Surface enhanced Raman spectroscopy of RNA samples extracted from blood of hepatitis C patients for quantification of viral loads

Background: Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis. Objectives: To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quan-titative analysis of HCV from blood samples. Methods: SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nano-particles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data. Results: The SERS spectral features due to biochemical changes in the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy. Conclusion: SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.

Automated summary

The study aimed to develop a SERS-based method for qualitative and quantitative analysis of HCV from blood samples. Results showed that SERS could identify the biochemical changes associated with different viral loads in HCV-infected samples, and aid in differentiating between hepatitis-infected and healthy samples. Additionally, a PLSR model was established to determine viral loads in HCV-positive RNA samples with 99% accuracy.