GEO series

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

GSE230029 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/07/03 Platform GPL20301

At a glance

RNA-seq (gene expression) · human · 24 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: 24 samples: 24 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: Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycleMolecular cell · 2024

Summary (written by the submitters)

Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation 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 cytoplasm.

Read the full summary ↓

These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles 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 GSE230029 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 GSE230029_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 PRJNA957227 and SRA study SRP433325. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38964322) ↗

Study design

24 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 24 sample titles: 24 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

GSM7184209 sgDDX3 arm, initial time point, replicate 1, ribosome footprints
GSM7184210 sgDDX3 arm, day 14 time point, replicate 1, ribosome footprints
GSM7184211 sgDDX3 arm, day 3 time point, replicate 1, ribosome footprints
GSM7184212 sgDDX3 arm, initial time point, replicate 2, ribosome footprints
GSM7184213 sgDDX3 arm, day 14 time point, replicate 2, ribosome footprints
GSM7184214 sgDDX3 arm, day 3 time point, replicate 2, ribosome footprints
GSM7184215 sgGal4 arm, initial time point, replicate 1, ribosome footprints
GSM7184216 sgGal4 arm, day 14 time point, replicate 1, ribosome footprints
GSM7184217 sgGal4 arm, day 3 time point, replicate 1, ribosome footprints
GSM7184218 sgGal4 arm, initial time point, replicate 2, ribosome footprints
GSM7184219 sgGal4 arm, day 14 time point, replicate 2, ribosome footprints
GSM7184220 sgGal4 arm, day 3 time point, replicate 2, ribosome footprints
GSM7184221 sgDDX3 arm, initial time point, replicate 1, RNAseq
GSM7184222 sgDDX3 arm, day 14 time point, replicate 1, RNAseq
GSM7184223 sgDDX3 arm, day 3 time point, replicate 1, RNAseq
GSM7184224 sgDDX3 arm, initial time point, replicate 2, RNAseq
GSM7184225 sgDDX3 arm, day 14 time point, replicate 2, RNAseq
GSM7184226 sgDDX3 arm, day 3 time point, replicate 2, RNAseq
GSM7184227 sgGal4 arm, initial time point, replicate 1, RNAseq
GSM7184228 sgGal4 arm, day 14 time point, replicate 1, RNAseq
GSM7184229 sgGal4 arm, day 3 time point, replicate 1, RNAseq
GSM7184230 sgGal4 arm, initial time point, replicate 2, RNAseq
GSM7184231 sgGal4 arm, day 14 time point, replicate 2, RNAseq
GSM7184232 sgGal4 arm, day 3 time point, replicate 2, RNAseq

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