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Cellular senescence in human liver under normal aging and cancer

GSE311064 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/01/01 Platform GPL34290
At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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
Cellular senescence in human liver under normal aging and cancerCell genomics · 2026
Summary (written by the submitters)
Cellular senescence, a stress-induced program causing stable cell-cycle arrest, is a hallmark of liver aging, fibrosis, and cancer. However, the cell-type-specific mechanisms, spatial organization, and cancer-associated alterations in the liver remain unclear. We profiled 43 normal human livers spanning ages and fibrosis stages using a single-cell multiome, Xenium spatial transcriptomics, and CODEX, complemented by fibrotic mouse models and 24 colorectal cancer liver metastases.
Read the full summary ↓
We found CDKN1A+ senescent hepatocytes, fibroblasts, cholangiocytes, and endothelial cells associated with age, liver disease, or cancer. Senescence differed between aged and fibrotic livers, with similar patterns in mice. Spatially, CDKN1A+ hepatocytes localized periportally, while SERPINE1+ aging-associated hepatocytes formed spatial clusters, potentially mediated by Claudins and THBS1. Fibrotic regions contained CXCL12+ senescent fibroblasts interacting with CXCR4+ immune cells. Chemotherapy intensified senescence in hepatocytes by 5-fold relative to aging and led to unique CDKN2A+ populations. Across conditions, senescent cells shared AP-1 activation, pro-inflammatory cytokines, and apoptosis resistance, suggesting therapeutic opportunities.
Published in
Cellular senescence in human liver under normal aging and cancer
Karpova A, Li X, Peng CW et al. · Cell genomics 2026 · PMID 41576948 · doi:10.1016/j.xgen.2025.101133
This dataset
Open GSE311064 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 GSE311064_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 PRJNA1368618 and SRA study SRP649386. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41576948) ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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
Datasets on a similar topic

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