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Single-Cell Splicing Isoform Atlas of the Adult Human Heart and Heart Failure

GSE288222 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/09/29 Platform GPL26167
At a glance

Single-cell RNA-seq · human · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
12 samples: 6 × non-diseased, patient ND vs 6 × end-stage heart failure, patient HFGroups 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
Single-Cell Splicing Isoform Atlas of the Adult Human Heart and Heart FailureCirculation · 2025
Summary (written by the submitters)
BACKGROUND: Alternative splicing plays crucial roles in normal heart development and cardiac disease by influencing protein-coding sequences, functional domains, and molecular networks. However, a detailed characterization of the human heart isoform landscape remains incomplete. METHODS: Leveraging long-read single-nucleus RNA sequencing and computational analysis, we dissected full-length isoform heterogeneities, expression patterns, and usage shifts across cell types, cell states, and cardiac conditions of the adult left ventricle.
Read the full summary ↓
We applied in silico approaches to assess the functional relevance of identified isoforms; validated isoform compositions of representative cardiac genes using reverse transcription quantitative polymerase chain reaction and targeted amplicon sequencing; and developed a web server for interactive navigation of our results. RESULTS: The data revealed that isoform heterogeneity is widespread in the cardiac cellular system, serving as a posttranscriptional buffer system that calibrates the molecule reservoirs in human hearts. In healthy left ventricles, ≈30% of cell type–specific genes were polyform, using multiple isoforms tailored to cell type–specific programs. Among ubiquitously expressed genes, >300 showed differential isoform usage with cell type specificity. Compared with heart failure, 379 genes in cardiomyocytes demonstrated marked isoform usage shifts, most of which are predicted to change protein coding outcomes through direct changes in protein coding sequences and switches between intron retention and non–protein-coding biotypes. In contrast, cell state–specific programs tend to operate on monoform genes associated with changes among cell states. In addition, our data revealed heart failure–associated differential isoform usage events in stromal and immune cell types in the cardiac microenvironment. CONCLUSIONS: We present a comprehensive atlas of splicing isoforms in the normal adult heart and heart failure through long-read single-nucleus RNA sequencing and comprehensive computational analyses. The results suggest crucial roles of isoforms in buffering core cellular programs and contributing to disease-associated cell states. The full-length details of these cell-specific isoforms serve as an important reference for downstream translational and mechanistic studies and are available on our online data portal at https://github.com/gaolabtools/heart-isoform-atlas. Github repo: https://github.com/gaolabtools/heart-isoform-atlas doi: 10.1161/CIRCULATIONAHA.125.074959
Published in
Single-Cell Splicing Isoform Atlas of the Adult Human Heart and Heart Failure
Pan T, Lu L, Youker K et al. · Circulation 2025 · PMID 41017471 · doi:10.1161/CIRCULATIONAHA.125.074959
This dataset
Open GSE288222 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 GSE288222_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 PRJNA1216590 and SRA study SRP560425. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41017471) ↗
Study design
6 × non-diseased, patient ND vs 6 × end-stage heart failure, patient HF

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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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