← BioTransfer GEO Dataset Finder
GEO series

Endocrine cell ER stress conservation

GSE238017 Mus musculus (mouse) RNA-seq (gene expression) 54 samples Submitted 2025/05/05 Platform GPL24247Platform GPL25947Platform GPL24676
At a glance

RNA-seq (gene expression) · mouse · 54 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
54 samples: 54 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
VDAC1 is a target for pharmacologically induced insulin hypersecretion in β cellsCell reports · 2025
Summary (written by the submitters)
Endocrine cells are dedicated to the production and processing of hormones, from peptides to small molecules, to regulate key physiological processes including glucose homeostasis and metabolism. Because of this relatively high productivity, endocrine cells must handle a variety of stresses from oxidative stress to the unfolded protein response of the endoplasmic reticulum (UPRER).
Read the full summary ↓
While much is known about the major pathways regulating the UPRER, the roles of endocrine cell type-specific, context-dependent, and time-dependent transcriptional changes are not well explored. To identify unique and shared responses to the UPRER across a subset of endocrine cell types, we tested representative lines for β-cells (insulin), α-cells (glucagon), δ-cells (somatostatin), X/A-cells (ghrelin), L-cells (glucagon-like peptide 1 (GLP1)), and thyrotropes (thyroid hormone and thyroglobulin). We exposed each cell type to the canonical ER stressor thapsigargin for 6 and 24 h, or vehicle (DMSO 0.1%) for 24 h and performed mRNA sequencing. Analysis of the data showed all lines responded to thapsigargin. Comparisons of up- and down-regulated genes between each line revealed both shared and unique transcriptional signatures. These data represent a valuable mineable set of candidate genes which may have cell type-specific functions during the UPRER, and have the potential to lead to new understanding about how different endocrine cells mitigate or succumb to ER stress.
Published in
VDAC1 is a target for pharmacologically induced insulin hypersecretion in β cells
Roy G, Ordóñez A, Binns DD et al. · Cell reports 2025 · PMID 40512624 · doi:10.1016/j.celrep.2025.115834
This dataset
Open GSE238017 in finder → Find more 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 GSE238017_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA997368 and SRA study SRP450830. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40512624) ↗
Study design
54 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 54 sample titles: 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Popular RNA-seq datasets

No close match found for this topic, so these are simply the most-viewed datasets of the same kind.

Search all RNA-seq datasets in GEO →

Share this dataset

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.