Single-cells RNA sequencing (scRNA-seq) is currently one of the most powerful techniques available to study the transcriptional response of cells to external perturbations. However, the use of conventional bulked RNA-seq analysis methods can miss important patterns underlying in the scRNA-seq data. Here, we present a reanalysis of scRNA-seq data from human bronchial epithelial cells and colon and ileum organoids using pseudo-time profiles based on the degree of virus accumulation which reflect the progress of infection. Our analysis revealed a transcriptional response to infection characterized by three distinct up- and down-regulatory phases, that cannot be detected using classical two-group comparisons. Interrogation of results, focused on genes involved in interferon-response, transcription factors and RNA-binding proteins, suggests a highly correlated transcriptional response for most genes. In addition, correlation network analysis revealed a distinct response of genes involved in translation and mitochondrially-encoded genes. Based on our data, we propose a model where modulation of nucleocytoplasmic traffic by the viral protein nsp1 explains the triphasic transcriptional response to SARS-CoV-2 infection.

This work was supported by European Commission – NextGenerationEU (Regulation EU 2020/2094) through CSIC’s Global Health Platform (PTI+ Salud Global) grants SGL2021-03-009 and SGL2021-03-052 to S.F.E.

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