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URL: https://dx.doi.org/DOI:10.17504/protocols.io.bsq5ndy6

Authors: Elisangela Bressan, Ashutosh Dhingra, Stella Donato, Peter Heutink

Summary: The derivation of human induced pluripotent stem cells (iPSCs) into midbrain dopaminergic (mDA) neurons presents an exciting opportunity to access a large number of patients-specific cells in vitro, to model disease, perform target screenings, and test drug candidates. Previously published small molecule-based protocols are straightforward, implementable in automated cell culture systems (Dhingra et al. J Vis Exp. 162, 2020), and suitable for the differentiation of large sets of cell lines (Bressan et al. Protocols.io, 2020) at relatively low cost and working time. However, the derivation of mDA from a large set of human iPSC lines shows variations in differentiation efficiency between lines. In addition, the current protocols produce heterogeneous cell populations in which only a small subset represents the cells of interest. To address these issues, we optimized a previously established mDA neuron differentiation protocol (Kriks et al., Nature 480, 547–551, 2011) by: (1) adjusting the SMAD inhibition to improve and achieve more homogeneous neuron conversion between iPSC lines; (2) reinforcing the WTN signaling activation to trigger more efficient midbrain floor plate induction and conversion into mDA neurons, and (3) applying the DNA cross-linker Mitomycin-C to eliminate remaining proliferating cells. The optimized mDA neuron derivation protocol presented here resulted in more homogeneous differentiation between iPSC lines, higher yields of neurons with higher proportion of mDA neurons, and completely elimination of contaminating proliferative cells. .justify:after { content: ""; display:inline-block; width: 100%; }

Affiliations: German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany

Version: 1

Publication Date: 2021

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