Cell ProliferationGenetic & Genomic MedicinePeer-reviewed journal article
Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19
Yunlong MaiD, Yijun ZhouiD, Dingping Jiang, Wei Dai, Jinɡjinɡ LiiD, Chunyu Deng, Chen ChengiD, Gongwei Zheng, Yaru ZhangiD, Fei QiuiD, Haojun Sun, Shilai XingiD, Haijun Han, Jia QuiD, Nan WuiD, Yinghao YaoiD, Jianzhong SuiD
Abstract
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 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 (MSCs) 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.
- organism: human
- assay: scRNA-seq
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 18
- FWCI
- 1.60
- Field percentile
- top 16%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Single cell sequencing analysis uncovers genetics-influenced CD16+monocytes and memory CD8+T cells involved in severe COVID-19
Yunlong Ma, Fei Qiu, … Jianzhong Su·Genome MedicinePeer-reviewed·Feb 16, 2022
- Comprehensive characterization of the transcriptional response to COVID-19 in multiple organs reveals shared signatures across tissues
Alejandro A. Granados, Simon Bucher, … Bruce Wang·eLifePeer-reviewed·Aug 7, 2023
- Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity
Sai Zhang, Johnathan Cooper‐Knock, … Michael P. Snyder·Cell SystemsPeer-reviewed·Jun 3, 2022
- Identification of Candidate COVID-19 Therapeutics using hPSC-derived Lung Organoids
- Characterization of cell-cell communication in COVID-19 patients
Yingxin Lin, Lipin Loo, … Jean Yee Hwa Yang·PLoS Computational BiologyPeer-reviewed·Oct 5, 2022
- The scRNA-seq expression profiling of the receptor ACE2 and the cellular protease TMPRSS2 reveals human organs susceptible to COVID-19 infection
Jing Qi, Yang Zhou, … Shuilin Jin·International Journal of Environmental Research and Public HealthPeer-reviewed·Jan 2, 2021