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Pigareva Y, Gladkov A, Kolpakov V, Bukatin A, Li S, Kazantsev VB, Mukhina I, Pimashkin A. Microfluidic Bi-Layer Platform to Study Functional Interaction between Co-Cultured Neural Networks with Unidirectional Synaptic Connectivity. MICROMACHINES 2023; 14:835. [PMID: 37421068 DOI: 10.3390/mi14040835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/05/2023] [Accepted: 04/09/2023] [Indexed: 07/09/2023]
Abstract
The complex synaptic connectivity architecture of neuronal networks underlies cognition and brain function. However, studying the spiking activity propagation and processing in heterogeneous networks in vivo poses significant challenges. In this study, we present a novel two-layer PDMS chip that facilitates the culturing and examination of the functional interaction of two interconnected neural networks. We utilized cultures of hippocampal neurons grown in a two-chamber microfluidic chip combined with a microelectrode array. The asymmetric configuration of the microchannels between the chambers ensured the growth of axons predominantly in one direction from the Source chamber to the Target chamber, forming two neuronal networks with unidirectional synaptic connectivity. We showed that the local application of tetrodotoxin (TTX) to the Source network did not alter the spiking rate in the Target network. The results indicate that stable network activity in the Target network was maintained for at least 1-3 h after TTX application, demonstrating the feasibility of local chemical activity modulation and the influence of electrical activity from one network on the other. Additionally, suppression of synaptic activity in the Source network by the application of CPP and CNQX reorganized spatio-temporal characteristics of spontaneous and stimulus-evoked spiking activity in the Target network. The proposed methodology and results provide a more in-depth examination of the network-level functional interaction between neural circuits with heterogeneous synaptic connectivity.
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Affiliation(s)
- Yana Pigareva
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Arseniy Gladkov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Vladimir Kolpakov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Anton Bukatin
- Department of Nanobiotechnology, Alferov Saint-Petersburg National Research Academic University of the Russian Academy of Sciences, Saint Petersburg 194021, Russia
- Institute for Analytical Instrumentation of the RAS, Saint Petersburg 198095, Russia
| | - Sergei Li
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
| | - Victor B Kazantsev
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Irina Mukhina
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Alexey Pimashkin
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
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2
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Jia X, Shao W, Hu N, Shi J, Fan X, Chen C, Wang Y, Chen L, Qiao H, Li X. Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning. Front Neurosci 2022; 16:854199. [PMID: 36061604 PMCID: PMC9433803 DOI: 10.3389/fnins.2022.854199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these "learning populations" had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.
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Affiliation(s)
- Xiaoli Jia
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Wenwei Shao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Nan Hu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Jianxin Shi
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Xiu Fan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Chong Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Youwei Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Liqun Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Huanhuan Qiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Xiaohong Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
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Kim D, Kang H, Nam Y. Compact 256-channel multi-well microelectrode array system for in vitro neuropharmacology test. LAB ON A CHIP 2020; 20:3410-3422. [PMID: 32785330 DOI: 10.1039/d0lc00384k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Microelectrode arrays (MEAs) have been extensively used to measure extracellular spike activity from cultured neurons using multiple electrodes embedded in a planar glass substrate. This system has been implemented to investigate drug effects by detecting pharmacological perturbation reflected in spontaneous network activity. By configuring multiple wells in an MEA, a high-throughput electrophysiological assay has become available, speeding up drug tests. Despite its merits in acquiring massive amounts of electrophysiological data, the high cost and the bulky size of commercial multi-well MEA systems and most importantly its lack of customizability prevent potential users from fully implementing the system in drug experiments. In this work, we have developed a microelectrode array based drug testing platform by incorporating a custom-made compact 256-channel multi-well MEA in a standard microscope slide and commercial application-specific integrated circuit (ASIC) chip based recording system. We arranged 256 electrodes in 16 wells to maximize data collection from a single chip. The multi-well MEA in this work has a more compact design with reduced chip size compared to previously reported multi-well MEAs. Four synaptic modulators (NMDA, AMPA, bicuculline (BIC) and ATP) were applied to a multi-well MEA and neural spike activity was analyzed to study their neurophysiological effects on cultured neurons. Analyzing various neuropharmacological compounds has become much more accessible by utilizing commercially available digital amplifier chips and customizing a user-preferred analog-front-end interface design with additional benefits in reduced platform size and cost.
