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Micro bulk sequencing on murine Bleomycin-induced fibrotic lungs sorted on SA-β-Gal and GD3

GSE262926 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2024/04/04 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 4 × GD3+ Bgal vs 4 × GD3- Bgal+ vs 4 × GD3- Bgal vs 3 × GD3+ Bgal+Groups 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
A ganglioside-based immune checkpoint enables senescent cells to evade immunosurveillance during agingNature aging · 2025
Summary (written by the submitters)
Aging is marked by a progressive decline in physiological function, leading to an increased susceptibility to age-related diseases and cancer. On the cellular level, aging correlates with an accumulation of senescent cells, which play a dual role: they are critical for tissue homeostasis, yet their over-accumulation may drive pathological aging.
Read the full summary ↓
We recently revealed that senescent cells could overexpress immunosuppressive molecules like the ganglioside GD3 at their cell surface to evade from immune surveillance and to facilitate their tolerogenicity. We investigate which cells expressed GD3 and whether this ganglioside could be considered as a new marker associated to senescence in a model of senescence associated disease: Bleomycin-induced lung fibrosis.
Published in
A ganglioside-based immune checkpoint enables senescent cells to evade immunosurveillance during aging
Iltis C, Moskalevska I, Debiesse A et al. · Nature aging 2025 · PMID 39730825 · doi:10.1038/s43587-024-00776-z
This dataset
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.

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 GSE262926_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1094922 and SRA study SRP499017. Searching any of these in the dataset finder brings you back here.

Study design
4 × GD3+ Bgal vs 4 × GD3- Bgal+ vs 4 × GD3- Bgal vs 3 × GD3+ Bgal+

Supports a between-group comparison across 15 samples.

How we read this: 4 replicated groups read from 15 sample titles; they account for 15 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
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