Calcium-Dependent Hyperexcitability in Human Stem Cell-Derived Rett Syndrome Neuronal Networks.

Background: Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell-derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell-derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue. Methods: We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34). Results: RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics. Conclusions: During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics.

Wide spectrum of neuronal and network phenotypes in human stem cell-derived excitatory neurons with Rett syndrome-associated MECP2 mutations.

Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily caused by heterozygous loss-of-function mutations in the X-linked gene MECP2 that is a global transcriptional regulator. Mutations in the methyl-CpG binding domain (MBD) of MECP2 disrupt its interaction with methylated DNA. Here, we investigate the effect of a novel MECP2 L124W missense mutation in the MBD of an atypical RTT patient with preserved speech in comparison to severe MECP2 null mutations. L124W protein had a limited ability to disrupt heterochromatic chromocenters due to decreased binding dynamics. We isolated two pairs of isogenic WT and L124W induced pluripotent stem cells. L124W induced excitatory neurons expressed stable protein, exhibited increased input resistance and decreased voltage-gated Na+ and K+ currents, and their neuronal dysmorphology was limited to decreased dendritic complexity. Three isogenic pairs of MECP2 null neurons had the expected more extreme morphological and electrophysiological phenotypes. We examined development and maturation of L124W and MECP2 null excitatory neural network activity using micro-electrode arrays. Relative to isogenic controls, L124W neurons had an increase in synchronous network burst frequency, in contrast to MECP2 null neurons that suffered a significant decrease in synchronous network burst frequency and a transient extension of network burst duration. A biologically motivated computational neural network model shows the observed changes in network dynamics are explained by changes in intrinsic Na+ and K+ currents in individual neurons. Our multilevel results demonstrate that RTT excitatory neurons show a wide spectrum of morphological, electrophysiological and circuitry phenotypes that are dependent on the severity of the MECP2 mutation.

Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments.

The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here, we hypothesized that the codes mediating neuronal communication in the HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals selected one of the two targets in the arms of the maze, according to a learned context-color rule. Our results show that during associative learning, HPC principal cells concentrate spikes in bursts, enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layer II/III LPFC pyramidal cells fire spikes more sparsely distributed over time. The latter would facilitate broadcasting of signals loaded in short-term memory across neuronal populations without necessarily triggering fast synaptic plasticity.

Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders.

In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.