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Dual roles of fibroblast-epithelial crosstalk in acute and chronic lung injury

GSE294260 Homo sapiens (human) RNA-seq (gene expression) 70 samples Submitted 2025/07/03 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 70 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
Homo sapiens (human)
Samples
70 samples: 14 conditions, mostly with about 5 replicates eachGroups 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 dual role of fibroblast-epithelial crosstalk in acute and chronic lung injuryThe Journal of biological chemistry · 2025
Summary (written by the submitters)
Dysfunctional interactions between fibroblasts and epithelial cells contribute to the progression of chronic lung diseases, including idiopathic pulmonary fibrosis. Our findings showed that chronic epithelial damage initiates a bidirectional fibrotic cascade between the epithelium and lung fibroblasts, leading to further epithelial damage and the release of pro-fibrotic mediators.
Read the full summary ↓
Conversely, our transcriptomic and proteomic analyses highlighted that, in the context of acute epithelial injury, a protective signaling environment merges that mitigates further damage. We observed that epithelial cells shift their metabolism towards increased aerobic fatty acid metabolism, promoting healing and repair. By delineating secreted regulators responsible for inducing these positive responses, we identified pentraxin 3 (PTX3) as a top hit harboring antifibrotic characteristics.
Published in
A dual role of fibroblast-epithelial crosstalk in acute and chronic lung injury
Bammert MT, Kollak I, Hoffmann J et al. · The Journal of biological chemistry 2025 · PMID 40581116 · doi:10.1016/j.jbc.2025.110408
This dataset
Open GSE294260 in finder → Find more human 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 GSE294260_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 PRJNA1248893 and SRA study SRP577684. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40581116) ↗
Study design
14 conditions, mostly with about 5 replicates each
LFs MoCu Control ×5 LFs MoCu TGFb ×5 LFs nSAECs CoCu Control ×5 LFs nSAECs CoCu TGFb ×5 LFs tSAECs CoCu Control ×5 LFs tSAECs CoCu TGFb ×5 nSAECs LFs CoCu Control ×5 nSAECs LFs CoCu TGFb ×5 +6 more

Supports a between-group comparison across 70 samples.

How we read this: 14 replicated groups read from 70 sample titles; they account for 70 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

+ 30 more — browse all 70 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.