Methods to Enhance Information Extraction from Microelectrode Array Measurements of Neuronal Networks

For the last couple of decades, hand-in-hand progresses in stem-cell technologies and culturing neuronal cells together with advances in microelectrode array (MEA) technology have enabled more efficient biological models. Thus, understanding the neuronal behavior in general or realizing some particular disease model by studying neuronal responses to pharmacological or neurotoxical assays has become more achievable.Moreover, with the widespread practical usage of MEA technology, a vast amount of new types of data has been collected to be analyzed. Conventionally, MEA data from neuronal networks have been analyzed, e.g., with the methods using predefined parameters and suitable for analyzing only specific neuronal behaviors or by considering only a portion of the data such as extracted extracellular action potentials (EAPs). Therefore, in addition to the current analysis methods, novel methods and newly acquired measures are needed to understand the new models. In fact, we hypothesized that existing measurement data carry a lot more information than is considered at present. In this thesis, we proposed novel methods and measures to increase the information which we can extract from MEA recordings; thus, we hope these to contribute to better understanding of neuronal behaviors and interactions.Firstly, to analyze firing properties of neuronal ensembles, we developed a method which identifies bursts based on spiking behavior of recordings; thus, the method is feasible for the cultures with variable firing dynamics. The developed method was also designed to process a large amount of data automatically for statistical justification. Therefore, we increased the analysis power in the subsequent analyses in comparison to the existing burst detection methods which are using pre-defined and strict definitions.Subsequently, we proposed novel metrics to evaluate and quantify the information content of the bursts. Entropy-based measures were employed for quantifying bursts according to their self-similarity and spectral uniformity. We showed that different types of bursts can be distinguished using entropy-based measures. Also, the joint analysis of bursts and action potential waveforms were proposed to obtain a novel type of information, i.e., spike type compositions of bursts. We presented that the spike type compositions of bursts would change under different pharmacological applications.In addition, we developed a novel method to calculate synchronization between neuronal ensembles by evaluating their time variant spectral distributions: For that, we assessed correlations of the spectral entropy (CorSE). We showed that CorSE was able to estimate synchronicity by studying both local field potentials (LFPs) and extracellular action potentials (EAPs); thus, we could contribute to understanding synchronicity between neuronal ensembles which also don’t exhibit detectable EAPs.In conclusion, motivated by the recent popularity of MEA usage in the neuroscience field, we developed novel and enhanced methods to derive new types of information. We showed that by using our developed methods one could extract additional information from MEA recordings. As a result, the proposed methods and metrics would enhance the analysis efficiency of the microelectrode array measurement based studies and provide different viewpoints for the analyses. The derived novel information would contribute to interpreting neuronal signals recorded from a single or multiple recording locations. Consequently, methods presented in this thesis are important complements to the existing methods to understand neuronal behavior and population-wise neuronal interactions

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate : comparison to rat cortical cultures

Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions.Peer reviewe

Methods to Enhance Information Extraction from Microelectrode Array Measurements of Neuronal Networks

For the last couple of decades, hand-in-hand progresses in stem-cell technologies and culturing neuronal cells together with advances in microelectrode array (MEA) technology have enabled more efficient biological models. Thus, understanding the neuronal behavior in general or realizing some particular disease model by studying neuronal responses to pharmacological or neurotoxical assays has become more achievable.Moreover, with the widespread practical usage of MEA technology, a vast amount of new types of data has been collected to be analyzed. Conventionally, MEA data from neuronal networks have been analyzed, e.g., with the methods using predefined parameters and suitable for analyzing only specific neuronal behaviors or by considering only a portion of the data such as extracted extracellular action potentials (EAPs). Therefore, in addition to the current analysis methods, novel methods and newly acquired measures are needed to understand the new models. In fact, we hypothesized that existing measurement data carry a lot more information than is considered at present. In this thesis, we proposed novel methods and measures to increase the information which we can extract from MEA recordings; thus, we hope these to contribute to better understanding of neuronal behaviors and interactions.Firstly, to analyze firing properties of neuronal ensembles, we developed a method which identifies bursts based on spiking behavior of recordings; thus, the method is feasible for the cultures with variable firing dynamics. The developed method was also designed to process a large amount of data automatically for statistical justification. Therefore, we increased the analysis power in the subsequent analyses in comparison to the existing burst detection methods which are using pre-defined and strict definitions.Subsequently, we proposed novel metrics to evaluate and quantify the information content of the bursts. Entropy-based measures were employed for quantifying bursts according to their self-similarity and spectral uniformity. We showed that different types of bursts can be distinguished using entropy-based measures. Also, the joint analysis of bursts and action potential waveforms were proposed to obtain a novel type of information, i.e., spike type compositions of bursts. We presented that the spike type compositions of bursts would change under different pharmacological applications.In addition, we developed a novel method to calculate synchronization between neuronal ensembles by evaluating their time variant spectral distributions: For that, we assessed correlations of the spectral entropy (CorSE). We showed that CorSE was able to estimate synchronicity by studying both local field potentials (LFPs) and extracellular action potentials (EAPs); thus, we could contribute to understanding synchronicity between neuronal ensembles which also don’t exhibit detectable EAPs.In conclusion, motivated by the recent popularity of MEA usage in the neuroscience field, we developed novel and enhanced methods to derive new types of information. We showed that by using our developed methods one could extract additional information from MEA recordings. As a result, the proposed methods and metrics would enhance the analysis efficiency of the microelectrode array measurement based studies and provide different viewpoints for the analyses. The derived novel information would contribute to interpreting neuronal signals recorded from a single or multiple recording locations. Consequently, methods presented in this thesis are important complements to the existing methods to understand neuronal behavior and population-wise neuronal interactions

