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Histone methyltransferase DOT1L affects the development of dendritic cell subsets [ChIP-Seq]

GSE301939 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2026/02/24 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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
Mus musculus (mouse)
Samples
12 samples: 6 conditions, mostly in duplicateGroups 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
Histone methyltransferase DOT1L differentially affects the development of dendritic cell subsetsLife science alliance · 2026
Summary (written by the submitters)
Dendritic cells (DCs) are important orchestrators of immune responses and can be divided into two conventional DC subsets, cDC1s and cDC2s, and a plasmacytoid DC (pDC) subset, which are involved in the activation of T cells and production of type I interferons (IFNs), respectively. The development of the DC subsets occurs in the bone marrow (BM) under control of different transcription factors and epigenetic modifiers.
Read the full summary ↓
The histone methyltransferase DOT1L, known as a druggable target in cancer, is emerging as a key epigenetic regulator of differentiation in lymphocytes. DOT1L writes methylation of lysine 79 of histone H3 in gene bodies of actively transcribed genes. Deletion or inhibition of DOT1L affects the differentiation trajectory of innate and adaptive immune cells, leading to altered cell states. Recent studies suggest that DOT1L also plays a role in DC function. Here, we investigated the role of DOT1L in the development of DCs in in vitro BM cultures and in vivo using a tamoxifen-inducible mouse model. H3K79me2 ChIPseq data of sorted DC subsets from the BM revealed distinct H3K79me2 peaks for all three subsets, which suggested differential regulation of the subsets by DOT1L. A comparison of the H3K79me2 ChIPseq data to bulk RNAseq of sorted DC subsets from the BM revealed a stronger correlation between H3K79me2 and global transcription in pDCs and cDC2s compared to cDC1s. In line with this, we observed that Dot1l deletion led to a decrease in pDCs and CpG A-stimulated IFNα production and an increase in cDC2s in in vitro BM cultures, while cDC1s were unchanged. In vivo deletion of Dot1l led to a decrease in common myeloid progenitors (CMPs), monocyte DC progenitors (MDPs), and common DC progenitors (CDPs) in the BM, while common lymphoid progenitor (CLP) and cDC2 numbers were increased. Inhibition of DOT1L resulted in a similar phenotype, which linked the observed effects to the methyltransferase activity of DOT1L. Interestingly, all Dot1l-KO DC subsets exhibited an enrichment of pathways related to antigen presentation and the immune response and MHCII expression was upregulated in Dot1l-KO pDCs and cDC2s in vivo. To conclude, our data indicate that DOT1L function differentially affects the development of DCs with predominant effects in pDCs and cDC2s.
Published in
Histone methyltransferase DOT1L differentially affects the development of dendritic cell subsets
Bouma RG, de Leeuw WJ, Wang AZ et al. · Life science alliance 2026 · PMID 41850725 · doi:10.26508/lsa.202603624
This dataset
Open GSE301939 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE301939_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 PRJNA1287755 and SRA study SRP598801. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41850725) ↗
Study design
6 conditions, mostly in duplicate
pDC, IP, ×2 DC1, IP, ×2 DC2, IP, ×2 pDC, INP, ×2 DC1, INP, ×2 DC2, INP, ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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