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Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2
Pooja Lahiri1, Souvik Das1,2, Shivani Thakur3
1Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.
Analytical Chemistry
|August 31, 2024
Summary
Fourier Transform Infrared microspectroscopy and machine learning accurately identify SARS-CoV-2 variants and influenza A. This rapid diagnostic tool aids in monitoring viral evolution and preparedness for future outbreaks.
Area of Science:
- Biophysics
- Infectious Disease Diagnostics
- Computational Biology
Background:
- The COVID-19 pandemic highlights the need for rapid diagnostics to track SARS-CoV-2 variants and prepare for future outbreaks.
- Existing diagnostic methods may not be sufficient for real-time variant surveillance and differentiation.
- Understanding viral evolution is crucial for public health preparedness.
Purpose of the Study:
- To develop and validate a multidisciplinary approach using FTIR microspectroscopy and machine learning for SARS-CoV-2 variant characterization and strain-typing.
- To identify spectral markers for differentiating SARS-CoV-2 variants, healthy individuals, and other respiratory viruses like influenza A.
- To investigate structural changes in viral proteins associated with transmissibility.
Main Methods:
- Fourier Transform Infrared (FTIR) microspectroscopy was used to analyze pharyngeal swab samples from different pandemic waves.
- Machine learning algorithms, particularly neural networks, were employed to classify spectral data.
- FTIR spectral data were correlated with viral variants and clinical outcomes.
Main Results:
- Distinct vibrational profiles were observed in FTIR spectra, with a specific wavenumber range (1150-1240 cm⁻¹) identified as a key classification marker.
- Machine learning models achieved 98.6% accuracy in classifying SARS-CoV-2 variants.
- Neural networks successfully differentiated between SARS-CoV-2, influenza A (H1N1, H3N2), and healthy samples.
- FTIR analysis revealed red shifts and secondary structural alterations in spike proteins of more transmissible SARS-CoV-2 variants.
Conclusions:
- The integrated FTIR microspectroscopy and machine learning approach offers a rapid and reliable method for SARS-CoV-2 variant identification.
- This technique can aid in monitoring viral evolution and improving diagnostic capabilities for emerging infectious diseases.
- The study provides experimental validation for computational findings on viral protein alterations and transmissibility.
