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Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction

GSE237916 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 197 samples Submitted 2024/03/01 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 197 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
Mus musculus (mouse)
Samples
197 samples: 86 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.
Paper
Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interactionImmunity · 2024
Summary (written by the submitters)
The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation.
Read the full summary ↓
Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ T cells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.
Published in
Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction
Liu S, Cao Y, Cui K et al. · Immunity 2024 · PMID 38614090 · doi:10.1016/j.immuni.2024.03.018
This dataset
Open GSE237916 in finder → Find more mouse 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 GSE237916_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 PRJNA997233 and SRA study SRP450763. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38614090) ↗
Study design
86 conditions, mostly in duplicate
Hi-TrAC_MLL4_WT_Th2_24h ×5 Hi-TrAC_HSS3_KO_Naive ×5 Hi-TrAC_HSS3_WT_Naive ×5 Hi-TrAC_MLL4_KO_Th2_24h ×4 RNA-seq_GATA3_KO_Naive ×4 RNA-seq_GATA3_WT_Naive ×4 Hi-TrAC_GATA3_KO_Naive ×4 Hi-TrAC_GATA3_WT_Naive ×4 +78 more

Supports a between-group comparison across 197 samples.

How we read this: 86 replicated groups read from 197 sample titles; they account for 197 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

+ 157 more — browse all 197 samples with per-sample file links →

Datasets on a similar topic

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