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The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequence [RNA-seq]

GSE326003 Mus musculus (mouse) RNA-seq (gene expression) 165 samples Submitted 2026/05/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 165 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
165 samples: 56 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
The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequencePLoS computational biology · 2026
Summary (written by the submitters)
The differentiation of hematopoietic progenitors into specialized types requires the transmittal of information from a few external or internal regulators to the thousands of genes that produce a cell type's characteristic phenotypes. While the main signaling pathways, transcription factors, and the genes eliciting the terminal phenotypes are known, how information flows from a few regulators to thousands of genes to change the state of the cell remains to be fleshed out.
Read the full summary ↓
To profile this information transfer process, we sampled the differentiation of the PUER myeloid cell line into macrophages and neutrophils at 27 time points over seven days. There is extensive transient regulation; the number of transcripts modulated in time is twice the number differentially expressed between endpoints. Differentiation is marked by two sharp transitions, at ~8h and ~80h, when transcriptomic state changes suddenly. We utilized non-negative matrix factorization to identify behaviors, characteristic temporal patterns of gene expression, and to classify transcripts by behavior. Only 10 distinct behaviors are sufficient to recapitulate the expression of ~36,000 transcripts with high fidelity. Gene expression in most of the behaviors occurs in pulses of varying initiation times and durations. This implies that information transfer during differentiation occurs in cascading waves of gene expression culminating in the permanent turning on of certain genes after ~80h. Each behavior is enriched in specific biological processes, so that physiological remodeling proceeds in a characteristic order---signal transduction, translation and mRNA processing, metabolism, and, ultimately, myeloid phenotypic processes. The sharp transition at 8h corresponds to the completion of transcriptional and translational remodeling and the initiation of metabolic remodeling; the one at 80h corresponds to the elicitation of myeloid phenotypes. Our analysis shows that differentiation relies upon a series of transient, rapid, and complex gene regulatory events and highlights the importance of profiling it at a high temporal resolution.
Published in
The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequence
Repele A, Handzlik J, Samarawickrama N et al. · PLoS computational biology 2026 · PMID 42118810 · doi:10.1371/journal.pcbi.1014276
This dataset
Open GSE326003 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 GSE326003_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1442805 and SRA study SRP686736. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42118810) ↗
Study design
56 conditions, mostly in triplicate
PUER cells, IL3, no OHT, 0h, ×3 PUER cells, GCSF, OHT, 0h, ×3 PUER cells, IL3, OHT, 1h, ×3 PUER cells, GCSF, OHT, 1h, ×3 PUER cells, IL3, OHT, 2h, ×3 PUER cells, GCSF, OHT, 2h, ×3 PUER cells, IL3, OHT, 3h, ×3 PUER cells, GCSF, OHT, 3h, ×3 +48 more

Supports a between-group comparison across 165 samples.

How we read this: 56 replicated groups read from 165 sample titles; they account for 165 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

+ 125 more — browse all 165 samples with per-sample file links →

Datasets on a similar topic

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