A Simplified Sanger Sequencing Method for Detection of Relevant SARS-CoV-2 Variants

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

Despite low viral titer in saliva samples, Sanger-based SARS-CoV-2 spike gene sequencing is highly applicable for the variant identification

BACKGROUND

This study aimed to compare the performance of Sanger-based SARS-CoV-2 spike gene sequencing and Next Generation Sequencing (NGS)-based full-genome sequencing for variant identification in saliva samples with low viral titer.

METHODS

Using 241 stocked saliva samples collected from confirmed COVID-19 patients between November 2020 and March 2022 in Hiroshima, SARS-CoV-2 spike gene sequencing (nt22735-nt23532) was performed by nested RT-PCR and Sanger platform using in-house primers. The same samples underwent full-genome sequencing by NGS using Illumina NextSeq2000.

RESULTS

Among 241 samples, 147 were amplified by both the Sanger and the Illumina NextSeq2000 NGS, 86 by Sanger only, and 8 were not amplified at all. The overall amplification rates of Illumina NextSeq2000 NGS and Sanger were 61% and 96.7%, respectively. At low viral titer (< 103 copies/mL), Illumina NextSeq2000 NGS provided 19.2% amplification, while Sanger was 89.7% (p < 0.0001). Both platforms identified 38 wild type, 54 Alpha variants, 84 Delta variants, and 57 Omicron variants.

CONCLUSIONS

Our study provided evidence to expand the capacity of Sanger-based SARS-CoV-2 spike gene sequencing for variants identification over full-genome by Illumina NextSeq2000 NGS for mass screening. Therefore, the feasible and simple Sanger-based SARS-CoV-2 spike gene sequencing is practical for the initial variants screening, which might reduce the gap between the rapid evolution of SARS-CoV-2 and its molecular surveillance.