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@ARTICLE{Provenzano:155703,
      author       = {Provenzano, Francesca and Pérez, María José and Deleidi,
                      Michela},
      title        = {{R}edefining {M}icroglial {I}dentity in {H}ealth and
                      {D}isease at {S}ingle-{C}ell {R}esolution.},
      journal      = {Trends in molecular medicine},
      volume       = {27},
      number       = {1},
      issn         = {1471-4914},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2021-00871},
      pages        = {47 - 59},
      year         = {2021},
      abstract     = {Microglia have long been considered a homogenous cell
                      population that uniformly responds to extrinsic factors.
                      Here, we describe how the recent development of single-cell
                      technologies has revealed the heterogeneity of both human
                      and mouse microglia and identified distinct microglial
                      states linked to specific developmental, aging, and disease
                      stages. We discuss progress and future developments in data
                      analysis, essential tools for the comprehension of big data
                      derived from single-cell omics, and the necessity of
                      integrating such data with functional studies to correlate
                      genetic cues with the relevant biological functions of
                      microglia. Defining the functional correlates of distinct
                      microglia states is fundamental to dissecting the
                      'microglial etiology' of aging and complex neurological
                      diseases and identifying novel therapeutic and diagnostic
                      targets.},
      subtyp        = {Review Article},
      keywords     = {Animals / Disease Susceptibility / Genomics: methods /
                      Homeostasis / Humans / Metabolomics: methods / Microglia:
                      physiology / Proteomics: methods / Single-Cell Analysis:
                      methods},
      cin          = {AG Deleidi},
      ddc          = {610},
      cid          = {I:(DE-2719)1210011},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33008729},
      doi          = {10.1016/j.molmed.2020.09.001},
      url          = {https://pub.dzne.de/record/155703},
}