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Transcriptional and functional architecture of the whole neutrophil compartment II

GSE266678 Mus musculus (mouse) Single-cell RNA-seq 13 samples Submitted 2025/09/30 Platform GPL17021
At a glance

Single-cell RNA-seq · mouse · 13 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
13 samples: 13 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
Architecture of the neutrophil compartmentNature · 2026
Summary (written by the submitters)
In this study, we present NeuMAP, a comprehensive single-cell analysis of neutrophils spanning over 40 anatomical, physiological, and pathological contexts in mice. NeuMAP confirms and expands previous models of neutrophil diversity, revealing the organization of neutrophils into distinct functional hubs under both normal and pathological conditions.
Read the full summary ↓
Furthermore, we delineate prototypical trajectories as key organizers of granulopoiesis and examine neutrophil dynamics along these trajectories during acute inflammation and cancer. Integrating insights from fate mapping, mutant mouse models, and in vitro experiments, we identified immunological signals guiding neutrophil through these typical trajectories of granulopoiesis, that balance immune protection, tissue homeostasis and repair. Specifically, we identified IFNB, GMCSF, and TGFB as drivers of pro-inflammatory, cancer-associated, and mature neutrophil states, respectively and found that the transcription factor JUNB drives the angiogenic and immunosuppressive function of neutrophils. Additionally, we uncover conserved transcriptional signatures for human neutrophil states, validate their prognostic significance in cancer patients, and introduce a proof-of-concept strategy for exploring the diagnostic potential of neutrophil functional diversity across various diseases using single-cell transcriptomics of blood neutrophils. Our study provides a model that delineates the global architecture of the neutrophil compartment in mammals, and stablishes a robust framework for further exploration of neutrophil biology.
Published in
Architecture of the neutrophil compartment
Cerezo-Wallis D, Rubio-Ponce A, Richter M et al. · Nature 2026 · PMID 41339555 · doi:10.1038/s41586-025-09807-0
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 GSE266678_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: 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 PRJNA1108258 and SRA study SRP505942. Searching any of these in the dataset finder brings you back here.

Study design
13 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 13 sample titles: 13 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.