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IL1A enhances TNF-induced retinal ganglion cell death

GSE318351 Mus musculus (mouse) RNA-seq (gene expression) 109 samples Submitted 2026/02/04 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 109 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
109 samples: 109 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
IL1A enhances TNF-induced retinal ganglion cell deathFrontiers in aging neuroscience · 2026
Summary (written by the submitters)
Introduction: A growing body of literature suggests a role for neuroinflammation in retinal ganglion cell (RGC) death in glaucoma. For instance, deficiency of three proinflammatory cytokines, complement component 1, subcomponent q (C1q), interleukin 1 alpha (Il1a), and tumor necrosis factor (Tnf), resulted in significant protection of RGCs after glaucoma-relevant insults.
Read the full summary ↓
While TNF and C1Q have been extensively investigated in glaucoma-relevant model systems, the role of IL1A in RGCs is not well defined. Methods: Eyes of 2-4 month-old C57BL/6J mice or mice deficient in either Jun or Sarm1 were intravitreally injected with IL1A alone, TNF alone, or IL1A and TNF together. Retinal flat mounts were assessed for RGC survival using immunostaining of RBPMS. Bulk RNA-sequencing and differential expression analyses of retinal tissue was performed to determine molecular changes in response to IL1A, TNF, and IL1A combined with TNF within C57BL/6J and Sarm1 deficient mice. Results: Intravitreal injection of IL1A did not result in RGC death at either 14 days or 12 weeks. Consistent with previous studies, TNF injection did not cause significant RGC loss at 14 days but did after 12 weeks. Together, IL1A+TNF resulted in a relatively rapid RGC death, driving significant loss two weeks after injection. We identified molecular changes which occur in response to IL1A and to combined IL1A+TNF treatment with limited changes identified in TNF alone treated eyes. Using mice deficient in Jun or Sarm1, we showed RGC loss after IL1A+TNF insult is JUN-independent and SARM1-dependent. Furthermore, RNA-seq analysis showed Sarm1 deficiency does not stop the neuroinflammatory response to IL1A+TNF. Discussion: We identified a novel role of IL1A, we found that IL1A acted as a sensitizer to TNF-induced death. Co-injection of IL1A and TNF resulted in rapid RGC death, with significant RGC loss 14 days after injection. TNF+IL1A-induced RGC death did not depend on JUN activation and was rather SARM1 dependent. Also, RNA-seq analyses indicated that while Sarm1 deficiency protected from IL1A+TNF induced RGC loss it did not significantly alter microglia and astrocyte responses. Altogether, these findings indicate that IL1A potentiates SARM1-dependent TNF-induced RGC death in vivo.
Published in
IL1A enhances TNF-induced retinal ganglion cell death
Andersh KM, MacLean M, Howell GR et al. · Frontiers in aging neuroscience 2026 · PMID 41728092 · doi:10.3389/fnagi.2026.1754881
This dataset
Open GSE318351 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 GSE318351_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 PRJNA1418622 and SRA study SRP674586. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41728092) ↗
Study design
109 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 109 sample titles: 109 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

+ 69 more — browse all 109 samples with per-sample file links →

Datasets on a similar topic

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