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      Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing

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          Abstract

          Characterising the hierarchy of mammary epithelial cells (MECs) and how they are regulated during adult development is important for understanding how breast cancer arises. Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells—especially in the luminal compartment—should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution. In summary, our results provide a global, unbiased view of adult mammary gland development.

          Abstract

          There is a need to understand how mammary epithelial cells respond to changes at various developmental stages. Here, the authors use single-cell RNA sequencing of mammary epithelial cells at different adult developmental stages, identifying different cell types and charting their developmental trajectory.

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          Most cited references 28

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          A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

          Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
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            Gata-3 is an essential regulator of mammary-gland morphogenesis and luminal-cell differentiation.

            The transcription factor Gata-3 is a defining marker of the 'luminal' subtypes of breast cancer. To gain insight into the role of Gata-3 in breast epithelial development and oncogenesis, we have explored its normal function within the mammary gland by conditionally deleting Gata-3 at different stages of development. We report that Gata-3 has essential roles in the morphogenesis of the mammary gland in both the embryo and adult. Through the discovery of a novel marker (beta3-integrin) of luminal progenitor cells and their purification, we demonstrate that Gata-3 deficiency leads to an expansion of luminal progenitors and a concomitant block in differentiation. Remarkably, introduction of Gata-3 into a stem cell-enriched population induced maturation along the alveolar luminal lineage. These studies provide evidence for the existence of an epithelial hierarchy within the mammary gland and establish Gata-3 as a critical regulator of luminal differentiation.
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              Diffusion maps for high-dimensional single-cell analysis of differentiation data.

              Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.
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                Author and article information

                Contributors
                marioni@ebi.ac.uk
                wtk22@cam.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 December 2017
                11 December 2017
                2017
                : 8
                Affiliations
                [1 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Pharmacology, , University of Cambridge, ; Cambridge, CB2 1PD UK
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Cancer Research UK Cambridge Institute, , University of Cambridge, ; Cambridge, CB2 0RE UK
                [3 ]Cancer Research UK Cambridge Cancer Centre, Cambridge, CB2 0RE UK
                [4 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1HH UK
                [5 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Bioinformatics Institute, , European Molecular Biology Laboratory, ; Hinxton, CB10 1 SD UK
                Article
                2001
                10.1038/s41467-017-02001-5
                5723634
                116e4069-258d-40d1-a01f-9d247efe17e2
                © The Author(s) 2017

                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/.

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