GEO series

Cellular senescence in skeletal muscle aging: new mechanisms and therapeutic potential for sarcopenia [snMultiome_ATAC+GEX]

GSE268953 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 20 samples Submitted 2025/05/09 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 20 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 20 samples: 20 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)

Skeletal muscle allows voluntary movement and plays a key role in regulating metabolism and homeostasis in the organism. Adult muscle stem cells, MuSCs, are responsible for muscle growth and regeneration. Skeletal muscle suffers from sarcopenia (muscle mass and function loss) during the aging process. Burgeoning evidence suggests that cellular senescence drives MuSC dysfunction in muscle aging.

Read the full summary ↓

Activation of senescence-associated secretory phenotype (SASP) factors, which is one of the hallmarks of senescence, may be a driving mechanism of sarcopenia but no study so far has clearly defined cellular senescence atlas and SASP components in aging human muscles. Here in this study we propose to profile aging and senescence atlases in aging muscles using the state-of-art multiomics approaches. Our pilot results demonstrate the prevalence of cellular senescence and the dynamics/heterogeneity of SASPs in aging human muscle. Furthermore, regulators governing senescence state and SASP induction were defined. Moreover, we demonstrated the potential of senotherapeutic targeting for sarcopenia treatment. In the project we propose (1) To complete single cell multiomics mapping of aging muscle atlas. (2) To further elucidate cellular senescence in aging muscle. (3) To investigate JUNB regulation of MuSC SASP induction in aging muscle. (4) To test the potential use of senotherapeutics on aging muscle. Once completed, findings from this study will uncover novel knowledge of how MuSC senescence is involved in niche alteration and muscle aging through SASPs and demonstrate the possibility of developing senotherapeutics for treating sarcopenia.

This dataset

Open GSE268953 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 GSE268953_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1119500 and SRA study SRP511500. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

20 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 20 sample titles: 20 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

GSM8303169 Muscle, Young, biol rep1, GEX
GSM8303170 Muscle, Young, biol rep1, ATAC
GSM8303171 Muscle, Young, biol rep2, GEX
GSM8303172 Muscle, Young, biol rep2, ATAC
GSM8303173 Muscle, Young, biol rep3, GEX
GSM8303174 Muscle, Young, biol rep3, ATAC
GSM8303175 Muscle, Young, biol rep4, GEX
GSM8303176 Muscle, Young, biol rep4, ATAC
GSM8303177 Muscle, Young, biol rep5, GEX
GSM8303178 Muscle, Young, biol rep5, ATAC
GSM8303179 Muscle, Aged, biol rep1, GEX
GSM8303180 Muscle, Aged, biol rep1, ATAC
GSM8303181 Muscle, Aged, biol rep2, GEX
GSM8303182 Muscle, Aged, biol rep2, ATAC
GSM8303183 Muscle, Aged, biol rep3, GEX
GSM8303184 Muscle, Aged, biol rep3, ATAC
GSM8303185 Muscle, Aged, biol rep4, GEX
GSM8303186 Muscle, Aged, biol rep4, ATAC
GSM8303187 Muscle, Aged, biol rep5, GEX
GSM8303188 Muscle, Aged, biol rep5, ATAC

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