A single-cell atlas of the mouse and human prostate reveals heterogeneity and conservation of epithelial progenitors
Abstract
Understanding the cellular constituents of the prostate is essential for identifying the cell of origin for prostate adenocarcinoma. Here we describe a comprehensive single-cell atlas of the adult mouse prostate epithelium, which displays extensive heterogeneity. We observe distal lobe-specific luminal epithelial populations (LumA, LumD, LumL, and LumV), a proximally-enriched luminal population (LumP) that is not lobe-specific, and a periurethral population (PrU) that shares both basal and luminal features. Functional analyses suggest that LumP and PrU cells have multipotent progenitor activity in organoid formation and tissue reconstitution assays. Furthermore, we show that mouse distal and proximal luminal cells are most similar to human acinar and ductal populations, that a PrU-like population is conserved between species, and that the mouse lateral prostate is most similar to the human peripheral zone. Our findings elucidate new prostate epithelial progenitors, and help resolve long-standing questions about anatomical relationships between the mouse and human prostate.
Data availability
Single-cell RNA-sequencing data from this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE150692, and can also be accessed through the Broad Institute Single-Cell Portal (www.singlecell.broadinstitute.org).
-
Single-cell RNA-seq analysis of mouse and human prostateNCBI Gene Expression Omnibus, GSE150692.
Article and author information
Author details
Funding
National Cancer Institute (R01CA238005)
- Michael M Shen
National Cancer Institute (U54CA193313)
- Raul Rabadan
- Michael M Shen
National Cancer Institute (P50CA211024)
- Massimo Loda
- Michael M Shen
National Cancer Institute (K99CA194287)
- Maho Shibata
T.J. Martell Foundation
- Michael M Shen
Department of Defense Prostate Cancer Research Program (W81XWH-18-1-0424)
- Francesco Cambuli
National Science Foundation
- Laura Crowley
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Nima Sharifi, Cleveland Clinic, United States
Ethics
Animal experimentation: Animal studies were conducted according to protocols (AC-AABE0556, AC-AABG0564, AC-AABE5557) approved by the Columbia University Irving Medical Center (CUIMC) Institutional Animal Care and Use Committee (IACUC).
Human subjects: Human prostate tissue specimens were obtained from patients undergoing cystoprostatectomy for bladder cancer or radical prostatectomy at Columbia University Irving Medical Center or at Weill Cornell Medicine. Patients gave informed consent under an Institutional Review Board-approved protocol (AAAN8850).
Version history
- Received: May 29, 2020
- Accepted: September 10, 2020
- Accepted Manuscript published: September 11, 2020 (version 1)
- Version of Record published: October 1, 2020 (version 2)
Copyright
© 2020, Crowley et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 8,620
- Page views
-
- 1,110
- Downloads
-
- 47
- Citations
Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.
-
- Cancer Biology
- Computational and Systems Biology
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.