Raman Spectra of Viral RNA Reveal a New Way to Tell SARS-CoV-2 Variants Apart

Researchers have taken a significant step toward what they call a “Raman genome” — a spectroscopic fingerprint of viral RNA that can distinguish SARS-CoV-2 variants and subvariants without sequencing a single nucleotide. In a study published in Advanced Science, a team led by Giuseppe Pezzotti analyzed purified RNA strands extracted from 20 different SARS-CoV-2 lineages, ranging from the ancestral Japanese isolate JPN/TY/WK-521 through Alpha, Beta, Gamma, Delta, Lambda, Theta, Mu and a broad panel of Omicron subvariants. Using high-resolution Raman spectroscopy combined with machine-learning-based spectral deconvolution, they showed that each viral strain produces a unique vibrational signature, one distinctive enough to be encoded as a barcode that unambiguously identifies the variant.

The motivation behind the work lies in the limitations of the current diagnostic toolkit. Next-generation sequencing is the gold standard for decoding viral genomes and tracking mutations, but it is a complex, multi-step process that can take weeks and may occasionally miss or misinterpret variants. Reverse transcription polymerase chain reaction, by contrast, is fast and widely deployed from bench to bedside, yet it can only detect sequences that are already known: if mutations arise in the primer-binding regions, the assay may fail entirely, and designing new primers for an emerging variant can take nearly as long as full sequencing. Raman spectroscopy offers a third path — one that does not read the base sequence at all, but instead captures the structural consequences of sequence variation as they are written into the RNA molecule’s vibrational behavior.

In the study, viral variants were propagated in VeroE6/TMPRSS2 cells, and RNA was extracted from culture supernatants using a commercial kit. Raman spectra were collected with a confocal LabRAM HR800 spectrometer excited at 532 nanometers, achieving spectral resolution better than one wavenumber with an internal neon lamp reference for calibration. Ten spectra were averaged for each RNA sample across an area of roughly two square millimeters, and the resulting spectra were baseline-corrected and deconvoluted into Gaussian-Lorentzian sub-band components using an automatic solver built on machine learning algorithms. Crucially, the optimization preserved the spectral positions and bandwidths of elementary molecular components while treating relative intensities as structure-dependent variables, a constrained approach that dramatically reduces the risk of overfitting in spectrally congested regions.

The spectra, recorded between 400 and 1800 wavenumbers, were morphologically similar across all 20 strains but differed in telling details. Ring-breathing markers for the four RNA bases appeared at characteristic positions: about 724 wavenumbers for adenine, 778 for cytosine, 672 for guanine and 791 for uracil. Backbone signals included the O-P-O stretching modes near 807 and 824 wavenumbers and a C3′-O-P bending band at 432 wavenumbers, while ribofuranose sugar rings contributed a strong C-O/C-C stretching signal at roughly 1045 wavenumbers. When the researchers compared the fractional intensities of the base signals with the actual base fractions computed from genomic data, the two disagreed substantially — pyrimidine signals systematically overestimated their base fractions and purine signals underestimated them. These mismatches could not be explained by base composition, which is nearly homogeneous across strains, but instead arise from differences in Raman cross-sections and from hyperchromic and hypochromic effects tied to RNA secondary structure.

That structural sensitivity is precisely what makes the approach powerful. Single-stranded viral RNA folds into stems, hairpin loops, pseudoknots, bulge loops, internal loops and multibranched junctions, and each motif modulates the Raman spectrum in its own way. Double-stranded stems enhance base stacking and hydrogen bonding, sharpening and intensifying purine ring signals; hairpin loops bend the phosphodiester backbone and alter its vibrational response; bulges and internal loops distort local geometry and reduce stacking-related intensity. To rationalize these effects, the team predicted the secondary structures of all 20 genomes with the ViennaRNA package using sequences from the GISAID database, computing positional descriptors including base-pairing probability, cumulative minimum-free-energy nesting depth, Shannon entropy and a custom composite “strain score” flagging nucleotides that are structurally engaged yet conformationally unstable.

Several quantitative relationships emerged. The ribofuranose ring signal at 1045 wavenumbers showed a clean linear dependence on genome length — the longer the RNA chain, the stronger the signal — because the sugar ring’s two additional dihedral angles affect only the pucker conformation and are not directly strained by backbone bending. Phosphodiester backbone signals, by contrast, scattered far more widely, since the six backbone dihedral angles are directly perturbed by kinks introduced through non-canonical base pairing. Searches for AUUCU repeats and kink-turn motifs, known sources of backbone deformation, found that their counts varied only marginally among the strains and could not alone account for the observed scatter. More strikingly, the 432-wavenumber C3′-O-P bending signal displayed an exponential hyperchromic dependence on the number of hairpin loops, with the ancestral strain — which had the most hairpins, 592 — showing the strongest intensity. The researchers interpret this as evidence that RNA backbone vibrations become more Raman-active as secondary-structure complexity increases, likely because tight structural constraints reduce vibrational energy dissipation and make the bending mode more cooperative.

