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Identifying specific functional roles for senescence across cell types

GSE271670 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2024/07/12 Platform GPL24247Platform GPL30215
At a glance

RNA-seq (gene expression) · mouse · 32 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
32 samples: 32 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 plays critical roles in aging, regeneration, and disease, yet the ability to discern its contributions across various cell types to pathophysiological processes in vivo remains limited. In this study, we generated an in vivotoolbox, consisting of three p16INK4a-related intersectional genetic systems, enabling pulse- chase tracing (Sn-pTracer), Cre-based tracing and ablation (Sn-cTracer), and gene manipulation combined with tracing (Sn-gTracer) of defined senescent cell types.
Read the full summary ↓
Using liver injury and repair as an example, we found that macrophages and endothelial cells (ECs) represent distinct senescent cell populations with different fates and functions during liver fibrosis and repair.Targeted reprogramming of p16INK4a+ ECs through Kdr overexpression markedly reduces liver fibrosis. This study illuminates the pathophysiological diversity of senescent cells and offers valuable insights for the development of future cell type-specific senolytic therapies. Furthermore, our model provides a versatile platform for investigating cellular senescence in any desired cell type.
This dataset
Open GSE271670 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 GSE271670_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 PRJNA1132889 and SRA study SRP518458. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 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 32 sample titles: 32 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

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.