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Retina-specific glucocorticoid receptor deletion disrupts rhythmic gene expression in the mouse retina under constant dark conditions.

GSE338377 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/07/22 Platform GPL28457
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: 6 × WT_CT vs 6 × cKO_CTGroups 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 ocular circadian clock has been thought to be regulated primarily by environmental light. In many peripheral tissues, however, circadian clocks are influenced by temporal information conveyed by adrenal glucocorticoids, while glucocorticoid-dependent regulation of the eye clock, a light-receptive organ, has remained unclear. To investigate this mechanism, we generated retina-specific Nr3c1 (glucocorticoid receptor; GR) knockout mice by crossing Chx10-Cre mice, in which Cre-recombinase is driven by the Chx10 promoter expressed in the developing optic cup, with Nr3c1 flox/flox mice.
Read the full summary ↓
Using these mice, we performed time-course tissue collection under constant dark conditions followed by time-series RNA-seq analysis. The analysis showed that many rhythmic genes detected in control retina were lost in retina-specific Nr3c1-deficient mice, suggesting that glucocorticoid signaling contributes to rhythmic retinal gene expression.
This dataset
Open GSE338377 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 GSE338377_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 PRJNA1494406 and SRA study SRP717500. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 × WT_CT vs 6 × cKO_CT

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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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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.