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Transcriptomic analysis of sarcopenic obese mice reveals alterations in regulators of muscle force production, such as extracellular matrix remodeling and shifts in muscle fiber type

GSE290570 Mus musculus (mouse) RNA-seq (gene expression) 19 samples Submitted 2025/08/05 Platform GPL30215
At a glance

RNA-seq (gene expression) · mouse · 19 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
19 samples: 19 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.
Paper
Transcriptomic analysis demonstrates moderators of muscle quality are altered in age-related sarcopenic obesityBMC genomics · 2025
Summary (written by the submitters)
Sarcopenic obesity (SO) is the combined condition of sarcopenia and obesity most commonly occurring in the older adult population. SO is associated with poor physical function, increased risk of musculoskeletal injury, and reduced quality of life and independence. There is little information on the molecular underpinnings of this condition.
Read the full summary ↓
The aim of this study was to provide a transcriptomic analysis characterizing SO and to provide deeper understanding to the etiology of the condition. Young (Y) (6 mo) and aged (A) (21-24 mo) mice were fed either normal chow (L) (12% kcal from fat) or high-fat (O) (60% kcal from fat) diets ad libitum. Through multiple analyses, we observed genes related to ECM remodeling are downregulated with aged-obesity compared with young obesity, providing insight to the effects of age in a life-long obese condition. Furthermore, genes related to slow-twitch muscle contraction and fast-to-slow muscle fiber type transitions were upregulated in with obesity in the aged condition. Together, our results demonstrate avenues of dysregulation in SO skeletal muscle, providing the molecular building blocks expanding our understanding of the etiology of poor muscle function in this condition. Further understanding and exploration of these dysregulations is critical to identify therapeutic treatments targeting the source of muscle functional impairment, reducing the risk of musculoskeletal injury, and improving quality of life and independence SO individuals.
Published in
Transcriptomic analysis demonstrates moderators of muscle quality are altered in age-related sarcopenic obesity
Schrems ER, Cabrera AR, Jones RG 3rd et al. · BMC genomics 2025 · PMID 41068575 · doi:10.1186/s12864-025-12080-0
This dataset
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.

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 GSE290570_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1228639 and SRA study SRP566566. Searching any of these in the dataset finder brings you back here.

Study design
19 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 19 sample titles: 19 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
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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.