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Nuclear Speckles enable processing of RNA from GC-rich Isochores [SON_Halo_CR_H3K9_H327me3]

GSE264300 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2026/03/09 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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
12 samples: 3 × HCT116 cells, control, H3K9me3 CUT & RUN, vs 3 × HCT116 cells, SON-depleted, H3K9me3 CUT & RUN, vs 3 × HCT116 cells, control, H3K27me3 CUT & RUN, vs 3 × HCT116 cells, SON-depleted, H3K27me3 CUT & RUN,Groups 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
Nuclear speckles enable processing of RNA from GC-rich isochoresCell · 2026
Summary (written by the submitters)
Nuclear Speckles are large, multi-component condensates in the mammalian nuclei that are thought to be involved in various stages of gene expression, including transcription, splicing, and export. Investigating the direct role of Speckles in these processes have been intractable due to their heterogeneous, multi-component nature. Here we show that Speckles are necessary for the splicing of GC-rich, levelled intron/exon architecture.
Read the full summary ↓
Acute removal of Speckles leads to a drastic downregulation of GC-rich genes which are clustered within GC-rich isochores. Genes outside these domains are physically away from Speckles and are not affected from loss of Speckles. Animal genomes that do not have GC-rich isochores, do not have GC-rich levelled intron/exon architecture and do not have Speckles. Our results suggest that the expansion of Speckles core proteins’ intrinsically disordered domains during vertebrate evolution facilitated GC-content elevation in Amniotes by creating a condensate necessary to splice the by-products of this process: GC-rich, levelled intron/exon architecture.
Published in
Nuclear speckles enable processing of RNA from GC-rich isochores
Małszycki M, Martina L, Ilık İA et al. · Cell 2026 · PMID 41747727 · doi:10.1016/j.cell.2026.01.011
This dataset
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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 GSE264300_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 PRJNA1101930 and SRA study SRP502591. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41747727) ↗
Study design
3 × HCT116 cells, control, H3K9me3 CUT & RUN, vs 3 × HCT116 cells, SON-depleted, H3K9me3 CUT & RUN, vs 3 × HCT116 cells, control, H3K27me3 CUT & RUN, vs 3 × HCT116 cells, SON-depleted, H3K27me3 CUT & RUN,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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