GEO series

Immunomodulatory Role of the Stem Cell Circadian Clock in Muscle Repair [RNA-Seq]

GSE278176 Mus musculus (mouse) Single-cell RNA-seq 22 samples Submitted 2025/03/12 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 22 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 22 samples: 6 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.
Paper: Immunomodulatory role of the stem cell circadian clock in muscle repairScience advances · 2025

Summary (written by the submitters)

The circadian clock orchestrates vital physiological processes such as metabolism, immune function, and tissue regeneration, aligning them with the optimal time of day. This study identifies an intricate interplay between the circadian clock within muscle stem cells (SCs) and their capacity to modulate the immune microenvironment during muscle regeneration.

Read the full summary ↓

We uncover that the SC clock provokes time-of-day (TOD)-dependent induction in inflammatory response genes following injury, particularly those related to neutrophil activity and chemotaxis. These responses are driven by rhythms of cytosolic regeneration of the signaling metabolite NAD+. We demonstrate that genetically enhancing cytosolic NAD+ regeneration in SCs is sufficient to induce robust inflammatory responses that significantly influence muscle regeneration. Furthermore, using mononuclear single-cell sequencing of the regenerating muscle niche, we uncover a key role for the cytokine CCL2 in mediating SC-neutrophil crosstalk in a TOD-dependent manner. Our findings highlight a crucial intersection between SC metabolic shifts and immune responses within the muscle microenvironment, dictated by the circadian rhythms, and underscore the potential for targeting circadian and metabolic pathways to enhance tissue regeneration.

Published in

Immunomodulatory role of the stem cell circadian clock in muscle repair

Zhu P, Pfrender EM, Steffeck AWT et al. · Science advances 2025 · PMID 40043110 · doi:10.1126/sciadv.adq8538

This dataset

Open GSE278176 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 GSE278176_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 PRJNA1165808 and SRA study SRP535121. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40043110) ↗

Study design

6 conditions, mostly with about 4 replicates each

1dpi_ASC_ZT4 ×4 1dpi_ASC_ZT16 ×4 3dpi_ASC_ZT4 ×4 3dpi_ASC_ZT16 ×4 QSC_ZT4 ×3 QSC_ZT16 ×3

Supports a between-group comparison across 22 samples.

How we read this: 6 replicated groups read from 22 sample titles; they account for 22 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

GSM8541234 1dpi_ASC_ZT4-1 1dpi_ASC_ZT4
GSM8541235 1dpi_ASC_ZT4-2 1dpi_ASC_ZT4
GSM8541236 1dpi_ASC_ZT4-3 1dpi_ASC_ZT4
GSM8541237 1dpi_ASC_ZT4-4 1dpi_ASC_ZT4
GSM8541238 1dpi_ASC_ZT16-1 1dpi_ASC_ZT16
GSM8541239 1dpi_ASC_ZT16-2 1dpi_ASC_ZT16
GSM8541240 1dpi_ASC_ZT16-3 1dpi_ASC_ZT16
GSM8541241 1dpi_ASC_ZT16-4 1dpi_ASC_ZT16
GSM8541242 3dpi_ASC_ZT4-1 3dpi_ASC_ZT4
GSM8541243 3dpi_ASC_ZT4-2 3dpi_ASC_ZT4
GSM8541244 3dpi_ASC_ZT4-3 3dpi_ASC_ZT4
GSM8541245 3dpi_ASC_ZT4-4 3dpi_ASC_ZT4
GSM8541246 3dpi_ASC_ZT16-1 3dpi_ASC_ZT16
GSM8541247 3dpi_ASC_ZT16-2 3dpi_ASC_ZT16
GSM8541248 3dpi_ASC_ZT16-3 3dpi_ASC_ZT16
GSM8541249 3dpi_ASC_ZT16-4 3dpi_ASC_ZT16
GSM8541250 QSC_ZT4-1 QSC_ZT4
GSM8541251 QSC_ZT4-2 QSC_ZT4
GSM8541252 QSC_ZT4-3 QSC_ZT4
GSM8541253 QSC_ZT16-1 QSC_ZT16
GSM8541254 QSC_ZT16-2 QSC_ZT16
GSM8541255 QSC_ZT16-3 QSC_ZT16

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