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The role of structural vs cellular remodeling in arrhythmogenesis: personalized computer models of atrial fibrillation

GSE269882 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/03/08 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 9 × right atrial appendage, sinus rhythm, patient n vs 8 × right atrial appendage, post-operative AF, patient n vs 5 × right atrial appendage, persistent AF, patient n vs 5 × right atrial appendage, paroxysmal AF, patient nGroups 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)
Atrial fibrillation (AF) is a progressive disease involving both structural and functional remodeling. To investigate the contribution of cell-scale functional remodeling to AF pathogenesis, we combined personalized 3D anatomical models with pathology-specific ionic models. The latter were developed using recordings in myocytes isolated from patients in sinus rhythm, paroxysmal, postoperative, and persistent AF.
Read the full summary ↓
To quantify AF dynamics, we developed a novel algorithm for locating RDs by backtracking the conduction velocity field from the wavebreak regions. We demonstrate that our novel algorithm is at least 700 times faster than the traditional phase singularity analysis. The inducibility of simulated AF was not pathology-dependent, but pathological models demonstrate a more extensive arrhythmogenic substrate compared to the sinus rhythm. AF driver locations depend on electrophysiological remodeling; differences between pathology-specific models are explained by differences in wavebreak patterns. Specifically, RDs tend to dwell in the regions with the highest wavebreak probability.
This dataset
Open GSE269882 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 GSE269882_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 PRJNA1124064 and SRA study SRP514001. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 × right atrial appendage, sinus rhythm, patient n vs 8 × right atrial appendage, post-operative AF, patient n vs 5 × right atrial appendage, persistent AF, patient n vs 5 × right atrial appendage, paroxysmal AF, patient n

Supports a between-group comparison across 27 samples.

How we read this: 4 replicated groups read from 27 sample titles; they account for 27 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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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.