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Single-cell analysis uncovers preserved prostate cancer lineages and universally altered pathways in Matrigel-free patient-derived organoids

GSE305752 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2025/10/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 17 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)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
17 samples: 17 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
Single-cell analysis uncovers preserved prostate cancer lineages and universally altered pathways in Matrigel-free patient-derived organoidsCell reports · 2025
Summary (written by the submitters)
The application of patient-derived organoids (PDOs) in prostate cancer (PCa) research has been hampered by poor take-rates and benign overgrowth. We highlight the limitations of existing culture conditions and identify extracellular matrix (ECM) composition as a determinant of organoid outcome. Single-cell RNA sequencing (scRNA-seq) reveals that Matrigel-free PDOs preserve patient-specific PCa cells with active AR signaling, and enrich in intermediate cells.
Read the full summary ↓
In contrast, Matrigel fails to maintain primary PCa cells and produces in vitro basal-like cells divergent from patient samples. Furthermore, we redefine cell type-signatures, identify biomarkers discriminating tumor versus all other cell types, and show that expression of laminin-binding integrins is a hallmark of Matrigel-derived organoids. Finally, integrating previously published datasets with our data, we generate a Prostate PDO single-cell atlas (PPScA). The PPScA captures a spectrum of cellular identities, and reveals pathways universally altered in vitro. Our study provides methodological improvements for short-term cultures and cellular biology insights.
Published in
Single-cell analysis uncovers preserved prostate cancer lineages and universally altered pathways in Matrigel-free patient-derived organoids
Dolgos R, Parmentier R, Wang J et al. · Cell reports 2025 · PMID 41037396 · doi:10.1016/j.celrep.2025.116352
This dataset
Open GSE305752 in finder → Find more human RNA-seq datasets → Prostate cancer landscape →
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 GSE305752_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 PRJNA1307553. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41037396) ↗
Study design
17 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 17 sample titles: 17 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
More Prostate cancer datasets

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.