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The H3K9me3 heterochromatin integrity and function are sustained by H3K9me3 methyltransferases-HP1 dependencies [TT-Seq]

GSE233040 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2024/09/19 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 30 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
30 samples: 15 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
Distinct H3K9me3 heterochromatin maintenance dynamics govern different gene programs and repeats in pluripotent cellsbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
H3K9me3-heterochromatin, compacted by HP1 proteins, restricts transcription factor binding and functions as a barrier to cell fate changes in development and reprogramming1,2. To investigate principles underlying H3K9me3-heterochromatin maintenance in mammalian cells, we applied degron engineering3,4 on mouse ESCs to conditionally deplete all three H3K9me3-methyltransferases within one hour and found an unexpectedly dynamic nature of heterochromatin maintenance.
Read the full summary ↓
Following H3K9me3 methyltransferases degradation, HP1β dissociates from heterochromatin within three hours, and transcription factors quickly access the de-compacted heterochromatin, activating transcription of their targets, which then promotes H3K9me3 decay. Both passive dilution and active removal contribute to the H3K9me3 decay. Mathematical modeling reveals distinct H3K9me3 heterochromatin states with diverse chromatin features which influence initial H3K9me3 levels and the stability of H3K9me3 domains, thereby expanding our understanding of distinct heterochromatin domains. Rampant transposable element de-repression interferes with transcription of poised early developmental and signaling genes outside H3K9me3-domains, highlighting the intricate roles of dynamic H3K9me3-heterochromatin maintenance in safeguarding developmental programs. Our findings deconvolve the hierarchical relationships between H3K9me3 marks, methyltransferases, HP1 and transcription factors to reveal principles of H3K9me3-heterochromatin maintenance and how its remodeling enables transcription and cell fate change.
Published in
Distinct H3K9me3 heterochromatin maintenance dynamics govern different gene programs and repeats in pluripotent cells
Zhang J, Donahue G, Gilbert MB et al. · bioRxiv : the preprint server for biology 2024 · PMID 39345615 · doi:10.1101/2024.09.16.613328
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 GSE233040_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA974827 and SRA study SRP438817. Searching any of these in the dataset finder brings you back here.

Study design
15 conditions, mostly in duplicate
TTseq.0h.rep1 ×2 TTseq.0h.rep2 ×2 TTseq.0h.rep3 ×2 TTseq.12h.rep1 ×2 TTseq.12h.rep2 ×2 TTseq.12h.rep3 ×2 TTseq.24h.rep1 ×2 TTseq.24h.rep2 ×2 +7 more

Supports a between-group comparison across 30 samples.

How we read this: 15 replicated groups read from 30 sample titles; they account for 30 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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