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BLIMP1 shapes germinal center B-cell clonal diversity by gating chromatin accessibility during light-to-dark zone transition

GSE305877 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2026/03/06 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 18 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
18 samples: 18 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.
Paper
BLIMP1 shapes germinal center B cell clonal diversity by gating chromatin accessibility during light-to-dark zone transitionNature immunology · 2026
Summary (written by the submitters)
Extensive studies have delineated signaling systems and transcription factors (TFs) that positively regulate GC B cell responses. In the LZ, T follicular helper (Tfh) derived CD40L and ICOS along with B cell receptor (BCR) signals license B cell clones to pass through the G1-S checkpoint; the cells then re-enter the DZ for clonal expansion and AID mediated somatic hypermutation (SHM).
Read the full summary ↓
Among TFs that promote GC initiation, selection, and output, BCL6 programs GC identity. MYC licenses positively selected LZ cells for re-entry and proliferation in the DZ, whereas FOXO1 controls the program underlying rapid cell divisions. PU.1 and SPIB via interactions with IRF8 help maintain GC identity and selection, acting on ETS–IRF composite elements (EICE). BATF supports robust GC responses and class switching. The POU TF module comprised of OCT1/POU2F1, OCT2/POU2F2, and their co-activator OBF1/POU2AF1 maintains BCR-dependent transcription required for GC expansion. Canonical and alternative NF-κB components c-Rel and p52/RelB also promote GC maintenance. A p52–ETS1 complex has been recently shown to induce OCT1 and OBF1, thereby generating a feedforward regulatory loop. In contrast, few signaling-induced negative regulators of the GC response that restrict clonal dominance have been identified, notably Nr4a1 (NUR77). We posited that BLIMP1 which promotes exit of GC B cells into the plasma cell (PC) pathway, could also act as a signaling-induced transient feedback gate to restrain the GC B cell response, thereby reducing clonal dominance. In keeping with this hypothesis, low and heterogenous expression of BLIMP1 has been reported in GC B cells and its loss has been shown to result in larger GCs. Furthermore, Conter et al. recently demonstrated using bone marrow chimeras that B-cell-intrinsic BLIMP1 loss results in more exuberant GC responses associated with enhanced proliferation of antigen-specific GC B cells. We note that in addition to the feedback gating model for BLIMP1 action in the GC considered by us, two other models could account for the GC phenotype: (i) increased GC seeding due to impaired plasmablast differentiation or (ii) failed GC exit due to blocked PC differentiation. We reasoned that integrated single cell (sc) transcriptional and chromatin analyses of GC B cells along with delineation of their clonal dynamics could distinguish among the three models. Furthermore, such high dimensional analyses of the perturbed and amplified GC response would yield insights into the underlying molecular mechanisms of BLIMP1 action. Using sc transcriptome, V(D)J and chromatin profiling coupled with single nucleotide-resolution accessibility modeling of regulatory DNA sequences, we show that Prdm1-deficient B cells mount an exaggerated GC reaction characterized by larger clone sizes and enhanced affinity maturation culminating in greater clonal dominance. This phenotype is not attributable to increased GC seeding nor can it be merely accounted for by impaired exit of GC B cells into the PC pathway. Rather, the integrated genomic analyses reveal that BLIMP1 constrains expression of genes encoding components of the BCR-signaling cascade. Loss of this feedback, results in enhanced Syk, Lyn and Btk activity in GC B cells and augments chromatin engagement of signaling-inducible TFs at EICE, NF-κB and POU (OCT) motifs, promoting the G1–S transition during LZ selection and fueling DZ expansion.
Published in
BLIMP1 shapes germinal center B cell clonal diversity by gating chromatin accessibility during light-to-dark zone transition
Manakkat Vijay GK, Ramaswami B, Gierlack S et al. · Nature immunology 2026 · PMID 42010057 · doi:10.1038/s41590-026-02495-6
This dataset
Open GSE305877 in finder → Find more mouse 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 GSE305877_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 PRJNA1308089 and SRA study SRP610116. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42010057) ↗
Study design
18 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 18 sample titles: 18 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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