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      Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing

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          SUMMARY

          Aging is closely associated with increased susceptibility to breast cancer, yet there have been limited systematic studies of aging-induced alterations in the mammary gland. Here, we leverage high-throughput single-cell RNA sequencing to generate a detailed transcriptomic atlas of young and aged murine mammary tissues. By analyzing epithelial, stromal, and immune cells, we identify age-dependent alterations in cell proportions and gene expression, providing evidence that suggests alveolar maturation and physiological decline. The analysis also uncovers potential pro-tumorigenic mechanisms coupled to the age-associated loss of tumor suppressor function and change in microenvironment. In addition, we identify a rare, age-dependent luminal population co-expressing hormone-sensing and secretory-alveolar lineage markers, as well as two macrophage populations expressing distinct gene signatures, underscoring the complex heterogeneity of the mammary epithelia and stroma. Collectively, this rich single-cell atlas reveals the effects of aging on mammary physiology and can serve as a useful resource for understanding aging-associated cancer risk.

          In Brief

          Using single-cell RNA-sequencing, Li et al. compare mammary epithelia and stroma in young and aged mice. Age-dependent changes at cell and gene levels provide evidence suggesting alveolar maturation, functional deterioration, and potential pro-tumorigenic and inflammatory alterations. Additionally, identification of heterogeneous luminal and macrophage subpopulations underscores the complexity of mammary lineages.

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          Most cited references155

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                10 January 2021
                29 December 2020
                22 February 2021
                : 33
                : 13
                : 108566
                Affiliations
                [1 ]Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
                [2 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [3 ]Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Charlestown, MA 02129, USA
                [4 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [5 ]Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA 02115, USA
                [6 ]Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
                [7 ]Present address: Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
                [8 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                C.M.L. and J.S.B. conceived the study and designed the experiments. C.M.L. performed the experiments and analyses with assistance from G.K.G. for tissue dissociation; A.R. for scRNA-seq; L.M.S., H.C., L.P., Y.O., and M.J.S. for bioinformatics analyses; H.S., C.T., and K.P.G. for tissue staining and microscopy; and H.S., J.R., and K.M. for FACS analysis of tissues and organoids. All authors contributed to the interpretation of experimental results. C.M.L. and J.S.B. wrote the manuscript, with contribution from all authors. J.S.B. provided funding and project supervision.

                Article
                NIHMS1658643
                10.1016/j.celrep.2020.108566
                7898263
                33378681
                3f5df3f6-9132-4647-bcdd-13c8811bf335

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

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                Cell biology
                Cell biology

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