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Longitudinal monitoring of cytoplasmic RBP-RNA interactions and transcriptome in living cells by engineered protein nanocages

GSE293919 Mus musculus (mouse) Non-coding RNA profiling 424 samples Submitted 2026/06/24 Platform GPL24247Platform GPL29480Platform GPL24676
At a glance

Non-coding RNA profiling · mouse · 424 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Mus musculus (mouse)
Samples
424 samples: 197 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
Longitudinal monitoring of cytoplasmic RBP-RNA interactions and transcriptome in living cells by engineered protein nanocagesMolecular cell · 2026
Summary (written by the submitters)
Most existing RNA sequencing methods rely on cell lysis or fixation, limiting their use in longitudinal studies of the same cell population. Here, we introduce POND-seq (Protein nanocage-empOwered Non-Destructive sequencing), a strategy that employs secretory protein nanocages fused to RNA-binding proteins (RBPs) to recover RBP-associated RNAs from living cells.
Read the full summary ↓
POND-seq robustly identifies RNA targets of cytoplasmic RBPs across multiple cell types and enables longitudinal tracking of dynamic changes in RBP-associated RNA profiles under stress conditions. Fusion to PABPC1 further allows monitoring of transcriptomic responses and selectively profile cell-type-specific transcriptomes from mixed populations without cell dissociation and sorting. Additionally, POND-seq supports functional interrogation of RBP domains and residues involved in RNA association and enables scalable analysis of RBP variants, as demonstrated by a systematic assessment of disease-associated FMR1 mutations. Together, POND-seq provides a versatile and scalable platform for non-destructive and longitudinal analysis of cytoplasmic transcriptome and RBP-associated RNAs.
Published in
Longitudinal monitoring of cytoplasmic RBP-RNA interactions and transcriptome in living cells by engineered protein nanocages
Hu LF, Xie G, Wu YX et al. · Molecular cell 2026 · PMID 42349405 · doi:10.1016/j.molcel.2026.06.007
This dataset
Open GSE293919 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 GSE293919_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 ↗

Also filed as BioProject PRJNA1247426 and SRA study SRP576915. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42349405) ↗
Study design
197 conditions, mostly in duplicate
POND-FMR1-WT ×6 293T-AGO2-miRNA_celllysate ×3 293T-EPN24-miRNA_celllysate ×3 POND-SLBP ×2 POND-GFP_48H ×2 POND-IGF2BP1_8H ×2 POND-IGF2BP1_16H ×2 POND-IGF2BP1_24H ×2 +189 more

Supports a between-group comparison across 400 samples.

How we read this: 197 replicated groups read from the first 400 of 424 sample titles; they account for 400 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

+ 384 more — browse all 424 samples with per-sample file links →

Datasets on a similar topic

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