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      Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states

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          Abstract

          Prostate cancer is the second most common malignancy in men worldwide and consists of a mixture of tumor and non-tumor cell types. To characterize the prostate cancer tumor microenvironment, we perform single-cell RNA-sequencing on prostate biopsies, prostatectomy specimens, and patient-derived organoids from localized prostate cancer patients. We uncover heterogeneous cellular states in prostate epithelial cells marked by high androgen signaling states that are enriched in prostate cancer and identify a population of tumor-associated club cells that may be associated with prostate carcinogenesis. ERG-negative tumor cells, compared to ERG-positive cells, demonstrate shared heterogeneity with surrounding luminal epithelial cells and appear to give rise to common tumor microenvironment responses. Finally, we show that prostate epithelial organoids harbor tumor-associated epithelial cell states and are enriched with distinct cell types and states from their parent tissues. Our results provide diagnostically relevant insights and advance our understanding of the cellular states associated with prostate carcinogenesis.

          Abstract

          The changes that prostate cancer (PCa) induces in its microenvironment are not fully understood. Here the authors use single-cell RNA-seq and organoids to characterise how the microenvironment responds to PCa, and also identify tumour-associated epithelial cell states and club cells.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                Franklin.Huang@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 January 2022
                10 January 2022
                2022
                : 13
                : 141
                Affiliations
                [1 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Division of Hematology/Oncology, Department of Medicine, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [2 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Helen Diller Family Comprehensive Cancer Center, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [3 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Bakar Computational Health Sciences Institute, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [4 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Institute for Human Genetics, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [5 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, The Ragon Institute of Massachusetts General Hospital, , Massachusetts Institute of Technology and Harvard University, ; Cambridge, MA 02139 USA
                [6 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Institute for Medical Engineering and Science (IMES), , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [7 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Chemistry, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [8 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Koch Institute for Integrative Cancer Research, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [9 ]GRID grid.66859.34, Broad Institute of Massachusetts Institute of Technology and Harvard, ; Cambridge, MA 02142 USA
                [10 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Urology, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [11 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Pathology, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [12 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Departments of Radiation Oncology, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                [13 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Division of Gastroenterology, Department of Medicine, , University of California, ; San Francisco, CA 94143 USA
                [14 ]GRID grid.410372.3, ISNI 0000 0004 0419 2775, Division of Hematology and Oncology, Department of Medicine, San Francisco Veterans Affairs Medical Center, ; San Francisco, CA 94121 USA
                Author information
                http://orcid.org/0000-0002-2164-8813
                http://orcid.org/0000-0001-8409-4654
                http://orcid.org/0000-0002-0963-7687
                http://orcid.org/0000-0003-3486-5494
                http://orcid.org/0000-0001-5670-8778
                http://orcid.org/0000-0001-5447-0436
                Article
                27322
                10.1038/s41467-021-27322-4
                8748675
                35013146
                69876fa4-f7ce-4514-b26b-5981b89f77b8
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022

                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
                : 28 October 2020
                : 29 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000005, U.S. Department of Defense (United States Department of Defense);
                Award ID: W81XWH-17-PCRP-HD
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: P20 CA233255
                Award Recipient :
                Funded by: Searle Scholars Program, Beckman Young Investigator Program, Sloan Fellowship in Chemistry, Pew-Stewart Scholars Program for Cancer Research, Prostate Cancer Foundation, Benioff Initiative for Prostate Cancer Research
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer genomics,cancer models,cancer microenvironment,tumour heterogeneity,prostate cancer

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