Biology subjects

Winter-Hjelm, N.

Publications and source records attributed to Winter-Hjelm, N..

9 recordsLinked to original sources

bioRxiv2026

A Microfluidic Platform for Spatiotemporal Dissection of Neurodegeneration Across Hierarchical Human Neural Circuits

Understanding how neurodegenerative diseases initiate and propagate through neural circuits remains a fundamental challenge in neuroscience. The earliest stages occur years before symptoms emerge, making them inaccessible to study in patients. Microfluidic platforms, where neurons communicate across chambers through microchannels accessible only to their axons, have opened new experimental avenues. However, existing models lack the complexity and precision needed to track how individual circuit components respond to focal pathological changes over time. Here we present a 33-chamber cortical network-on-chip integrating human iPSC-derived excitatory neurons, inhibitory neurons, and astrocytes in a six-layer feedforward architecture recapitulating the laminar structure of the neocortex. Amyloid-{beta} is applied globally, while progerin-induced accelerated ageing in a single chamber establishes a defined disease core. Continuous recordings using high-density microelectrode arrays reveal progressive, layer-dependent changes in firing dynamics and network topology. Machine-learning-based feature analysis identifies a multiparametric electrophysiological signature distinguishing healthy from disease-affected chambers, enabling studies of the earliest timepoint at which pathology becomes detectable. This establishes a scalable framework for mechanistic studies of neurodegeneration and identification of electrophysiological biomarkers of disease progression.

neuroscience↗

bioRxiv2025

Microscale dysfunction and mesoscale compensation in degenerating neuronal networks

Progressive neurodegenerative diseases involve neuronal dysfunction from cellular to circuit to whole-brain levels, but complexity and variability, both between and within diseases, pose significant research challenges. However, although they are differentiated by anatomical origins, vulnerable neuronal subtypes, and specific misfolded proteins, neurodegenerative diseases also share many important features. During presymptomatic disease phases, neural networks initiate multiple compensatory processes to maintain network function, including increased network centralisation and reliance on a rich-club of hub nodes, which have been proposed as common reconfigurations to neural network damage. Currently, while supporting evidence for such mechanisms has been found in some neurodegenerative diseases, it is limited in others, like ALS. This knowledge gap makes it challenging to ascertain if there are indeed common pre-symptomatic mechanisms within and across different neurodegenerative diseases. To address this, we investigated the structural and functional properties of ALS patient derived motor neuron networks and counterpart networks from a healthy donor using longitudinal multielectrode array recordings and graph theory-based network analysis. We demonstrate microscale-level motor neuron dysfunction, including TDP-43 proteinopathy, hyperactivity and reduced spike amplitude. Structurally, we observed neurite hypertrophy, indicating that degenerating networks attempt to establish new connections. We furthermore document mesoscale-level functional reconfigurations, including increased rich-club connectivity and network assortativity, indicating functional compensation in ALS where networks become more centralised to maintain computational capacity. We thus provide novel evidence that ALS networks become increasingly centralised, which places progressively mounting demands on a rich-club, predisposing networks to further damage, consistent with existing models of common reconfigurations in neurodegenerative disease. Significance statementThis study makes significant contributions to preclinical modelling of neurodegenerative disease, with specific relevance for ALS research. By utilising human cellular models, longitudinal extracellular electrophysiology, and advanced network analysis, we show that known features of ALS can be recapitulated in in vitro engineered neural networks, and that these networks allow for novel hypothesis testing and identification of pre-symptomatic pathological processes including increased centralisation. This has previously been observed in other neurodegenerative diseases, but evidence has been limited in ALS. Our results advance our understanding of motor neuron network dynamics in ALS and contribute to a shared understanding of how neurodegenerative diseases affect neural networks which go beyond specific disease diagnosis, elucidating fundamental processes in neural network function and disease response.

neuroscience↗

bioRxiv2024

Nanoporous Platinum Microelectrode Arrays for Neuroscience Applications

Microelectrode arrays are invaluable tools for investigating the electrophysiological behaviour of neuronal networks with high spatiotemporal precision. In recent years, it has become increasingly common to functionalize such electrodes with highly porous platinum to increase their effective surface area, and hence their signal-to-noise ratio. Although such functionalization significantly improves the electrochemical performance of the electrodes, the impact of various electrode morphologies on biocompatibility and electrophysiological performance in cell cultures remains poorly understood. In this study, we introduce reproducible protocols for depositing highly porous platinum with varying morphologies on microelectrodes designed for neural cell cultures. We also evaluate the impact of morphology and electrode size on the signal-to-noise ratio in recordings from rat cortical neurons cultured on these electrodes. Our results indicate that electrodes with a uniform layer of highly nanoporous platinum offer the best trade-off between biocompatibility, electrochemical, and electrophysiological performance. While more microporous electrodes exhibited lower impedance, nanoporous electrodes detected higher extracellular signal amplitudes from neurons, suggesting reduced distance between perisomatic neuronal areas and the electrodes. Additionally, these nanoporous electrodes showed fewer thickness variations at their edges compared to the more porous electrodes. Such edges can be mechanically broken off during cell culturing and contribute to long-term cytotoxic effects, which is highly undesirable. We hope this work will contribute to better standardization in creating and utilizing nanoporous platinum microelectrodes for neuroscience applications. Improving the accessibility and reproducibility of this technology is crucial for enhancing the quality of electrophysiological data and advancing our understanding of neuronal network function and dysfunction.

