Surface-Enhanced Raman Spectroscopy for Monitoring the Biochemical Changes Due to DNA Mutations Induced by CRISPR-Cas9 Genome Editing in the Aspergillus niger Fungus.

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

Quantitative analysis of cephalexin in solid dosage form by Raman spectroscopy and chemometric tools.

OBJECTIVE: To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. SIGNIFICANCE: Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. METHOD: For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. RESULTS: PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. CONCLUSION: These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.

Surface-enhanced Raman spectroscopy (SERS) for the characterization of supernatants of bacterial cultures of bacterial strains causing sinusitis.

BACKGROUND: Sinusitis is defined as inflammation of the paranasal sinus mucous membrane lining caused by bacteria which usually invade the sinus by upper respiratory tract viral infections (UTI). OBJECTIVES: In the present study, Surface-enhanced Raman spectroscopy (SERS) has been applied to differentiate and characterize supernatant samples, in triplicate, of three different types of bacteria which are considered leading cause of sinusitis disease. METHODS: For this purpose, supernatant samples of three different strains of bacteria namely Staphylococcus aureus, Klebsiella pneumoniae and Enterococcus faecalis. The SERS has identified significant changes as a result of secretions of biomolecules by these bacteria in their supernatants which can be helpful to explore the potential of this technique for the identification and characterization of different strains of bacteria causing same disease. RESULTS: These differentiating characteristic SERS spectral features including 552 cm-1 (C-S-S-C bonds), 951 cm-1 (CN stretching), 1008 cm-1 (Phenylalanine), 1032 cm-1 (In plane CH bending mode Phenylalanine), 1280 cm-1, 1320 cm-1, 1329 cm-1 (Amide III band), 1368 cm-1, 1400 cm-1, 1420 cm-1 (COO-sym. stretching and CH bending), 1583 cm-1 (Tyrosine) correspond to Proteins and 1051 cm-1 (C-C, C-O, -C-OH def.) correspond to carbohydrates contents of these three different types of bacterial secretions in their respective supernatants. Furthermore, multivariate data analysis techniques like principal component analysis (PCA) and a supervised method partial least squares-discriminant analysis (PLS-DA) were found to be useful for the identification and characterization of different bacterial supernatants. CONCLUSIONS: Surface-enhanced Raman spectroscopy is proven to be a helpful approach for the characterization and discrimination of three bacterial supernatants including S. aureus, K. pneumonia and E. faecalis.

Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients.

BACKGROUND: Raman spectroscopy is an effective tool for detecting and discriminating microorganisms that is robust, reliable, and rapid. OBJECTIVES: To develop a polymerase chain reaction technique (PCR) based on Surface Enhanced Raman Spectroscopy (SERS) technique with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to assess diagnostic capability of SERS for distinguishing between tuberculosis (TB) positive rifampin resistant and tuberculosis (TB) positive rifampin susceptible samples. METHODS: Silver nanoparticles (Ag NPs) were used as SERS substrates and technique was used to distinguish TB positive rifampin (RIF) resistant and TB positive rifampin (RIF) susceptible patients on the basis of characteristic SERS spectral features of their respective PCR products. SERS spectra were acquired from 52 samples of PCR products including 22 samples of TB positive rifampin susceptible, 30 samples of TB positive rifampin resistant and negative control samples. All these samples were collected from individuals of same age. Furthermore, multivariate data analyses techniques such as PCA and PLS-DA were used to assess diagnostic capability of SERS for distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples. RESULTS: PCA is found helpful for successful differentiation among these two groups of spectral data sets. Moreover, PLS-DA provides this classification quantitatively by predicting the class of SERS spectral data set with 73% area under curve, 96% sensitivity, 95.6% specificity and 95% accuracy. CONCLUSION: SERS can be employed for the rapid distinguishing between TB positive rifampin resistant and TB positive rifampin susceptible samples.