The latest RNA genomic mutation of
SARS-CoV-2 virus, termed the
Omicron variant, has generated a stream of highly contagious and antibody-resistant
strains, which in turn led to classifying Omicron as a variant of
concern. We systematically collected Raman spectra from six Omicron
subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE,
and BA.2.75) and applied machine-learning algorithms to decrypt their
structural characteristics at the molecular scale. Unique Raman fingerprints
of sulfur-containing amino acid rotamers, RNA purines and pyrimidines,
tyrosine phenol ring configurations, and secondary protein structures
clearly differentiated the six Omicron subvariants. These spectral
characteristics, which were linked to infectiousness, transmissibility,
and propensity for immune evasion, revealed evolutionary motifs to
be compared with the outputs of genomic studies. The availability
of a Raman “metabolomic snapshot”, which was then translated
into a barcode to enable a prompt subvariant identification, opened
the way to rationalize in real-time SARS-CoV-2 activity and variability.
As a proof of concept, we applied the Raman barcode procedure to a
nasal swab sample retrieved from a SARS-CoV-2 patient and identified
its Omicron subvariant by coupling a commercially available magnetic
bead technology with our newly developed Raman analyses.