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CNOT3 maintains ILC2 integrity by destabilizing Tbx21 and Rorc mRNAs [RNA-seq]

GSE263342 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/04/25 Platform GPL21273
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 8 conditions, mostly in triplicateGroups 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
CNOT3 supports ILC2 differentiation and function by destabilizing Tbx21 and Rorc transcriptsThe Journal of experimental medicine · 2026
Summary (written by the submitters)
Immune responses are initiated by activation of innate lymphoid cells (ILCs), T-bet-dependent natural killer and ILC1 cells, GATA-3-dependent ILC2 cells, and RORγt-dependent ILC3 cells. Despite accumulated knowledge of translational regulation in ILCs, comprehensive impact of mRNA decay on ILCs has not been clarified yet. CNOT3 is a main subunit of the CCR4-NOT complex which mainly destabilizes mRNAs by poly (A) deadenylation in eukaryotes.
Read the full summary ↓
Here, we showed that CNOT3 is required for cell survival of all ILCs and that the absence of CNOT3 promoted T-bet and RORγt expression due to increased stability of Tbx21 and Rorc mRNAs in ILC2 cells and enabled them to produce granzyme B and IL-22. Likewise, Th1 and Th17 skewing was induced in CD4 T cells lacking CNOT3. Furthermore, CNOT3 in each ILC subset contributes to their type-specific immune responses. Thus, the CCR4-NOT complex controls ILC differentiation by inhibiting the potential for type 1 and 3 immunity.
Published in
CNOT3 supports ILC2 differentiation and function by destabilizing Tbx21 and Rorc transcripts
Tatematsu M, Takasuga S, Fuchimukai A et al. · The Journal of experimental medicine 2026 · PMID 42118148 · doi:10.1084/jem.20250895
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 GSE263342_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 PRJNA1096737 and SRA study SRP500024. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in triplicate
NK_control ×3 NK_Cnot3CKO ×3 LungILC2_control ×3 LungILC2_Cnot3CKO ×3 SIILC2_control ×3 SIILC2_Cnot3CKO ×3 SIILC3_control ×3 SIILC3_Cnot3CKO ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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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