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Decoding cellular stress states for toxicology using single-cell transcriptomics

GSE299113 Homo sapiens Expression profiling by high throughput sequencing 478 samples 2025/06/10 GPL16791
Summary
Single-cell transcriptomics (SCTr) offers a powerful platform for decoding the complexities of cellular stress responses to chemical exposures, capturing rare and transient subpopulations that can be obscured in bulk measurements. This study employs SCTr to profile the responses of HepaRG liver cells to various stress-inducing chemicals, providing detailed insights into adaptive stress response pathways (SRPs), encompassing the response to unfolded proteins, oxidative stress, heat shock, and DNA damage. By leveraging SCTr, we identify distinct cellular subpopulations and elucidate their transcriptomic profiles, linking specific gene expression patterns to biological mechanisms underlying stress responses. Our findings demonstrate the utility of SCTr in discerning the dose-dependent effects of chemicals at a granular level, highlighted by key differences between pseudo-bulk and single-cell analysis. Chemicals such as troglitazone, brefeldin A, rotenone, and tunicamycin induced unique transcriptional signatures associated with specific stress response pathways. Single-cell connectivity mapping with signatures of SRPs and cell fates, including autophagy and apoptosis, revealed clusters of cells with adaptive and potentially adverse phenotypes. These SCTr data have implications for identifying toxicological and disease mechanisms involving adaptive stress pathways. The data can also provide unique insights into toxicological processes by beginning to resolve adaptive chemical exposures, where cells recover, from those that are adverse from which cells do not recover, offering potentially new approaches for chemical safety assessments.
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NCBI GEO page ↗ Paper (PMID 40766395) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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