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Assessing the role of RHIM proteins in RIPK3-transcription in MEFs

GSE281136 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/05/07 Platform GPL30172
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
RIPK3 coordinates RHIM domain-dependent antiviral inflammatory transcription in neuronsScience signaling · 2025
Summary (written by the submitters)
We previously have found that RIPK3 induces robust anti-viral and inflammatory transcriptional signaling in death-resistant MEFs, however the molecular mechanism that governs this transcription is not well understood. Here we found that RIPK3-depedent transcription is RIPK1 and TRIF dependent in MEFs.
Published in
RIPK3 coordinates RHIM domain-dependent antiviral inflammatory transcription in neurons
Kofman SB, Chu LH, Ames JM et al. · Science signaling 2025 · PMID 40168465 · doi:10.1126/scisignal.ado9745
This dataset
Open GSE281136 in finder → Find more mouse RNA-seq datasets →
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.

Series matrix ↓
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 GSE281136_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1182360 and SRA study SRP543328. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40168465) ↗
Study design
8 conditions, mostly in triplicate
RXMM_siSCR_Mock ×3 RXMM_siTICAM_Mock ×3 RXMM_siRIPK1_Mock ×3 RXMM_siZBP1_Mock ×3 RXMM_siSCR_AP1 ×3 RXMM_siTICAM_AP1 ×3 RXMM_siRIPK1_AP1 ×3 RXMM_siZBP1_AP1 ×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
Datasets on a similar topic

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