15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Transcriptomic profiling of microglia and astrocytes throughout aging

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Activation of microglia and astrocytes, a prominent hallmark of both aging and Alzheimer’s disease (AD), has been suggested to contribute to aging and AD progression, but the underlying cellular and molecular mechanisms are largely unknown.

          Methods

          We performed RNA-seq analyses on microglia and astrocytes freshly isolated from wild-type and APP-PS1 (AD) mouse brains at five time points to elucidate their age-related gene-expression profiles.

          Results

          Our results showed that from 4 months onward, a set of age-related genes in microglia and astrocytes exhibited consistent upregulation or downregulation (termed “age-up”/“age-down” genes) relative to their expression at the young-adult stage (2 months). And most age-up genes were more highly expressed in AD mice at the same time points. Bioinformatic analyses revealed that the age-up genes in microglia were associated with the inflammatory response, whereas these genes in astrocytes included widely recognized AD risk genes, genes associated with synaptic transmission or elimination, and peptidase-inhibitor genes.

          Conclusions

          Overall, our RNA-seq data provide a valuable resource for future investigations into the roles of microglia and astrocytes in aging- and amyloid-β-induced AD pathologies.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          STEM: a tool for the analysis of short time series gene expression data

          Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            High-Dimensional Single-Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Occurrence of T cells in the brain of Alzheimer's disease and other neurological diseases.

              We investigated the occurrence of T cells in the brain parenchyma of Alzheimer's disease (AD), non-AD degenerative dementias and controls by semi-quantitative analysis of immunohistochemically stained tissue sections. In all cases, we found at least some T cells. The number of T cells was increased in the majority of AD cases compared with other cases. The phenotype of T cells in the AD brain indicates that they are activated but are not fully differentiated. Antigen-triggered clonal expansion is not likely to take place. Local inflammatory conditions might cause accumulation and activation of T cells in the AD brain.
                Bookmark

                Author and article information

                Contributors
                zhangweispace@163.com
                wanj@ust.hk
                Journal
                J Neuroinflammation
                J Neuroinflammation
                Journal of Neuroinflammation
                BioMed Central (London )
                1742-2094
                1 April 2020
                1 April 2020
                2020
                : 17
                : 97
                Affiliations
                [1 ]Shenzhen Key Laboratory for Neuronal Structural Biology, Biomedical Research Institute, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong Province China
                [2 ]Shenzhen Key Laboratory for Translational Medicine of Dermatology, Biomedical Research Institute, Shenzhen Peking University - The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong Province China
                [3 ]GRID grid.440601.7, Department of Dermatology, , Peking University Shenzhen Hospital, ; Shenzhen, Guangdong Province China
                [4 ]GRID grid.24515.37, ISNI 0000 0004 1937 1450, Division of Life Science, , The Hong Kong University of Science and Technology, ; Clear Water Bay Road, Kowloon, Hong Kong, China
                Article
                1774
                10.1186/s12974-020-01774-9
                7115095
                32238175
                c16456f6-9fe3-4f4f-bdaa-0b6f1d4737e6
                © The Author(s) 2020

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 November 2019
                : 17 March 2020
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2016YFA0501903
                Award Recipient :
                Funded by: National Natural Scientific Foundation of China
                Award ID: 81673053
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003453, Natural Science Foundation of Guangdong Province;
                Award ID: 2016A030312016
                Award Recipient :
                Funded by: Shenzhen Basic Research Grant
                Award ID: JCYJ20170411090739316
                Award ID: JCYJ20170815153617033
                Award ID: JCYJ20180507182657867
                Award ID: JCYJ20170306161450254
                Award ID: JCYJ20170306161807726
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Neurosciences
                microglia,astrocyte,alzheimer’s disease (ad),aging,rna-seq
                Neurosciences
                microglia, astrocyte, alzheimer’s disease (ad), aging, rna-seq

                Comments

                Comment on this article