A Step-by-Step Refined Strategy for Highly Efficient Generation of Neural Progenitors and Motor Neurons from Human Pluripotent Stem Cells.
Ontology highlight
ABSTRACT: Limited access to human neurons, especially motor neurons (MNs), was a major challenge for studying neurobiology and neurological diseases. Human pluripotent stem cells (hPSCs) could be induced as neural progenitor cells (NPCs) and further multiple neural subtypes, which provide excellent cellular sources for studying neural development, cell therapy, disease modeling and drug screening. It is thus important to establish robust and highly efficient methods of neural differentiation. Enormous efforts have been dedicated to dissecting key signalings during neural commitment and accordingly establishing reliable differentiation protocols. In this study, we refined a step-by-step strategy for rapid differentiation of hPSCs towards NPCs within merely 18 days, combining the adherent and neurosphere-floating methods, as well as highly efficient generation (~90%) of MNs from NPCs by introducing refined sets of transcription factors for around 21 days. This strategy made use of, and compared, retinoic acid (RA) induction and dual-SMAD pathway inhibition, respectively, for neural induction. Both methods could give rise to highly efficient and complete generation of preservable NPCs, but with different regional identities. Given that the generated NPCs can be differentiated into the majority of excitatory and inhibitory neurons, but hardly MNs, we thus further differentiate NPCs towards MNs by overexpressing refined sets of transcription factors, especially by adding human SOX11, whilst improving a series of differentiation conditions to yield mature MNs for good modeling of motor neuron diseases. We thus refined a detailed step-by-step strategy for inducing hPSCs towards long-term preservable NPCs, and further specified MNs based on the NPC platform.
SUBMITTER: Ren J
PROVIDER: S-EPMC8625124 | biostudies-literature |
REPOSITORIES: biostudies-literature
ACCESS DATA