Raman Molecular Fingerprints of SARS-CoV-2 British Variant and the Concept of Raman Barcode

Reprogramming exosomes for immunity-remodeled photodynamic therapy against non-small cell lung cancer

Traditional treatments against advanced non-small cell lung cancer (NSCLC) with high morbidity and mortality continue to be dissatisfactory. Given this situation, there is an urgent requirement for alternative modalities that provide lower invasiveness, superior clinical effectiveness, and minimal adverse effects. The combination of photodynamic therapy (PDT) and immunotherapy gradually become a promising approach for high-grade malignant NSCLC. Nevertheless, owing to the absence of precise drug delivery techniques as well as the hypoxic and immunosuppressive characteristics of the tumor microenvironment (TME), the efficacy of this combination therapy approach is less than ideal. In this study, we construct a novel nanoplatform that indocyanine green (ICG), a photosensitizer, loads into hollow manganese dioxide (MnO2) nanospheres (NPs) (ICG@MnO2), and then encapsulated in PD-L1 monoclonal antibodies (anti-PD-L1) reprogrammed exosomes (named ICG@MnO2@Exo-anti-PD-L1), to effectively modulate the TME to oppose NSCLC by the synergy of PDT and immunotherapy modalities. The ICG@MnO2@Exo-anti-PD-L1 NPs are precisely delivered to the tumor sites by targeting specially PD-L1 highly expressed cancer cells to controllably release anti-PD-L1 in the acidic TME, thereby activating T cell response. Subsequently, upon endocytic uptake by cancer cells, MnO2 catalyzes the conversion of H2O2 to O2, thereby alleviating tumor hypoxia. Meanwhile, ICG further utilizes O2 to produce singlet oxygen (1O2) to kill tumor cells under 808 nm near-infrared (NIR) irradiation. Furthermore, a high level of intratumoral H2O2 reduces MnO2 to Mn2+, which remodels the immune microenvironment by polarizing macrophages from M2 to M1, further driving T cells. Taken together, the current study suggests that the ICG@MnO2@Exo-anti-PD-L1 NPs could act as a novel drug delivery platform for achieving multimodal therapy in treating NSCLC.

SARS-CoV-2 causes dysfunction in human iPSC-derived brain microvascular endothelial cells potentially by modulating the Wnt signaling pathway

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which is associated with various neurological symptoms, including nausea, dizziness, headache, encephalitis, and epileptic seizures. SARS-CoV-2 is considered to affect the central nervous system (CNS) by interacting with the blood-brain barrier (BBB), which is defined by tight junctions that seal paracellular gaps between brain microvascular endothelial cells (BMECs). Although SARS-CoV-2 infection of BMECs has been reported, the detailed mechanism has not been fully elucidated.

METHODS

Using the original strain of SARS-CoV-2, the infection in BMECs was confirmed by a detection of intracellular RNA copy number and localization of viral particles. BMEC functions were evaluated by measuring transendothelial electrical resistance (TEER), which evaluates the integrity of tight junction dynamics, and expression levels of proinflammatory genes. BMEC signaling pathway was examined by comprehensive RNA-seq analysis.

RESULTS

We observed that iPSC derived brain microvascular endothelial like cells (iPSC-BMELCs) were infected with SARS-CoV-2. SARS-CoV-2 infection resulted in decreased TEER. In addition, SARS-CoV-2 infection decreased expression levels of tight junction markers CLDN3 and CLDN11. SARS-CoV-2 infection also increased expression levels of proinflammatory genes, which are known to be elevated in patients with COVID-19. Furthermore, RNA-seq analysis revealed that SARS-CoV-2 dysregulated the canonical Wnt signaling pathway in iPSC-BMELCs. Modulation of the Wnt signaling by CHIR99021 partially inhibited the infection and the subsequent inflammatory responses.

CONCLUSION

These findings suggest that SARS-CoV-2 infection causes BBB dysfunction via Wnt signaling. Thus, iPSC-BMELCs are a useful in vitro model for elucidating COVID-19 neuropathology and drug development.

Machine learning-driven SERS fingerprinting of disintegrated viral components for rapid detection of SARS-CoV-2 in environmental dust

Surveillance of airborne viruses in crowded indoor spaces is crucial for managing outbreaks, as highlighted by the SARS-CoV-2 pandemic. However, the rapid and on-site detection of fast-mutating viruses, such as SARS-CoV-2, in complex environmental backgrounds remains challenging. Our study introduces a machine learning (ML)-driven surface-enhanced Raman spectroscopy (SERS) approach for detecting viruses within environmental dust matrices. By decomposing intact virions into individual structural components via a Raman-background-free lysis protocol and concentrating them into nanogap SERS hotspots, we significantly enhance the SERS signal intensity and fingerprint information density from viral structural components. Utilizing Principal Component Analysis (PCA), we establish a robust connection between the SERS data of these structural components and their biological sequences, laying a solid foundation for virus detection through SERS. Furthermore, we demonstrate reliable quantitative detection of SARS-CoV-2 using identified SARS-CoV-2 peaks at concentrations down to 102 pfu/ml through Gaussian Process Regression (GPR) and a digital SERS methodology. Finally, applying a Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm, we identify SARS-CoV-2, influenza A virus, and Zika virus within an environmental dust background with over 86% accuracy. Therefore, our ML-driven SERS approach holds promise for rapid environmental virus monitoring to manage future outbreaks.

Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning

Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machine-learning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman "metabolomic snapshot", which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses.

Analysis of SARS-CoV-2 spike RBD binding to ACE2 and its inhibition by fungal cohaerin C using surface enhanced Raman spectroscopy

The structure of the SARS-CoV-2 spike RBD and human ACE2 as well as changes in the structure due to binding activities were analysed using surface enhanced Raman spectroscopy. The inhibitor cohaerin C was applied to inhibit the binding between spike RBD and ACE2. Differences and changes in the Raman spectra were determined using deconvolution of the amide bands and principal component analysis. We thus demonstrate a fast and label-free analysis of the protein structures and the differentiation between bound and unbound states. The approach is suitable for sensing and screening and might be relevant to investigate other protein systems as well.

Secondary Structures of MERS-CoV, SARS-CoV, and SARS-CoV-2 Spike Proteins Revealed by Infrared Vibrational Spectroscopy

All coronaviruses are characterized by spike glycoproteins whose S1 subunits contain the receptor binding domain (RBD). The RBD anchors the virus to the host cellular membrane to regulate the virus transmissibility and infectious process. Although the protein/receptor interaction mainly depends on the spike's conformation, particularly on its S1 unit, their secondary structures are poorly known. In this paper, the S1 conformation was investigated for MERS-CoV, SARS-CoV, and SARS-CoV-2 at serological pH by measuring their Amide I infrared absorption bands. The SARS-CoV-2 S1 secondary structure revealed a strong difference compared to those of MERS-CoV and SARS-CoV, with a significant presence of extended β-sheets. Furthermore, the conformation of the SARS-CoV-2 S1 showed a significant change by moving from serological pH to mild acidic and alkaline pH conditions. Both results suggest the capability of infrared spectroscopy to follow the secondary structure adaptation of the SARS-CoV-2 S1 to different environments.

Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

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.

Molecular Computation for Molecular Classification

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

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.

Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols

Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO₂ nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO₂ SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO₂ nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 10¹-10⁴ pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO₂ SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols.

Raman Metabolomics of Candida auris Clades: Profiling and Barcode Identification

This study targets on-site/real-time taxonomic identification and metabolic profiling of seven different Candida auris clades/subclades by means of Raman spectroscopy and imaging. Representative Raman spectra from different Candida auris samples were systematically deconvoluted by means of a customized machine-learning algorithm linked to a Raman database in order to decode structural differences at the molecular scale. Raman analyses of metabolites revealed clear differences in cell walls and membrane structure among clades/subclades. Such differences are key in maintaining the integrity and physical strength of the cell walls in the dynamic response to external stress and drugs. It was found that Candida cells use the glucan structure of the extracellular matrix, the degree of α-chitin crystallinity, and the concentration of hydrogen bonds between its antiparallel chains to tailor cell walls’ flexibility. Besides being an effective ploy in survivorship by providing stiff shields in the α–1,3–glucan polymorph, the α–1,3–glycosidic linkages are also water-insoluble, thus forming a rigid and hydrophobic scaffold surrounded by a matrix of pliable and hydrated β–glucans. Raman analysis revealed a variety of strategies by different clades to balance stiffness, hydrophobicity, and impermeability in their cell walls. The selected strategies lead to differences in resistance toward specific environmental stresses of cationic/osmotic, oxidative, and nitrosative origins. A statistical validation based on principal component analysis was found only partially capable of distinguishing among Raman spectra of clades and subclades. Raman barcoding based on an algorithm converting spectrally deconvoluted Raman sub-bands into barcodes allowed for circumventing any speciation deficiency. Empowered by barcoding bioinformatics, Raman analyses, which are fast and require no sample preparation, allow on-site speciation and real-time selection of appropriate treatments.

Raman Fingerprints of the SARS-CoV-2 Delta Variant and Mechanisms of Its Instantaneous Inactivation by Silicon Nitride Bioceramics

Raman spectroscopy uncovered molecular scale markers of the viral structure of the SARS-CoV-2 Delta variant and related viral inactivation mechanisms at the biological interface with silicon nitride (Si3N4) bioceramics. A comparison of Raman spectra collected on the TY11-927 variant (lineage B.1.617.2; simply referred to as the Delta variant henceforth) with those of the JPN/TY/WK-521 variant (lineage B.1.617.1; referred to as the Kappa variant or simply as the Japanese isolate henceforth) revealed the occurrence of key mutations of the spike receptor together with profound structural differences in the molecular structure/symmetry of sulfur-containing amino acid and altered hydrophobic interactions of the tyrosine residue. Additionally, different vibrational fractions of RNA purines and pyrimidines and dissimilar protein secondary structures were also recorded. Despite mutations, hydrolytic reactions at the surface of silicon nitride (Si3N4) bioceramics induced instantaneous inactivation of the Delta variant at the same rate as that of the Kappa variant. Contact between virions and micrometric Si3N4 particles yielded post-translational deimination of arginine spike residues, methionine sulfoxidation, tyrosine nitration, and oxidation of RNA purines to form formamidopyrimidines. Si3N4 bioceramics proved to be a safe and effective inorganic compound for instantaneous environmental sanitation.

