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Aged bone marrow myeloid and mesenchymal cells develop unique senescence phenotypes

GSE294362 Mus musculus (mouse) RNA-seq (gene expression) 80 samples Submitted 2026/01/07 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 80 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
80 samples: 80 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.
Summary (written by the submitters)
Cellular senescence was originally characterized in mesenchymal cells and subsequently this concept was extended to immune cells. However, the extent to which immune cells differ from mesenchymal cells in their senescence phenotype, or “senotype”, is unclear. Here, we evaluated the senotypes of bone marrow/bone immune versus mesenchymal cells.
Read the full summary ↓
Among immune cells, myeloid cells exhibited the highest expression of p16. However, targeted clearance of p16+ myeloid cells using LysM-Cre x p16-LOX-ATTAC mice only had minor effects on age-related bone loss in male mice, with no effects in females. After clearance, p16+ myeloid cells were only transiently reduced, unlike when targeting p16+ mesenchymal cells. In the same bones, aged myeloid cells demonstrated lower expression of senescence markers than mesenchymal cells and, when induced in culture, the senescence phenotype of myeloid cells differed substantially from that of mesenchymal cells. Our findings indicate that aged bone marrow myeloid cells do not achieve the fully developed senescent phenotype originally described in mesenchymal cells, justifying further characterization of senotypes of immune cells across tissues.
This dataset
Open GSE294362 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 GSE294362_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 PRJNA1249087 and SRA study SRP578067. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
80 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 80 sample titles: 80 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

+ 40 more — browse all 80 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.