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Highly multiplexed single cell chromatin regulator and accessibility profiling elucidates chromatin dynamics in human B cell development and oncogenesis, and reveals an adverse prognostic gene expression signature in B lymphoblastic leukemias

GSE268580 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2026/06/01 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 15 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
15 samples: 7 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.
Summary (written by the submitters)
Developing B cells undergo successive stages of chromatin reorganization which modulate DNA accessibility to control V(D)J recombination and gene transcription. These epigenetic shifts are mediated by histone modifications (e.g., H3K4me3, H3K27me3), histone-modifying enzymes (e.g., MLL1, LSD1), and chromatin remodelers (e.g., CTCF, DNMT1).
Read the full summary ↓
We combined highly-multiplexed, multi-omic approaches to capture this choreographed dance – from single cell changes in chromatin modulators, to alterations in histone modifications, to genome-wide chromatin accessibility. This epigenetic map of normal human B cell differentiation enabled us to peer into the early origins of B cell leukemias and lymphomas – specifically, stage-specific chromatin accessibility at sites of recurrent chromosomal translocations and oncogenic transcription factors (TF). It further identified a chromatin state characterized by relative depletion of chromatin structure modulators and high leukemic TF accessibility, enriched in a small cohort of B lymphoblastic leukemias and associated with adverse prognosis.
This dataset
Open GSE268580 in finder → Find more human 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 GSE268580_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1117833 and SRA study SRP510563. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in duplicate
LSP_biorep1_tech ×3 LSP_biorep2_tech ×2 S1_biorep1_tech ×2 S1_biorep2_tech ×2 S2_biorep1_tech ×2 S2_biorep2_tech ×2 S4_biorep2_tech ×2

Supports a between-group comparison across 15 samples.

How we read this: 7 replicated groups read from 15 sample titles; they account for 15 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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