GEO series

Dysregulated ribonucleoprotein granules impair mitochondrial activity in RBM20-dilated cardiomyopathy

GSE305648 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/07/17 Platform GPL30173

At a glance

RNA-seq (gene expression) · human · 12 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: 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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)

Pathogenic variants in the RNA-binding motif protein 20 (RBM20) cause severe dilated cardiomyopathy (DCM). While loss-of-function (LoF) variants lead to splicing dysfunction, gain-of-function (GoF) variants also mislocalize the protein to cytoplasmic ribonucleoprotein (RNP) granules resulting in a more aggressive disease phenotype with a yet unknown pathogenic mechanism.

Read the full summary ↓

Here, we show that the induction of severe mitochondrial dysfunction is a key consequence of RBM20 mislocalization. Proteomic profiling of RBM20-GoF mouse hearts reveals a predominant reduction in mitochondrial membrane protein solubility and widespread post-transcriptional downregulation of mitochondrial proteins, including Transmembrane Protein 65 (TMEM65), a regulator of mitochondrial calcium efflux. Ultrastructural analysis shows enlarged mitochondria with aberrant morphology. Functional assays confirm impaired mitochondrial respiration, transmembrane potential, and calcium handling in RBM20-GoF, but not LoF models. Using human iPSC-derived cardioids, we validate that RBM20 mislocalization, not splicing deficiency, drives mitochondrial dysfunction in DCM. These findings establish a mechanistic link between dysregulated RNP granules and mitochondrial defects in RBM20-DCM, resembling pathogenic features observed in Fused in Sarcoma protein (FUS)-associated neurodegeneration causing amyotrophic lateral sclerosis (ALS), where cytoplasmic mislocalization of an RNA-binding protein disrupts granule dynamics and organelle function.

This dataset

Open GSE305648 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 GSE305648_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 PRJNA1307415 and SRA study SRP609695. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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

GSM9181353 RBM20_KO_Rep1_bA
GSM9181354 RBM20_KO_Rep2_bC
GSM9181355 RBM20_KO_Rep3_bB
GSM9181356 RBM20_PL_Rep1_bA
GSM9181357 RBM20_PL_Rep2_bB
GSM9181358 RBM20_PL_Rep3_bC
GSM9181359 RBM20_RQ_Rep1_bB
GSM9181360 RBM20_RQ_Rep2_bC
GSM9181361 RBM20_RQ_Rep3_bA
GSM9181362 RBM20_WT_Rep1_bC
GSM9181363 RBM20_WT_Rep2_bA
GSM9181364 RBM20_WT_Rep3_bB

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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.