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Ontogenically distinct alveolar macrophages mediate cigarette smoke-induced inflammation and damage

GSE303713 Mus musculus (mouse) Single-cell RNA-seq 14 samples Submitted 2026/07/05 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 14 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
14 samples: 14 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.
Summary (written by the submitters)
We employed single-cell RNA sequencing (scRNA seq) using the 10x Genomics platform to investigate how varying durations of cigarette smoke (CS) exposure affect myeloid cells of different ontogenies in mice. Chronic obstructive pulmonary disease (COPD) is often associated with CS and the third leading cause of death. While airway-recruited macrophages contribute to COPD pathogenesis, the role of embryonic alveolar macrophages (Em-AM) remains unclear.
Read the full summary ↓
Using a CS-induced COPD mouse model, we found that CS-triggered lung inflammation and emphysema-like tissue damage correlated with substantial changes in the lung myeloid compartment. Using fate-mapping, we showed that, while most CS-induced AM were recruited bone marrow monocyte-derived AM (BM-AM), Em-AM persisted and underwent similar inflammation- and emphysema-associated adaptations as BM-AM upon CS. Therefore, our data support that the CS-exposed niche, rather than ontogeny, is the main determinant of AM fate in COPD, and that CS-induced AM changes may be potential therapeutic targets to mitigate AM dysfunction in COPD.
This dataset
Open GSE303713 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 GSE303713_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 PRJNA1297138 and SRA study SRP603774. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 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 14 sample titles: 14 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.