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Characterizing dynamics and lineage of bihormonal cell in mammalian pancreatic islets [Smart-seq3xpress]

GSE248194 Mus musculus (mouse) Single-cell RNA-seq 1430 samples Submitted 2025/06/16 Platform GPL30215
At a glance

Single-cell RNA-seq · mouse · 1430 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
1430 samples: 6 conditions, mostly with about 116 replicates eachGroups 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
Comprehensive Characterization of Bihormonal Cells and Endocrine Cell Lineages in Mammalian Pancreatic IsletsAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
Summary (written by the submitters)
The precise roles and prevalence of bihormonal cells within pancreatic islets are not fully understood. Here, we utilized genetically engineered mouse strains with specific fluorescent markers for pancreatic endocrine cells and advanced imaging flow cytometry to investigate the presence of bihormonal cells in adult mouse islets. Our results revealed a marginal presence of such cells, contradicting the hypothesis that transdifferentiation contributes significantly to β-cell restoration in diabetic mice or during pregnancy-induced β-cell proliferation.
Read the full summary ↓
Employing single-cell RNA sequencing (scRNA-seq), we observed that Gcg+Ppy+ bihormonal cells closely resemble either α- or PP-cells, lacking distinct gene expression profiles. This finding suggests bihormonal cells may not represent a unique lineage but could be transitional states in cellular differentiation. Furthermore, a dual-genetic tracing approach revealed that embryonic Ins+Gcg+ cells differentiate into α-cells postnatally. By integrating gene co-expression network analysis with 10x Genomics scRNA-seq data, we established a hierarchical classification of endocrine cells in mouse and human islets, identifying distinct populations with minimal overlap in conserved genes between species. This study not only refines our understanding of bihormonal cells in islet function and development but also highlights the complexities in translating murine model findings to human applications, emphasizing the need for species-specific considerations in diabetes research and therapy development.
Published in
Comprehensive Characterization of Bihormonal Cells and Endocrine Cell Lineages in Mammalian Pancreatic Islets
Yu XX, Peng P, Wang YN et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025 · PMID 40439685 · doi:10.1002/advs.202416326
This dataset
Open GSE248194 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 GSE248194_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 PRJNA1042636 and SRA study SRP472791. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40439685) ↗
Study design
6 conditions, mostly with about 116 replicates each
P60_GcgPpy_Bihormonal ×116 E13.5TAM_E14.5 ×77 P60_alpha ×68 P60_PP ×61 P60_GcgPpy_Doublet ×44 E13.5TAM_E16.5 ×33 E14.0TAM_E14.5 ×1

Supports a between-group comparison across 399 samples.

How we read this: 6 replicated groups read from the first 400 of 1430 sample titles; they account for 399 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

+ 1390 more — browse all 1430 samples with per-sample file links →

Datasets on a similar topic

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