Expression profiling by high throughput sequencing
Summary
COVID-19, caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple-organ failure, but little is known about its pathophysiology. Here, we generated single-cell atlases of 23 lung, 16 kidney, 15 liver and 18 heart COVID-19 autopsy donor tissue samples, and spatial atlases of 14 lung donors. Integrated computational analysis uncovered substantial remodeling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of myofibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in COVID-19 donor heart tissue, and mapped cell types and genes implicated with disease severity based on COVID-19 GWAS. Our foundational dataset elucidates the biological impact of severe SARS-CoV-2 infection across the body a key step towards new treatments.
Overall design
We developed a large cross-body COVID-19 autopsy biobank of 420 autopsy specimens, spanning 11 organs and 17 donors and used it to generate a single cell atlas of COVID-19 lung, kidney, liver and heart from up to 16 donors, and a lung spatial atlas. For our single-cell and single-nucleus RNA-Seq data, we provide both Cellranger raw counts matrices and counts matrices following ambient and emtpy drop removal using Cellbender remove-background for all lung, heart, liver, and kidney samples included in our published atlases as well as for a some airway and lymph node samples that we generated data for but did not analyze or include in our publication. We provide a metadata file including a list of barcodes that passed QC, basic QC metrics, and annotations for the lung, heart, liver, and kidney atlases. We provide an RSEM countsmatrix for our bulk RNA-sequencing samples, including data for the lung samples we analyzed and included in our publication as well as one heart and one brain sample that we did not include in our publication. NOTE from submitter: This submission does not contain raw data files as the fastq files contain protected patient information. Fastq files will be deposited to DUOS.
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