GEO series

Cartography of emergency myelopoiesis identifies conserved molecular activation modules

GSE296404 Mus musculus (mouse) Single-cell RNA-seq 15 samples Submitted 2026/06/25 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 15 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: 15 samples: 15 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: Quantitative molecular cartography of emergency myelopoiesis reveals conserved modules of hematopoietic activationCell stem cell · 2026

Summary (written by the submitters)

Hematopoietic stem and progenitor cells (HSPC) respond to infections, inflammation, and regenerative challenges using cellular and molecular mechanisms termed emergency myelopoiesis (EM) pathways. However, it remains unclear whether all parts of the hematopoietic hierarchy are similarly activated by different EM inducers. Here, we generate a generalizable and comprehensive cell annotation method together with a refined quantitative model of hematopoietic differentiation using single cell RNA sequencing (scRNA-seq) of HSPCs, which we apply to a broad range of EM contexts.

Read the full summary ↓

We uncover multiple strategies to enhance myelopoiesis acting at different levels of the HSPC hierarchy, which are also associated with both unique and shared transcriptional response modules. In particular, we identify a myeloid progenitor-based signature associated with EM pathway engagement across multiple inflammatory challenges, which predicts outcome in both pediatric and adult human acute myeloid leukemia. Collectively, our work illuminates fundamental regulatory mechanisms in hematopoietic regeneration and identifies a common EM signature that informs outcome in disease contexts.

Published in

Quantitative molecular cartography of emergency myelopoiesis reveals conserved modules of hematopoietic activation

Swann JW, Fung JH, Chen Z et al. · Cell stem cell 2026 · PMID 42314682 · doi:10.1016/j.stem.2026.05.007

This dataset

Open GSE296404 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 GSE296404_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 PRJNA1259230 and SRA study SRP583281. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42314682) ↗

Study design

15 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 15 sample titles: 15 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

GSM8969792 Chronic_IL1_Control_LK
GSM8969793 Chronic_IL1_Control_LSK
GSM8969794 Chronic_IL1_20dIL1_LK
GSM8969795 Chronic_IL1_20dIL1_LSK
GSM8969796 Acute_IL1_7dIL1_LSK
GSM8969797 pIC_Control_LK
GSM8969798 pIC_3dpIC_LK
GSM8969799 5FU_d0_LSK
GSM8969800 5FU_d8_LSK
GSM8969801 5FU_d10_LSK
GSM8969802 5FU_d12_LSK
GSM8969803 5FU_d14_LSK
GSM8969804 Hashing_5FU_F
GSM8969805 Hashing_5FU_M
GSM8969806 5FU_d0_LK

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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.