A second key marker appeared near 1460 wavenumbers, a composite band dominated by C=N and C=C stretching in guanine and adenine together with C-H and N-H bending. Its relative intensity rose linearly with average stem length, tracking the increased base stacking and hydrogen bonding of longer double-stranded regions. Neither of these trends could be reproduced for neighboring bands of similar origin, underscoring the unique sensitivity of the 1460-wavenumber signal to base-stacking effects. Because hairpins and stems regulate viral infectivity — stabilizing the genome against host nucleases, enabling translation initiation of the ORF1a/ORF1ab replicase polyproteins through conserved stem-loop structures in the 5′ untranslated region, and hiding immunostimulatory double-stranded motifs from innate immune sensors such as RIG-I, MDA5, PKR and the OAS family — the two Raman markers can be read as proxies for replication efficiency and immune evasion, respectively.

Plotted as a rescaled intensity timeline across the 20 strains, these markers tell an evolutionary story. The ancestral variant scored highest on both functions. Successive variants showed a general decline in the translation/replication indicator, with the Theta variant as a notable exception, its secondary structure scoring closest to the ancestor’s — consistent with reports of concerning spike mutations in that lineage. From the Mu variant onward, the immune-evasion indicator climbed sharply through the first wave of Omicron subvariants, in agreement with the known immune-escape character of Omicron, and a further, smaller spike appeared in a later Omicron subvariant. The authors note that such trade-offs between immune escape and replication fitness are well documented in HIV, influenza and hepatitis C, and that Omicron’s emergence indeed traded replication efficiency for immune evasion.

Classification posed its own challenge. Principal component analysis, a standard chemometric tool in biomedical spectroscopy, succeeded in separating only a minority of the 20 RNA samples, a partial failure the researchers attribute to the extreme complexity of RNA secondary structure. Instead, they converted each deconvoluted spectrum into a Raman barcode, assigning to each sub-band a line whose thickness and spacing encode the band’s width and area. Every one of the 20 barcodes proved unique, univocally locating the molecular structure of each sample, including the spectroscopic imprint of its secondary structure. Compared with DNA barcoding initiatives that require sequencing short genetic markers, the Raman approach skips sequence analysis altogether, potentially enabling on-site pathogen identification in a time dictated solely by RNA extraction, once a comprehensive spectral library and standardized deconvolution protocols are in place.

The authors are careful to frame the work as a proof of concept with clear limitations. The study used purified viral RNA from controlled cell cultures rather than clinical specimens containing host RNA, proteins and debris; it cannot identify individual nucleotide substitutions or determine primary sequence, and should be regarded as complementary to genomic sequencing rather than a replacement; and the barcode algorithm will require inter-laboratory validation, standardized acquisition protocols and expanded spectral libraries before clinical translation. Its ability to resolve newly emerging variants differing by only a few mutations remains to be systematically tested. Nevertheless, the study establishes that Raman spectroscopy captures structural information encoded within viral RNA that conventional compositional analyses cannot reach — amplified spectral differences arising from small genomic ones — and demonstrates that a Raman library of viral RNA barcodes could one day give virologists a rapid, low-cost route to variant identification in emergency and critical-care settings, precisely where timely classification of emerging strains matters most.

Subject of Research: Raman spectroscopic fingerprinting of SARS-CoV-2 viral RNA secondary structure for variant classification

Article Title: Raman Analysis of RNA Nucleotide Strands From SARS‐CoV‐2 Variants and Subvariants: A Step Forward in the Definition of “Raman Genome”

Article References: Pezzotti, G., Yasukochi, Y., Angiola, G., Ueno, T., Ikegami, S., Okawa, R., Adachi, T., Zhu, W., Mazda, O., Grillo, A., Higasa, K., & Okuma, K. (2026). Raman Analysis of RNA Nucleotide Strands From SARS‐CoV‐2 Variants and Subvariants: A Step Forward in the Definition of “Raman Genome”. Advanced Science, Article e77679. https://doi.org/10.1002/advs.77679

Image Credits: AI Generated

DOI: 10.1002/advs.77679

Keywords: SARS-CoV-2, Raman spectroscopy, viral RNA, Raman genome, variants and subvariants, RNA secondary structure, Raman barcode, molecular diagnostics, machine learning, ViennaRNA, immune evasion, viral evolution