GSE162256
Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response
The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anti-cancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here we used single-cell RNA sequencing (scRNA-seq) to examine transcriptomic profiles of cancer cells treated with chemotherapy drug paclitaxel, anti-inflammatory drug celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that global mRNA abundance shows dynamic regulation across single cells after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification, and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription at variable levels across single cells. Interestingly, the combination of the two drugs resulted in transcriptional amplification and hyperactivation of the mitochondrial oxidative phosphorylation pathway, which is linked to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammatory genes that was activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across human cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response. The results also emphasize the importance of adding spike-in molecules to study gene expression regulation using scRNA-seq experiments. Overall design: scRNA-seq of cancer cells treated with paclitaxel, celecoxib and the two drug combination Please note that the Evalues.txt contains total 338 data columns as some bad quality samples were removed during the data analyses.