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      Single-Cell Transcriptomes Distinguish Stem Cell State Changes and Lineage Specification Programs in Early Mammary Gland Development

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          SUMMARY

          The mammary gland consists of cells with gene expression patterns reflecting their cellular origins, function, and spatiotemporal context. However, knowledge of developmental kinetics and mechanisms of lineage specification is lacking. We address this significant knowledge gap by generating a single-cell transcriptome atlas encompassing embryonic, postnatal, and adult mouse mammary development. From these data, we map the chronology of transcriptionally and epigenetically distinct cell states and distinguish fetal mammary stem cells (fMaSCs) from their precursors and progeny. fMaSCs show balanced co-expression of factors associated with discrete adult lineages and a metabolic gene signature that subsides during maturation but reemerges in some human breast cancers and metastases. These data provide a useful resource for illuminating mammary cell heterogeneity, the kinetics of differentiation, and developmental correlates of tumorigenesis.

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          In Brief

          Single-cell RNA sequencing of developing mouse mammary epithelia reveals the timing of lineage specification. Giraddi et al. find that fetal mammary stem cells co-express factors that define distinct lineages in their progeny and bear functionally relevant metabolic program signatures that change with differentiation and are resurrected in human breast cancers and metastases.

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          Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.

          One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.
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            A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

            Chromatin states are the key to gene regulation and cell identity. Chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-Seq) is increasingly being used to map epigenetic states across genomes of diverse species. Chromatin modification profiles are frequently noisy and diffuse, spanning regions ranging from several nucleosomes to large domains of multiple genes. Much of the early work on the identification of ChIP-enriched regions for ChIP-Seq data has focused on identifying localized regions, such as transcription factor binding sites. Bioinformatic tools to identify diffuse domains of ChIP-enriched regions have been lacking. Based on the biological observation that histone modifications tend to cluster to form domains, we present a method that identifies spatial clusters of signals unlikely to appear by chance. This method pools together enrichment information from neighboring nucleosomes to increase sensitivity and specificity. By using genomic-scale analysis, as well as the examination of loci with validated epigenetic states, we demonstrate that this method outperforms existing methods in the identification of ChIP-enriched signals for histone modification profiles. We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions. http://home.gwu.edu/ approximately wpeng/Software.htm. Supplementary data are available at Bioinformatics online.
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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

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                11 October 2018
                07 August 2018
                20 December 2018
                : 24
                : 6
                : 1653-1666.e7
                Affiliations
                [1 ]Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
                [2 ]Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
                [3 ]J. Craig Venter Institute, La Jolla, CA 92037, USA
                [4 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel HI, NC 27599, USA
                [5 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Concept and Supervision, G.M.W. and B.T.S.; Writing, R.R.G., G.M.W., and B.T.S.; Resources, R.L., K.E.V., and C.M.P.; Experimental Design, R.R.G., C.-Y.C., R.E.H., O.B., C.L.T., B.M.H., C.M.P., G.M.W., and B.T.S.; Experimentation, R.R.G., C.-Y.C., R.E.H., O.B., M.N., C.L.T., C.D., B.M.H., L.W.R., and B.T.S.; Computational Design, R.R.G., C.-Y.C., C.D., K.E.V., C.M.P., G.M.W., and B.T.S.; Computational Analysis, C.-Y.C., E.M.M., J.Y.H., C.F., and B.T.S.; Editorial Revision, R.R.G., O.B., E.M.M., G.M.W., and B.T.S. All authors interpreted the data.

                [* ]Correspondence: wahl@ 123456salk.edu (G.M.W.), benjamin.spike@ 123456hci.Utah.edu (B.T.S.)
                Article
                NIHMS991890
                10.1016/j.celrep.2018.07.025
                6301014
                30089273
                a0155409-cf80-4406-b8c4-860226b3e679

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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