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      Aging hallmarks exhibit organ-specific temporal signatures

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          Abstract

          Aging is the single greatest cause of disease and death worldwide, and understanding the associated processes could vastly improve quality of life. While the field has identified major categories of aging damage such as altered intercellular communication, loss of proteostasis, and eroded mitochondrial function 1 , these deleterious processes interact with extraordinary complexity within and between organs. Yet, a comprehensive analysis of aging dynamics organism-wide has been lacking. Here we performed bulk RNA-sequencing of 17 organs and plasma proteomics at 10 ages across the mouse lifespan, and integrated these findings with data from the companion Tabula Muris Senis 2, 3 . We uncover previously unknown linear and non-linear expression shifts during aging, which cluster in strikingly consistent trajectory groups with coherent biological functions, including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Remarkably, these gene sets are expressed similarly across tissues, differing merely in age of onset and amplitude. Widespread immune cell activation is especially pronounced and first detectable in white adipose depots during middle age. Single-cell RNA-sequencing confirms the accumulation of adipose T and B cells, including immunoglobulin J-expressing plasma cells, which also accrue concurrently across diverse organs. Finally, we show how expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to aging of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of aging, thereby providing a foundation to track systemic sources of declining health at old age.

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          Most cited references38

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                2 December 2020
                15 July 2020
                July 2020
                15 January 2021
                : 583
                : 7817
                : 596-602
                Affiliations
                [1 ]Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA.
                [2 ]Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.
                [3 ]Chan Zuckerberg Biohub, San Francisco, California, USA.
                [4 ]Veterans Administration Palo Alto Healthcare System, Palo Alto, California, USA.
                [5 ]Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.
                [6 ]Department of Bioengineering, Stanford University, Stanford, California, USA.
                [7 ]Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, California, USA.
                [8 ]Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California, USA.
                Author notes
                [#]

                These authors contributed equally.

                [†]

                A full list of authors and affiliations appears in the online version of the paper.

                Author Contributions

                N.S., B.L., S.R.Q., and T.W.-C. conceptualized the study. N.S., O.H., B.L., R.P., and T.W.-C. conceptualized the analysis. O.H., B.L., and R.P. with contributions from A.K., and T.F. conducted the transcriptomic analyses. N.S., S.E.L., D.P.L., M.E.Z., H.Z., and D.B. collected samples and extracted RNA. S.H., A.Z., and W.T. conducted cDNA and library preparation. P.M.L. created the Shiny web interface. R.S. and M.T. performed sequencing and library quality control. S.H., A.O.P., J.W., and A.M. processed raw sequencing data. The Tabula Muris Senis Consortium generated the single-cell sequencing database. N.S. and K.C. performed and analyzed FACS and RNAscope experiments. N.S., B.L., O.H., T.W.-C., and S.R.Q. wrote and edited the manuscript. T.W.-C., S.R.Q., S.D., N.F.N. and J.K. supervised the work.

                Article
                NIHMS1592414
                10.1038/s41586-020-2499-y
                7757734
                32669715
                e9289e23-3592-43f7-b30f-773a21eca8a9

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