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Modulation of inflammatory responses in mouse models of pericyte-depletion retinopathy

GSE296651 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/05/26 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 12 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
Immunomodulation of inflammatory responses preserves retinal integrity in murine models of pericyte-depletion retinopathyJCI insight · 2025
Summary (written by the submitters)
A major feature of diabetic retinopathy is pericyte loss. Here, we have utilzed a model of pericyte depletion retinopathy that recapitulates key features of human diabetic retinopathy to study the cellular and molecular components of the diease and response to immunomodulation with minocylcine. Pericyte depletion was achieved via injection of a rat anti-mouse PDGFRB mAb at Postnatal day 1 (P1) in C57BL6/J WT mice.
Read the full summary ↓
Treatment with minocyline begaun at P5, daily until P27, and retinas analysed from P28 mice. Transcriptomic analyses identifies signature pathways in pericyt-depletion retinopathy and response to minoccyline
Published in
Immunomodulation of inflammatory responses preserves retinal integrity in murine models of pericyte-depletion retinopathy
Kinuthia UM, Moehle C, Adams RH et al. · JCI insight 2025 · PMID 40591415 · doi:10.1172/jci.insight.184465
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 GSE296651_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 PRJNA1260457 and SRA study SRP583865. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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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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.