GEO series

Genome-wide Profiles of DNA Damage Represent Highly Accurate Predictors of Chronological Age

GSE239751 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2024/03/25 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 36 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: 36 samples: 18 conditions, mostly in duplicateGroups 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: Genome-wide profiles of DNA damage represent highly accurate predictors of mammalian ageAging cell · 2024

Summary (written by the submitters)

Accurate prediction of a true biological age of an individual represents a very important problem for both the clinics and basic science, as well as for the society as a whole, which led to a wealth of efforts to identify multiple types of various age-related biomarkers. However, the predictive models based on these biomarkers still require major improvements calling for identification of novel age-related biomarkers.

Read the full summary ↓

Despite multiple studies associating DNA damage with aging, a glaring paucity of DNA damage-based age-related biomarkers exist, in no small part due to the lack of precise methods for genome-wide surveys of different types of DNA damage. Recently, we developed two techniques for genome-wide mapping of the most prevalent types of DNA damage, single-strand breaks and abasic sites, with nucleotide-level resolution. Here, we explored the potential of the data generated using these methods as the source of universal age biomarkers in mammals. Strikingly, we found that patterns of either DNA lesion could very accurately predict age with higher precision than the commonly used transcriptome analysis. These findings show that previously unexplored high-resolution patterns of genome-wide DNA damage contain potential treasure troves of useful information that can significantly enrich both practical applications and basic science

Published in

Genome-wide profiles of DNA damage represent highly accurate predictors of mammalian age

Cao H, Deng B, Song T et al. · Aging cell 2024 · PMID 38391092 · doi:10.1111/acel.14122

This dataset

Open GSE239751 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 GSE239751_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: 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 PRJNA1000908 and SRA study SRP452498. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38391092) ↗

Study design

18 conditions, mostly in duplicate

SSB 24-M ×2 SSB 24-B ×2 SSB 24-H ×2 SSB 24-L ×2 SSB 24-P ×2 SSB 24-S ×2 24-M ×2 24-B ×2 +10 more

Supports a between-group comparison across 36 samples.

How we read this: 18 replicated groups read from 36 sample titles; they account for 36 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

GSM7670917 SSB 24-M-1 SSB 24-M
GSM7670918 SSB 24-M-2 SSB 24-M
GSM7670919 SSB 24-B-1 SSB 24-B
GSM7670920 SSB 24-B-2 SSB 24-B
GSM7670921 SSB 24-H-1 SSB 24-H
GSM7670922 SSB 24-H-2 SSB 24-H
GSM7670923 SSB 24-L-1 SSB 24-L
GSM7670924 SSB 24-L-2 SSB 24-L
GSM7670925 SSB 24-P-1 SSB 24-P
GSM7670926 SSB 24-P-2 SSB 24-P
GSM7670927 SSB 24-S-1 SSB 24-S
GSM7670928 SSB 24-S-2 SSB 24-S
GSM7670929 24-M-1 24-M
GSM7670930 24-M-2 24-M
GSM7670931 24-B-1 24-B
GSM7670932 24-B-2 24-B
GSM7670933 24-H-1 24-H
GSM7670934 24-H-2 24-H
GSM7670935 24-L-1 24-L
GSM7670936 24-L-2 24-L
GSM7670937 24-P-1 24-P
GSM7670938 24-P-2 24-P
GSM7670939 24-S-1 24-S
GSM7670940 24-S-2 24-S
GSM7670941 RNA-24M-1 RNA-24M
GSM7670942 RNA-24M-2 RNA-24M
GSM7670943 RNA-24B-1 RNA-24B
GSM7670944 RNA-24B-2 RNA-24B
GSM7670945 RNA-24H-1 RNA-24H
GSM7670946 RNA-24H-2 RNA-24H
GSM7670947 RNA-24L-1 RNA-24L
GSM7670948 RNA-24L-2 RNA-24L
GSM7670949 RNA-24P-1 RNA-24P
GSM7670950 RNA-24P-2 RNA-24P
GSM7670951 RNA-24S-1 RNA-24S
GSM7670952 RNA-24S-2 RNA-24S

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