Signal Propagation between Neuronal Populations Controlled by Micropatterning

Localisation of oestrogen receptors in stem cells and in stem cell-derived neurons of the mouse

Oestrogen receptors (ER) transduce the effects of the endogenous ligand, 17β-estradiol in cells to regulate a number of important processes such as reproduction, neuroprotection, learning and memory and anxiety. The ERα or ERβ are classical intracellular nuclear hormone receptors while some of their variants or novel proteins such as the G-protein coupled receptor (GPCR), GPER1/GPR30 are reported to localise in intracellular as well as plasma membrane locations. Although the brain is an important target for oestrogen with oestrogen receptors expressed differentially in various nuclei, subcellular organisation and crosstalk between these receptors is under-explored. Using an adapted protocol that is rapid, we first generated neurons from mouse embryonic stem cells. Our immunocytochemistry approach shows that the full length ERα (ERα-66) and for the first time, that an ERα variant, ERα-36, as well as GPER1 is present in embryonic stem cells. In addition, these receptors typically decrease their nuclear localisation as neuronal maturation proceeds. Finally, although these ERs are present in many subcellular compartments such as the nucleus and plasma membrane, we show that they are specifically not colocalised with each other, suggesting that they initiate distinct signalling pathways.

Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks

Neuronal networks in dissociated culture combined with cell engineering technology offer a pivotal platform to constructively explore the relationship between structure and function in living neuronal networks. Here, we fabricated defined neuronal networks possessing a modular architecture on high-density microelectrode arrays (HD-MEAs), a state-of-the-art electrophysiological tool for recording neural activity with high spatial and temporal resolutions. We first established a surface coating protocol using a cell-permissive hydrogel to stably attach a polydimethylsiloxane microfluidic film on the HD-MEA. We then recorded the spontaneous neural activity of the engineered neuronal network, which revealed an important portrait of the engineered neuronal network-modular architecture enhances functional complexity by reducing the excessive neural correlation between spatially segregated modules. The results of this study highlight the impact of HD-MEA recordings combined with cell engineering technologies as a novel tool in neuroscience to constructively assess the structure-function relationships in neuronal networks.

An Overview of In Vitro Biological Neural Networks for Robot Intelligence

In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Neurons-on-a-Chip: In Vitro NeuroTools

Neurons-on-a-Chip technology has been developed to provide diverse in vitro neuro-tools to study neuritogenesis, synaptogensis, axon guidance, and network dynamics. The two core enabling technologies are soft-lithography and microelectrode array technology. Soft lithography technology made it possible to fabricate microstamps and microfluidic channel devices with a simple replica molding method in a biological laboratory and innovatively reduced the turn-around time from assay design to chip fabrication, facilitating various experimental designs. To control nerve cell behaviors at the single cell level via chemical cues, surface biofunctionalization methods and micropatterning techniques were developed. Microelectrode chip technology, which provides a functional readout by measuring the electrophysiological signals from individual neurons, has become a popular platform to investigate neural information processing in networks. Due to these key advances, it is possible to study the relationship between the network structure and functions, and they have opened a new era of neurobiology and will become standard tools in the near future.

Experimental Platform to Study Spiking Pattern Propagation in Modular Networks In Vitro

The structured organization of connectivity in neural networks is associated with highly efficient information propagation and processing in the brain, in contrast with disordered homogeneous network architectures. Using microfluidic methods, we engineered modular networks of cultures using dissociated cells with unidirectional synaptic connections formed by asymmetric microchannels. The complexity of the microchannel geometry defined the strength of the synaptic connectivity and the properties of spiking activity propagation. In this study, we developed an experimental platform to study the effects of synaptic plasticity on a network level with predefined locations of unidirectionally connected cellular assemblies using multisite extracellular electrophysiology.

Improvements of Microcontact Printing for Micropatterned Cell Growth by Contrast Enhancement

Patterned neuronal cell cultures are important tools for investigating neuronal signal integration, network function, and cell-substrate interactions. Because of the variable nature of neuronal cells, the widely used coating method of microcontact printing is in constant need of improvements and adaptations depending on the pattern, cell type, and coating solutions available for a certain experimental system. In this work, we report on three approaches to modify microcontact printing on borosilicate glass surfaces, which we evaluate with contact angle measurements and by determining the quality of patterned neuronal growth. Although background toxification with manganese salt does not result in the desired pattern enhancement, a simple heat treatment of the glass substrates leads to improved background hydrophobicity and therefore neuronal patterning. Thirdly, we extended a microcontact printing process based on covalently linking the glass surface and the coating molecule via an epoxysilane. This extension is an additional hydrophobization step with dodecylamine. We demonstrate that shelf life of the silanized glass is at least 22 weeks, leading to consistently reliable neuronal patterning by microcontact printing. Thus, we compared three practical additions to microcontact printing, two of which can easily be implemented into a workflow for the investigation of patterned neuronal networks.

Asymmetric confinement for defining outgrowth directionality

Directional connectivity is required to develop accurate in vitro models of the nervous system. This research investigated the interaction of murine neuronal outgrowths with asymmetric microstructured geometries to provide insights into the mechanisms governing unidirectional outgrowth bias. The structures were designed using edge-guidance and critical turning angle principles to study different prohibitive to permissive edge-guidance ratios. The different structures enable outgrowth in the permissive direction, while reducing outgrowth in the prohibitive direction. Outgrowth bias was probabilistic in nature, requiring multiple structures for effective unidirectional bias in primary hippocampal cultures at 14 days in vitro. Arrowhead structures with acute posterior corners were optimal, enabling 100% unidirectional outgrowth bias by virtue of re-routing and delay effects.

Impact of modular organization on dynamical richness in cortical networks

As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.

Connecting the Brain to Itself through an Emulation

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions.

Neural Circuits on a Chip

Neural circuits are responsible for the brain's ability to process and store information. Reductionist approaches to understanding the brain include isolation of individual neurons for detailed characterization. When maintained in vitro for several days or weeks, dissociated neurons self-assemble into randomly connected networks that produce synchronized activity and are capable of learning. This review focuses on efforts to control neuronal connectivity in vitro and construct living neural circuits of increasing complexity and precision. Microfabrication-based methods have been developed to guide network self-assembly, accomplishing control over in vitro circuit size and connectivity. The ability to control neural connectivity and synchronized activity led to the implementation of logic functions using living neurons. Techniques to construct and control three-dimensional circuits have also been established. Advances in multiple electrode arrays as well as genetically encoded, optical activity sensors and transducers enabled highly specific interfaces to circuits composed of thousands of neurons. Further advances in on-chip neural circuits may lead to better understanding of the brain.