Back

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

Hughes, J.-W. B.; Sandholm, A.; Croll, D.; Senchyna, F.; Schneider, K.; Butterfield, R.; McHugh, T. L. M.; Brown, I.; Deguchi, H.; Hilsabeck, T. A. U.; Mak, S.; Wilson, K. A.; Davtyan, H.; Blurton-Jones, M.; Herdy, J.; Higuchi-Sanabria, R.; Gage, F. H.; Furman, D.; Ellerby, L. M.; Desprez, P.-Y.; Campisi, J.

2026-04-03 cell biology
10.64898/2026.04.02.716205 bioRxiv
Show abstract

Alzheimers 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. 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-{kappa}1. In contrast, fibroblasts develop a p16-associated senescent state lacking a SASP phenotype and show a predicted repression of NF-{kappa}1. 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.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Molecular Cell
308 papers in training set
Top 1%
11.9%
2
Cell Reports
1338 papers in training set
Top 4%
8.8%
3
Nature Aging
51 papers in training set
Top 0.2%
8.1%
4
Nature Communications
4913 papers in training set
Top 24%
7.9%
5
Developmental Cell
168 papers in training set
Top 3%
6.9%
6
Nature Cell Biology
99 papers in training set
Top 0.5%
6.6%
50% of probability mass above
7
Immunity
58 papers in training set
Top 1%
4.7%
8
Nature
575 papers in training set
Top 7%
3.5%
9
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 22%
3.5%
10
Aging Cell
144 papers in training set
Top 1%
3.1%
11
eLife
5422 papers in training set
Top 33%
2.5%
12
Science Advances
1098 papers in training set
Top 13%
2.0%
13
Science
429 papers in training set
Top 12%
2.0%
14
Nature Genetics
240 papers in training set
Top 4%
1.8%
15
Nucleic Acids Research
1128 papers in training set
Top 10%
1.8%
16
Nature Medicine
117 papers in training set
Top 2%
1.6%
17
Nature Neuroscience
216 papers in training set
Top 4%
1.6%
18
Cell Systems
167 papers in training set
Top 8%
1.6%
19
Nature Metabolism
56 papers in training set
Top 1%
1.6%
20
Cell
370 papers in training set
Top 13%
1.4%
21
Advanced Science
249 papers in training set
Top 13%
1.4%
22
Cell Metabolism
49 papers in training set
Top 1%
1.3%
23
Cell Stem Cell
57 papers in training set
Top 1%
1.3%
24
Science Translational Medicine
111 papers in training set
Top 6%
0.8%
25
The EMBO Journal
267 papers in training set
Top 6%
0.7%
26
Journal of Cell Biology
333 papers in training set
Top 6%
0.6%