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KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease [ChIP-seq]

GSE299829 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/09/10 Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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
12 samples: 12 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
KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS diseaseiScience · 2026
Summary (written by the submitters)
Understanding transcriptional regulatory networks (TRNs) in microglia is key to uncovering mechanisms driving central nervous system (CNS) disorders. Human iPSC-derived models offer a tractable system for studying microglia, yet variability between lines has limited reproducibility. Here, we use the standardized KOLF2.1J iTF line to rapidly generate microglia-like cells (iTF-Microglia) and profile TRNs under homeostatic and inflammatory conditions.
Read the full summary ↓
iTF-Microglia closely resemble primary brain microglia at both transcriptomic and epigenomic levels. Integrative analyses reveal microglia-enriched candidate cis-regulatory elements (cCREs) and dynamic enhancer remodeling upon differentiation and LPS+IFNG stimulation, involving key transcription factors (TFs) including NF-kB, IRF, and STAT families. TRNs active in iTF-Microglia are enriched for genetic variants linked to Alzheimer’s disease and other CNS disorders. These findings establish KOLF2.1J iTF-Microglia as a reproducible and genetically tractable platform for studying human microglial gene regulation and provide mechanistic insight into how TRN remodeling may contribute to CNS disease risk.
Published in
KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease
Rogers BB, Anderson AG, Rodriguez-Nunez I et al. · iScience 2026 · PMID 42338472 · doi:10.1016/j.isci.2026.116412
This dataset
Open GSE299829 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 GSE299829_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 PRJNA1276584 and SRA study SRP591960. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42338472) ↗
Study design
12 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 12 sample titles: 12 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
Datasets on a similar topic

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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.