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Multi-omic analysis of chronic myelomonocytic leukemia monocytes reveals metabolic and immune dysregulation leading to altered macrophage polarization [ChIPseq]

GSE283202 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 42 samples Submitted 2025/01/28 Platform GPL16791
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 42 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
42 samples: 34 distinct titles, 8 of them repeatedGroups 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
Multi-omic analysis of chronic myelomonocytic leukemia monocytes reveals metabolic and immune dysregulation leading to altered macrophage polarizationLeukemia · 2025
Summary (written by the submitters)
Chronic myelomonocytic leukemia (CMML) is a myelodysplastic/myeloproliferative overlap neoplasm with few treatment options and generally poor prognosis. A defining feature is expansion of classical monocytes, which are responsible for various clinical sequelae and thus represent viable therapeutic targets.
Read the full summary ↓
Given the near-ubiquity of mutations involving epigenetic regulators, we profiled the epigenome (open chromatin configuration; H3K4-monomethylation; H3K27-acetylation; H3K27/H3K4-trimethylation) and transcriptome of primary CD14+ monocytes from CMML patients and age-matched healthy controls. Compared with healthy monocytes, CMML monocytes displayed heterogeneous but distinct epigenetic and transcriptome signatures. Most epigenetic changes mapped to distal elements, suggesting generalized enhancer dysregulation. We observed strong epigenetic (but not transcriptional) suppression of the NF-κB pathway, suggesting poised potential to repress downstream targets and modulate inflammatory responses. RNA-seq revealed up-regulation of genes involved in the oxidative phosphorylation (OxPhos) machinery, with significantly heightened OxPhos activity of primary CMML monocytes validated in mitochondrial stress tests, highlighting a potentially tractable therapeutic vulnerability. In line with the observed metabolic phenotype of CMML monocytes, unpolarized CMML monocyte-derived macrophages exhibited transcriptional bias towards an M2 (“anti-inflammatory”) phenotype, reflected by an increased Th2/Th1 ratio in comparison with control monocytes. These observations may be germane to the observed clinical heterogeneity and prominent immune dysfunction characteristic of CMML.
Published in
Multi-omic analysis of chronic myelomonocytic leukemia monocytes reveals metabolic and immune dysregulation leading to altered macrophage polarization
Addinsell HM, Cant R, Hull NJ et al. · Leukemia 2025 · PMID 39815051 · doi:10.1038/s41375-024-02511-4
This dataset
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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 GSE283202_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1192314 and SRA study SRP548437. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39815051) ↗
Study design
34 distinct titles, 8 of them repeated
HV4_H3K4me ×2 HV2_H3K4me ×2 HV3_H3K4me ×2 BC830_H3K4me ×2 BC932_H3K4me ×2 BC786_H3K4me ×2 BC812_H3K4me ×2 BC776_H3K4me ×2

How we read this: Read from 42 sample titles: 34 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

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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.