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.

Early functional changes associated with alpha-synuclein proteinopathy in engineered human neural networks.

A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson's disease (PD), misfolded forms of α-synuclein proteins accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites. Such protein aggregates seem to affect selectively vulnerable neuronal populations in the substantia nigra and to propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early time points in disease development, even before clear behavioral symptoms of dysfunction arise. In this study, we investigate the early pathophysiology developing after induced formation of such PD-related α-synuclein inclusions in a physiologically relevant in vitro setup using engineered human neural networks. We monitor the neural network activity using multielectrode arrays (MEAs) for a period of 3 wk following proteinopathy induction to identify associated changes in network function, with a special emphasis on the measure of network criticality. Self-organized criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organizing neural networks, both in vitro and in vivo. We show that although developing pathology at early onset is not clearly manifest in standard measurements of network function, it may be discerned by investigating differences in network criticality states.

A novel lab-on-chip platform enabling axotomy and neuromodulation in a multi-nodal network.

Lab-on-chip platforms, such as microfluidic chips and micro-electrode arrays (MEAs) are powerful tools that allow us to manipulate and study neurons in vitro. Microfluidic chips provide a controlled extracellular environment that structures neural networks and facilitates isolation and manipulation at a sub-cellular level. Furthermore, MEAs enable measurement of extracellular electrophysiological activity from single neurons to entire networks. Here, we demonstrate the design, fabrication and application of a 3-nodal microfluidic chip integrated with MEAs as a versatile study platform for neurobiology and pathophysiology. In this work, we evaluate the use of the microfluidic chip to structure a neural network into three separate nodes, interconnected through tunnels that isolate and guide axons into a channel, thus facilitating synaptic contacts between neurons originating from opposite nodes. Furthermore, we demonstrate the utility of the MEA for monitoring developing activity and intra-/inter nodal connectivity of the structured neural network. Finally, we demonstrate the versatility of the platform in two separate experiments. First, we demonstrate the ability to measure intra- and inter-nodal dynamic responses to a fluidically isolated chemical stimulation. Then, we demonstrate the feature of the microfluidic chip enabling the disruption of functional connectivity between nodes and examination of the immediate activity response of the neural network. The platform enables in vitro modelling of neural networks to study their functional connectomes in the context of neurodegenerative disease and CNS trauma, including spinal cord injury.

Structuring a multi-nodal neural network in vitro within a novel design microfluidic chip.

Neural network formation is a complex process involving axon outgrowth and guidance. Axon guidance is facilitated by structural and molecular cues from the surrounding microenvironment. Micro-fabrication techniques can be employed to produce microfluidic chips with a highly controlled microenvironment for neural cells enabling longitudinal studies of complex processes associated with network formation. In this work, we demonstrate a novel open microfluidic chip design that encompasses a freely variable number of nodes interconnected by axon-permissible tunnels, enabling structuring of multi-nodal neural networks in vitro. The chip employs a partially open design to allow high level of control and reproducibility of cell seeding, while reducing shear stress on the cells. We show that by culturing dorsal root ganglion cells (DRGs) in our microfluidic chip, we were able to structure a neural network in vitro. These neurons were compartmentalized within six nodes interconnected through axon growth tunnels. Furthermore, we demonstrate the additional benefit of open top design by establishing a 3D neural culture in matrigel and a neuronal aggregate 3D culture within the chips. In conclusion, our results demonstrate a novel microfluidic chip design applicable to structuring complex neural networks in vitro, thus providing a versatile, highly relevant platform for the study of neural network dynamics applicable to developmental and regenerative neuroscience.