GEO series

High-fidelity and differential nonsense suppression in live cells and a frontotemporal dementia allele with human transfer RNAs

GSE300389 Homo sapiens (human) Single-nucleus RNA-seq 15 samples Submitted 2025/07/28 Platform GPL30173

At a glance

Single-nucleus RNA-seq · human · 15 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 15 samples: 5 × WTPAN vs 5 × tRNA-Arg-TCA-3-1 vs 5 × tRNA-Arg-TCA-4-1Groups 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: High-fidelity and differential nonsense suppression in live cells and a frontotemporal dementia allele with human transfer RNAsNucleic acids research · 2025

Summary (written by the submitters)

Nonsense mutations generate premature termination codons (PTCs) that are responsible for 11% of genetic disease alleles. The arginine (Arg, CGA) to stop (UGA) mutation is the most common PTC. Humans encode >600 tRNA genes with many identical and similar copies. We developed a dual fluorescent reporter to quantify PTC readthrough in live cells and found single nucleotide mutations of human tRNAArg gene variants enabled differential nonsense suppression that depended on the tRNA sequence and the cell type.

Read the full summary ↓

We investigated G36A variants of all six human tRNAArgUCG isodecoders, and only the TCG-6-1 tRNA, where G36A occurs in 0.01% of human genomes, was unable to translate nonsense codons. With tRNA sequencing, we showed that a suppressor tRNA derived from the TCG-3-1 gene was expressed 2.1-fold higher and generated 1.8-fold more nonsense suppression than a tRNA derived from the TCG-4-1 gene. In a neuroblastoma model of frontotemporal dementia (FTD), we observed >70% readthrough of progranulin R493X with a suppressor tRNA that represented 5% of the total tRNAArg pool. The tRNAs outperformed aminoglycoside induced nonsense suppression in efficacy, tolerability to the cells, and translation fidelity according to mass spectrometry. Our studies show that human nonsense suppressor tRNAs can correct genetic defects that cause disease.

Published in

High-fidelity and differential nonsense suppression in live cells and a frontotemporal dementia allele with human transfer RNAs

Beharry A, Ward C, Moore H et al. · Nucleic acids research 2025 · PMID 40794874 · doi:10.1093/nar/gkaf730

This dataset

Open GSE300389 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 GSE300389_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1280181 and SRA study SRP593492. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40794874) ↗

Study design

5 × WTPAN vs 5 × tRNA-Arg-TCA-3-1 vs 5 × tRNA-Arg-TCA-4-1

Supports a between-group comparison across 15 samples.

How we read this: 3 replicated groups read from 15 sample titles; they account for 15 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

GSM9059464 WTPAN-1 WTPAN
GSM9059465 WTPAN-2 WTPAN
GSM9059466 WTPAN-3 WTPAN
GSM9059467 WTPAN-4 WTPAN
GSM9059468 WTPAN-5 WTPAN
GSM9059469 tRNA-Arg-TCA-3-1-1 tRNA-Arg-TCA-3-1
GSM9059470 tRNA-Arg-TCA-3-1-2 tRNA-Arg-TCA-3-1
GSM9059471 tRNA-Arg-TCA-3-1-3 tRNA-Arg-TCA-3-1
GSM9059472 tRNA-Arg-TCA-3-1-4 tRNA-Arg-TCA-3-1
GSM9059473 tRNA-Arg-TCA-3-1-5 tRNA-Arg-TCA-3-1
GSM9059474 tRNA-Arg-TCA-4-1-1 tRNA-Arg-TCA-4-1
GSM9059475 tRNA-Arg-TCA-4-1-2 tRNA-Arg-TCA-4-1
GSM9059476 tRNA-Arg-TCA-4-1-3 tRNA-Arg-TCA-4-1
GSM9059477 tRNA-Arg-TCA-4-1-4 tRNA-Arg-TCA-4-1
GSM9059478 tRNA-Arg-TCA-4-1-5 tRNA-Arg-TCA-4-1

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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.