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Timestamp tracing delineates thymic regulatory T cell ontogeny in native conditions [scRNA-seq]

GSE274125 Mus musculus (mouse) Single-cell RNA-seq 20 samples Submitted 2026/07/21 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 20 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
20 samples: 20 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.
Summary (written by the submitters)
Thymic T-cell selection and regulatory T (Treg) cell development are executed via mechanisms that remain unclear. Here, we develop a timestamp tracing method using Rag1CreER knockin mice in combination with Rosa reporters to reveal the processes governing thymic Treg differentiation in native physiological conditions. It enables tracing of thymic T cells at the double negative and double positive stages upon tamoxifen administration.
Read the full summary ↓
Following labeled cells over time, we define the processes, kinetics, and gene expression changes during thymic T-cell differentiation; specify thymic Treg maturation with CD73, gene expression changes, and Treg-specific DNA demethylation; characterize precursor cells with 4-1BB and/or CD25 expression that are induced by TCR signaling and differentiate to Treg cells in vitro and in vivo; and determine the features of recirculating or retained mature Treg cells. Thus, Rag1CreER–based timestamp tracing delineates thymic Treg ontogeny, proving the concept for exploring T-cell development and heterogeneity with age-resolved precision.
This dataset
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.

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 GSE274125_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1144995 and SRA study SRP524532. Searching any of these in the dataset finder brings you back here.

Study design
20 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 20 sample titles: 20 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
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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.