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Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas

GSE291551 Homo sapiens (human) Single-cell RNA-seq 11 samples Submitted 2025/06/23 Platform GPL21103Platform GPL24676
At a glance

Single-cell RNA-seq · human · 11 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
11 samples: 5 × human kidney biopsy, disease, sample vs 4 × Folic acid model, mouse kidney, biolGroups 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
Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlasNature genetics · 2025
Summary (written by the submitters)
The use of single-cell RNA sequencing in clinical and translational research is limited by cross-species differences and the challenge of identifying cell-type-specific, targetable molecular changes in individual patients. Here, we created an Integrated Single-cell Kidney Atlas including over one million cells from 140 samples, defining more than 70 conserved cell states in human and rodent models.
Read the full summary ↓
We developed CellSpectra, a computational tool that quantifies changes in gene expression coordination across cellular functions, which we applied to kidney and lung cancer data. This tool powers our Patient-Level Single-Cell Functional Profiling Report, which highlights cell-type-specific changes in pathway gene expression coordination in individuals. Our cross-species atlas facilitates the selection of a rodent model that closely reflects the cellular and pathway-level signatures observed in a patient sample, advancing the application of single-cell methodologies in clinical precision medicine. Finally, by using experimental models, we demonstrate how our informatics approach can be applied for the potential selection of suitable therapeutics.
Published in
Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas
Klötzer KA, Abedini A, Li S et al. · Nature genetics 2025 · PMID 40775269 · doi:10.1038/s41588-025-02285-0
This dataset
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.

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 GSE291551_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1234133 and SRA study SRP569200. Searching any of these in the dataset finder brings you back here.

Study design
5 × human kidney biopsy, disease, sample vs 4 × Folic acid model, mouse kidney, biol

Supports a case/control comparison: 5 samples read as cases, 1 as controls.

How we read this: 2 replicated groups read from 11 sample titles; they account for 9 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.