4.7 Article

Generation and optimization of highly pure motor neurons from human induced pluripotent stem cells via lentiviral delivery of transcription factors

Journal

AMERICAN JOURNAL OF PHYSIOLOGY-CELL PHYSIOLOGY
Volume 319, Issue 4, Pages C771-C780

Publisher

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/ajpcell.00279.2020

Keywords

human induced pluripotent stem cells (hiPSCs); lentivirus; motor neurons; transcription factors

Funding

  1. NIH [R21NS112910]
  2. Department of Defense Peer Reviewed Medical Research Program [W81XWH2010186]

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Generation of neurons from human induced pluripotent stem cells (hiPSCs) overcomes the limited access to human brain samples and greatly facilitates the progress of research in neurological diseases. However, it is still a challenge to generate a particular neuronal subtype with high purity and yield for determining the pathogenesis of diseased neurons using biochemical approaches. Motor neurons (MNs) are a specialized neuronal subtype responsible for governing both autonomic and volitional movement. Dysfunctions in MNs are implicated in a variety of movement diseases, such as amyotrophic lateral sclerosis (ALS). In this study, we generated functional MNs from human iPSCs via lentiviral delivery of transcription factors. Moreover, we optimized induction conditions by using different combinations of transcription factors and found that a single lentiviral vector expressing three factors [neurogenin-2 (NGN2), insulin gene enhancer 1 (ISL1), and LIM/homeobox 3 (LHX3)] is necessary and sufficient to induce iPSC-derived MNs (iPSC-MNs). These MNs robustly expressed general neuron markers [microtubule-associated protein 2 (MAP2), neurofilament protein (SMI-32), and tubulin beta-3 class III (TUBB3)] and MN-specific markers [HB9 and choline acetyltransferase (ChAT)] and showed electrical maturation and firing of action potentials within 3 wk. This approach significantly improved the neuronal survival, yield, and purity, making it feasible to obtain abundant materials for biochemical studies in modeling movement diseases.

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