RESUMEN
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.
RESUMEN
The ability of surface-enhanced Raman spectroscopy (SERS) to generate spectroscopic fingerprints has made it an emerging tool for biomedical applications. The objective of this study is to confirm the potential use of Raman spectroscopy for early disease diagnosis based on blood serum. In this study, a total of sixty blood serum samples, consisting of forty from diseased patients and twenty (controls) from healthy individuals, was used. Because disease biomarkers, found in the lower molecular weight fraction, are suppressed by higher molecular weight proteins, 50 kDa Amicon ultrafiltration centrifugation devices were used to produce two fractions from whole blood serum consisting of a filtrate, which is a low molecular weight fraction, and a residue, which is a high molecular weight fraction. These fractions were then analyzed, and their SERS spectral data were compared with those of healthy fractions. The SERS technique was utilized on blood serum, filtrate and residue of patients with tuberculosis to identify characteristic SERS spectral features associated with the development of disease, which can be used to differentiate them from healthy samples using silver nanoparticles as a SERS substrate. For further analysis, the effective chemometric technique of principal component analysis (PCA) was used to qualitatively differentiate all the analyzed samples based on their SERS spectral features. Partial least squares discriminant analysis (PLS-DA) accurately classified the filtrate portions of healthy and tuberculosis samples with 97% accuracy, 97% specificity, 98% sensitivity, and an area under the receiver operating characteristic (AUROC) curve of 0.74.