GEO series

Emergence of inflammatory fibroblasts with aging in Hermansky-Pudlak syndrome associated pulmonary fibrosis

GSE272904 Mus musculus (mouse) Single-cell RNA-seq 23 samples Submitted 2025/02/07 Platform GPL17021

At a glance

Single-cell RNA-seq · mouse · 23 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 23 samples: 23 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: Emergence of inflammatory fibroblasts with aging in Hermansky-Pudlak syndrome associated pulmonary fibrosisCommunications biology · 2025

Summary (written by the submitters)

The longitudinal cellular interactions that drive pulmonary fibrosis are not well understood. To investigate the disease underpinnings associated with fibrosis onset and progression, we generated a scRNAseq atlas of lungs from young and aged mouse models of multiple subtypes of Hermansky-Pudlak syndrome (HPS), a collection of rare autosomal recessive diseases associated with albinism, platelet dysfunction, and pulmonary fibrosis.

Read the full summary ↓

We identified an age-dependent increase in SAA3+ inflammatory lung fibroblasts in HPS mice, including in double-mutant HPS1-2 mice which develop spontaneous fibrosis. HPS1 fibroblasts showed increased expression of IL-1R1, whereas alveolar type II epithelial cells from HPS2 mice induced the inflammatory gene signature in co-cultured fibroblasts. scRNAseq of lung tissue from three HPS1 patients similarly showed the presence of inflammatory fibroblasts and increased IL1R1 expression on fibroblasts. These data posit complex interactions between dysfunctional epithelial cells, inflammatory fibroblasts, and recruited immune cells, suggesting potential opportunities for mitigation of the fibrotic cascade.

Published in

Emergence of inflammatory fibroblasts with aging in Hermansky-Pudlak syndrome associated pulmonary fibrosis

Banaschewski BJH, Michki SN, Sitaraman S et al. · Communications biology 2025 · PMID 39987372 · doi:10.1038/s42003-025-07589-9

This dataset

Open GSE272904 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 GSE272904_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1139212 and SRA study SRP521759. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39987372) ↗

Study design

23 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 23 sample titles: 23 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

GSM8414436 lung, 60wk, HPS1-2, cohort_e
GSM8414437 lung, 60wk, HPS1, cohort_e
GSM8414438 lung, 60wk, HPS2, cohort_e
GSM8414439 lung, 60wk, HPS2-TG, cohort_e
GSM8414440 lung, 60wk, HPS3, cohort_e
GSM8414441 lung, 60wk, C57BL6, cohort_e
GSM8414442 lung, 8wk, HPS1-2, cohort_f
GSM8414443 lung, 8wk, HPS1, cohort_f
GSM8414444 lung, 8wk, HPS2, cohort_f
GSM8414445 lung, 8wk, HPS3, cohort_f
GSM8414446 lung, 8wk, HPS2-TG, cohort_f
GSM8414447 lung, 8wk, C57BL6, cohort_f
GSM8414448 lung, 60wk, C57BL6, cohort_d
GSM8414449 lung, 60wk, HPS1-2, cohort_d
GSM8414450 lung, 8wk, HPS1-2, cohort_d
GSM8414451 lung, 8wk, HPS1, cohort_d
GSM8414452 lung, 60wk, HPS1-2, cohort_a
GSM8414453 lung, 60wk, HPS1, cohort_a
GSM8414454 lung, 60wk, HPS2, cohort_a
GSM8414455 lung, 60wk, C57BL6, cohort_a
GSM8414456 lung, 8wk, C57BL6, cohort_b
GSM8414457 lung, 8wk, C57BL6, cohort_c
GSM8414458 lung, 8wk, HPS2, cohort_c

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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.