Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV‑2

• Published in: Analytical chemistry (Washington) Vol. 96; no. 37; pp. 14749 - 14758
• Main Authors: Lahiri, Pooja, Das, Souvik, Thakur, Shivani, Mehra, Rukmankesh, Ranjan, Piyush, Wig, Naveet, Dar, Lalit, Bhattacharyya, Tarun Kanti, Sengupta, Sanghamitra, Lahiri, Basudev
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 17.09.2024
• Subjects: Algorithms; analytical chemistry; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - virology; diagnostic techniques; energy; etiology; evolution; Fourier transform infrared spectroscopy; Fourier transforms; Humans; Infectious diseases; influenza; Influenza A; Influenza A Virus, H1N1 Subtype - genetics; Influenza A Virus, H1N1 Subtype - isolation & purification; Influenza A Virus, H3N2 Subtype - genetics; Influenza A Virus, H3N2 Subtype - isolation & purification; Infrared analysis; Infrared spectroscopy; Learning algorithms; Machine Learning; Neural networks; Neural Networks, Computer; Nucleic acids; pandemic; Pandemics; Pharynx; Proteins; Public health; SARS-CoV-2 - chemistry; SARS-CoV-2 - genetics; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Spectroscopy, Fourier Transform Infrared - methods; Spike Glycoprotein, Coronavirus - chemistry; Structural analysis; Vibrations; Viral diseases; Wavelengths
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.4c01260

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm–1 was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.