Single-cell RNA sequencing outlines the immune landscape of severe COVID-19

A new single-cell RNA sequencing analysis of more than 59,000 cells from three different patient cohorts provides a detailed look at patients’ immune responses to severe cases of COVID-19. The results suggest that patients with severe COVID-19 experience increased regulation of the type I interferon (IFN-I) inflammation-triggering pathway – a signature that the researchers also observed in patients hospitalized with severe cases of influenza. Their findings suggest that anti-inflammatory treatment strategies for COVID-19 should also be aimed toward the IFN-I signaling pathway, in addition to targeting inflammatory molecules such as TNF, IL-1?, and IL-6 that have been implicated in COVID-19.

Seong Seok Lee and colleagues at the Korea Advanced Institute of Science and Technology sequenced the RNA from a total of 59,572 blood cells obtained from four healthy donors, eight patients with mild or severe COVID-19, and five patients with severe influenza. Patients in both the mild and severe COVID-19 cohorts all showed increased regulation of the TNF/IL-1ß-driven inflammatory response, while patients with severe COVID-19 also exhibited an increased IFN-I response. By comparison, patients with severe flu showed increased expression of various IFN-stimulated genes, but did not experience TNF/IL-1ß responses as seen in COVID-19 patients. Unlike the flu cohort, patients in the severe COVID-19 cohort exhibited the IFN-I signature concurrently with TNF/IL-1ß-driven inflammation – a combination also not seen in patients with milder cases of COVID-19. Based on their results, the scientists propose that the IFN-I response exacerbates inflammation in patients with severe COVID-19. Their results, along with past mouse studies that highlight how the timing of IFN-I expression is critical to determining the outcome of SARS-CoV-2 infection, support targeting IFN-I as a potential treatment strategy for severe COVID-19.

Immune landscape of COVID-19

(A) Hierarchical clustering using the Pearson correlation coefficient (PCC) of a normalized transcriptome between diseases in cell type resolution (n = 33). The color intensity of the heat map indicates the PCC values. The color bars above the heat map indicate the cell type and disease group. The black box indicates the cell types that highly correlate between the severe COVID-19 and FLU groups. (B) Illustration of the enrichment p-values for the select GO biological pathways (n = 49) of differentially expressed genes (DEGs) in COVID-19 and FLU patients (left 6 columns: DEGs for COVID-19 and FLU groups compared to HD, right 2 columns: DEGs between COVID-19 and FLU groups). (C) tSNE plot of representative gene expression patterns for GBP1 (FLU specific), CREM (COVID-19 specific), and CCL3 (COVID-19/FLU common). (D) Top, dendrogram from WGCNA analysis performed using relative normalized gene expression between the COVID-19 and FLU groups for the genes belonging to the select biological pathways in (B) (n=316). Bottom, heat map of relative normalized gene expression between the COVID-19 and FLU groups. The color bar (left) indicates cell type information clustered by hierarchical clustering based on the PCC for relative normalized gene expression. Modularized gene expression patterns by WGCNA are shown together (G1, n=10; G2, n=147; G3, n=27; G4, n=17; G5, n=12; G6, n=64; G7, n=34; G8, n=5).

Source – American Association for the Advancement of Science