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Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice [part20]

GSE294983 Mus musculus (mouse) RNA-seq (gene expression) 1000 samples Submitted 2026/04/11 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 1000 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
1000 samples: 1000 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.
Summary (written by the submitters)
The central nervous system represents a uniquely immune-privileged environment, with inflammatory responses involving several resident CNS-specific cell types. While stereotyped cellular and transcriptional responses recur across varied diseases, relevant signaling pathways and regulatory networks are not fully understood. Here, we describe a screening platform based on high-throughput RNA-seq analysis and glial cell state gene set scores that enables multimodal investigation of inflammatory networks at scale.
Read the full summary ↓
As proof-of-concept, we investigate genetically heterogeneous mice from a large-scale chemical mutagenesis screen to identify novel functionally relevant variants in six genes previously linked to human CNS disorders: Nrros, Ctsd, Smpd1, Idua, Nlrp1a, and Inpp5d . We leverage the rich and readily-interpretable data from our study to demarcate distinct inflammatory states arising from each mutation. In all, our work provides a methodology for discovering novel regulators of CNS neuroimmune homeostasis and provides a validated analysis framework for identifying discrete neuroinflammatory modules that are engaged divergently across disease contexts.
This dataset
Open GSE294983 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 GSE294983_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 PRJNA1252149 and SRA study SRP579494. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
1000 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 the first 400 of 1000 sample titles: 400 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

+ 960 more — browse all 1000 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.