DOI: 10.5281/zenodo.13945274
title: Single-cell analysis uncovers preserved prostate cancer lineages and universally altered pathways in Matrigel-free patient-derived organoids

description: Patient-derived organoids (PDOs) offer new opportunities to model various cancers. However, their application in prostate cancer (PCa) has been hampered by poor take-rates and overgrowth of benign cell types. Initial experiments with 136 samples highlighted the limitations of existing culture conditions, while 30 additional samples were subsequently used to explore the effects of niche factors, carbon source, and extracellular matrix (ECM) composition on organoid outcome. Single-cell RNA sequencing (scRNA-seq) reveals that Matrigel-free PDOs exhibit cellular heterogeneity and preserve patient-specific PCa cell populations with active AR signaling, while enriching in intermediate club cell populations. In contrast, Matrigel fails to maintain primary PCa cells and produces in vitro basal-like benign transcriptomic profiles that are divergent from patient samples. Furthermore, we redefine cell type-signatures, identifying RNA- and protein-based biomarkers discriminating tumor versus all other cell types ex vivo, and show that expression of laminin-binding integrins is a hallmark of Matrigel-derived organoids. Finally, integrating previously-published datasets with our new data, we generate the first Prostate PDO single-cell atlas (PPScA). The PPScA captures a spectrum of cellular identities and malignancies, while revealing pathways universally altered in PDOs as compared to primary PCa tissues. Altogether, our study represents a significant advancement in the field, providing methodological improvements and novel cellular biology insights.
description: File Name



Description





scRNAseq_metadata.csv



Contains sequencing statistics and sample-specific annotations for all 17 samples.





SR_sample_matrix.mtx



Sparse matrix file for each sample, containing gene expression data (genes x cells) after genome alignment and cell demultiplexing. No quality control has been applied yet.





SR_sample_features.tsv



Tab-separated file containing gene identifiers (e.g., gene symbols or Ensembl IDs) for each sample.





SR_sample_barcodes.tsv



Tab-separated file containing unique cell barcodes for each sample.





Dolgos_early_passage_PDOs_matrix.mtx



Sparse matrix file for the combined early passage PDO samples. This data has undergone quality control to exclude low-quality cells.





Dolgos_early_passage_PDOs_features.tsv



Tab-separated file containing gene identifiers for the combined early passage PDO samples.





Dolgos_early_passage_PDOs_barcodes.tsv



Tab-separated file containing unique cell barcodes for the combined early passage PDO samples.





Dolgos_early_passage_PDOs_metadata.csv



Contains per-cell metadata for the combined early passage PDO samples. Includes UMAP coordinates, size factors used for log normalization, clustering annotations, cell cycle phase annotations, and signature scores.





Atlas_PCa_PDOs_matrix.mtx



Sparse matrix file for the combined PCa PDOs datasets (Dolgos_2024, Huang_2023, Song_2022, McCray_2019). This data has undergone quality control to exclude low-quality cells.





Atlas_PCa_PDOs_features.tsv



Tab-separated file containing gene identifiers for the combined PCa PDOs datasets (Dolgos_2024, Huang_2023, Song_2022, McCray_2019) .





Atlas_PCa_PDOs_barcodes.tsv



Tab-separated file containing unique cell barcodes for the combined PCa PDOs datasets (Dolgos_2024, Huang_2023, Song_2022, McCray_2019) .





Atlas_PCa_PDOs_metadata.csv



Contains per-cell metadata for the combined early passage PDO samples. Includes UMAP coordinates, size factors used for log normalization, and cell malignancy.