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Cdkn2a/p16INK4A Loss affects hippocampal transcription in Young Adult Mice Independently of Alzheimer ’s-Associated Genetic Pathways

GSE300348 Mus musculus (mouse) RNA-seq (gene expression) 23 samples Submitted 2026/03/11 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 23 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
23 samples: 6 × 5 month old mouse brain hippocampus - WT genotype - sample vs 6 × 5 month old mouse brain hippocampus - P16 KO genotype - sample vs 6 × 5 month old mouse brain hippocampus - APP<NLGF/NLGF>P16<KO/KO> genotype - sample vs 5 × 5 month old mouse brain hippocampus - APP NLGF genotype - sampleGroups 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
Cdkn2a/p16Ink4a loss impairs Spatial memory independently of Alzheimer's-associated genetic pathways in young adult miceCellular and molecular life sciences : CMLS · 2026
Summary (written by the submitters)
Background – The cyclin-dependent kinase inhibitor CDKN2A/p16INK4A is a central regulator of cellular senescence, widely studied in aging and cancer. Although its expression increases with age and disease, its role in the adult brain remains poorly defined. Recent evidence suggests a possible contribution to neuroinflammation and cognitive decline in Alzheimer’s disease (AD), but underlying mechanisms are unclear.
Read the full summary ↓
We investigated the functional impact of p16INK4A inactivation on hippocampal gene expression in adult mice under normal and amyloidogenic conditions using the AppNL-G-F AD model. Methods –. Hippocampal transcriptomes from control, p16INK4Aknockout (p16INK4AKO), AppNL-G-F, and double mutant mice were analysed by RNA sequencing. We performed differential gene expression analysis, gene set enrichment analysis (GSEA), and interaction modelling to assess the molecular consequences of p16INK4A loss alone and in combination with amyloid pathology. Results –Transcriptomic analysis revealed that p16INK4AKO upregulated metabolic, mitochondrial, and translational pathways, while downregulating synaptic and cytoskeletal genes. In contrast, AppNL-G-F mice displayed strong immune activation. The double mutants showed additive transcriptional changes, yet GSEA indicated non-linear interactions in synaptic and immune-related pathways. Unexpectedly, p16INK4A deletion alone enriched senescence-associated signatures, despite the loss of this canonical senescence gene. Conclusion – Constitutive p16INK4A loss causes broad transcriptomic remodelling in the adult hippocampus, independently of amyloid pathology. These findings uncover non-canonical roles for p16INK4A in neuronal homeostasis and emphasise the need for temporally controlled models to better dissect the brain-specific functions of senescence regulators in ageing and neurodegeneration.
Published in
Cdkn2a/p16Ink4a loss impairs Spatial memory independently of Alzheimer's-associated genetic pathways in young adult mice
Stephan P, Plassard D, Keyes WM et al. · Cellular and molecular life sciences : CMLS 2026 · PMID 41741874 · doi:10.1007/s00018-026-06079-3
This dataset
Open GSE300348 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 GSE300348_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 PRJNA1280130 and SRA study SRP593439. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41741874) ↗
Study design
6 × 5 month old mouse brain hippocampus - WT genotype - sample vs 6 × 5 month old mouse brain hippocampus - P16 KO genotype - sample vs 6 × 5 month old mouse brain hippocampus - APP<NLGF/NLGF>P16<KO/KO> genotype - sample vs 5 × 5 month old mouse brain hippocampus - APP NLGF genotype - sample

Supports a between-group comparison across 23 samples.

How we read this: 4 replicated groups read from 23 sample titles; they account for 23 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
Datasets on a similar topic

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