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Efficient Conversion of Astrocytes to Functional Midbrain Dopaminergic Neurons Using a Single Polycistronic Vector

Overview of attention for article published in PLOS ONE, December 2011
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Title
Efficient Conversion of Astrocytes to Functional Midbrain Dopaminergic Neurons Using a Single Polycistronic Vector
Published in
PLOS ONE, December 2011
DOI 10.1371/journal.pone.0028719
Pubmed ID
Authors

Russell C. Addis, Fu-Chun Hsu, Rebecca L. Wright, Marc A. Dichter, Douglas A. Coulter, John D. Gearhart

Abstract

Direct cellular reprogramming is a powerful new tool for regenerative medicine. In efforts to understand and treat Parkinson's Disease (PD), which is marked by the degeneration of dopaminergic neurons in the midbrain, direct reprogramming provides a valuable new source of these cells. Astrocytes, the most plentiful cells in the central nervous system, are an ideal starting population for the direct generation of dopaminergic neurons. In addition to their potential utility in cell replacement therapies for PD or in modeling the disease in vitro, astrocyte-derived dopaminergic neurons offer the prospect of direct in vivo reprogramming within the brain. As a first step toward this goal, we report the reprogramming of astrocytes to dopaminergic neurons using three transcription factors - ASCL1, LMX1B, and NURR1 - delivered in a single polycistronic lentiviral vector. The process is efficient, with 18.2±1.5% of cells expressing markers of dopaminergic neurons after two weeks. The neurons exhibit expression profiles and electrophysiological characteristics consistent with midbrain dopaminergic neurons, notably including spontaneous pacemaking activity, stimulated release of dopamine, and calcium oscillations. The present study is the first demonstration that a single vector can mediate reprogramming to dopaminergic neurons, and indicates that astrocytes are an ideal starting population for the direct generation of dopaminergic neurons.

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Geographical breakdown

Country Count As %
Italy 1 <1%
Australia 1 <1%
China 1 <1%
Korea, Republic of 1 <1%
Japan 1 <1%
Unknown 209 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 27%
Researcher 46 21%
Student > Master 27 13%
Student > Bachelor 15 7%
Professor > Associate Professor 11 5%
Other 28 13%
Unknown 30 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 37%
Neuroscience 46 21%
Biochemistry, Genetics and Molecular Biology 28 13%
Medicine and Dentistry 10 5%
Engineering 8 4%
Other 14 7%
Unknown 29 14%