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      Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity

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          Abstract

          Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals. Unbiased clustering analysis reveals the existence of three distinct epithelial cell populations, one basal and two luminal cell types, which we identify as secretory L1- and hormone-responsive L2-type cells. Pseudotemporal reconstruction of differentiation trajectories produces one continuous lineage hierarchy that closely connects the basal lineage to the two differentiated luminal branches. Our comprehensive cell atlas provides insights into the cellular blueprint of the human breast epithelium and will form the foundation to understand how the system goes awry during breast cancer.

          Abstract

          Epithelial subpopulations are present in the human breast but how these differentiate or form is unclear. Here, the authors use single-cell RNA sequencing of primary human breast epithelial cells to define previously undescribed luminal, basal epithelial subpopulations and ZEB1-positive basal cells.

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          Most cited references15

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          Defining cell types and states with single-cell genomics

          A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. Such experiments will allow us to discover new cell types and states and trace their developmental origins. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve cellular states. Single-cell genomics and proteomics enable not only precise characterization of cell state, but also provide a stunningly high-resolution view of transitions between states. These measurements may finally make explicit the metaphor that C.H. Waddington posed nearly 60 years ago to explain cellular plasticity: Cells are residents of a vast “landscape” of possible states, over which they travel during development and in disease. Single-cell technology helps not only locate cells on this landscape, but illuminates the molecular mechanisms that shape the landscape itself. However, single-cell genomics is a field in its infancy, with many experimental and computational advances needed to fully realize its full potential.
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            Classification of low quality cells from single-cell RNA-seq data

            Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.
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              Molecular biology in breast cancer: intrinsic subtypes and signaling pathways.

              The last decade has brought a breakthrough in the knowledge of the biology of breast cancer. The technological development, and in particular the high throughput technologies, have allowed researchers to inquire more deeply into the nature of the disease through the comparative study of large numbers of samples. The classification of breast cancer by traditional parameters has been joined by rankings based on gene expression. Among the most popular platforms are MammaPrint®, Oncotype DX® the wound-response model, the rate of two genes model, the genomic grade index and the intrinsic subtype model. The latter one provides the amplest biological information and allows for the classification of breast cancer into six intrinsic subtypes: luminal A, luminal B, HER2-enriched, basal-like, normal breast and claudin-low. These new classifications are not yet fully applicable to clinical practice not only because they have not been standardized, but also because they entail a substantial economic outlay. Nevertheless, they have provided valuable information on tumor biology that has led to a better understanding of the signaling pathways governing the processes of formation, maintenance and expansion of the tumors. Researchers now know more about the HER2, estrogen receptor, IGF1R, PI3K/AKT, mTOR, AMPK and angiogenesis pathways which has allowed for the development of new targeted therapeutics now being tested in ongoing clinical trials. In general, one can say that the last decade has changed the way researchers understand, classify and study breast cancer, and it has reshaped the way doctors diagnose and treat this disease. In addition, it has undoubtedly changed the search for alternative therapies by integrating molecular studies and the selection of study populations based on their molecular markers into clinical trials. The present review summarizes the advances that have allowed researchers to both better classify the disease, as well as explore some of the most important signaling pathways. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                zena.werb@ucsf.edu
                kai.kessenbrock@uci.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                23 May 2018
                23 May 2018
                2018
                : 9
                : 2028
                Affiliations
                [1 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, Department of Biological Chemistry, , University of California, Irvine, ; Irvine, CA 92697 USA
                [2 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, Center for Complex Biological Systems, University of California, Irvine, ; Irvine, CA 92697 USA
                [3 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, Department of Physiology and Biophysics, , University of California, Irvine, ; Irvine, CA 92697 USA
                [4 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Anatomy and Biomedical Sciences Program, , University of California, ; San Francisco, CA 94143-0452 USA
                [5 ]GRID grid.422873.8, ProteinSimple, 3001 Orchard Parkway, ; San Jose, CA 95134 USA
                [6 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Cell and Tissue Biology, , University of California, ; San Francisco, CA 94143-0452 USA
                [7 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Department of Surgery, , Feinberg School of Medicine, Northwestern University, ; Chicago, IL 60611 USA
                Author information
                http://orcid.org/0000-0001-9127-0986
                http://orcid.org/0000-0002-6525-3872
                Article
                4334
                10.1038/s41467-018-04334-1
                5966421
                29795293
                5a7af020-6286-4556-9682-fefaf4ad4ff6
                © The Author(s) 2018

                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
                : 3 December 2017
                : 23 April 2018
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