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Autonomous circadian bladder capacity rhythm independent of the central clock

GSE295306 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2026/08/03 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples: 12 conditions, mostly in triplicateGroups 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)
This study investigates the autonomous regulatory function of the bladder for its capacity circadian rhythm using a central nervous Bmal1 conditional knockout (cKO) mouse. Under constant darkness (DD), these mice lose behavioral rhythmicity but preserve rhythmicity of the bladder capacity and bladder clock gene expression for short term.
Read the full summary ↓
However, prolonged exposure to DD results in loss of bladder capacity rhythm and decreased clock gene amplitude. RNA-seq analysis in the bladder of Bmal1 cKO mice revealed that clock genes and extracellular matrix-related genes were disrupted in rhythmicity under long DD. These findings highlight that the bladder has an autonomous regulatory function for the diurnal rhythm of bladder capacity independent of the central clock. However, the vulnerability of peripheral bladder clocks is revealed in the long-term absence of central clock input. This study provides molecular insights into how circadian dysregulation may contribute to bladder dysfunction.
This dataset
Open GSE295306 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 GSE295306_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 PRJNA1254161 and SRA study SRP580484. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
ShortDD_04 ×3 ShortDD_08 ×3 ShortDD_12 ×3 ShortDD_16 ×3 ShortDD_20 ×3 ShortDD_24 ×3 LongDD_04 ×3 LongDD_08 ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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.