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Single cell transcriptomics identifies stem cell-derived graft composition in a model of Parkinson’s disease

Tiklová, Katarína ; Nolbrant, Sara LU ; Fiorenzano, Alessandro LU ; Björklund, Åsa K. ; Sharma, Yogita LU ; Heuer, Andreas LU ; Gillberg, Linda ; Hoban, Deirdre B. LU ; Cardoso, Tiago LU and Adler, Andrew F. LU , et al. (2020) In Nature Communications 11(1).
Abstract

Cell replacement is a long-standing and realistic goal for the treatment of Parkinsonʼs disease (PD). Cells for transplantation can be obtained from fetal brain tissue or from stem cells. However, after transplantation, dopamine (DA) neurons are seen to be a minor component of grafts, and it has remained difficult to determine the identity of other cell types. Here, we report analysis by single-cell RNA sequencing (scRNA-seq) combined with comprehensive histological analyses to characterize intracerebral grafts from human embryonic stem cells (hESCs) and fetal tissue after functional maturation in a pre-clinical rat PD model. We show that neurons and astrocytes are major components in both fetal and stem cell-derived grafts.... (More)

Cell replacement is a long-standing and realistic goal for the treatment of Parkinsonʼs disease (PD). Cells for transplantation can be obtained from fetal brain tissue or from stem cells. However, after transplantation, dopamine (DA) neurons are seen to be a minor component of grafts, and it has remained difficult to determine the identity of other cell types. Here, we report analysis by single-cell RNA sequencing (scRNA-seq) combined with comprehensive histological analyses to characterize intracerebral grafts from human embryonic stem cells (hESCs) and fetal tissue after functional maturation in a pre-clinical rat PD model. We show that neurons and astrocytes are major components in both fetal and stem cell-derived grafts. Additionally, we identify a cell type closely resembling a class of recently identified perivascular-like cells in stem cell-derived grafts. Thus, this study uncovers previously unknown cellular diversity in a clinically relevant cell replacement PD model.

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Please use this url to cite or link to this publication:
@article{017981da-35f8-4829-bd8d-375e3d7595ee,
  abstract     = {{<p>Cell replacement is a long-standing and realistic goal for the treatment of Parkinsonʼs disease (PD). Cells for transplantation can be obtained from fetal brain tissue or from stem cells. However, after transplantation, dopamine (DA) neurons are seen to be a minor component of grafts, and it has remained difficult to determine the identity of other cell types. Here, we report analysis by single-cell RNA sequencing (scRNA-seq) combined with comprehensive histological analyses to characterize intracerebral grafts from human embryonic stem cells (hESCs) and fetal tissue after functional maturation in a pre-clinical rat PD model. We show that neurons and astrocytes are major components in both fetal and stem cell-derived grafts. Additionally, we identify a cell type closely resembling a class of recently identified perivascular-like cells in stem cell-derived grafts. Thus, this study uncovers previously unknown cellular diversity in a clinically relevant cell replacement PD model.</p>}},
  author       = {{Tiklová, Katarína and Nolbrant, Sara and Fiorenzano, Alessandro and Björklund, Åsa K. and Sharma, Yogita and Heuer, Andreas and Gillberg, Linda and Hoban, Deirdre B. and Cardoso, Tiago and Adler, Andrew F. and Birtele, Marcella and Lundén-Miguel, Hilda and Volakakis, Nikolaos and Kirkeby, Agnete and Perlmann, Thomas and Parmar, Malin}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Single cell transcriptomics identifies stem cell-derived graft composition in a model of Parkinson’s disease}},
  url          = {{https://lup.lub.lu.se/search/files/100828168/Tiklova_pdf_publication_in_nature_com.pdf}},
  doi          = {{10.1038/s41467-020-16225-5}},
  volume       = {{11}},
  year         = {{2020}},
}