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Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lung

GSE280003 Mus musculus (mouse) Single-cell RNA-seq 81 samples Submitted 2024/12/01 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 81 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: 81 samples: 28 conditions, mostly in triplicateGroups 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: Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lungJCI insight · 2024

Summary (written by the submitters)

Macrophages are required for healthy repair of the lungs following injury, but they are also implicated in driving dysregulated repair with fibrosis. How these two distinct outcomes of lung injury are mediated by different macrophage subsets is unknown. To assess this, single-cell RNA sequencing was performed on lung macrophages isolated from mice treated with lipopolysaccharide or bleomycin.

Read the full summary ↓

Macrophages were categorized based on anatomic location (airspace versus interstitium), developmental origin (embryonic versus recruited monocyte-derived), time after inflammatory challenge, and injury model. Analysis of the integrated dataset revealed that macrophage subset clustering was driven by macrophage origin and tissue compartment rather than injury model. Gpnmb-expressing recruited macrophages that were enriched for genes typically associated with fibrosis were present in both injury models. Analogous GPNMB-expressing macrophages were identified in datasets from both fibrotic and non-fibrotic lung disease in humans. We conclude that this subset represents a conserved response to tissue injury and is not sufficient to drive fibrosis. Beyond this conserved response, we identified that recruited macrophages failed to gain resident-like programming during fibrotic repair. Overall, fibrotic versus non-fibrotic tissue repair is dictated by dynamic shifts in macrophage subset programming and persistence of recruited macrophages.

Published in

Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lung

King EM, Zhao Y, Moore CM et al. · JCI insight 2024 · PMID 39509324 · doi:10.1172/jci.insight.182700

This dataset

Open GSE280003 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 GSE280003_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 PRJNA1175717 and SRA study SRP540053. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39509324) ↗

Study design

28 conditions, mostly in triplicate

LPS, D03, AM, ×4 LPS, D03, IMrec, ×4 LPS, D03, IMres, ×4 Homeostasis, D00, AM, ×3 Homeostasis, D00, CD45, ×3 Homeostasis, D00, IMrec, ×3 Homeostasis, D00, IMres, ×3 LPS, D03, CD45, ×3 +20 more

Supports a between-group comparison across 81 samples.

How we read this: 28 replicated groups read from 81 sample titles; they account for 81 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

GSM8585418 Homeostasis, D00, AM, rep1 Homeostasis, D00, AM,
GSM8585419 Homeostasis, D00, AM, rep2 Homeostasis, D00, AM,
GSM8585420 Homeostasis, D00, AM, rep3 Homeostasis, D00, AM,
GSM8585421 Homeostasis, D00, CD45, rep1 Homeostasis, D00, CD45,
GSM8585422 Homeostasis, D00, CD45, rep2 Homeostasis, D00, CD45,
GSM8585423 Homeostasis, D00, CD45, rep3 Homeostasis, D00, CD45,
GSM8585424 Homeostasis, D00, IMrec, rep1 Homeostasis, D00, IMrec,
GSM8585425 Homeostasis, D00, IMrec, rep2 Homeostasis, D00, IMrec,
GSM8585426 Homeostasis, D00, IMrec, rep3 Homeostasis, D00, IMrec,
GSM8585427 Homeostasis, D00, IMres, rep1 Homeostasis, D00, IMres,
GSM8585428 Homeostasis, D00, IMres, rep2 Homeostasis, D00, IMres,
GSM8585429 Homeostasis, D00, IMres, rep3 Homeostasis, D00, IMres,
GSM8585430 LPS, D03, AM, rep1 LPS, D03, AM,
GSM8585431 LPS, D03, AM, rep2 LPS, D03, AM,
GSM8585432 LPS, D03, AM, rep3 LPS, D03, AM,
GSM8585433 LPS, D03, AM, rep4 LPS, D03, AM,
GSM8585434 LPS, D03, CD45, rep1 LPS, D03, CD45,
GSM8585435 LPS, D03, CD45, rep2 LPS, D03, CD45,
GSM8585436 LPS, D03, CD45, rep3 LPS, D03, CD45,
GSM8585437 LPS, D03, IMrec, rep1 LPS, D03, IMrec,
GSM8585438 LPS, D03, IMrec, rep2 LPS, D03, IMrec,
GSM8585439 LPS, D03, IMrec, rep3 LPS, D03, IMrec,
GSM8585440 LPS, D03, IMrec, rep4 LPS, D03, IMrec,
GSM8585441 LPS, D03, IMres, rep1 LPS, D03, IMres,
GSM8585442 LPS, D03, IMres, rep2 LPS, D03, IMres,
GSM8585443 LPS, D03, IMres, rep3 LPS, D03, IMres,
GSM8585444 LPS, D03, IMres, rep4 LPS, D03, IMres,
GSM8585445 LPS, D06, AM, rep1 LPS, D06, AM,
GSM8585446 LPS, D06, AM, rep2 LPS, D06, AM,
GSM8585447 LPS, D06, AM, rep3 LPS, D06, AM,
GSM8585448 LPS, D06, CD45, rep1 LPS, D06, CD45,
GSM8585449 LPS, D06, CD45, rep2 LPS, D06, CD45,
GSM8585450 LPS, D06, CD45, rep3 LPS, D06, CD45,
GSM8585451 LPS, D06, IMrec, rep1 LPS, D06, IMrec,
GSM8585452 LPS, D06, IMrec, rep2 LPS, D06, IMrec,
GSM8585453 LPS, D06, IMrec, rep3 LPS, D06, IMrec,
GSM8585454 LPS, D06, IMres, rep1 LPS, D06, IMres,
GSM8585455 LPS, D06, IMres, rep2 LPS, D06, IMres,
GSM8585456 LPS, D06, IMres, rep3 LPS, D06, IMres,
GSM8585457 LPS, D15, AM, rep1 LPS, D15, AM,

+ 41 more — browse all 81 samples with per-sample file links →

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