Spectral entropy based neuronal network synchronization analysis based on microelectrode array measurements

Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the real in vitro MEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.peerReviewe

A kainic acid-induced seizure model in human pluripotent stem cell-derived cortical neurons for studying the role of IL-6 in the functional activity

Human pluripotent stem cell (hPSC)-derived neural cultures have attracted interest for modeling epilepsy and seizure-like activity in vitro. Clinical and experimental evidence have shown that the multifunctional inflammatory cytokine interleukin (IL)-6 plays a significant role in epilepsy. However, the role of IL-6 in neuronal networks remains unclear. In this study, we modelled seizure-like activity in hPSC-derived cortical neurons using kainic acid (KA) and explored the effects of IL-6 and its counterpart, hyper-IL-6 (H-IL-6), a fusion protein consisting of IL-6 and its soluble receptor, IL-6R. In the seizure-like model, functionally mature neuronal networks responded to KA induction with an increased bursting phenotype at the single electrode level, while network level bursts decreased. The IL-6 receptors, IL6R and gp130, were expressed in hPSC-derived cortical neurons, and the gene expression of IL6R increased during maturation. Furthermore, the expression of IL-6R increased not only after IL-6 and H-IL-6 treatment but also after KA treatment. Stimulation with IL-6 or H-IL-6 was not toxic to the neurons and cytokine pretreatment did not independently modulate neuronal network activity or KA-induced seizures. Furthermore, the increased expression of IL-6R in response to IL-6, H-IL-6 and KA implies that neurons can respond through both classical and trans-signaling pathways. Acute treatment with IL-6 and H-IL-6 did not alter functional activity, suggesting that IL-6 does not affect the induction or modulation of newly induced seizures in healthy cultures. Overall, we propose this model as a useful tool to study seizure-like activity in neuronal networks in vitro.publishedVersionPeer reviewe

Simultaneous induction of vasculature and neuronal network formation on a chip reveals a dynamic interrelationship between cell types

Background: Neuronal networks receive and deliver information to regulate bodily functions while the vascular network provides oxygen, nutrients, and signaling molecules to tissues. Neurovascular interactions are vital for both tissue development and maintaining homeostasis in adulthood; these two network systems align and reciprocally communicate with one another. Although communication between network systems has been acknowledged, the lack of relevant in vitro models has hindered research at the mechanistic level. For example, the current used in vitro neurovascular models are typically established to be short-term (≤ 7 days) culture models, and they miss the supporting vascular mural cells. Methods: In this study, we utilized human induced pluripotent stem cell (hiPSC) -derived neurons, fluorescence tagged human umbilical vein endothelial cells (HUVECs), and either human bone marrow or adipose stem/stromal cells (BMSCs or ASCs) as the mural cell types to create a novel 3D neurovascular network-on-a-chip model. Collagen 1–fibrin matrix was used to establish long-term (≥ 14 days) 3D cell culture in a perfusable microphysiological environment. Results: Aprotinin-supplemented endothelial cell growth medium-2 (EGM-2) supported the simultaneous formation of neuronal networks, vascular structures, mural cell differentiation, and the stability of the 3D matrix. The formed neuronal and vascular networks were morphologically and functionally characterized. Neuronal networks supported vasculature formation based on direct cell contacts and by dramatically increasing the secretion of angiogenesis-related factors in multicultures in contrast to cocultures without neurons. Both utilized mural cell types supported the formation of neurovascular networks; however, the BMSCs seemed to boost neurovascular networks to greater extent. Conclusions: Overall, our study provides a novel human neurovascular network model that is applicable for creating in vivo-like tissue models with intrinsic neurovascular interactions. The 3D neurovascular network model on chip forms an initial platform for the development of vascularized and innervated organ-on-chip and further body-on-chip concepts and offers the possibility for mechanistic studies on neurovascular communication both under healthy and in disease conditions. [MediaObject not available: see fulltext.]Peer reviewe

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures

Funder: Tampereen Yliopisto (University of Tampere); doi: https://doi.org/10.13039/501100004371Funder: Tekes (Finnish Funding Agency for Innovation); doi: https://doi.org/10.13039/501100003406Abstract: Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures.

Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions

Directional Growth of Human Neuronal Axons in a Microfluidic Device with Nanotopography on Azobenzene-Based Material

Axonal dysfunction and degeneration are important pathological features of central nervous system (CNS) diseases and traumas, such as Alzheimer's disease, traumatic brain injury, ischemic stroke and spinal cord injury. Engineered microfluidic chips combined with human pluripotent stem cell (hPSC)-derived neurons provide valuable tools for targeted in vitro research on axons to improve understanding of disease mechanisms and enhance drug development. Here, a polydimethylsiloxane (PDMS) microfluidic chip integrated with a light patterned substrate is utilized to achieve both isolated and unidirectional axonal growth of hPSC-derived neurons. The isolation of axons from somas and dendrites and robust axonal outgrowth to adjacent, axonal compartment, is achieved by optimized cross-sectional area and length of PDMS microtunnels in the microfluidic device. In the axonal compartment, the photoinscribed nanotopography on a thin film of azobenzene-containing molecular glass efficiently guides the growth of axons. Integration of nanotopographic patterns with a compartmentalized microfluidic chip creates a human neuron-based model that supports superior axonal alignment in an isolated microenvironment. The practical utility of the chip by studying oxygen-glucose deprivation-induced damage for the isolated and aligned axons is demonstrated here. The created chip model represents a sophisticated platform and a novel tool for enhanced and long-term axon-targeted in vitro studies.publishedVersionPeer reviewe