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Profiling transcriptome composition and dynamics within nuclear compartments using SLAM-RT&Tag

GSE272219 Homo sapiens (human) RNA-seq (gene expression) 140 samples Submitted 2025/03/04 Platform GPL30173Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 140 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
140 samples: 140 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.
Paper
Profiling transcriptome composition and dynamics within nuclear compartments using SLAM-RT&TagMolecular cell · 2025
Summary (written by the submitters)
Nuclear compartments are membrane-less nuclear regions that are enriched in functionally related molecules. RNA is a major component of many nuclear compartments, but the identity and dynamics of transcripts within nuclear compartments are poorly understood. Here, we applied Reverse Transcribe & Tagment (RT&Tag) to identify the transcript populations of Polycomb domains and of nuclear speckles within the nucleus.
Read the full summary ↓
We also developed SLAM-RT&Tag, which combines RNA metabolic labeling with RT&Tag, to quantify transcript dynamics within nuclear compartments. Intriguingly, exceptionally long genes are transcribed adjacent to Polycomb domains, and are transiently associated with chromatin. In contrast, diverse incompletely spliced mature transcripts migrate to nuclear speckles, and are generally transient within speckles. Speckle transcripts are retained longer when splicing is inhibited, suggesting post-transcriptional processing is coupled to speckle dynamics. Our findings argue that nuclear speckles act as quality control checkpoints that transiently confine incompletely spliced transcripts and facilitate their post-transcriptional splicing.
Published in
Profiling transcriptome composition and dynamics within nuclear compartments using SLAM-RT&Tag
Khyzha N, Ahmad K, Henikoff S · Molecular cell 2025 · PMID 40073862 · doi:10.1016/j.molcel.2025.02.012
This dataset
Open GSE272219 in finder → Find more human 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 GSE272219_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 PRJNA1135881 and SRA study SRP521704. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40073862) ↗
Study design
140 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 140 sample titles: 140 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

+ 100 more — browse all 140 samples with per-sample file links →

Datasets on a similar topic

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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.