GEO series

Semaglutide and exercise synergy in obesity: preserving muscle mass and uncovering organ crosstalk

GSE304862 Mus musculus (mouse) RNA-seq (gene expression) 209 samples Submitted 2026/08/07 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 209 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: 209 samples: 12 conditions, mostly with about 29 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)

Background and aims: The glucagon-like peptide-1 receptor agonist semaglutide is an antidiabetic medication that has recently been approved for treatment of obesity as well. While its efficacy on weight management is well established, associated concomitant loss of muscle mass can be a potential drawback. In this study, we investigated whether combining semaglutide with exercise could enhance its metabolic effects while mitigating muscle loss.

Read the full summary ↓

Method: Ldlr−/−.Leiden mice were fed a high fat diet (HFD) for 20 weeks to induce obesity, insulin resistance, metabolic dysfunction-associated steatohepatitis (MASH) and atherosclerosis. Mice were subsequently left untreated (control) or treated for 14 weeks with semaglutide, exercise (running wheel) or the combination thereof. Histological examination and comprehensive transcriptome analyses were conducted on white adipose tissue, muscle, liver and heart to explore underlying mechanisms. Results: Semaglutide significantly reduced fat mass (-31%) but also lean mass (-11%). Combining semaglutide with exercise further reduced fat mass (-45%) and lean mass as well but to a lesser extent (-8%). Semaglutide alone or combined with exercise significantly improved insulin sensitivity, plasma cholesterol and triglycerides. The combination of semaglutide with exercise improved , adipose tissue inflammation, liver steatosis, liver inflammation and atherosclerotic lesion area. With regard to muscle parameters, combination treatment significantly improved grip strength and diameter of gastrocnemius myocytes, while these parameters were not affected by either treatment alone. Pathway analysis revealed complementary effects of semaglutide and exercise on metabolic, inflammatory and a variety of other pathways, thereby substantiating the added value of combining semaglutide with exercise. Conclusion: Semaglutide has broad beneficial effects on circulating risk factors of metabolic syndrome, liver, vascular and adipose parameters but decreases lean mass and muscle strength. By combining semaglutide intervention with exercise, these parameters are further improved with a partial preservation of lean mass and muscle function.

This dataset

Open GSE304862 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 GSE304862_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 PRJNA1303092 and SRA study SRP607365. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 conditions, mostly with about 29 replicates each

M.HH ×30 L.HH ×29 M.HHS ×29 L.HHS ×28 W.HH ×20 A.HH ×16 W.HHS ×15 A.HHS ×15 +4 more

Supports a between-group comparison across 209 samples.

How we read this: 12 replicated groups read from 209 sample titles; they account for 209 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

GSM9158544 L.CC.1 L.CC
GSM9158545 L.CC.2 L.CC
GSM9158546 L.CC.3 L.CC
GSM9158547 L.CC.4 L.CC
GSM9158548 L.CC.5 L.CC
GSM9158549 L.CC.6 L.CC
GSM9158550 L.CC.7 L.CC
GSM9158551 L.CC.8 L.CC
GSM9158552 L.HH.24 L.HH
GSM9158553 L.HH.25 L.HH
GSM9158554 L.HH.27 L.HH
GSM9158555 L.HH.28 L.HH
GSM9158556 L.HH.29 L.HH
GSM9158557 L.HH.30 L.HH
GSM9158558 L.HH.31 L.HH
GSM9158559 L.HH.32 L.HH
GSM9158560 L.HH.33 L.HH
GSM9158561 L.HH.34 L.HH
GSM9158562 L.HH.35 L.HH
GSM9158563 L.HH.36 L.HH
GSM9158564 L.HH.37 L.HH
GSM9158565 L.HH.38 L.HH
GSM9158566 L.HHS.39 L.HHS
GSM9158567 L.HHS.40 L.HHS
GSM9158568 L.HHS.41 L.HHS
GSM9158569 L.HHS.42 L.HHS
GSM9158570 L.HHS.43 L.HHS
GSM9158571 L.HHS.44 L.HHS
GSM9158572 L.HHS.45 L.HHS
GSM9158573 L.HHS.46 L.HHS
GSM9158574 L.HHS.47 L.HHS
GSM9158575 L.HHS.49 L.HHS
GSM9158576 L.HHS.50 L.HHS
GSM9158577 L.HHS.51 L.HHS
GSM9158578 L.HHS.52 L.HHS
GSM9158579 L.HHS.53 L.HHS
GSM9158580 L.HHR.54 L.HH
GSM9158581 L.HHR.55 L.HH
GSM9158582 L.HHR.56 L.HH
GSM9158583 L.HHR.57 L.HH

+ 169 more — browse all 209 samples with per-sample file links →

Datasets on a similar topic

GSE310028 Dissecting Semaglutide-dependent transcriptome response in obesity and osteoarthritis across tissues at single-cell resolution 28 samples shares semaglutide, obesity
GSE339586 Extensive Crosstalk between BMP and Notch Signaling Pathways in Activated Adult Muscle Stem Cells [RNA-seq III] 24 samples shares crosstalk, muscle
GSE307570 Localized FLASH Radiotherapy Reduces Long-Term Skin and Muscle Damage While Preserving Systemic Homeostasis 20 samples shares preserving, muscle
GSE255524 Remote limb ischemic conditioning alleviates nonalcoholic steatohepatitis via extracellular vesicle-mediated muscle-liver crosstalk [RIC Sham] 19 samples shares crosstalk, muscle
GSE326952 Semaglutide-induced Loss of Skeletal Muscle Mass is Blunted by Co-administration of Ketone Esters 13 samples shares semaglutide, muscle
GSE250202 Yin Yang 1 Deficiency in Skeletal Muscle Stem Cell Aggravates Muscle Fibrosis in Duchenne Muscular Dystrophy Mouse by Disrupting Muscle Stem Cell/Macrophage/Fibro-adipogenic Progenitor Crosstalk [RNA-Seq] 12 samples shares crosstalk, muscle
GSE255526 Remote limb ischemic conditioning alleviates nonalcoholic steatohepatitis via extracellular vesicle-mediated muscle-liver crosstalk [miR_181_RNAseq] 10 samples shares crosstalk, muscle
GSE255528 Remote limb ischemic conditioning alleviates nonalcoholic steatohepatitis via extracellular vesicle-mediated muscle-liver crosstalk 45 samples shares crosstalk, muscle

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.