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P21+TREM2+-Senescent Macrophages Fuel Inflammaging and Metabolic Dysfunction-Associated Steatotic Liver Disease [NAFLD_time_course]

GSE319035 Mus musculus (mouse) RNA-seq (gene expression) 77 samples Submitted 2026/02/16 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 77 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
77 samples: 77 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
p21(+)TREM2(+) senescent macrophages fuel inflammaging and metabolic dysfunction-associated steatotic liver diseaseNature aging · 2026
Summary (written by the submitters)
Cellular senescence drives chronic sterile inflammation during aging via the senescence-associated secretory phenotype (SASP); however, the cell types responsible for this pathology remain poorly defined. Here, we identify p21⁺Trem2⁺ senescent macrophages as a major source of inflammaging. Using primary mouse and human macrophages, we developed a model of DNA damage and cholesterol-induced senescence and applied multi-omic profiling to fully characterize senescent macrophages.
Read the full summary ↓
We found that senescent macrophages exhibit a distinctive p21-TREM2 expression profile and SASP, driven in part by type-I interferon signaling via secreted mitochondrial DNA. We also found that senescent macrophage accumulation occurs in aged and MASLD mouse livers and is enriched in human cirrhotic liver tissue. Finally, senolytic treatment targeting senescent macrophages reduced liver inflammation and steatosis in both aged and MASLD mice. Together, these findings establish macrophage senescence as a central driver of chronic inflammation in aging and metabolic liver disease, highlighting a promising, tractable therapeutic target.
Published in
p21(+)TREM2(+) senescent macrophages fuel inflammaging and metabolic dysfunction-associated steatotic liver disease
Salladay-Perez IA, Avila I, Estrada L et al. · Nature aging 2026 · PMID 41991686 · doi:10.1038/s43587-026-01101-6
This dataset
Open GSE319035 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 GSE319035_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 PRJNA1422001 and SRA study SRP676109. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41991686) ↗
Study design
77 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 77 sample titles: 77 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

+ 37 more — browse all 77 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.