SRA

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 current methods for qualitative single cell characterization. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses. We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and show that the ensuing biases can effectively be removed by a novel bioinformatics method. Applying this 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. This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells allows to clarify the complexities of the cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy. Overall design: Single-cell RNA sequencing using 10 X of pancreatic islets from 3 human donors and 3 mice. Islets were treated ex-vivo with drugs that alter islet cell identity for 72 hrs. Methanol-fixed cells were spiked-in with the islet cells to bioinformatically detect and remove contaminating cell-free RNA. Upon data cleanup and an algorithm to assign cell types, we could study cell-specific drug effects on the transcriptome.