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Hepatic leukemia factor directs tissue residency of proinflammatory CD4+ T cells

GSE295436 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 22 samples Submitted 2025/07/04 Platform GPL24676Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 22 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
22 samples: 5 × ECRS_PBMC vs 5 × ECRS_Polyp vs 2 × scRNA_7w_Asp_ivnega vs 2 × scRNA_7w_Asp_ivposiGroups 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
Hepatic leukemia factor directs tissue residency of proinflammatory memory CD4(+) T cellsScience (New York, N.Y.) · 2025
Summary (written by the submitters)
CD4+ tissue-resident memory T cells (TRM) contribute to both host defense and pathogenesis of chronic inflammatory diseases. However, the molecular mechanisms that direct tissue residency and functional heterogeneity of CD4+ TRM remain unknown. We herein show that the transcription factor, hepatic leukemia factor (HLF), directs the tissue residency program and functionality of CD4+ TRM.
Read the full summary ↓
HLF simultaneously upregulates tissue retention receptors and downregulates tissue egress receptors via changes in chromatin accessibility, and drives proinflammatory CD4+ TRM by inducing Bhlhe40. Genetic deletion of Hlf inhibits CD4+ TRM generation, consequently ameliorating airway tissue inflammation in vivo. In humans, HLF-positive CD4+ TRM from inflamed airway tissue have a tissue residency signature and express inflammatory cytokines. HLF is therefore a central regulator of proinflammatory CD4+ TRM development and function.
Published in
Hepatic leukemia factor directs tissue residency of proinflammatory memory CD4(+) T cells
Kiuchi M, Nemoto M, Yagyu H et al. · Science (New York, N.Y.) 2025 · PMID 41379973 · doi:10.1126/science.adp0714
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 GSE295436_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 PRJNA1254670 and SRA study SRP580812. Searching any of these in the dataset finder brings you back here.

Study design
5 × ECRS_PBMC vs 5 × ECRS_Polyp vs 2 × scRNA_7w_Asp_ivnega vs 2 × scRNA_7w_Asp_ivposi

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 22 sample titles; they account for 14 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
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