NAR Genomics and BioinformaticsGenomicsPeer-reviewed journal article
Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response
Xin WangiD, Jane FrederickiD, Hongbin WangiD, Sheng Hui, Vadim BackmaniD, Zhe JiiD
Abstract
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-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that celluar mRNA abundance shows dynamic regulation 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 across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 8
- FWCI
- 0.47
- Field percentile
- top 45%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action
J. M. McFarland, B. R. Paolella, … A. Tsherniak·bioRxiv·Dec 8, 2019
- Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq
W. Zhao, A. Dovas, … P. A. Sims·bioRxiv·Apr 22, 2020
- COMPARISON OF HIGH-THROUGHPUT SINGLE-CELL RNA-SEQ METHODS FOR EX VIVO DRUG SCREENING
Henrik Gezelius, Anna Pia Enblad, … Jessica Nordlund·NAR Genomics and BioinformaticsPeer-reviewed·Jan 5, 2024
- Modeling non-genetic dynamics of cancer cell states measured by single-cell analysis: Uncovering bifurcations that explain why treatment either kills a cancer cell or makes it resistant
S. Huang·bioRxiv·Mar 12, 2019
- Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
Sanju Sinha, Rahulsimham Vegesna, … Eytan Ruppin·bioRxiv·Jan 11, 2022
- Deep Transfer Learning of Drug Responses by Integrating Bulk and Single-cell RNA-seq data
Junyi Chen, Zhenyu Wu, … Qin Ma·bioRxiv·Aug 1, 2021