Bulk RNAseq of mononucleated cells isolated from resting and regenerating adult and aged skeletal mouse muscle
RNA-seq (gene expression) · mouse · 107 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
- 107 samples: 36 conditions, mostly in triplicateGroups 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
- Nicotinamide and pyridoxine stimulate muscle stem cell expansion and enhance regenerative capacity during agingThe Journal of clinical investigation · 2024
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE271744_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1133018 and SRA study SRP518562. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 106 samples.
How we read this: 36 replicated groups read from 107 sample titles; they account for 106 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.
- GSM8383879 D2_Young_M2_Replicate 1 D2_Young_M2
- GSM8383880 D7_Young_M2_Replicate 2 D7_Young_M2
- GSM8383881 D2_Young_M1_Replicate 1 D2_Young_M1
- GSM8383882 D2_Old_M2_Replicate 3 D2_Old_M2
- GSM8383883 D4_Old_M2_Replicate 3 D4_Old_M2
- GSM8383884 D4_Old_M1_Replicate 3 D4_Old_M1
- GSM8383885 D4_Young_M1_Replicate 2 D4_Young_M1
- GSM8383886 D2_Old_M1_Replicate 3 D2_Old_M1
- GSM8383887 D4_Young_M2_Replicate 2 D4_Young_M2
- GSM8383888 D7_Young_M2_Replicate 1 D7_Young_M2
- GSM8383889 D4_Old_M2_Replicate 2 D4_Old_M2
- GSM8383890 D4_Old_M1_Replicate 2 D4_Old_M1
- GSM8383891 D4_Young_M2_Replicate 1 D4_Young_M2
- GSM8383892 D4_Young_M1_Replicate 1 D4_Young_M1
- GSM8383893 D2_Old_M2_Replicate 2 D2_Old_M2
- GSM8383894 D7_Old_M2_Replicate 3 D7_Old_M2
- GSM8383895 D2_Old_M1_Replicate 2 D2_Old_M1
- GSM8383896 D4_Old_M2_Replicate 1 D4_Old_M2
- GSM8383897 D4_Old_M1_Replicate 1 D4_Old_M1
- GSM8383898 D2_Young_M2_Replicate 3 D2_Young_M2
- GSM8383899 D2_Young_M1_Replicate 3 D2_Young_M1
- GSM8383900 D2_Old_M1_Replicate 1 D2_Old_M1
- GSM8383901 D2_Old_M2_Replicate 1 D2_Old_M2
- GSM8383902 D7_Old_M2_Replicate 2 D7_Old_M2
- GSM8383903 D2_Young_M2_Replicate 2 D2_Young_M2
- GSM8383904 D7_Young_M2_Replicate 3 D7_Young_M2
- GSM8383905 D2_Young_M1_Replicate 2 D2_Young_M1
- GSM8383906 D7_Old_M2_Replicate 1 D7_Old_M2
- GSM8383907 D4_Young_M2_Replicate 3 D4_Young_M2
- GSM8383908 D4_Young_M1_Replicate 3 D4_Young_M1
- GSM8383909 D2_Young_FAPs_Replicate 1 D2_Young_FAPs
- GSM8383910 D7_Young_FAPs_Replicate 2 D7_Young_FAPs
- GSM8383911 D4_Young_FAPs_Replicate 1 D4_Young_FAPs
- GSM8383912 D0_Old_FAPs_Replicate 2 D0_Old_FAPs
- GSM8383913 D4_Old_FAPs_Replicate 3 D4_Old_FAPs
- GSM8383914 D4_Young_FAPs_Replicate 2 D4_Young_FAPs
- GSM8383915 D0_Old_FAPs_Replicate 3 D0_Old_FAPs
- GSM8383916 D2_Old_FAPs_Replicate 3 D2_Old_FAPs
- GSM8383917 D0_Young_FAPs_Replicate 1 D0_Young_FAPs
- GSM8383918 D4_Old_FAPs_Replicate 2 D4_Old_FAPs
+ 67 more — browse all 107 samples with per-sample file links →
- GSE308174 Bulk mRNA-seq analysis of mouse skeletal muscle, heart and liver expressing wild-type and mutant H2B 75 samples shares skeletal, muscle, bulk
- GSE267909 Single-nuclei sequencing of skeletal muscle reveals subsynaptic-specific transcripts involved in neuromuscular junction maintenance (bulk RNA-Seq) 14 samples shares skeletal, muscle, bulk
- GSE248916 Metabolic Promotion of Adult and Aging Skeletal Muscle Regeneration by the Serine Biosynthesis Pathway 30 samples shares skeletal, muscle, adult
- GSE292230 Genomic characterization of mouse hindlimb skeletal muscles with divergent slow myofiber content at an embryonic and adult time point [ATAC-Seq] 24 samples shares skeletal, muscle, adult
- GSE237747 Transcriptomic profiling across 20 CC-RIX genetic backgrounds with and without high-fat high-sugar (HFHS) diet, and with and without metformin [skeletal muscle] 703 samples shares skeletal, muscle
- GSE267727 Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, heart, and skeletal muscle tissue from mice fed high-fat, high-sugar diet [heart] 482 samples shares skeletal, muscle
- GSE266549 Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, and skeletal muscle tissue from mice fed high-fat, high-sugar diet. [Adipose] 468 samples shares skeletal, muscle
- GSE280699 The mitochondrial-targeted peptide therapeutic elamipretide improves cardiac and skeletal muscle function during aging without detectable changes in tissue epigenetic or transcriptomic age [RNA-seq] 78 samples shares skeletal, muscle
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.