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Transcriptomic analysis of RNA (mRNA/ lncRNA/ circRNA) expression and epitranscriptomic profiling of N4-acetylcytidine-related RNA (mRNA/ lncRNA/ circRNA) acetylation in renal cortex from mice with ischemia reperfusion injury

GSE286507 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/01/12 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: 12 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.
Paper
CircAASS alleviates renal injury and fibrosis by regulating mitochondrial homeostasis in tubular epithelial cellsAutophagy · 2026
Summary (written by the submitters)
N4-acetylation of cytidine (ac4C) in RNA are crucial for regulating the expression level and promoting the translate efficiency of mRNA. However, epitranscriptomic profiling of ac4C-related RNA (mRNA/ lncRNA/ circRNA) acetylation in renal cortex are largely unknown. Therefore, we profiled the transcriptome-wide ac4C modification in mRNA/ lncRNA/ circRNA in renal cortex from either ischemia reperfusion-acute kidney injury (I/R-AKI) mouse or Sham-operated mouse.
Read the full summary ↓
At the same time, the expression levels of mRNA/ lncRNA/ circRNA were also evaluated by the sequencing experiment in input group. Therefore, this is an experiment for quantifying both the expression levels and the ac4C modification alterations in mRNA/ lncRNA/ circRNA within renal cortex from I/R-AKI mouse.
Published in
CircAASS alleviates renal injury and fibrosis by regulating mitochondrial homeostasis in tubular epithelial cells
Ma T, Yu Y, Luo H et al. · Autophagy 2026 · PMID 41162865 · doi:10.1080/15548627.2025.2581212
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 GSE286507_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 PRJNA1209825 and SRA study SRP556880. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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
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