The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Regenerative potential of prostate luminal cells revealed by single-cell analysis
Open accessAbstract
Equal opportunity tissue regeneration: Tissue regeneration is thought to be driven primarily by rare stem cells with distinctive properties. Single-cell RNA sequencing allows rigorous testing of this hypothesis. Karthaus et al. examined the regeneration of normal prostate tissue in mice after androgen ablation, a common treatment for prostate cancer (see the Perspective by Kelly). Unexpectedly, they found that in addition to rare stem cells, a large population of differentiated cells was a major contributor to prostate regeneration, a result that they confirmed in a study of human prostate tissue. Investigation of the molecular mechanism by which the differentiated cells acquired regenerative potential yielded insights that could potentially lead to improved therapies for prostate cancer. Science , this issue p. 497 ; see also p. 467
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Self-citationIntegrative Genomic Profiling of Human Prostate Cancer
Taylor, B. S., Schultz, N., Hieronymus, H., Gopalan, A., Xiao, Y., Carver, B. S., Arora, V. K., Kaushik, P., Cerami, E., Reva, B., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2010.05.026 · Cited by 3,701
Self-citationScrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Accelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · doi:10.5555/2627435.2697068 · Cited by 2,226
Snippert, H. J., van der Flier, L. G., Sato, T., van Es, J. H., van den Born, M., Kroon-Veenboer, C., Barker, N., Klein, A. M., van Rheenen, J., Simons, B. D., et al. (2010) · Cell · doi:10.1016/j.cell.2010.09.016 · Cited by 1,951
A DISTRIBUTION-FREE k-SAMPLE TEST AGAINST ORDERED ALTERNATIVES
JONCKHEERE, A. R. (1954) · Biometrika · doi:10.1093/biomet/41.1-2.133 · Cited by 1,500
Cited by
Ferroptosis surveillance independent of GPX4 and differentially regulated by sex hormones
Liang, D., Feng, Y., Zandkarimi, F., Wang, H., Zhang, Z., Kim, J., Cai, Y., Gu, W., Stockwell, B. R., & Jiang, X. (2023) · Cell · doi:10.1016/j.cell.2023.05.003 · Cited by 722
Tumor Microenvironment-Derived NRG1 Promotes Antiandrogen Resistance in Prostate Cancer
Zhang, Z., Karthaus, W. R., Lee, Y. S., Gao, V. R., Wu, C., Russo, J. W., Liu, M., Mota, J. M., Abida, W., Linton, E., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.005 · Cited by 259
Self-citationBiological determinants of PSMA expression, regulation and heterogeneity in prostate cancer
Bakht, M. K., & Beltran, H. (2025) · Nature Reviews Urology · doi:10.1038/s41585-024-00900-z · Cited by 158
An improved epigenetic counter to track mitotic age in normal and precancerous tissues
Zhu, T., Tong, H., Du, Z., Beck, S., & Teschendorff, A. E. (2024) · Nature Communications · doi:10.1038/s41467-024-48649-8 · Cited by 27
Rapid interrogation of cancer cell of origin through CRISPR editing
Feng, W., Cao, Z., Lim, P. X., Zhao, H., Luo, H., Mao, N., Lee, Y. S., Rivera, A. A., Choi, D., Wu, C., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110344118 · Cited by 25
Self-citation
Metadata
- Authors
- Karthaus, W. R., Hofree, M., Choi, D., Linton, E. L., Turkekul, M., Bejnood, A., Carver, B., Gopalan, A., Abida, W., Laudone, V., Biton, M., Chaudhary, O., Xu, T., Masilionis, I., Manova, K., Mazutis, L., Pe’er, D., Regev, A., & Sawyers, C. L.
- Venue
- Science
- Published
- 2020-05
- Volume
- 368
- Issue
- 6490
- Pages
- 497-505
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3021988909
- Cited by
- 283
- References
- 50
- FWCI
- 18.96
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Regenerative potential of prostate luminal cells revealed by single-cell analysis. https://app.cochat.ai/papers/10.1126/science.aay0267
- The paper
- Karthaus, W. R., Hofree, M., Choi, D., Linton, E. L., Turkekul, M., Bejnood, A., Carver, B., Gopalan, A., Abida, W., Laudone, V., Biton, M., Chaudhary, O., Xu, T., Masilionis, I., Manova, K., Mazutis, L., Pe’er, D., Regev, A., & Sawyers, C. L. (2020). Regenerative potential of prostate luminal cells revealed by single-cell analysis. Science. https://doi.org/10.1126/science.aay0267
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
