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Naïve CD8 T cell RNA sequencing in C57BL/6 and MD4 mice

GSE263132 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/04/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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
10 samples: 5 × B6_naïve CD8 T cells vs 5 × MD4_naïve CD8 T cellsGroups 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
B cells shape naive CD8+ T cell programmingThe Journal of clinical investigation · 2025
Summary (written by the submitters)
The presence of B cells is essential for the formation of CD8 T cell memory after infection and vaccination. In this study, we investigated whether B cells influence the programming of naïve CD8 T cells prior to their involvement in an immune response. RNA sequencing indicated that B cells are necessary for sustaining the FOXO1-controlled transcriptional program, which is critical for their homeostasis.
Read the full summary ↓
Without an appropriate B cell repertoire, mouse naïve CD8 T cells exhibit a terminal, effector-skewed phenotype, which significantly impacts their response to vaccination. A similar effector-skewed phenotype with reduced FOXO1 expression was observed in naïve CD8 T cells from human patients undergoing B cell-depleting therapies. Furthermore, we show that patients without B cells have a defect in generating long-lived CD8 T cell memory following COVID vaccination. In summary, we demonstrate that B cells promote the quiescence of naïve CD8 T cells, poising them to become memory cells upon vaccination.
Published in
B cells shape naive CD8+ T cell programming
Manes C, Guerrero Moreno M, Cimons J et al. · The Journal of clinical investigation 2025 · PMID 40244703 · doi:10.1172/JCI190106
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 GSE263132_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 PRJNA1095917 and SRA study SRP499597. Searching any of these in the dataset finder brings you back here.

Study design
5 × B6_naïve CD8 T cells vs 5 × MD4_naïve CD8 T cells

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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
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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.