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Setdb1 regulates proper differentiation of adult intestinal stem cells via restraining permissive chromatin structure and transcriptional variability

GSE279796 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2024/10/21 Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 18 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
18 samples: 18 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.
Summary (written by the submitters)
The histone methylase Setdb1 plays a pivotal role in embryonic stem cell maintenance and developmental lineage specification. However, its function in adult stem cells remains elusive. Here we show that conditional inactivation of Setdb1 in Lgr5+ intestinal stem cells alters the transcriptional programs of the progeny cell types and results in increased cell-to-cell transcriptional variability.
Read the full summary ↓
Loss of Setdb1 blocked differentiation towards the absorptive enterocyte lineage, while the generation of secretory cell types was only marginally affected due to the activation of alternative developmental trajectories. Setdb1 inactivation did not alter H3K9 methylation at large heterochromatin domains but led to reduced localized modifications at islands of gene-rich areas around transposase-accessible chromatin regions, which have doubled in number, became broader and more heterogeneous in size. The results demonstrate that Setdb1 regulates intestinal stem cell properties and epithelial lineage specification by limiting chromatin accessibility at open genomic areas and controlling cell-to-cell transcriptional variability.
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 GSE279796_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 PRJNA1174708 and SRA study SRP539356. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
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