B004 Epigenetic and transcriptional characterisation of hiPSC-derived striatal medium spiny neurons for Huntington’s disease research: insights and limitations

<h3>Background</h3> As medium spiny neuron (MSN) degeneration is central to Huntington’s disease (HD) pathology, it is important to develop accurate MSN models. Human induced pluripotent stem cells (hiPSCs) can be differentiated into MSN-like cells and retain epigenetic features of their original cell-type that can enhance their differentiation capacity, potentially producing more accurate phenotypes. <h3>Aim</h3> To investigated whether hiPSCs derived from the lateral ganglionic eminence (LGE, the embryonic origin of MSNs), are epigenetically primed to differentiate into more authentic MSNs compared to isogenic controls. <h3>Methods/Techniques</h3> In vitro culture, bulk DNA methylation, single cell RNA sequencing (scRNAseq) <h3>Results/Outcome</h3> Despite being isogenic, hiPSCs exhibited subtle tissue-related methylation signatures, and also variable capacity for MSN differentiation. However, direct comparison of hiPSC-MSNs with ‘authentic’ LGE-derived MSNs revealed significant differences in DNA methylation patterns, and some hiPSC-MSN subpopulations did not accurately reflect the transcriptional profile of genuine MSNs. Thus, while the LGE-hiPSC derived MSNs had epigenetic advantages over hiPSCs derived from other tissues, this was not sufficient to accurately model the authentic MSN. <h3>Conclusions</h3> Pluripotent cell lines are unique and subtle differences between them can have far reaching effects on their differentiation and the resulting phenotype. Additionally, while hPSC-derived MSNs can reproduce many aspects of MSN development and function, there are still epigenetic and transcriptional differences that limit the full recapitulation of the authentic LGE-derived MSN phenotype. We suggest that more research is required to better understand these differences and to identify strategies to differentiate MSNs more accurately to ensure their relevance in disease modelling.