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3D porous microscaffold promoted hPSC-macrophage large-scale production by facilitating proliferation and self-renewal gene network

GSE272205 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 54 samples Submitted 2026/07/15 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 54 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
54 samples: 18 conditions, mostly in triplicateGroups 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)
Macrophages play crucial roles in various physiological and pathological processes, including immune response and tissue homeostasis. Therefore, the importance of macrophages in preclinical study and clinical application is increasing, necessitating the development of more efficient human pluripotent stem cell(hPSCs) derived macrophage(iMac) differentiation methods to meet the growing demand.
Read the full summary ↓
Utilizing three-dimensional porous microscaffold (PMS), we have devised a novel approach to induce hPSCs to generate a substantial number of macrophages. This method differs from existing hematopoietic differentiation methods for iMacs. Interestingly, it leads to the formation of a vascular-like hematopoiesis structure resembling the early aorta-gonad-mesonephros(AGM) during embryogenesis. The subsequent event involved the generation of a substantial quantity of macrophages undergoing robust proliferation. Multi-omics data analysis revealed that the 3D microenvironment may generate numerous ligands that promote macrophage proliferation and activate the expression of proliferation and self-renewal genes during iMac generation stage. The subsequent functional experiments demonstrated iMacs can serve as off-the-shelf products for the treatment of infection symptoms caused by drug-resistant Streptococcus pneumoniae infections, and may allow for further innovative cell-based treatment strategies.
This dataset
Open GSE272205 in finder → Find more human RNA-seq 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 GSE272205_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 PRJNA1135690 and SRA study SRP519788. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 conditions, mostly in triplicate
ATAC_2D-iMac_day12 ×4 ATAC_3D-iMac_day12 ×4 RNA_2D-iMac_day12 ×4 RNA_3D-iMac_day12 ×4 3D PMS iMac at day ×3 ChIP_2D-iMac_day12_K4me3 ×3 ChIP_3D-iMac_day12_K4me3 ×3 ChIP_3D-iMac_day12_K27me3 ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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