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Integrated Transcriptomic and Metabolomic Analyses for Circadian Disruption biomarker identification

GSE299370 Mus musculus (mouse) RNA-seq (gene expression) 48 samples Submitted 2025/09/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 48 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
48 samples: 12 conditions, mostly with about 4 replicates eachGroups 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)
Although the impact of circadian disruption is well recognized, definitive indicators and time windows for its detection are currently lacking. Here, by conducting transcriptomic, metabolomic, and integrated analysis, we comprehensively investigated the gene and metabolite changes on the 7th day after a single 6-hour phase advance jet lag in mice.
Read the full summary ↓
Our findings revealed significant alterations during this post-jet lag window, especially in core clock genes Bmal1 and Cry1, and metabolites L-Arginine and SM(d18:1/18:1(11Z)), with notable differences at Zeitgeber Time 0 (ZT0), suggesting ZT0 as a key diagnostic time point. Additionally, we identified L-Serine as a potential biomarker for indexing circadian disruption irrespective of time points. Our study provides new insights into potential biomarkers for detecting circadian clock disruption.
This dataset
Open GSE299370 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 GSE299370_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 PRJNA1273983 and SRA study SRP591814. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly with about 4 replicates each
Control-ZT0 ×4 Control-ZT4 ×4 Control-ZT8 ×4 Control-ZT12 ×4 Control-ZT16 ×4 Control-ZT20 ×4 PS-ZT0 ×4 PS-ZT4 ×4 +4 more

Supports a between-group comparison across 48 samples.

How we read this: 12 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.