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Affiliation(s)
- Daejeong Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
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4
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Shimba K, Chang CH, Asahina T, Moriya F, Kotani K, Jimbo Y, Gladkov A, Antipova O, Pigareva Y, Kolpakov V, Mukhina I, Kazantsev V, Pimashkin A. Functional Scaffolding for Brain Implants: Engineered Neuronal Network by Microfabrication and iPSC Technology. Front Neurosci 2019; 13:890. [PMID: 31555074 PMCID: PMC6727854 DOI: 10.3389/fnins.2019.00890] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/08/2019] [Indexed: 01/10/2023] Open
Abstract
Neuroengineering methods can be effectively used in the design of new approaches to treat central nervous system and brain injury caused by neurotrauma, ischemia, or neurodegenerative disorders. During the last decade, significant results were achieved in the field of implant (scaffold) development using various biocompatible and biodegradable materials carrying neuronal cells for implantation into the injury site of the brain to repair its function. Neurons derived from animal or human induced pluripotent stem (iPS) cells are expected to be an ideal cell source, and induction methods for specific cell types have been actively studied to improve efficacy and specificity. A critical goal of neuro-regeneration is structural and functional restoration of the injury site. The target treatment area has heterogeneous and complex network topology with various types of cells that need to be restored with similar neuronal network structure to recover correct functionality. However, current scaffold-based technology for brain implants operates with homogeneous neuronal cell distribution, which limits recovery in the damaged area of the brain and prevents a return to fully functional biological tissue. In this study, we present a neuroengineering concept for designing a neural circuit with a pre-defined unidirectional network architecture that provides a balance of excitation/inhibition in the scaffold to form tissue similar to that in the injured area using various types of iPS cells. Such tissue will mimic the surrounding niche in the injured site and will morphologically and topologically integrate into the brain, recovering lost function.
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Affiliation(s)
- Kenta Shimba
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Chih-Hsiang Chang
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Takahiro Asahina
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Fumika Moriya
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kiyoshi Kotani
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Yasuhiko Jimbo
- Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Arseniy Gladkov
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Molecular and Cellular Technologies, Central Research Laboratory, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Oksana Antipova
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Yana Pigareva
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Vladimir Kolpakov
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Irina Mukhina
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Molecular and Cellular Technologies, Central Research Laboratory, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Victor Kazantsev
- Department of Neurotechnology, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Pimashkin
- Department of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Neurotechnology, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
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5
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Okujeni S, Egert U. Inhomogeneities in Network Structure and Excitability Govern Initiation and Propagation of Spontaneous Burst Activity. Front Neurosci 2019; 13:543. [PMID: 31213971 PMCID: PMC6554329 DOI: 10.3389/fnins.2019.00543] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 05/10/2019] [Indexed: 11/13/2022] Open
Abstract
The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in developing neuronal networks in vivo. Synchronous bursts are mostly thought to be triggered at burst initiation sites due to build-up of noise or by highly active neurons, or to reflect reverberating activity that circulates within larger networks, although neither of these has been observed directly. Inferring such collective dynamics in neuronal populations from electrophysiological recordings crucially depends on the spatial resolution and sampling ratio relative to the size of the networks assessed. Using large-scale microelectrode arrays with 1024 electrodes at 0.3 mm pitch that covered the full extent of in vitro networks on about 1 cm2, we investigated where bursts of spontaneous activity arise and how their propagation patterns relate to the regions of origin, the network's structure, and to the overall distribution of activity. A set of alternating burst initiation zones (BIZ) dominated the initiation of distinct bursting events and triggered specific propagation patterns. Moreover, BIZs were typically located in areas with moderate activity levels, i.e., at transitions between hot and cold spots. The activity-dependent alternation between these zones suggests that the local networks forming the dominating BIZ enter a transient depressed state after several cycles (similar to Eytan et al., 2003), allowing other BIZs to take over temporarily. We propose that inhomogeneities in the network structure define such BIZs and that the depletion of local synaptic resources limit repetitive burst initiation.
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Affiliation(s)
- Samora Okujeni
- Biomicrotechnology, IMTEK - Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | - Ulrich Egert
- Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
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Keren H, Partzsch J, Marom S, Mayr CG. A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks. Front Neurosci 2019; 13:432. [PMID: 31133779 PMCID: PMC6517490 DOI: 10.3389/fnins.2019.00432] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/15/2019] [Indexed: 12/30/2022] Open
Abstract
Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.
