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The molecular landscape in autosomal dominant polycystic kidney disease is characterized by common alterations in purine metabolism

GSE289843 Homo sapiens (human) RNA-seq (gene expression) 55 samples Submitted 2025/08/07 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 55 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
55 samples: 45 distinct titles, 8 of them repeatedGroups 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)
Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease. Cysts develop through dedifferentiation of tubular epithelial cells, but the sequence of molecular events and their relative importance remain unclear. To address this gap in knowledge, 40 cysts from 4 ADPKD kidneys and 4 microcystic tissues were mapped on transcriptomic and histological level.
Read the full summary ↓
Cyst were heterogenous and we identified 6 cystic subclusters with 2 deviations from the main trajectory, dependent on the rate of interstitial remodeling, inflammation and dedifferentiation. Loss of proximal tubular marker gene expression was more pronounced compared to those of other tubular segments. Altered expression of metabolic pathways was consistent among the cysts, which was further analyzed in human and mouse cell lines. Purine metabolism was similarly altered in all ADPKD cell lines, and its modulation with azathioprine suppressed cyst formation in vitro. In conclusion, by focusing on common altered pathways in cysts and cell models, we have identified purine metabolism as a novel potential target in ADPKD.
This dataset
Open GSE289843 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 GSE289843_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 PRJNA1225177 and SRA study SRP564613. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
45 distinct titles, 8 of them repeated
Healthy ×4 Patient1_Cyst 1C ×2 Patient1_Cyst 1D ×2 Patient1_Cyst 1E ×2 Patient2_Cyst2B ×2 Patient2_Cyst2D ×2 Patient2_Cyst2G ×2 Patient2_Cyst2I ×2

Supports a case/control comparison: 51 samples read as cases, 4 as controls.

How we read this: Read from 55 sample titles: 45 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

+ 15 more — browse all 55 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.