GEO series

Global epigenetic stability of liver-resident invariant natural killer T cells in response to cues capable of inducing transcriptional re-programming

GSE250192 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2024/10/02 Platform GPL24247Platform GPL17021

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 24 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: 24 samples: 20 distinct titles, 2 of them repeatedGroups 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: Transcriptional re-programming of liver-resident iNKT cells into T-regulatory type-1-like liver iNKT cells involves extensive gene de-methylationFrontiers in immunology · 2024

Summary (written by the submitters)

Unlike conventional CD4+ T cells, which are phenotypically and functionally plastic, invariant NKT (iNKT) cells generally exist in a terminally differentiated state. Although peripheral CD4+ T cells can acquire alternative epigenetic states in response to different cues, it remains unclear whether peripheral iNKT cells are epigenetically stable or maleable.

Read the full summary ↓

Repetitive encounters of liver-resident iNKT cells (LiNKTs) with galactosylceramide (aGalCer)/CD1d-coated nanoparticles (NPs) can trigger their differentiation into a LiNKT cell subset (LiNKTR1) expressing a T regulatory type 1 (TR1)-like transcriptional signature. Here we use a broad range of omics tools to dissect the epigenetic underpinnings of the LiNKT-LiNKTR1 transdifferentiation process as compared to those underlying the T-conv-to-T-follicular helper (TFH) and TFH-to-TR1 differentiation pathways. We show that LiNKTR1 cell generation is partially associated with treatment induced hypo-methylation of gene bodies and regulatory elements of upregulated genes, in the absence of significant changes in chromatin exposure, deposition of expression-promoting histone marks, or removal of their repressive counterparts from bivalent regulatory elements. In contrast, the Tconv-TFH differentiation process involves extensive remodeling of the chromatin and the acquisition of a broad repertoire of epigenetic modifications that are then inherited by TFH cell-derived TR1 cell progeny. These results suggest that peripheral tissue-resident iNKT cells, unlike conventional T cells but like TFH cells, are epigenetically stable and that such stability contributes to their phenotypic and functional stability.

Published in

Transcriptional re-programming of liver-resident iNKT cells into T-regulatory type-1-like liver iNKT cells involves extensive gene de-methylation

Montaño J, Garnica J, Yamanouchi J et al. · Frontiers in immunology 2024 · PMID 39315110 · doi:10.3389/fimmu.2024.1454314

This dataset

Open GSE250192 in finder → Find more mouse methylation 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 GSE250192_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 PRJNA1052646 and SRA study SRP478001. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39315110) ↗

Study design

20 distinct titles, 2 of them repeated

ATACseq CTL, ×3 ATACseq KRN, ×3

How we read this: Read from 24 sample titles: 20 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

GSM7975337 ATACseq CTL, replicate 1
GSM7975338 ATACseq CTL, replicate 2
GSM7975339 ATACseq CTL, replicate 3
GSM7975340 ATACseq KRN, replicate 1
GSM7975341 ATACseq KRN, replicate 2
GSM7975342 ATACseq KRN, replicate 3
GSM7975343 ChIPseq H3K27me3, CTL
GSM7975344 ChIPseq H3K27me3, KRN
GSM7975345 ChIPseq H3K27me3, Input CTL
GSM7975346 ChIPseq H3K27me3, Input KRN
GSM7975347 ChIPseq H3K27Ac, CTL
GSM7975348 ChIPseq H3K27Ac, KRN
GSM7975349 ChIPseq H3K27Ac, Input
GSM7975350 ChIPseq H3K4me3, CTL
GSM7975351 ChIPseq H3K4me3, KRN
GSM7975352 ChIPseq H3K4me3, Input
GSM7975353 10X scRNAseq, CTL
GSM7975354 10X scRNAseq, KRN
GSM7975355 10X scATACseq, CTL
GSM7975356 10X scATACseq, KRN
GSM7975357 Whole Genome Bisulfite Sequencing (WGBS), CTL
GSM7975358 Whole Genome Bisulfite Sequencing (WGBS), KRN
GSM7975359 SmartSeq2 scRNA-seq, CTL
GSM7975360 SmartSeq2 scRNA-seq, KRN

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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.