Background Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy—beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses. Results We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples. Conclusions This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.

中文翻译：

---

具有刺入细胞的单细胞 RNA-seq 能够准确量化胰岛中细胞特异性药物效应


背景 单细胞 RNA-seq (scRNA-seq) 正在成为分析复杂组织中药物治疗的细胞特异性效应的强大工具。该应用需要高水平的精度、稳健性和定量准确度——超出了主要定性单细胞分析的现有方法所能达到的水平。在这里，我们使用标准化参考细胞作为掺入对照，以准确、稳健地剖析单细胞药物反应。结果我们发现，在人类原代组织样本中，无细胞 RNA 的污染可占读数的 20%，并且我们表明，使用新型生物信息学算法可以有效消除随之而来的偏差。将我们的方法应用于离体处理的人类和小鼠胰岛，我们获得了细胞特异性药物对转录组的影响的准确和定量评估。我们观察到 FOXO 抑制会诱导 α 和 β 细胞去分化，而蒿甲醚治疗则上调一部分 α 细胞中的胰岛素和其他 β 细胞标记基因。在β细胞中，蒿甲醚治疗后的去分化和胰岛素抑制主要发生在小鼠身上，但在人类样本中则不然。结论 这种使用掺入参比细胞进行定量、纠错、scRNA-seq 数据标准化的新方法有助于以单细胞分辨率和高定量精度阐明药理扰动的复杂细胞特异性效应。