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DNA damage drives a unique, Alzheimer’s disease-relevant senescent state in neurons

GSE329677 Homo sapiens (human) RNA-seq (gene expression) 231 samples Submitted 2026/05/29 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 231 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
Homo sapiens (human)
Samples
231 samples: 209 distinct titles, 22 of them repeatedGroups 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.
Summary (written by the submitters)
Alzheimer’s disease (AD) shares molecular hallmarks with the canonical drivers of cellular senescence. Senescent cells have also been shown to accumulate in the brain with age, yet the mechanisms linking AD pathology to the accumulation of senescent cells in the brain remain unclear. Here, we demonstrate that DNA damage in patient-derived directly induced neurons (iNs) drives a senescent-like cell state with relevance to AD.
Read the full summary ↓
DNA damage-induced senescent iNs show significant transcriptional concordance with human AD neurons and a weighted gene co-expression network analysis (WGCNA) uncovers candidate regulators associated with the senescent-like state in neurons. Direct comparison of iNs to the original patient fibroblasts reveals striking cell-type specific senescence signatures following DNA damage. iNs adopt a p21-associated senescent-like state characterized by a senescence-associated secretory phenotype (SASP) and predicted activation of NF-κꞴ1. In contrast, fibroblasts develop a p16-associated senescent state lacking a SASP phenotype and show a predicted repression of NF-κꞴ1. Early responses to DNA damage further reveal divergent DNA damage response (DDR), with neurons exhibiting higher accumulation of damage lesions relative to fibroblasts. Together, these findings demonstrate that DNA damage drives a unique senescent-like neuronal state that models molecular features of AD, while also revealing fundamental cell-type specific differences in senescent-like phenotypes and DDR.
This dataset
Open GSE329677 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 GSE329677_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 PRJNA1460693 and SRA study SRP696543. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
209 distinct titles, 22 of them repeated
iN, CTL, time 0, donor A27, batch R002 ×2 iN, CTL, time 0, donor A48, batch R002 ×2 iN, IR, time 8, donor A27, batch R002 ×2 iN, IR, time 8, donor A48, batch R002 ×2 iN, IR, time 24, donor A27, batch R002 ×2 iN, IR, time 24, donor A48, batch R002 ×2 iN, IR, time 48, donor A27, batch R002 ×2 iN, IR, time 48, donor A48, batch R002 ×2 +14 more

How we read this: Read from 231 sample titles: 209 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

+ 191 more — browse all 231 samples with per-sample file links →

Datasets on a similar topic

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