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Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the transcript life cycle

GSE264556 Homo sapiens (human) RNA-seq (gene expression) 19 samples Submitted 2024/07/03 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 19 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
19 samples: 5 conditions, mostly with about 4 replicates eachGroups 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
Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycleMolecular cell · 2024
Summary (written by the submitters)
Series of regulatory mechanisms control eukaryotic mRNAs, from their production on chromatin to their eventual degradation. Dissecting these pathways requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and the cytoplasm.
Read the full summary ↓
All rates displayed substantial variability genome-wide, and transcripts from genes with related functions flowed across subcellular compartments with similar kinetics. For some genes, most transcripts were rapidly degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. With compartment-specific measurements of poly(A) tail lengths, we found that relationships between tail length and stability differ across the cell. The targets of RNA binding proteins experience distinct RNA flow kinetics, and we found that two, DDX3X and PABPC4, act by controlling the nuclear export of their targets. Finally, we developed a machine learning model to propose additional molecular features that underlie the diverse life cycle of mammalian mRNAs.
Published in
Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle
Ietswaart R, Smalec BM, Xu A et al. · Molecular cell 2024 · PMID 38964322 · doi:10.1016/j.molcel.2024.06.008
This dataset
Open GSE264556 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 GSE264556_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 PRJNA1103145 and SRA study SRP503374. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38964322) ↗
Study design
5 conditions, mostly with about 4 replicates each
K562_WholeCell ×4 K562_Cytoplasm ×4 K562_NuclearWash ×4 K562_Nucleus ×4 K562_NuclearWashNorRNADepletion ×3

Supports a between-group comparison across 19 samples.

How we read this: 5 replicated groups read from 19 sample titles; they account for 19 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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