GEO series

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

GSE310860 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/03/09 Platform GPL34295

At a glance

RNA-seq (gene expression) · human · 27 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: Homo sapiens (human)
Samples: 27 samples: 9 conditions, mostly in triplicateGroups 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

Open GSE310860 in finder → Find more human RNA-seq 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 GSE310860_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: 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 PRJNA1367361 and SRA study SRP647974. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41747727) ↗

Study design

9 conditions, mostly in triplicate

WT_siMOCK ×3 WT_siSON ×3 SON_C-term-only_siSON ×3 SON_C-term-RSRP_siSON ×3 SON_C-term-RSRP-NEG_siSON ×3 SON_full-length_siSON ×3 SON_deltaC-term_siSON ×3 fly_SON_siSON ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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

GSM9310200 WT_siMOCK_rep1 WT_siMOCK
GSM9310201 WT_siMOCK_rep2 WT_siMOCK
GSM9310202 WT_siMOCK_rep3 WT_siMOCK
GSM9310203 WT_siSON_rep1 WT_siSON
GSM9310204 WT_siSON_rep2 WT_siSON
GSM9310205 WT_siSON_rep3 WT_siSON
GSM9310206 SON_C-term-only_siSON_rep1 SON_C-term-only_siSON
GSM9310207 SON_C-term-only_siSON_rep2 SON_C-term-only_siSON
GSM9310208 SON_C-term-only_siSON_rep3 SON_C-term-only_siSON
GSM9310209 SON_C-term-RSRP_siSON_rep1 SON_C-term-RSRP_siSON
GSM9310210 SON_C-term-RSRP_siSON_rep2 SON_C-term-RSRP_siSON
GSM9310211 SON_C-term-RSRP_siSON_rep3 SON_C-term-RSRP_siSON
GSM9310212 SON_C-term-RSRP-NEG_siSON_rep1 SON_C-term-RSRP-NEG_siS…
GSM9310213 SON_C-term-RSRP-NEG_siSON_rep2 SON_C-term-RSRP-NEG_siS…
GSM9310214 SON_C-term-RSRP-NEG_siSON_rep3 SON_C-term-RSRP-NEG_siS…
GSM9310215 SON_full-length_siSON_rep1 SON_full-length_siSON
GSM9310216 SON_full-length_siSON_rep2 SON_full-length_siSON
GSM9310217 SON_full-length_siSON_rep3 SON_full-length_siSON
GSM9310218 SON_deltaC-term_siSON_rep1 SON_deltaC-term_siSON
GSM9310219 SON_deltaC-term_siSON_rep2 SON_deltaC-term_siSON
GSM9310220 SON_deltaC-term_siSON_rep3 SON_deltaC-term_siSON
GSM9310221 fly_SON_siSON_rep1 fly_SON_siSON
GSM9310222 fly_SON_siSON_rep2 fly_SON_siSON
GSM9310223 fly_SON_siSON_rep3 fly_SON_siSON
GSM9310224 fish_SON_siSON_rep1 fish_SON_siSON
GSM9310225 fish_SON_siSON_rep2 fish_SON_siSON
GSM9310226 fish_SON_siSON_rep3 fish_SON_siSON

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