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The expression order determines the pioneer functions of Ngn3 and NeuroD1 in pancreatic endocrine differentiation -2

GSE271546 Mus musculus (mouse) Single-cell RNA-seq 447 samples Submitted 2025/02/15 Platform GPL21103
At a glance

Single-cell RNA-seq · mouse · 447 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
447 samples: 205 × Pdx1CreNgn3KONeurod1OE_E15.5_2 vs 117 × Pdx1CreNeurod1OE_E15.5_2 vs 78 × Pdx1CreNeurod1OE_E15.5_3Groups 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
The expression order determines the pioneer functions of NGN3 and NEUROD1 in pancreatic endocrine differentiationScience advances · 2025
Summary (written by the submitters)
This study investigates the roles of Ngn3 and NeuroD1 in pancreatic endocrine differentiation. Using a new mouse model, we mapped NGN3 binding sites by CUT&RUN and demonstrated its pioneering role in making chromatin accessible by scATAC-seq. According to conditional knock-out and over-expression experiments and scRNA-seq, we domonstrated that NGN3's pioneering function was dose tolerance, with low doses sufficing, while NeuroD1, as a conventional TF, has no significant pioneer effects under normal phenomena.
Read the full summary ↓
But when NGN3 was absent, NeuroD1 can alternatively acted as a pioneer factor, and sequential expression of NeuroD1 ahead of Ngn3 predominantly drives α-cell generation.
Published in
The expression order determines the pioneer functions of NGN3 and NEUROD1 in pancreatic endocrine differentiation
Yang L, Yu XX, Wang X et al. · Science advances 2025 · PMID 40138419 · doi:10.1126/sciadv.adt4770
This dataset
Open GSE271546 in finder → Find more mouse 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 GSE271546_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 PRJNA1132201 and SRA study SRP518160. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40138419) ↗
Study design
205 × Pdx1CreNgn3KONeurod1OE_E15.5_2 vs 117 × Pdx1CreNeurod1OE_E15.5_2 vs 78 × Pdx1CreNeurod1OE_E15.5_3

Supports a between-group comparison across 400 samples.

How we read this: 3 replicated groups read from the first 400 of 447 sample titles; they account for 400 of them. 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

+ 407 more — browse all 447 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.