Developmental effect of RASopathy mutations on neuronal network activity on a chip
RASopathies are a group of genetic disorders caused by mutations in genes encoding components and regulators of the RAS/MAPK signaling pathway, resulting in overactivation of signaling. RASopathy patients exhibit distinctive facial features, cardiopathies, growth and skeletal abnormalities, and varying degrees of developmental delay, neurocognitive impairment, intellectual disabilities, and attention deficits. At present, it is unclear how RASopathy mutations cause neurocognitive impairment and what their neuron-specific cellular and network phenotypes are. Such knowledge would be crucial for the development of specific therapies, which are still lacking for the treatment of the neurocognitive and behavioral symptoms in RASopathies. Here, we investigated the effect of RASopathy mutations on the establishment and functional maturation of neuronal networks. We isolated cortical neurons from RASopathy mouse models and cultured them for several weeks in multiwell plates with implanted multielectrode arrays. The arrays were used for longitudinal recordings of spontaneous activity in developing networks as well as for recordings of evoked responses in mature neurons. To facilitate the analysis of large and complex data sets resulting from long-term multielectrode recordings, we developed MATLAB-based tools for data processing, analysis and statistical evaluation. Longitudinal analysis of spontaneous network activity revealed a convergent developmental phenotype in neurons carrying the gain-of-function RASopathy mutations Ptpn11D61Y and KrasV14l. The phenotype was more pronounced at early time points and faded out over time, suggesting compensatory effects during network maturation. Nevertheless, persistent differences in excitatory/inhibitory balance and network excitability were observed in mature networks. This study improves the understanding of the complex relationship between genetic mutations and clinical manifestations in RASopathies by adding insights into functional network processes as an additional piece of the puzzle.