Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis.

COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2 via polyethyleneimine (PEI). Non-plasmid transfected HEK293 cells were used as the control group. Cellular uptake was optimized with green fluorescence protein (GFP) plasmids and evaluated by fluorescence microscopy and flow cytometry. The transfection efficiency was found to be around 60%. The expression of M, N, and E proteins was demonstrated by western blotting. The SERS spectra of the total proteins of transfected cells were obtained using a gold nanoparticle-based SERS substrate. Proteins of the transfected cells have peak positions at 646, 680, 713, 768, 780, 953, 1014, 1046, 1213, 1243, 1424, 2102, and 2124 cm-1. To reveal spectral differences between plasmid transfected cells and non-transfected control cells, PCA was applied to the spectra. The results demonstrated that SERS coupled with PCA might be a favorable and reliable way to develop a rapid, low-cost, and promising technique for the detection of COVID-19.

Determination of Optimum Ratio of Cationic Polymers and Small Interfering RNA with Agarose Gel Retardation Assay.

Nanomaterials have aroused attention in the recent years for their high potential for gene delivery applications. Most of the nanoformulations used in gene delivery are positively charged to carry negatively charged oligonucleotides. However, excessive positively charged carriers are cytotoxic. Therefore, the complexed oligonucleotide/nanoparticles should be well-examined before the application. In that manner, agarose gel electrophoresis, which is a basic method utilized for separation, identification, and purification of nucleic acid molecules because of its poriferous nature, is one of the strategies to determine the most efficient complexation rate. When the electric field is applied, RNA fragments can migrate through anode due to the negatively charged phosphate backbone. Because RNA has a uniform mass/charge ratio, RNA molecules run in agarose gel proportional according to their size and molecular weight. In this chapter, the determination of complexation efficiency between cationic polymer carriers and small interfering RNA (siRNA) cargos by using agarose gel electrophoresis is described. siRNA/cationic polymer carrier complexes are placed in an electric field and the charged molecules move through the counter-charged electrodes due to the phenomenon of electrostatic attraction. Nucleic acid cargos are loaded to cationic carriers via the electrostatic interaction between positively charged amine groups (N) of the carrier and negatively charged phosphate groups (P) of RNA. The N/P ratio determines the loading efficiency of the cationic polymer carrier. In here, the determination of N/P ratio, where the most efficient complexation occurs, by exposure to the electric field with a gel retardation assay is explained.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering.

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.