Exploring Single-Cell Gene Regulatory Dynamics in Rett Syndrome

Abstract

Rett syndrome is a monogenic disorder with an incidence of 95% in women, characterized by the complexity of studying the associated phenotype due to the heterogeneity in patient tissues from the stochastic silencing of the affected X chromosome. Furthermore, we are largely unaware of the cascade of alterations that occur in neurons due to transcriptional changes induced by the affected MECP2 gene. To address these challenges, an in-depth network analysis was implemented using organoid single-cell transcriptomic data derived from human patients. We performed a Weighted correlation network analysis and trajectory analysis to understand the differences in the developmental processes between samples, we followed by the generation of gene regulatory networks for each relevant cell developmental pathways to assess the master regulator that are involved in this process, with the differential expressed genes with potential therapeutic implications being identified by integration with SFARI and Genes4Epi. The results were adapted into dynamic Boolean models fitted with the transcriptomic data for validation in which we evaluated the attractor field from each reachable state. These approaches allowed us to explore differences in regulatory behavior in the developmental pathways. Our study provides an insight that pinpoints the cellular stages on which the regulation and compensatory mechanism activate and regulate Rett syndrome. We identified 19 Master regulators for the Dopaminergic developmental trajectory, as well as 34 Master regulator genes for the Gabaergic developmental trajectory. Dynamic Boolean modeling of these systems showcased a comprehensive understanding of the disrupted developmental pathways of Rett syndrome, highlighting the transitional states of potential within maturation trajectories as the key point of divergence in regulation for Rett syndrome. After complementing with enrichment and clinical relevant variant analysis, we identify the key actors in this system as NR2F1 and TCF4, with TCF4 suggesting a symmetrical compensatory relationship with MeCP2, and NR2F1 as possible link with wider developmental conditions, this concluded with highlighting the possibility of regulation in this condition being affected by the MAPK-ERK pathway of transcriptional regulation, offering a novel angle for targeted research.