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Modeling Parkinson's disease with induced pluripotent stem cells harboring α‐synuclein mutations
Author(s) -
Singh Dolt Karamjit,
Hammachi Fella,
Kunath Tilo
Publication year - 2017
Publication title -
brain pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.986
H-Index - 132
eISSN - 1750-3639
pISSN - 1015-6305
DOI - 10.1111/bpa.12526
Subject(s) - induced pluripotent stem cell , reprogramming , substantia nigra , neuroscience , parkinson's disease , biology , disease , dopaminergic , phenotype , bioinformatics , gene , genetics , embryonic stem cell , medicine , pathology , dopamine
Abstract Parkinson's disease (PD) is a common neurodegenerative condition affecting more than 8 million people worldwide. Although, the majority of PD cases are sporadic in nature, there are a growing number of monogenic mutations identified to cause PD in a highly penetrant manner. Many of these familial mutations give rise to a condition that is clinically and neuropathologically similar, if not identical, to sporadic PD. Mutations in genes such as SNCA cause PD in an autosomal dominant manner and patients have motor and non‐motor symptoms that are typical for sporadic PD. With the advent of reprogramming technology it is now possible to capture these mutations in induced pluripotent stem cells (iPSCs) to establish models of PD in a dish. There are multiple neuronal subtypes affected in PD including the midbrain dopaminergic (mDA) neurons of the substantia nigra . Robust neuronal differentiation into mDA or other relevant neural cell types are critical to accurately model the disease and ensure the findings are relevant to understanding the disease process. Another challenge for establishing accurate models of PD is being met by the generation of isogenic control iPSC lines with precise correction of mutations using advanced gene editing technology. The contributions of ageing and environmental factors present further challenges to this field, but significant progress is being made in these areas to establish highly relevant and robust models of PD. These human neuronal models, used in conjunction with other model systems, will vastly improve our understanding of the early stages of the PD, which will be key to identifying disease‐modifying and preventative treatments.

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