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ResearchIn-Press PreviewNeuroscience Open Access | 10.1172/JCI156768

Human midbrain dopaminergic neuronal differentiation markers predict cell therapy outcome in a Parkinson’s disease model

Peibo Xu,1 Hui He,1 Qinqin Gao,1 Yingying Zhou,1 Ziyan Wu,1 Xiao Zhang,1 Linyu Sun,1 Gang Hu,1 Qian Guan,1 Zhiwen You,1 Xinyue Zhang,1 Wenping Zheng,1 Man Xiong,2 and Yuejun Chen1

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Xu, P. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by He, H. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Gao, Q. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Zhou, Y. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Wu, Z. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Zhang, X. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Sun, L. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Hu, G. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Guan, Q. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by You, Z. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Zhang, X. in: JCI | PubMed | Google Scholar |

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Zheng, W. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Xiong, M. in: JCI | PubMed | Google Scholar

1CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

2Institute State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China

Find articles by Chen, Y. in: JCI | PubMed | Google Scholar |

Published June 14, 2022 - More info

J Clin Invest. https://doi.org/10.1172/JCI156768.
Copyright © 2022, Xu et al. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Published June 14, 2022 - Version history
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Abstract

Human pluripotent stem cell (hPSC)-based replacement therapy holds great promise in treating Parkinson’s disease (PD). However, the heterogeneity of hPSC-derived donor cells and the low yield of midbrain dopaminergic (mDA) neurons after transplantation hinder its broad clinical application. Here, we depicted the single-cell molecular landscape during mDA neuron differentiation. We found that this process recapitulated the development of multiple but adjacent fetal brain regions including ventral midbrain, isthmus, and ventral hindbrain, resulting in heterogenous donor cell population. We reconstructed the differentiation trajectory of mDA lineage and identified CLSTN2 and PTPRO as specific surface markers of mDA progenitors, which were predictive of mDA neuron differentiation and could facilitate highly enriched mDA neurons (up to 80%) following progenitor sorting and transplantation. Marker sorted progenitors exhibited higher therapeutic potency in correcting motor deficits of PD mice. Different marker sorted grafts had a strikingly consistent cellular composition, in which mDA neurons were enriched, while off-target neuron types were mostly depleted, suggesting stable graft outcomes. Our study provides a better understanding of cellular heterogeneity during mDA neuron differentiation, and establishes a strategy to generate highly purified donor cells to achieve stable and predictable therapeutic outcomes, raising the prospect of hPSC-based PD cell replacement therapies.

Graphical Abstract
graphical abstract
Supplemental material

View Supplemental Table 3. Marker genes of day 21 and day 28 bulk-seq clusters. Table with marker genes for each cluster calculated using edgeR package using ‘glmTreat’ function.

View Supplemental Table 4. Marker genes of scRNA-seq clusters for each stage. Table with marker genes for each cluster calculated using Scanpy package using ‘scanpy.tl.rank_genes_groups’ function. Clusters were labeled in red and filtered out for downstream analysis., including low number counts or genes detected, high ribosome protein genes detected ratio, enriched in stressed, apoptotic, and hypoxic pathway.

View Supplemental Table 1. Marker genes of 19 scRNA-seq clusters from all stages. Table with marker genes for each cluster calculated using Seurat package using Wilcoxon test.

View Supplemental Table 2. Marker genes of day 21 and day 28 scRNA-seq clusters. Table with marker genes for each cluster calculated using Seurat package using Wilcoxon test.

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Version history
  • Version 1 (June 14, 2022): In-Press Preview
  • Version 2 (July 15, 2022): Electronic publication
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