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Decoding cellular stress states for toxicology using single-cell transcriptomics

GSE299113 Homo sapiens (human) Single-cell RNA-seq 478 samples Submitted 2025/06/10 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 478 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
478 samples: 478 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Decoding Cellular Stress States for Toxicology Using Single-Cell TranscriptomicsbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
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.
Read the full summary ↓
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.
Published in
Decoding Cellular Stress States for Toxicology Using Single-Cell Transcriptomics
Shah I, Gallegos D, Robinette B et al. · bioRxiv : the preprint server for biology 2025 · PMID 40766395 · doi:10.1101/2025.06.10.657506
This dataset
Open GSE299113 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE299113_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1256510 and SRA study SRP585310. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40766395) ↗
Study design
478 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from the first 400 of 478 sample titles: 400 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 438 more — browse all 478 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.