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Role of ATF3 in the transcription of GI-SINEs (growth inducing B2-SINEs) in DRG neurons in response to sciatic nerve crush

GSE279906 Mus musculus (mouse) RNA-seq (gene expression) 34 samples Submitted 2025/05/16 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 34 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
34 samples: 34 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
Repeat-element RNAs integrate a neuronal growth circuitCell · 2025
Summary (written by the submitters)
Injury of the sciatic nerve upregulates a specific subset of B2-SINE RNAs in Dorsal Root Ganglia (DRG) neurons that enhances axon regeneration, which we termed GI-SINEs (growth inducing SINEs). Based on ATAC-seq and transcription factor binding site analysis and in-culture perturbation of AP-1 TFs, we hypotheiszed that GI-SINEs specifically regulated by injury activated AP-1 TFs.
Read the full summary ↓
Here we experimentally tested the role of ATF3, a CREB TF that belongs to the AP-1 TF superfamily, in the transcriptional upregulation of GI-SINEs. ATF3 was previously identified and characterized as a key activator of nerve-injury induced regeneration associated genes (RAGs) mRNA. Cre recombinase expressed under the Advillin promoter was used for ATF3-exon3 excision in a broad and specific manner in the DRG. Sciatic nerve injury was carried on on mice with a conditional knockout of ATF3 in sensory neurons and ATF3-wt mice and total RNAseq was carried on RNA extracred from on sciatic innervating DRGs (L4-L6) from the injured side ("Inj") and non-injured side (naive / "Na").
Published in
Repeat-element RNAs integrate a neuronal growth circuit
Zahavi EE, Koppel I, Kawaguchi R et al. · Cell 2025 · PMID 40381624 · doi:10.1016/j.cell.2025.04.030
This dataset
Open GSE279906 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 GSE279906_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 PRJNA1175360 and SRA study SRP539677. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40381624) ↗
Study design
34 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 34 sample titles: 34 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
Datasets on a similar topic

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