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Abstract The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer 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 cellular 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.
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This content will become publicly available on March 14, 2026
High-throughput single-cell transcriptomics data informs mechanisms underlying cell-to-cell differences in stress responses
Abstract Over recent decades, studies of cell-to-cell heterogeneity have shown that meaningful variation in stress responses is an evolved trait, exemplified by bet hedging. Here, we expand upon this work by using a high-throughput single-cell transcriptomics approach inS. cerevisiaeto study cell-to-cell variation in stress responses. We measured individual transcriptional profiles of over ten thousand cells subjected to two types of stress: protein misfolding (an intrinsic stress) and nutrient deprivation (an extrinsic stress). Doing so, we saw substantial heterogeneity in responses to stress across and within different conditions. A previously defined canonical stress response is elevated in all stressful conditions, making it useful as a barometer of stress. However, it does not include the majority of the genes that are the most upregulated in response to any of the stresses we study, suggesting stress responses vary across environments. On the other hand, the genes that were downregulated with stress show a much stronger overlap across conditions. Even more striking is the cell-to-cell heterogeneity we observed in stress responses where some stressed cells exhibit neither a canonical stress response nor a transcriptional response that matches most other cells in the same vessel. Further investigation of these cell-to-cell differences revealed a misconception of population-averaged data: While transposable elements (TEs) and chaperones both appear positively correlated with stress at the population level, single-cell analysis revealed they are actually anticorrelated. In other words, some stressed cells upregulate TEs, while others upregulate chaperones. One possible mechanism for this involves chaperones, such as Hsp90, which are thought to help to silence TEs but are unable to do so effectively when these chaperones become overwhelmed during stress. In sum, we have used a powerful single-cell transcriptomics approach to quantify stress-response heterogeneity and to shed light on the mechanisms underlying cell-to-cell variation in stress responses, both of which are crucial for understanding cell evolution and disease.
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- Award ID(s):
- 2119963
- PAR ID:
- 10594793
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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