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

GSE207924 Homo sapiens (human) RNA-seq (gene expression) 160 samples Submitted 2024/07/03 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · human · 160 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
160 samples: 65 conditions, mostly in duplicateGroups 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.
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.
This dataset
Open GSE207924 in finder → Find more 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 GSE207924_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 PRJNA857695 and SRA study SRP385916. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
65 conditions, mostly in duplicate
3T3_nuclear_WT_0min ×2 3T3_nuclear_WT_15min ×2 3T3_nuclear_WT_30min ×2 3T3_nuclear_WT_60min ×2 3T3_nuclear_WT_120min ×2 3T3_cytoplasm_WT_0min ×2 3T3_cytoplasm_WT_15min ×2 3T3_cytoplasm_WT_30min ×2 +87 more

Supports a between-group comparison across 130 samples.

How we read this: 65 replicated groups read from 160 sample titles; they account for 130 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

+ 120 more — browse all 160 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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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.