GEO series

Cryptic mitochondrial DNA mutations coincide with mid-late life and are pathophysiologically informative in single cells across tissues and species

GSE284767 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/02/10 Platform GPL21697

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 12 samples: 6 × WT vs 3 × LHON_11778 cybrid vs 3 × LHON_3460 cybridGroups 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: Cryptic mitochondrial DNA mutations coincide with mid-late life and are pathophysiologically informative in single cells across tissues and speciesNature communications · 2025

Summary (written by the submitters)

Ageing is associated with a range of chronic diseases and has diverse hallmarks. Mitochondrial dysfunction is implicated in ageing, and mouse-models with artificially enhanced mitochondrial DNA (mtDNA) mutation rates show accelerated ageing. A scarcely studied aspect of ageing, because it is invisible in aggregate analyses, is the accumulation of somatic mtDNA mutations which are unique to single cells (cryptic mutations).

Read the full summary ↓

We find evidence of cryptic mtDNA mutations from diverse single-cell datasets, from three species, and discover: cryptic mutations constitute the vast majority of mtDNA mutations in aged post-mitotic tissues, that they can avoid selection, that their accumulation is consonant with theory we develop, hitting high levels coinciding with species specific mid-late life, and that their presence covaries with a majority of the hallmarks of ageing including protein misfolding and ER stress. We identify mechanistic links to ER stress experimentally and further give an indication that aged brain cells with high levels of cryptic mutations show markers of neurodegeneration and that calorie restriction slows the accumulation of cryptic mutations.

Published in

Cryptic mitochondrial DNA mutations coincide with mid-late life and are pathophysiologically informative in single cells across tissues and species

Green AP, Klimm F, Marshall AS et al. · Nature communications 2025 · PMID 40050638 · doi:10.1038/s41467-025-57286-8

This dataset

Open GSE284767 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 GSE284767_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 PRJNA1200632 and SRA study SRP552679. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40050638) ↗

Study design

6 × WT vs 3 × LHON_11778 cybrid vs 3 × LHON_3460 cybrid

Supports a between-group comparison across 12 samples.

How we read this: 3 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

GSM8692802 LHON_11778 cybrid 1 LHON_11778 cybrid
GSM8692803 LHON_11778 cybrid 2 LHON_11778 cybrid
GSM8692804 LHON_11778 cybrid 3 LHON_11778 cybrid
GSM8692805 LHON_3460 cybrid 1 LHON_3460 cybrid
GSM8692806 LHON_3460 cybrid 2 LHON_3460 cybrid
GSM8692807 LHON_3460 cybrid 3 LHON_3460 cybrid
GSM8692808 WT 1 WT
GSM8692809 WT 2 WT
GSM8692810 WT 3 WT
GSM8692811 WT 4 WT
GSM8692812 WT 5 WT
GSM8692813 WT 6 WT

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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.