← BioTransfer GEO Dataset Finder
GEO series

Myeloid progenitor dysregulation fuels immunosuppressive macrophages in tumors [dataset 1]

GSE255330 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 65 samples Submitted 2025/07/07 Platform GPL24247Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 65 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
Homo sapiens (human)
Samples
65 samples: 65 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
Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratificationCancer cell · 2021
Summary (written by the submitters)
Monocyte–derived macrophages (mo-macs) drive immunosuppression in the tumor microenvironment (TME) and tumor-enhanced myelopoiesis in the bone marrow (BM) fuels these populations. Here, we performed paired transcriptome and chromatin analysis over the continuum of BM myeloid progenitors, circulating monocytes, and tumor-infiltrating mo-macs in mice and in patients with lung cancer to identify myeloid progenitor programs that fuel pro-tumorigenic mo-macs.
Read the full summary ↓
Analyzing chromatin accessibility and histone mark changes, we show that lung tumors prime accessibility for Nfe2l2 (NRF2) in BM myeloid progenitors as a cytoprotective response to oxidative stress. NRF2 activity is sustained and increased during monocyte differentiation into mo-macs in the lung TME to regulate oxidative stress, in turn promoting metabolic adaptation, resistance to cell death, and contributing to immunosuppressive phenotype. NRF2 genetic deletion and pharmacological inhibition significantly reduced mo-macs’ survival and immunosuppression in the TME, enabling NK and T cell therapeutic antitumor immunity and synergizing with checkpoint blockade strategies. Altogether, our study identifies a targetable epigenetic node of myeloid progenitor dysregulation that sustains immunoregulatory mo-macs in the TME.
Published in
Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification
Leader AM, Grout JA, Maier BB et al. · Cancer cell 2021 · PMID 34767762 · doi:10.1016/j.ccell.2021.10.009
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 GSE255330_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1074689 and SRA study SRP488933. Searching any of these in the dataset finder brings you back here.

Study design
65 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 43 samples read as cases, 3 as controls.

How we read this: Read from 65 sample titles: 65 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

+ 25 more — browse all 65 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.