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Multi-organ proteomic atlas of obesity regression in male mice

GSE338985 Mus musculus (mouse) RNA-seq (gene expression) 94 samples Submitted 2026/07/17 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 94 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
94 samples: 94 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.
Summary (written by the submitters)
Obesity drives systemic metabolic dysfunction, yet how the body adapts and recovers at a system-wide level remains unclear. Here we generate a multi-organ proteomic atlas of diet-induced obesity and its regression in male mice. Using a standardized, semi-automated sample preparation workflow, we quantify 12,936 unique proteins across 15 organs and 4 timepoints.
Read the full summary ↓
We find proteomic changes to be highly tissue-dependent with a small number of proteins displaying shared changes across multiple tissues. While proteomes of most tissues revert to lean levels after weight loss, white adipose tissue retains strong phagocytic and inflammatory responses, and brain, kidney, bone, thymus, and spleen display delayed obesity-induced alterations. We map the adipose tissue-specific immune regulators using ligand-target inference and show reduced expression of proteasomal subunits in brown adipose tissue, resulting in stalled ubiquitin turnover. Finally, we make all datasets open access and create an interactive webtool to facilitate further community-driven discovery.
This dataset
Open GSE338985 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 GSE338985_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 PRJNA1496755 and SRA study SRP718849. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
94 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 10 samples read as cases, 48 as controls.

How we read this: Read from 94 sample titles: 94 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

+ 54 more — browse all 94 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.