Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Dysregulation of synaptic transcripts underlies network abnormalities in ALS patient-derived motor neurons

Amyotrophic lateral sclerosis (ALS) is characterized by dysfunction and loss of upper and lower motor neurons. Several studies have identified structural and functional alterations in the motor neurons before the manifestation of symptoms, yet the underlying cause of such alterations and how they contribute to the progressive degeneration of affected motor neuron networks remain unclear. Importantly, the short- and long-term spatiotemporal dynamics of neuronal network activity make it challenging to discern how ALS-related network reconfigurations emerge and evolve. To address this, we systematically monitored the structural and functional dynamics of motor neuron networks with a confirmed endogenous C9orf72 mutation. We show that ALS patient-derived motor neurons display time-dependent neural network dysfunction, specifically reduced firing rate and spike amplitude, impaired bursting, but higher overall synchrony in network activity. These changes coincided with altered neurite outgrowth and branching within the networks. Moreover, transcriptional analyses revealed dysregulation of molecular pathways involved in synaptic development and maintenance, neurite outgrowth, and cell adhesion, suggesting impaired synaptic stabilization. This study identifies early synaptic dysfunction as a contributing mechanism resulting in network-wide structural and functional compensation, which may over time render the networks vulnerable to neurodegeneration.NEW & NOTEWORTHY RNA-sequencing of ALS patient-derived motor neurons revealed altered expression of genes involved in cell adhesion, neurite outgrowth, synaptic development and maintenance, and synaptic plasticity. These alterations were accompanied by time-dependent structural impairments and disrupted neuronal activity, suggesting that early synaptic changes and network-wide structural and functional compensations contribute to motor neuron vulnerability in ALS.

Evolving alterations of structural organization and functional connectivity in feedforward neural networks after induced P301L tau mutation

Reciprocal structure-function relationships underlie both healthy and pathological behaviours in complex neural networks. Thus, understanding neuropathology and network dysfunction requires a thorough investigation of the complex interactions between structural and functional network reconfigurations in response to perturbation. Such adaptations are often difficult to study in vivo. For example, subtle, evolving changes in synaptic connectivity, transmission and the electrophysiological shift from healthy to pathological states, for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheimer's, are difficult to study in the brain. Engineered in vitro neural networks are powerful models that enable selective targeting, manipulation and monitoring of dynamic neural network behaviour at the micro- and mesoscale in physiological and pathological conditions. In this study, we engineered feedforward cortical neural networks using two-nodal microfluidic devices with controllable connectivity interfaced with microelectrode arrays (mMEAs). We induced P301L mutated tau protein to the presynaptic node of these networks and monitored network dynamics over three weeks. Induced perturbation resulted in altered structural organization and extensive axonal retraction starting in the perturbed node. Perturbed networks also exhibited functional changes in intranodal activity, which manifested as an overall decline in both firing rate and bursting activity, with a progressive increase in synchrony over time and a decrease in internodal signal propagation between pre- and post-synaptic nodes. These results provide insights into dynamic structural and functional reconfigurations at the micro- and mesoscale as a result of evolving pathology and illustrate the utility of engineered networks as models of network function and dysfunction.

Reverse engineering of feedforward cortical-Hippocampal microcircuits for modelling neural network function and dysfunction

Engineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuroengineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feedforward neural networks of primary cortical and hippocampal neurons, using a custom-designed multinodal microfluidic device with Tesla valve inspired microtunnels. By interfacing this device with nanoporous microelectrodes, we show that the reverse engineered multinodal neural networks exhibit capacity for both segregated and integrated functional activity, mimicking brain network dynamics. To advocate the broader applicability of our model system, we induced localized perturbations with amyloid beta to study the impact of pathology on network functionality. Additionally, we demonstrate long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimer's-model mice and rats. Our results thus highlight the potential of our approach for reverse engineering of anatomically relevant multinodal neural networks to study dynamic structure-function relationships in both healthy and pathological conditions.

Engineered cortical microcircuits for investigations of neuroplasticity

Recent advances in neural engineering have opened new ways to investigate the impact of topology on neural network function. Leveraging microfluidic technologies, it is possible to establish modular circuit motifs that promote both segregation and integration of information processing in the engineered neural networks, similar to those observed in vivo. However, the impact of the underlying topologies on network dynamics and response to pathological perturbation remains largely unresolved. In this work, we demonstrate the utilization of microfluidic platforms with 12 interconnected nodes to structure modular, cortical engineered neural networks. By implementing geometrical constraints inspired by a Tesla valve within the connecting microtunnels, we additionally exert control over the direction of axonal outgrowth between the nodes. Interfacing these platforms with nanoporous microelectrode arrays reveals that the resulting laminar cortical networks exhibit pronounced segregated and integrated functional dynamics across layers, mirroring key elements of the feedforward, hierarchical information processing observed in the neocortex. The multi-nodal configuration also facilitates selective perturbation of individual nodes within the networks. To illustrate this, we induced hypoxia, a key factor in the pathogenesis of various neurological disorders, in well-connected nodes within the networks. Our findings demonstrate that such perturbations induce ablation of information flow across the hypoxic node, while enabling the study of plasticity and information processing adaptations in neighboring nodes and neural communication pathways. In summary, our presented model system recapitulates fundamental attributes of the microcircuit organization of neocortical neural networks, rendering it highly pertinent for preclinical neuroscience research. This model system holds promise for yielding new insights into the development, topological organization, and neuroplasticity mechanisms of the neocortex across the micro- and mesoscale level, in both healthy and pathological conditions.

Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.

Rheological Characterization of Three-Dimensional Neuronal Cultures Embedded in PEGylated Fibrin Hydrogels

Three-dimensional (3D) neuronal cultures are valuable models for studying brain complexity in vitro, and the choice of the bulk material in which the neurons grow is a crucial factor in establishing successful cultures. Indeed, neuronal development and network functionality are influenced by the mechanical properties of the selected material; in turn, these properties may change due to neuron-matrix interactions that alter the microstructure of the material. To advance our understanding of the interplay between neurons and their environment, here we utilized a PEGylated fibrin hydrogel as a scaffold for mouse primary neuronal cultures and carried out a rheological characterization of the scaffold over a three-week period, both with and without cells. We observed that the hydrogels exhibited an elastic response that could be described in terms of the Young's modulus E. The hydrogels without neurons procured a stable E≃420 Pa, while the neuron-laden hydrogels showed a higher E≃590 Pa during the early stages of development that decreased to E≃340 Pa at maturer stages. Our results suggest that neurons and their processes dynamically modify the hydrogel structure during development, potentially compromising both the stability of the material and the functional traits of the developing neuronal network.

Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes

We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation-inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations.