scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.
Cell Rep Med
; 5(6): 101568, 2024 Jun 18.
Article
em En
| MEDLINE
| ID: mdl-38754419
ABSTRACT
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Reguladoras de Genes
/
Análise de Célula Única
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Cell Rep Med
Ano de publicação:
2024
Tipo de documento:
Article