A Spatiotemporal Dynamic Immune Landscape of the COVID-19 Hamster Lung [Spatiotemporal]
IDSTT0000006(Source: STOmics DB)
STOmics technology:BGI Stereomics Stereo-Seq
Data type:Raw sequence reads, Spatial transcriptomics
Sample scope:Monoisolate
Summary:Although SARS‐CoV‐2‐mediated inflammation has attracted global health concerns since 2019, its pulmonary immunopathology is not fully understood. Here we generated a comprehensive cellular and molecular landscape of healthy and COVID-19 hamster lungs at different timepoints after infection, using single-cell RNA sequencing and spatial transcriptomic sequencing to map the entire progression of COVID-19. We found SARS-CoV-2 could infect naïve T cells and induced cell death to decrease T cell number at the early stage of COVID-19. Besides, we observed the activation and depletion of tissue resident myeloid cells after infection, the accumulation of Isg12+Cst7+ neutrophils and Il10+Spp1+ M2-like macrophages to clean up virus and resolve inflammation. Finally, we identified Trem2+AM and Fbp1+AM subsets during the resolution stage of COVID-19. Our study provided spatiotemporally-resolved insights into the lung cells transcriptome, identified distinct tissue regions of viral infection, lung injury, repair and remodeling.
Contributor(s):Xuetao Cao.
Publication(s):
  • Xuetao Cao. A Spatiotemporal Dynamic Immune Landscape of the COVID-19 Hamster Lung.
Submitter:Yupeng Zang(Yupeng Zang),华大生命科学研究院
Release date:2024-09-25
Updated:2024-09-25
Relations:
Statistics:
  • Sample: 15
  • Tissue Section: 15
Datasize:3.72GB
ProjectSampleTissue SectionOrganismFiles