On May 16 2023 Shan Wang successfully defended her thesis, entitled: ‘Modeling the pathogenesis of neurodevelopmental disorders using human induced pluripotent stem cells: From genetics to cellular phenotypes’ at the Radboudumc in Nijmegen. Her research was performed under the supervision of Dirk Schubert, Hans van Bokhoven and Nael Nadif Kasri.
In her thesis Shan Wang used brain-on-a-chip approaches in order to bridge the gap between genetic findings in NDDs and relevant cellular phenotypes. To do this, she focused on two NDD-associated genes, i.e., SETD1A, which has also been identified as high-risk gene for schizophrenia, and EHMT1, which is associated with Kleefstra syndrome.
Her first aim was to improve the understanding of how LoF variants in SETD1A affect the human neuronal development and function and which underlying molecular mechanisms are involved. She used human
iPSC-derived models to investigate genotype-phenotype correlation and to identify potential therapeutic targets. In her thesis work she described a new protocol for the differentiation of human iPSCs into functional glutamatergic and GABAergic neurons by overexpression of Ngn2 and Ascl1, respectively.
This protocol allowed her to establish an excitatory/inhibitory (E/I) neuronal network in vitro, which is a highly relevant model for studying NDDs considering E/I balance is often disrupted in these diseases. She used that protocol to phenotype SETD1A-deficient networks from different aspects, ranging from network activity to transcriptomic profile. Her second aim was to empathize the role of astrocytes in the pathogenesis of NDDs. She investigated the contribution of astrocytes to the pathophysiology of Kleefstra syndrome where she succeeded to denote S100β as a promising biomarker for studying the psychopathology of the syndrome.
With human iPSC-derived neurons on MEAs having been the main methodology she used to gain insight into the neuronal network activity. Shan Wang showed that human iPSC-derived neurons grown on MEAs can be a robust and powerful tool to identify disease/patient specific network phenotypes and can be employed as a first-tier approach to provide mechanistic insights into the underlying molecular changes in disease neuronal networks.
