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Bulk RNA sequencing of liver NK cells and ILC1s from T-bet WT vs. inducible Ncr1-T-bet Δ/Δ mice

GSE309461 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/04/06 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 16 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
Inducible T-bet deletion reveals tissue-specific requirements in NKp46+ ILC immunobiology and response to murine cytomegalovirusNature communications · 2026
Summary (written by the submitters)
The role of the T-box transcription factors T-bet and Eomes in innate lymphoid cells (ILCs) beyond their development is not well understood. We generated an inducible, NKp46 (Ncr1)-specific T-bet floxed knock-out mouse (Ncr1-T-betΔ/Δ) model. Bulk RNA sequencing of liver NK cells after tamoxifen treatment from T-bet WT (NKp46-CreERT2 x T-bet fl/fl) vs.
Read the full summary ↓
Ncr1-T-bet Δ/Δ (NKp46-CreERT2 x T-bet fl/fl) mice had minimal transcriptional changes, whereas T-bet-deficient ILC1 exhibited significant gene expression changes. This sequencing data along with their rapid loss in vivo in Ncr1-T-betΔ/Δ mice reveal the requirement for continuous T-bet expression in liver ILC1.
Published in
Inducible T-bet deletion reveals tissue-specific requirements in NKp46+ ILC immunobiology and response to murine cytomegalovirus
Wong P, Paik Y, Chang L et al. · Nature communications 2026 · PMID 42315839 · doi:10.1038/s41467-026-74478-y
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 GSE309461_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 PRJNA1336123 and SRA study SRP629438. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 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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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.