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Insulin-like growth factor 2 (IGF2) as a Foxn1-independent driving force of exponential expansion in neonatal thymus

GSE267706 Homo sapiens (human) Single-cell RNA-seq 50 samples Submitted 2025/03/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 50 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
Homo sapiens (human)
Samples
50 samples: 50 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
Insulin-like growth factor 2 as a driving force for exponential expansion and differentiation of the neonatal thymusDevelopment (Cambridge, England) · 2025
Summary (written by the submitters)
Like all organs, the thymus grows in size and function rapidly during development, which comes to a halt after birth. However, the molecular mechanisms behind such a transition in the thymus remain obscure. Using single-cell RNA sequencing (scRNA-seq) of the murine thymic stroma, we identified that major transcriptomic changes occur in the endothelium and mesenchyme across the transition to homeostasis.
Read the full summary ↓
Differentially expressed gene and intercellular network analyses of temporally resolved scRNA-seq data revealed fibroblast-derived insulin-like growth factor 2 (IGF2) as a candidate driving neonatal thymic expansion. We demonstrated IGF2 activity promotes a cortical TEC-specific proliferation and is tightly regulated at the thymic growth transition. Bulk RNA-seq of human thymi across the transition also revealed IGF2 to drive thymic expansion, suggesting an evolutionarily conserved role. Our study highlights the role of a fibroblast-derived IGF2 in promoting cTEC proliferation, resulting in early thymic expansion that is followed by down-regulation to establish homeostasis.
Published in
Insulin-like growth factor 2 as a driving force for exponential expansion and differentiation of the neonatal thymus
Kang SW, Helm BR, Wang Y et al. · Development (Cambridge, England) 2025 · PMID 40110795 · doi:10.1242/dev.204347
This dataset
Open GSE267706 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 GSE267706_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 PRJNA1112436 and SRA study SRP508209. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40110795) ↗
Study design
50 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 50 sample titles: 50 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

+ 10 more — browse all 50 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.