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Selective RNA sequestration in biomolecular condensates directs cell fate transitions

GSE302363 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2025/09/30 Platform GPL24676Platform GPL24247
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: 20 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.
Paper
Selective RNA sequestration in biomolecular condensates directs cell fate transitionsNature biotechnology · 2025
Summary (written by the submitters)
Recent studies have emphasized the significance of biomolecular condensates in modulating gene expression through RNA processing and translational control. However, the functional roles of RNA condensates in cell fate specification remains poorly understood. Here, we profiled the coding and non-coding transcriptome within intact biomolecular condensates, specifically P-bodies, in diverse developmental contexts, spanning multiple vertebrate species.
Read the full summary ↓
Our analyses revealed the conserved, cell type-specific sequestration of untranslated RNAs encoding key cell fate regulators. Notably, P-body contents did not directly reflect active gene expression profiles for a given cell type, but rather were enriched for translationally repressed transcripts characteristic of the preceding developmental stage. Mechanistically, microRNAs (miRNAs) direct the selective sequestration of RNAs into P-bodies in a context-dependent manner, and perturbing AGO2 or alternative polyadenylation profoundly reshapes P-body RNA content. Building on these mechanistic insights, we demonstrate that modulating P-body assembly or miRNA activity dramatically enhances both activation of a totipotency transcriptional program in naïve pluripotent stem cells as well as the programming of primed human embryonic cells towards the germ cell lineage. Collectively, our findings establish a direct link between biomolecular condensates and cell fate decisions across vertebrate species and provide a novel framework for harnessing condensate biology to expand clinically relevant cell populations.
Published in
Selective RNA sequestration in biomolecular condensates directs cell fate transitions
Pessina P, Nevo M, Shi J et al. · Nature biotechnology 2025 · PMID 41152623 · doi:10.1038/s41587-025-02853-z
This dataset
Open GSE302363 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 GSE302363_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 PRJNA1290254 and SRA study SRP600115. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41152623) ↗
Study design
20 conditions, mostly in duplicate
m2C_shCTRL_Neg ×2 m2C_shCTRL_Pos ×2 m2C_shDdx6-82_Neg ×2 m2C_shDdx6-82_Pos ×2 m2C_shDdx6-83_Neg ×2 m2C_shDdx6-83_Pos ×2 PGCs_Ctrl_KD_neg ×2 PGCs_Ctrl_KD_double_positive ×2 +12 more

Supports a between-group comparison across 40 samples.

How we read this: 20 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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