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Browsing by Subject "neurodevelopment"

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  • Sundaresh, Adithi (2022)
    Human induced pluripotent stem cells (iPSCs) are an important in vitro model of disease and development. iPSCs can be differentiated in culture into cell types which are difficult to access from patients, such as neurons. Applying iPSC-derived cellular models to disease studies requires a thorough characterization of the derived cell types, as well as assessing reproducibility across cell lines or differentiation batches. With the aim of providing such a comprehensive molecular characterization at an early stage of cortical neuronal differentiation in vitro, six iPSC lines from four donors were differentiated to cortical neural progenitors using a modification of an established protocol (Shi et al., 2012a). The protocol successfully produced neural progenitors, with over 75% of the differentiated cells aligning with a cortical identity, as confirmed via qPCR and immunocytochemistry of established markers such as PAX6, NES and SOX1. To further classify the cell types produced as well as identify potential differences between cell lines, gene expression of the obtained cells was profiled with single cell RNA sequencing of ~22,000 cells, which uncovered the heterogeneity of neural progenitors produced. Further, although two differentiation batches produced similar cell-type compositions on a whole, a fraction of the lines showed inter-individual differences in cell type composition, which correlated with expression variability of known marker genes. Additionally, the cell types produced in vitro were compared to those produced in vivo by mapping our dataset to a reference fetal brain dataset (Polioudakis et al., 2019). It was observed that the in vitro dataset represented a subset of the cell types present at mid-gestation. Overall, the single cell characterization of differentiated cells allowed greater resolution in understanding cell-type heterogeneity of cortical neurogenesis, which is of key relevance for future applications such as disease modeling.