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RNA-Seq analysis of Mapk14 knockout and myogenic differentiation in C2C12 cells

GSE315765 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/01/07 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × WT C2C12 cells, DM. biol vs 3 × WT C2C12 cells, GM. biol vs 3 × MAPK14 knockout C2C12 cells, DM. biol vs 3 × MAPK14 knockout C2C12 cells, GM. biolGroups 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
p38 MAPK orchestrates cross-tissue potassium homeostasis for survivalNature communications · 2026
Summary (written by the submitters)
This study investigates the role of p38/Mapk14 in myogenic differentiation using C2C12 mouse myoblast cells. Wild-type and Mapk14 knockout cells in the logarithmic growth phase were cultured in growth medium (GM) or differentiation medium (DM) for 5 days. RNA was extracted for sequencing to examine gene expression changes caused by Mapk14 deletion and by differentiation induction.
Read the full summary ↓
Differential expression analysis identifies genes downregulated in Mapk14 knockout cells compared to wild type and genes upregulated upon differentiation, providing insight into the molecular mechanisms by which p38/Mapk14 regulates myogenesis.
Published in
p38 MAPK orchestrates cross-tissue potassium homeostasis for survival
Huang R, Hu F, Li Y et al. · Nature communications 2026 · PMID 41796133 · doi:10.1038/s41467-026-70641-7
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 GSE315765_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 PRJNA1378441 and SRA study SRP653900. Searching any of these in the dataset finder brings you back here.

Study design
3 × WT C2C12 cells, DM. biol vs 3 × WT C2C12 cells, GM. biol vs 3 × MAPK14 knockout C2C12 cells, DM. biol vs 3 × MAPK14 knockout C2C12 cells, GM. biol

Supports a case/control comparison: 6 samples read as cases, 6 as controls.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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