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Tissue-specific clonal selection and differentiation of CD4⁺ T cells during infection.

GSE317035 Mus musculus (mouse) RNA-seq (gene expression) 23 samples Submitted 2026/02/20 Platform GPL19057Platform GPL16417
At a glance

RNA-seq (gene expression) · mouse · 23 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
23 samples: 16 distinct titles, 2 of them repeatedGroups 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)
Pathogen-specific CD4⁺ T cells expand and contract during infection, generating memory clones that shape subsequent immune responses. How distinct tissue environments influence differentiation and clonal selection of polyclonal T cells remains unclear. We developed Tracking Recently Activated Cell Kinetics (TRACK) mice, a dual-recombinase fate-mapping system enabling spatial and temporal labeling of recently activated CD4⁺ T cells.
Read the full summary ↓
Using TRACK mice during influenza infection, we observed organ-specific transcriptional differentiation and clonal selection in lung, mediastinal lymph nodes (medLNs), and spleen. During the effector phase, spleen-derived CD4⁺ T cells adopted a stem-like migratory phenotype, whereas medLN-activated cells differentiated into T follicular helper cells. T cell receptor sequencing showed low clonal overlap between tissues during the effector response, consistent with distinct antigenic landscapes. During memory formation, overlap increased between lung- and medLN-derived cells, while splenic clones retained a distinct repertoire. These findings define tissue-dependent mechanisms that shape CD4⁺ T cell fate and clonal architecture.
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 GSE317035_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: 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 PRJNA1405698 and SRA study SRP664839. Searching any of these in the dataset finder brings you back here.

Study design
16 distinct titles, 2 of them repeated
Single cell CD4+CD44hiTomato+ isolated from P… ×5 Single cell CD4+CD44hiTomato+ isolated from P… ×4

How we read this: Read from 23 sample titles: 16 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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