PDF(Pubmed)
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.
摘要：
由于细胞群体之间的异质性,细胞对药物反应不同。因此，为了准确阐明药物作用机制，确定药物反应性细胞群体至关重要，这仍然是一个巨大的挑战。这里,我们用scRank解决这个问题，它采用目标扰动的基因调控网络，通过使用未处理的单细胞转录组数据的计算机药物扰动对药物反应性细胞群体进行排名。我们在模拟和真实数据集上对scRank进行基准测试，这表明scRank的性能优于现有方法。当应用于髓母细胞瘤和重度抑郁症数据集时，scRank识别与文献一致的药物反应性细胞类型。此外，scRank准确地揭示了对丹参酮IIA反应的巨噬细胞亚群及其在心肌梗死中的潜在靶标，通过实验验证。总之,scRank能够使用未经处理的单细胞数据推断药物反应性细胞类型，从而提供对治疗干预的细胞水平影响的见解。
