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      Molecular and spatial signatures of mouse brain aging at single-cell resolution

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          SUMMARY

          The diversity and complex organization of cells in the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline during aging. Here, we generated a high-resolution cell atlas of brain aging within the frontal cortex and striatum using spatially resolved single-cell transcriptomics and quantified changes in gene expression and spatial organization of major cell types in these regions over the mouse lifespan. We observed substantially more pronounced changes in cell state, gene expression, and spatial organization of non-neuronal cells over neurons. Our data revealed molecular and spatial signatures of glial and immune cell activation during aging, particularly enriched in the subcortical white matter, and identified both similarities and notable differences in cell-activation patterns induced by aging and systemic inflammatory challenge. These results provide critical insights into age-related decline and inflammation in the brain.

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          In brief

          Single-cell transcriptome imaging and sequencing of aging mouse brain reveals spatially dependent and cell-type-specific changes in cellular state shared in part with systemic inflammatory challenge.

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          Fast, sensitive, and accurate integration of single cell data with Harmony

          The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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            Neurotoxic reactive astrocytes are induced by activated microglia

            A reactive astrocyte subtype termed A1 is induced after injury or disease of the central nervous system and subsequently promotes the death of neurons and oligodendrocytes.
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              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                12 March 2023
                05 January 2023
                28 December 2022
                18 March 2023
                : 186
                : 1
                : 194-208.e18
                Affiliations
                [1 ]Society of Fellows, Harvard University, Cambridge, MA 02138, USA
                [2 ]Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, and Department of Physics, Harvard University, Cambridge, MA 02138, USA
                [3 ]Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
                [4 ]Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
                [5 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                W.E.A., C.D., and X.Z. designed the experiments. W.E.A., T.R.B., and Z.A.S. performed experiments. W.E.A. analyzed the data. W.E.A., C.D., and X.Z. interpreted data and wrote the manuscript.

                Article
                HHMIMS1881768
                10.1016/j.cell.2022.12.010
                10024607
                36580914
                1b1d3c9e-7a74-419a-9e76-169ba4a0eb2b

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                Cell biology
                Cell biology

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