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

SARS-CoV-2 infection remains spread worldwide and requires a better understanding of virus-host interactions. Here, we analyzed biochemical modifications due to SARS-CoV-2 infection in cells by confocal Raman microscopy. Obtained results were compared with the infection with another RNA virus, the measles virus. Our results have demonstrated a virus-specific Raman molecular signature, reflecting intracellular modification during each infection. Advanced data analysis has been used to distinguish non-infected versus infected cells for two RNA viruses. Further, classification between non-infected and SARS-CoV-2 and measles virus-infected cells yielded an accuracy of 98.9 and 97.2 respectively, with a significant increase of the essential amino-acid tryptophan in SARS-CoV-2-infected cells. These results present proof of concept for the application of Raman spectroscopy to study virus-host interaction and to identify factors that contribute to the efficient SARS-CoV-2 infection and may thus provide novel insights on viral pathogenesis, targets of therapeutic intervention and development of new COVID-19 biomarkers.

SARS-CoV-2 induces barrier damage and inflammatory responses in the human iPSC-derived intestinal epithelium

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread and led to global health crises. COVID-19 causes well-known respiratory failure and gastrointestinal symptoms, such as diarrhea, nausea, and vomiting. Thus, human gastrointestinal cell models are urgently needed for COVID-19 research; however, it is difficult to obtain primary human intestinal cells. In this study, we examined whether human induced pluripotent stem cell (iPSC)-derived small intestinal epithelial cells (iPSC-SIECs) could be used as a SARS-CoV-2 infection model. We observed that iPSC-SIECs, such as absorptive and Paneth cells, were infected with SARS-CoV-2, and remdesivir treatment decreased intracellular SARS-CoV-2 replication in iPSC-SIECs. SARS-CoV-2 infection decreased expression levels of tight junction markers, ZO-3 and CLDN1, and transepithelial electrical resistance (TEER), which evaluates the integrity of tight junction dynamics. In addition, SARS-CoV-2 infection increased expression levels of proinflammatory genes, which are elevated in patients with COVID-19. These findings suggest iPSC-SIECs as a useful in vitro model for elucidating COVID-19 pathology and drug development.

Molecular Fingerprinting of the Omicron Variant Genome of SARS-CoV-2 by SERS Spectroscopy

The continuing accumulation of mutations in the RNA genome of the SARS-CoV-2 virus generates an endless succession of highly contagious variants that cause concern around the world due to their antibody resistance and the failure of current diagnostic techniques to detect them in a timely manner. Raman spectroscopy represents a promising alternative to variants detection and recognition techniques, thanks to its ability to provide a characteristic spectral fingerprint of the biological samples examined under all circumstances. In this work we exploit the surface-enhanced Raman scattering (SERS) properties of a silver dendrite layer to explore, for the first time to our knowledge, the distinctive features of the Omicron variant genome. We obtain a complex spectral signal of the Omicron variant genome where the fingerprints of nucleobases in nucleosides are clearly unveiled and assigned in detail. Furthermore, the fractal SERS layer offers the presence of confined spatial regions in which the analyte remains trapped under hydration conditions. This opens up the prospects for a prompt spectral identification of the genome in its physiological habitat and for a study on its activity and variability.

Raman Spectroscopy of Oral Candida Species: Molecular-Scale Analyses, Chemometrics, and Barcode Identification

Oral candidiasis, a common opportunistic infection of the oral cavity, is mainly caused by the following four Candida species (in decreasing incidence rate): Candida albicans, Candida glabrata, Candida tropicalis, and Candida krusei. This study offers in-depth Raman spectroscopy analyses of these species and proposes procedures for an accurate and rapid identification of oral yeast species. We first obtained average spectra for different Candida species and systematically analyzed them in order to decode structural differences among species at the molecular scale. Then, we searched for a statistical validation through a chemometric method based on principal component analysis (PCA). This method was found only partially capable to mechanistically distinguish among Candida species. We thus proposed a new Raman barcoding approach based on an algorithm that converts spectrally deconvoluted Raman sub-bands into barcodes. Barcode-assisted Raman analyses could enable on-site identification in nearly real-time, thus implementing preventive oral control, enabling prompt selection of the most effective drug, and increasing the probability to interrupt disease transmission.

Tailoring Vibrational Signature and Functionality of 2D-Ordered Linear-Chain Carbon-Based Nanocarriers for Predictive Performance Enhancement of High-End Energetic Materials

A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers.