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Fibroblast-derived osteoglycin promotes alveolar epithelial cell repair

GSE271272 Mus musculus (mouse) RNA-seq (gene expression) 32 samples Submitted 2025/02/01 Platform GPL24247
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.
Paper
Fibroblast-derived osteoglycin promotes epithelial cell repairNPJ Regenerative medicine · 2025
Summary (written by the submitters)
There is an increasing need for pharmacological treatments that target defective epithelial repair in chronic diseases, such as chronic obstructive pulmonary disease. The mesenchymal niche plays a major regulatory role in guiding epithelial progenitor cell activation during repair. We persuaded that the secreted factors involved in this interaction hold potential as drug targets.
Read the full summary ↓
In this study, we utilized a proteomics-guided drug discovery strategy, leveraging the secretome of lung fibroblasts, to uncover drug targets affecting epithelial progenitor behavior. Using lung organoids, we identified several ligands with regenerative potential, of which the matrix protein osteoglycin (OGN) surprisingly had the most profound effect. RNA sequencing revealed that OGN promoted alveolar epithelial type II cell differentiation, whilst increasing reactive oxygen species detoxification, reducing senescence, and enhancing growth factor-mediated fibroblast- epithelial crosstalk. In accordance with this OGN protein expression was reduced in damaged lung tissue of COPD patients and in smoke-exposed mice. Additionally, we found an active fragment of OGN comprising the leucine-rich repeat regions 4-7 to have comparable regenerative potential in regulating lung organoid formation. The active fragment of OGN ameliorated elastase-induced lung injury in precision-cut lung slices (PCLS) and improved lung function in mice. These findings identify lung fibroblast-derived OGN as a matrix protein supporting alveolar epithelial growth and its active fragment as a promising therapeutic target for epithelial cell repair in individuals with accelerated tissue damage.
Published in
Fibroblast-derived osteoglycin promotes epithelial cell repair
van der Koog L, Woest ME, Gorter IC et al. · NPJ Regenerative medicine 2025 · PMID 40133363 · doi:10.1038/s41536-025-00404-3
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 GSE271272_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 PRJNA1130843 and SRA study SRP517484. Searching any of these in the dataset finder brings you back here.

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
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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.