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Affiliation(s)
- Hanna Keren
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.,Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.,Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Johannes Partzsch
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Shimon Marom
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.,Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Christian G Mayr
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
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7
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Haroush N, Marom S. Inhibition increases response variability and reduces stimulus discrimination in random networks of cortical neurons. Sci Rep 2019; 9:4969. [PMID: 30899035 PMCID: PMC6428807 DOI: 10.1038/s41598-019-41220-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 02/25/2019] [Indexed: 11/11/2022] Open
Abstract
Much of what is known about the contribution of inhibition to stimulus discrimination is due to extensively studied sensory systems, which are highly structured neural circuits. The effect of inhibition on stimulus representation in less structured networks is not as clear. Here we exercise a biosynthetic approach in order to study the impacts of inhibition on stimulus representation in non-specialized network anatomy. Combining pharmacological manipulation, multisite electrical stimulation and recording from ex-vivo randomly rewired networks of cortical neurons, we quantified the effects of inhibition on response variability and stimulus discrimination at the population and single unit levels. We find that blocking inhibition quenches variability of responses evoked by repeated stimuli and enhances discrimination between stimuli that invade the network from different spatial loci. Enhanced stimulus discrimination is reserved for representation schemes that are based on temporal relation between spikes emitted in groups of neurons. Our data indicate that - under intact inhibition - the response to a given stimulus is a noisy version of the response evoked in the absence of inhibition. Spatial analysis suggests that the dispersion effect of inhibition is due to disruption of an otherwise coherent, wave-like propagation of activity.
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Affiliation(s)
- Netta Haroush
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel.
- Department of Physiology, Biophysics and Systems Biology, Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 32000, Israel.
| | - Shimon Marom
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel
- Department of Physiology, Biophysics and Systems Biology, Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, 32000, Israel
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8
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Gladkov A, Grinchuk O, Pigareva Y, Mukhina I, Kazantsev V, Pimashkin A. Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro. PLoS One 2018; 13:e0192468. [PMID: 29415033 PMCID: PMC5802926 DOI: 10.1371/journal.pone.0192468] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 01/24/2018] [Indexed: 12/12/2022] Open
Abstract
The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.
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Affiliation(s)
- Arseniy Gladkov
- Laboratory of Neuroengineering, Center of Translational Technologies, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russia
- Cell Technology Department, Central Research Laboratory, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia
| | - Oleg Grinchuk
- Information Science and Technology Department, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Yana Pigareva
- Laboratory of Neuroengineering, Center of Translational Technologies, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russia
| | - Irina Mukhina
- Laboratory of Neuroengineering, Center of Translational Technologies, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russia
- Cell Technology Department, Central Research Laboratory, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia
| | - Victor Kazantsev
- Laboratory of Neuroengineering, Center of Translational Technologies, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russia
| | - Alexey Pimashkin
- Laboratory of Neuroengineering, Center of Translational Technologies, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russia
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9
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Annecchino LA, Schultz SR. Progress in automating patch clamp cellular physiology. Brain Neurosci Adv 2018; 2:2398212818776561. [PMID: 32166142 PMCID: PMC7058203 DOI: 10.1177/2398212818776561] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/19/2018] [Indexed: 12/30/2022] Open
Abstract
Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since their inception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address both voltage- and ligand-gated channels, and showing the potential to play a pivotal role in drug discovery and biomedical research. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologically relevant cellular environments, or capture higher order aspects of synaptic physiology and network dynamics. In vivo patch clamp electrophysiology has the potential to yield more biologically complex information and be especially useful in reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, while capturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it is a difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, in vivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we document the development of automated patch clamp technology, from early systems based on multi-well plates through to automated planar-array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cell electrophysiological recordings in vivo.
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Affiliation(s)
- Luca A. Annecchino
- Centre for Neurotechnology and Department of Bioengineering, Imperial College London, London, UK
| | - Simon R. Schultz
- Centre for Neurotechnology and Department of Bioengineering, Imperial College London, London, UK
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Paraskevov AV, Zendrikov DK. A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves. Phys Biol 2017; 14:026003. [PMID: 28333685 DOI: 10.1088/1478-3975/aa5fc3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.
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Affiliation(s)
- A V Paraskevov
- National Research Centre "Kurchatov Institute", 123182 Moscow, Russia. Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Russia
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