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Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progression

GSE329941 Mus musculus (mouse) Single-cell RNA-seq 9 samples Submitted 2026/07/01 Platform GPL28330
At a glance

Single-cell RNA-seq · mouse · 9 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
9 samples: 9 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
Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progressionPloS one · 2026
Summary (written by the submitters)
This study investigates the role and mechanism of ergothioneine (EGT) in mitigating the progression of acute kidney injury (AKI) to chronic kidney disease (CKD). Using a cisplatin-induced mouse model of the AKI-to-CKD transition with EGT intervention, we combined histopathological examination, biochemical assays, and single-cell RNA sequencing (scRNA-seq) to demonstrate that EGT may significantly improve renal function parameters and attenuate renal injury and fibrosis.
Read the full summary ↓
scRNA-seq analysis revealed that EGT may restore the expression of mitochondrial function-related genes in renal tubular epithelial cells, enhance oxidative phosphorylation and electron transport chain activity, thereby potentially promoting mitochondrial homeostasis. Furthermore, our in vitro experiments confirmed the protective effects of EGT on mitochondria in injured renal tubular epithelial cells, including reducing reactive oxygen species (ROS) generation, restoring diminished mitochondrial membrane potential, and increasing ATP production. These findings elucidate a mechanism by which EGT might delay AKI-to-CKD progression through multi-cellular coordination aimed at improving mitochondrial function, offering a promising therapeutic strategy for kidney diseases.
Published in
Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progression
Peng J, Chen J, Qian Z et al. · PloS one 2026 · PMID 42335181 · doi:10.1371/journal.pone.0351630
This dataset
Open GSE329941 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 GSE329941_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 PRJNA1462235 and SRA study SRP697625. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42335181) ↗
Study design
9 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 9 sample titles: 9 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
Datasets on a similar topic

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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.