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The Heme-regulated transcriptional repressor, BACH1 safeguards macrophage enhancer selection, activation andtissue adaptation [RNA-seq_BMDM]

GSE245648 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/07/13 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 24 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.
Summary (written by the submitters)
Transcriptional master regulators of the myeloid lineage (like PU.1, Cebpa/b, etc.) are believed to be the main drivers of chromatin reprogramming during differentiation and polarization. Such developmental transcription factors act via their pioneer ability to open chromatin de novo and bookmark genomic regulatory sites (enhancers and promoters) to later recruit signal-dependent TFs, co-factors, and the basal transcriptional machinery.
Read the full summary ↓
However, recent studies by us and others challenged this “developmental TF-centric” mode as they suggested that signal-dependent TFs, particularly repressors, can also have a pioneer and thus safeguarding role in the MF epigenome at baseline and during acute inflammation. Such an expanded model predicts that active transcriptional repression and chromatin bookmarking by signal-dependent master regulators are key mechanisms for safeguarding the enhancer repertoire and proper priming, deployment, and resolution of an inflammatory response. Here, we show that BACH1, a heme-sensitive transcriptional repressor, acts as such a pervasive epigenomic safeguard to regulate tissue macrophage identity. We show that BACH1 is part of the core hardwired transcriptional program of ground-state macrophages shaping the chromatin accessibility of thousands of distal regulatory elements. Furthermore, BACH1 cistrome is extensive, not static but dynamic, and expanding upon different inflammatory stimuli and correlates with pre-formed and de novo formed enhancer-promoter networks. We further show that myeloid BACH1 is indispensable in vivo for tissue regeneration upon injury as it regulates the kinetics of Ly6C+ to Ly6C- transition of inflammatory macrophages and regulates the onset, the kinetics and magnitude of expression of critical inflammatory and repair-related genes. Finally, BACH1 was found to be required in vivo for competitive fitness and tissue adaptation of several tissue-resident macrophage populations (eg. lung, peritoneal macrophages), further supporting its pervasive role in acquiring and maintaining the functional specificity and diversity of tissue macrophages. Taken together, our data suggest that BACH1 acts as a signal-dependent master regulator that directly controls key transcriptional circuits during tissue macrophage subtype function, specification and inflammatory responses in vivo.
This dataset
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.

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 GSE245648_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1029499 and SRA study SRP466985. Searching any of these in the dataset finder brings you back here.

Study design
24 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 24 sample titles: 24 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
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