neuroscience↗

bioRxiv2024

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.

neuroscience↗

bioRxiv2024

Functional Complexity of Engineered Neural Networks Self-Organized on Novel 3D Interfaces

Engineered neural networks are indispensable tools for studying neural function and dysfunction in controlled microenvironments. In vitro, neurons self-organize into complex assemblies with structural and functional similarities to in vivo circuits. Traditionally, these models are established on planar interfaces, but studies suggest that the lack of a three-dimensional growth space affects neuronal organization and function. While methods supporting 3D growth exist, reproducible 3D neuroengineering techniques compatible with electrophysiological recording methods are still needed. In this study, we developed a novel biocompatible interface made of the polymer SU-8 to support 3D network development. Using electron microscopy and immunocytochemistry, we show that neurons utilize these 3D scaffolds to self-assemble into complex, multi-layered networks. Furthermore, interfacing the scaffolds with custom microelectrode arrays enabled characterizing of electrophysiological activity. Both planar control networks and 3D networks displayed complex interactions with integrated and segregated functional dynamics. However, control networks showed stronger functional interconnections, higher entropy, and increased firing rates. In summary, our interfaces provide a versatile approach for supporting neural networks with a 3D growth environment, compatible with assorted electrophysiology and imaging techniques. This system can offer new insights into the impact of 3D topologies on neural network organization and function.

neuroscience↗

bioRxiv2024

ALS patient-derived motor neuron networks exhibit microscale dysfunction and mesoscale compensation rendering them highly vulnerable to perturbation

Amyotrophic lateral sclerosis affects upper and lower motor neurons, causing progressive neuropathology leading to structural and functional alterations of affected neural networks long prior to development of symptoms. Certain genetic mutations, such as expansions in C9orf72, predispose motor neuron populations to pathological dysfunction. However, it is not known how underlying pathological predisposition affects structural and functional dynamics within vulnerable networks. Here, we studied micro-and mesoscale dynamics of ALS patient derived motor neuron networks over time. We show, for the first time, that ALS patient derived motor neurons with endogenous genetic predisposition develop classical ALS cytopathology in the form of cytoplasmic TDP-43 inclusions and self-organise into computationally efficient networks, albeit with functional hallmarks of higher metabolic cost compared to healthy controls. These hallmarks included microscale impairments and mesoscale compensation including increased centralisation of function. Moreover, we show that these networks are highly susceptible to transient perturbation by exhibiting induced hyperactivity.

neuroscience↗

bioRxiv2023

Altered structural organization and functional connectivity in feedforward neural networks after induced perturbation

Reciprocal structure-function relationships underlie both healthy and pathological behaviors 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, as for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheime[r]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 behavior 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 decrease in internodal signal propagation between pre- and postsynaptic 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.

neuroscience↗

bioRxiv2023

Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

Engineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuro-engineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feed-forward 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 ss-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.

neuroscience↗

bioRxiv2022

Structure-Function Dynamics of Engineered, Modular Neuronal Networks with Controllable Afferent-Efferent Connectivity

Microfluidic devices interfaced with microelectrode arrays have in recent years emerged as powerful platforms for studying and manipulating in vitro neuronal networks at the micro- and mesoscale. By segregating neuronal populations using microchannels only permissible to axons, neuronal networks can be designed to mimic the highly organized, modular topology of neuronal assemblies in the brain. However, little is known about how the underlying topological features of such engineered neuronal networks contribute to their functional profile. To start addressing this question, a key parameter is control of afferent or efferent connectivity within the network. In this study, we show that a microfluidic device featuring axon guiding channels with geometrical constraints inspired by a Tesla valve effectively promotes unidirectional axonal outgrowth between neuronal nodes, thereby enabling us to control afferent connectivity. Our results moreover indicate that these networks exhibit a more efficient network organization with higher modularity compared to single nodal controls. We verified this by applying designer viral tools to fluorescently label the neurons to visualize the structure of the networks, combined with extracellular electrophysiological recordings using embedded nanoporous microelectrodes to study the functional dynamics of these networks during maturation. We furthermore show that electrical stimulations of the networks induce signals selectively transmitted in a feedforward fashion between the neuronal populations. A key advantage with our microdevice is the ability to longitudinally study and manipulate both the structure and function of neuronal networks with high accuracy. This model system has the potential to provide novel insights into the development, topological organization, and neuroplasticity mechanisms of neuronal assemblies at the micro- and mesoscale in healthy and perturbed conditions.

neuroscience↗