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Integrated single-cell multiome analysis resolves muscle fiber-type gene regulatory circuitry modulated by endurance exercise

GSE240061 Homo sapiens (human) Single-cell RNA-seq 40 samples Submitted 2025/05/05 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 40 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
40 samples: 16 conditions, mostly in duplicateGroups 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
Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exerciseGenome research · 2025
Summary (written by the submitters)
Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2 max in four subjects, as well as in matched time of day samples from two supine resting circadian controls.
Read the full summary ↓
High quality same cell RNAseq and ATACseq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Comprehensive single cell regulatory circuit analysis identified distinct fiber type programs involving 272 TFs regulating 1579 genes at specific regulated chromatin sites, with fast fibers showing more robust circuit responses. These data and comprehensive circuit reconstruction provide a high resolution map of the molecular adaptations to exercise at single cell resolution.
Published in
Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise
Rubenstein AB, Smith GR, Zhang Z et al. · Genome research 2025 · PMID 40393809 · doi:10.1101/gr.280051.124
This dataset
Open GSE240061 in finder → Find more human 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 GSE240061_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1002144 and SRA study SRP453335. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40393809) ↗
Study design
16 conditions, mostly in duplicate
8035073276-N, Circadian control (Pre), GEX, ×2 8035073605-N, Circadian control (Post), GEX, ×2 8035073530-L, Circadian control (Pre), GEX, ×2 8035073603-L, Circadian control (Post), GEX, ×2 8035073800-E, Exercise Response (Pre), GEX, ×2 8035073790-E, Exercise Response (Post), GEX, ×2 8035073981-G, Exercise Response (Pre), GEX, ×2 8035073962-G, Exercise Response (Post), GEX, ×2 +16 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 40 sample titles; they account for 32 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
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