Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single-cell RNA sequencing

Area of Science:

• Molecular Biology
• Virology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-Seq) offers insights into SARS-CoV-2 pathogenesis.
• Existing scRNA-Seq methods are not optimized for detecting viral RNAs, particularly subgenomic mRNAs (sgmRNAs).
• Accurate quantification of viral RNA at single-cell resolution is crucial for understanding host-virus interactions.

Purpose of the Study:

• To compare scRNA-Seq library preparation methods for SARS-CoV-2 RNA detection and quantification.
• To develop and validate a data processing workflow (scCoVseq) for quantifying viral RNAs.
• To enable high-resolution studies of coronavirus RNA biology and pathogenesis.

Main Methods:

• Comparison of 10X Genomics Chromium Next GEM Single Cell 3' (10X 3') and 10X 5' libraries with standard and extended read 1 (R1) configurations.
• Development of the single-cell coronavirus sequencing (scCoVseq) workflow for quantifying viral sgmRNAs and genomic RNA (gRNA).
• Validation of scRNA-Seq results against bulk RNA-Seq data for SARS-CoV-2 analysis.

Main Results:

• 10X 5' libraries with extended R1 sequencing maximized unambiguous reads spanning leader-sgmRNA junction sites.
• The scCoVseq workflow combined with 10X 5' extended R1 libraries yielded the highest number of viral UMIs per cell.
• SARS-CoV-2 gene expression levels were highly correlated across infected cells, suggesting consistent sgmRNA proportions.

Conclusions:

• The combination of 10X 5' extended R1 library preparation/sequencing and scCoVseq data processing robustly quantifies coronavirus sgmRNA expression at single-cell resolution.
• This optimized approach enhances the study of viral RNA biology and pathogenesis in individual cells.
• The findings support high-resolution investigations into host-virus interactions during COVID-19.