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Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium

Authors :
Renata B. Filler
David van Dijk
Guilin Wang
Nicholas C. Huston
Akiko Iwasaki
Han Wan
Mia Madel Alfajaro
Anna Marie Pyle
Victor Gasque
Bao Wang
Victoria Habet
Craig B. Wilen
Richard W. Pierce
Stephanie C. Eisenbarth
Neal G. Ravindra
Jin Wei
Tamas L. Horvath
Adam Williams
Ruth R. Montgomery
Ellen F. Foxman
Klara Szigeti-Buck
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angiogenesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........e1ee286f5e3ccf102ba095af2fd6fa7b