scSLAM-seq and GRAND-SLAM reveal core features of the intrinsic immune response in single virus-infe
Current single-cell RNA sequencing approaches reveal intercellular heterogeneity in gene expression but convey little information about the underlying temporal dynamics. We developed single-cell SLAM-seq (scSLAM-seq), which combines metabolic RNA labeling, biochemical nucleoside conversion, deep single-cell RNA-seq and computational analyses to add a temporal dimension to scRNA-seq by directly quantifying newly transcribed and old, pre-existing RNA within total RNA at single-cell level. By analyzing transcriptional regulation during the first two hours of lytic cytomegalovirus (CMV), we demonstrate this approach to provide reliable quantitative information on transcriptional activity for thousands of genes per cell infection. scSLAM-seq thereby enables dose response analysis that can be utilized to identify novel pro-/antiviral states and cellular factors. Furthermore, scSLAM-seq reveals a sophisticated layer of RNA dynamics inaccessible with classical approaches including sub-populations defined by active regulatory programs, on-off dynamics of gene expression and transcriptional bursts. In summary, scSLAM-seq represents a broadly applicable, powerful approach for delineating alterations in transcriptional activity and decay in single cells under perturbed experimental conditions.
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