgent need for novel biomarkers to guide personalized treatment. Cellular responses to viral infection, encompassing entry, replication, and egress, offer potential avenues for early detection. Raman microscopy, capable of single-cell analysis, provides a label-free approach to detect biochemical modifications resulting from the host's response to viral infection. Unlike conventional methods, Raman spectroscopy directly identifies chemical modifications, bypassing the need for genetic or proteomic information about the virus. Previous biomedical applications of Raman spectroscopy have highlighted its non-invasive capabilities and capacity for examining single live cells without labeling. The technique's high spatial resolution allows precise identification of cellular behavior and subtle modifications. Data analysis approaches range from unsupervised methods like principal component analysis (PCA) to supervised methods like k-means cluster analysis (KMCA) and support vector machines (SVM). While prior studies have explored viral detection using surface-enhanced Raman scattering (SERS), this research utilizes spontaneous Raman spectroscopy to avoid complex substrate design, offering cost-effectiveness and simplicity. This study aims to compare the biomolecular modifications in cells infected with SARS-CoV-2 to those infected with another RNA virus, measles virus (MeV), to identify virus-specific intracellular signatures and potential novel biomarkers for COVID-19.

The literature review extensively covers existing viral detection methods (ELISA, PCR, next-generation sequencing), highlighting their limitations in terms of speed, cost, and the requirement for prior knowledge of the virus. It emphasizes the need for new, rapid, and cost-effective methods. The review also discusses the applications of Raman spectroscopy in biological systems and its use in previous studies involving virus detection, focusing on both the strengths and limitations of label-free techniques and the various data analysis methods employed. Existing studies exploring Raman spectroscopy for SARS-CoV-2 detection, primarily focusing on SERS techniques, are analyzed, highlighting the novelty of the current work's approach using spontaneous Raman spectroscopy to avoid the need for complex substrate design. The review also covers the use of Raman spectroscopy in studying the interactions between different viruses and host cells, with a focus on identifying changes in various biomolecules.

Vero E6 cells were infected with SARS-CoV-2 or MeV for 24 hours. Infection was verified using immunoblots, immunofluorescence, and electron microscopy. Confocal Raman microscopy with 532 nm laser excitation was used to analyze intracellular biochemical modifications in the 400-1800 cm⁻¹ and 2700-3050 cm⁻¹ spectral regions. Data analysis involved k-means cluster analysis (KMCA) to separate cellular organelles (nucleus, nucleolus, mitochondria, Golgi, cytoplasm, lipid droplets), followed by principal component analysis (PCA) and support vector machine (SVM) analysis to classify cells as non-infected, MeV-infected, or SARS-CoV-2-infected. Raman difference spectra were generated by comparing spectra from infected and non-infected cells, and between SARS-CoV-2- and MeV-infected cells. The SVM models were built using the first 13 principal components obtained from PCA and evaluated using tenfold cross-validation. The accuracy, sensitivity, and specificity of the method were evaluated based on the classification results. The study includes detailed descriptions of the cell culture, virus propagation, infection protocol, immunoblots, immunofluorescence microscopy, transmission electron microscopy, Raman spectroscopy imaging, data preprocessing (SNIP background subtraction, vector normalization), PCA, and SVM analysis.

Raman spectroscopy successfully identified a virus-specific intracellular signature distinguishing SARS-CoV-2 and measles virus infections from non-infected cells. The two-class SVM models achieved high accuracy in classifying non-infected versus infected cells (98.9% for SARS-CoV-2, 97.2% for MeV). A key finding is the significant increase in tryptophan levels in SARS-CoV-2-infected cells, a feature not observed in non-infected or MeV-infected cells. PCA analysis revealed distinct chemical profiles between SARS-CoV-2 and MeV infections, supporting the virus-specificity of the Raman signature. The analysis of the Raman difference spectra showed distinct changes in lipids, proteins, and nucleic acids between non-infected and infected cells, and between SARS-CoV-2- and MeV-infected cells. Specific Raman peaks associated with different biomolecules were identified and assigned, highlighting differences in the chemical composition of various cellular compartments in infected and non-infected cells, and between SARS-CoV-2 and MeV infections (e.g., tryptophan vibrations (566 and 749 cm⁻¹) in SARS-CoV-2-infected cells, tyrosine (815 and 1629 cm⁻¹) in MeV-infected cells). The three-class SVM model demonstrated the ability to classify cells into non-infected, SARS-CoV-2-infected, and MeV-infected categories. The study provides a detailed analysis of the Raman peaks in each cellular compartment and their association with various biomolecules. The increased tryptophan in SARS-CoV-2 infected cells was observed in the cytoplasm, Golgi-mitochondria bodies, and nucleus regions.

The findings demonstrate the potential of confocal Raman microscopy as a rapid, label-free, and cost-effective method for detecting viral infections and differentiating between different RNA viruses. The identification of a unique Raman signature for SARS-CoV-2, characterized by elevated tryptophan levels, suggests a potential novel biomarker for COVID-19. This biomarker may be useful for early detection and personalized treatment. The ability to differentiate between SARS-CoV-2 and MeV infections highlights the specificity and potential clinical applications of this technique. The increased tryptophan observed in SARS-CoV-2-infected cells is potentially linked to the higher tryptophan content in the SARS-CoV-2 proteome compared to MeV. The observed changes in other biomolecules, such as lipids and nucleic acids, also contribute to the distinct Raman signatures of the different viral infections. Future studies should investigate the use of this approach on a wider range of cell types and clinical samples to validate the findings and explore its translational potential. The potential correlation between increased intracellular tryptophan and decreased serum tryptophan levels in COVID-19 patients, as indicated by previous studies, warrants further investigation.

This study provides compelling evidence for using confocal Raman microscopy coupled with advanced data analysis techniques to detect and differentiate SARS-CoV-2 and MeV infections. The increased tryptophan levels in SARS-CoV-2-infected cells offer a promising new biomarker for COVID-19. The methodology presents a rapid, label-free, and potentially cost-effective diagnostic tool. Future research should focus on validating these findings in larger clinical studies and exploring the potential of this approach for point-of-care diagnostics and personalized treatment.

The study was conducted using only the Vero E6 cell line. Further studies are needed to validate these findings in other cell types relevant to SARS-CoV-2 infection in humans. The sample size might be considered relatively small, necessitating validation in a larger cohort of samples. The study focused on a specific time point (24 hours post-infection). Further investigation across multiple time points is needed to capture the dynamic changes occurring during the infection process. The use of a specific strain of SARS-CoV-2 could limit the generalizability of the results. The findings should be tested against different viral strains, particularly emerging variants. Finally, more work is needed to fully understand the biological mechanisms underlying the observed biochemical changes, especially the role of tryptophan in SARS-CoV-2 infection.