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Revealing an unconventional JAK1/2-STAT3 branch in macrophage IFNγ signaling [ChIP-Seq]

GSE304589 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/12/23 Platform GPL34290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 16 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
16 samples: 10 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
Revealing an unconventional JAK1/2-STAT3 branch in macrophage IFN-γ signalingCell reports · 2026
Summary (written by the submitters)
Interferon-γ (IFNγ) is a key cytokine in immune activation and anti-viral responses that classically signals via JAK1/2-mediated STAT1 homodimers to drive macrophage activation. Here, we identify a non-canonical signaling component in which IFNγ also activates STAT3. Our results show that IFNγ activates STAT3 rapidly and directly through JAK1 and JAK2.
Read the full summary ↓
We provide the first evidence that STAT3 can form heterodimers with STAT1 in this context and demonstrate that STAT3 is co-recruited to a subset of IFNγ-induced, STAT1-bound regulatory elements. While IFNγ directly activates STAT3, our results reveal that its contribution to gene regulation is limited, indicating that STAT1 easily substitutes the STAT1–STAT3 heterodimer for STAT1 homodimers when STAT3 is absent. These findings uncover STAT3 as a new unconventional player in macrophage IFNγ signaling, underscoring the complex and context-dependent nature of cytokine signaling networks.
Published in
Revealing an unconventional JAK1/2-STAT3 branch in macrophage IFN-γ signaling
Botman M, Huisman JMA, Drakaki A et al. · Cell reports 2026 · PMID 41671082 · doi:10.1016/j.celrep.2026.116985
This dataset
Open GSE304589 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 GSE304589_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 PRJNA1301844 and SRA study SRP606837. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41671082) ↗
Study design
10 conditions, mostly in duplicate
pSTAT1_ctrl ×2 pSTAT1_2hIFNy ×2 pSTAT3_ctrl ×2 pSTAT3_2hIFNy ×2 Pol2_ctrl ×2 Pol2_2hIFNy ×2 pSTAT1_2hIFNyIL6 ×2 pSTAT3_2hIFNyIL6 ×2 +4 more

Supports a between-group comparison across 20 samples.

How we read this: 10 replicated groups read from 22 sample titles; they account for 20 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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