Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.
Yunlong MaYijun ZhouDingping JiangWei DaiJingjing LiChunyu DengCheng ChenGongwei ZhengYaru ZhangFei QiuHaojun SunShilai XingHaijun HanJia QuNan WuYinghao YaoJianzhong SuPublished in: Cell proliferation (2023)
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
Keyphrases
- single cell
- coronavirus disease
- rna seq
- sars cov
- endothelial cells
- cell therapy
- genome wide
- mesenchymal stem cells
- high throughput
- gene expression
- genome wide association study
- multiple sclerosis
- early onset
- emergency department
- type diabetes
- drug induced
- dna methylation
- bone marrow
- extracellular matrix
- transcription factor
- risk factors
- adipose tissue
- mass spectrometry
- long non coding rna
- inflammatory response
- white matter
- brain injury
- machine learning
- adverse drug
- data analysis