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      Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer

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          Abstract

          Neuroendocrine prostate cancer is one of the most aggressive subtypes of prostate tumor. Although much progress has been made in understanding the development of neuroendocrine prostate cancer, the cellular architecture associated with neuroendocrine differentiation in human prostate cancer remain incompletely understood. Here, we use single-cell RNA sequencing to profile the transcriptomes of 21,292 cells from needle biopsies of 6 castration-resistant prostate cancers. Our analyses reveal that all neuroendocrine tumor cells display a luminal-like epithelial phenotype. In particular, lineage trajectory analysis suggests that focal neuroendocrine differentiation exclusively originate from luminal-like malignant cells rather than basal compartment. Further tissue microarray analysis validates the generality of the luminal phenotype of neuroendocrine cells. Moreover, we uncover neuroendocrine differentiation-associated gene signatures that may help us to further explore other intrinsic molecular mechanisms deriving neuroendocrine prostate cancer. In summary, our single-cell study provides direct evidence into the cellular states underlying neuroendocrine transdifferentiation in human prostate cancer.

          Abstract

          Using single-cell RNA sequencing, Dong, Miao, Wang et al. profile the transcriptomes of 21,292 cells from biopsies of 6 castration-resistant prostate cancers. They find that all neuroendocrine tumor cells display a luminal-like epithelial phenotype, providing insights into the cellular states underlying neuroendocrine transdifferentiation in human prostate cancer.

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          Fast, sensitive, and accurate integration of single cell data with Harmony

          The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Author and article information

                Contributors
                wj860520@163.com
                xuewei@renji.com
                gao.weiqiang@sjtu.edu.cn
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                16 December 2020
                16 December 2020
                2020
                : 3
                : 778
                Affiliations
                [1 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Urology, Renji Hospital, School of Medicine, , Shanghai Jiao Tong University, ; Shanghai, 200127 China
                [2 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, State Key Laboratory of Oncogenes and Related Genes, Renji-Med-X Stem Cell Research Center, Department of Urology, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, , Shanghai Jiao Tong University, ; Shanghai, 200127 China
                [3 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, School of Biomedical Engineering and Med-X Research Institute, , Shanghai Jiao Tong University, ; Shanghai, 200030 China
                Author information
                http://orcid.org/0000-0002-5721-9586
                http://orcid.org/0000-0003-0562-2477
                http://orcid.org/0000-0002-9634-7265
                Article
                1476
                10.1038/s42003-020-01476-1
                7745034
                33328604
                6612f52b-81bf-4eed-9494-4bc6c7ca6608
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 May 2020
                : 28 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002855, Ministry of Science and Technology of the People’s Republic of China (Chinese Ministry of Science and Technology);
                Award ID: 2017YFA0102900
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81872406
                Award ID: 81630073
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2020

                neuroendocrine cancer,computational biology and bioinformatics

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