NUK - logo
E-viri
Recenzirano Odprti dostop
  • Human iPSC-Based Modeling o...
    Miller, Justine D.; Ganat, Yosif M.; Kishinevsky, Sarah; Bowman, Robert L.; Liu, Becky; Tu, Edmund Y.; Mandal, Pankaj K.; Vera, Elsa; Shim, Jae-won; Kriks, Sonja; Taldone, Tony; Fusaki, Noemi; Tomishima, Mark J.; Krainc, Dimitri; Milner, Teresa A.; Rossi, Derrick J.; Studer, Lorenz

    Cell stem cell, 12/2013, Letnik: 13, Številka: 6
    Journal Article

    Reprogramming somatic cells to induced pluripotent stem cells (iPSCs) resets their identity back to an embryonic age and, thus, presents a significant hurdle for modeling late-onset disorders. In this study, we describe a strategy for inducing aging-related features in human iPSC-derived lineages and apply it to the modeling of Parkinson’s disease (PD). Our approach involves expression of progerin, a truncated form of lamin A associated with premature aging. We found that expression of progerin in iPSC-derived fibroblasts and neurons induces multiple aging-related markers and characteristics, including dopamine-specific phenotypes such as neuromelanin accumulation. Induced aging in PD iPSC-derived dopamine neurons revealed disease phenotypes that require both aging and genetic susceptibility, such as pronounced dendrite degeneration, progressive loss of tyrosine hydroxylase (TH) expression, and enlarged mitochondria or Lewy-body-precursor inclusions. Thus, our study suggests that progerin-induced aging can be used to reveal late-onset age-related disease features in hiPSC-based disease models. Display omitted •Reprogramming rejuvenates old donor fibroblasts by erasing age-related markers•Differentiation of old donor iPSCs is not sufficient to trigger memory of age•Progerin induces age-associated phenotypes in iPSC-derived fibroblasts and neurons•Progerin reveals late-onset disease phenotypes in iPSC-based models of genetic PD The induction of aging-related features in human iPS-derived cells through expression of progerin addresses one of the major limitations of human iPS-based disease modeling and enables analysis of late-onset phenotypes in conditions such as Parkinson’s disease.