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Long read subcellular fractionation and sequencing reveals the translational fate of full length mRNA isoforms during neuronal differentiation

GSE244655 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2024/03/11 Platform GPL26167Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 16 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
Long-read subcellular fractionation and sequencing reveals the translational fate of full-length mRNA isoforms during neuronal differentiationGenome research · 2024
Summary (written by the submitters)
Alternative splicing (AS) alters the cis-regulatory landscape of mRNA isoforms leading to transcripts with distinct localization, stability and translational efficiency. To rigorously investigate mRNA isoform-specific ribosome association, we generated subcellular fractionation and sequencing (Frac-seq) libraries using both conventional short reads and long reads from human embryonic stem cells (ESC) and neural progenitor cells (NPC) derived from the same ESC.
Read the full summary ↓
We performed de novo transcriptome assembly from high-confidence long reads from cytosolic, monosomal, light and heavy polyribosomal fractions and quantified their abundance using short reads from their respective subcellular fractions. Thousands of transcripts in each cell type exhibited association with particular subcellular fractions relative to the cytosol. Of the multi-isoform genes, 27% and 19% exhibited significant differential isoform sedimentation in ESC and NPC respectively. Alternative promoter usage and internal exon skipping accounted for the majority of differences between isoforms from the same gene. Random forest classifiers implicated coding sequence (CDS) and UTR lengths as important determinants of isoform-specific sedimentation profiles, and motif analyses reveal potential cell type-specific and subcellular fraction-associated RNA-binding protein signatures. Taken together our data demonstrate that alternative mRNA processing within the CDS and UTRs impacts the translational control of mRNA isoforms during stem cell differentiation, and highlights the utility of using a novel long-read sequencing-based method to study translational control.
Published in
Long-read subcellular fractionation and sequencing reveals the translational fate of full-length mRNA isoforms during neuronal differentiation
Ritter AJ, Draper JM, Vollmers C et al. · Genome research 2024 · PMID 38839373 · doi:10.1101/gr.279170.124
This dataset
Open GSE244655 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 GSE244655_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 PRJNA1023968 and SRA study SRP464529. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38839373) ↗
Study design
16 conditions, mostly in triplicate
ESC, short read, cytosol, ×3 ESC, short read, monosome, ×3 ESC, short read, heavy polyribosome (5+ ribos… ×3 ESC, short read, light polyribosome (2-4 ribo… ×3 NPC, short read, cytosol, ×3 NPC, short read, monosome, ×3 NPC, short read, heavy polyribosome (5+ ribos… ×3 NPC, short read, light polyribosome (2-4 ribo… ×3 +8 more

Supports a between-group comparison across 40 samples.

How we read this: 16 replicated groups read from 40 sample titles; they account for 40 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
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