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Spatial Transcriptional Landscape of Human Heart Failure

GSE271676 Homo sapiens (human) RNA-seq (gene expression) 178 samples Submitted 2025/05/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 178 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
178 samples: 178 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.
Paper
Spatial transcriptional landscape of human heart failureEuropean heart journal · 2025
Summary (written by the submitters)
Heart failure (HF) poses a major clinical challenge with diverse underlying causes. Despite advances, the intricate molecular alterations specific to distinct cell types and histological patterns during HF progression remain inadequately characterized. Using the GeoMx whole human transcriptome digital spatial profiler, we discerned gene expression dynamics in failing human hearts, integrating cell type, clinical, and histological nuances.
Read the full summary ↓
We demonstrated the cellular composition of the left ventricle myocardium and validated the GeoMx platform's compartmentalization efficiency, showing high accuracy for cardiomyocytes but limitations for endothelial cells and fibroblasts. Gene regulation in HF was influenced by cellular context, with contrasting patterns across cell types. Differentially expressed genes, such as UCHL1, IGFBP2, CCL14, ACKR1, and PLVAP, were linked to specific histologic features like degeneration and fibrosis. Multiplex immunohistochemistry confirmed the co-localization of PLVAP and ACKR1 in endothelial cells, correlating with fibrosis severity. Furthermore, our data highlight pronounced inflammatory responses in end-stage HF. Integrative analysis, combining histological and clinical perspectives, spotlighted CRIP3, PFKFB2, and TAX1BP3 as potential contributors to HF progression. Our findings underscore the pivotal role of cell enriched and histology-specific transcriptome mapping in understanding the complex pathophysiological landscape of failing hearts, unveiling potential therapeutic targets and molecular insights.
Published in
Spatial transcriptional landscape of human heart failure
Lee SE, Joo JH, Hwang HS et al. · European heart journal 2025 · PMID 40335066 · doi:10.1093/eurheartj/ehaf272
This dataset
Open GSE271676 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 GSE271676_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 PRJNA1132899 and SRA study SRP518469. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40335066) ↗
Study design
178 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 178 sample titles: 178 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

+ 138 more — browse all 178 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.