A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro

Seamless integration of CMOS microsensors into open microfluidic systems

As traditional two-dimensional (2D) cell cultures offer limited predictive capabilities for drug development, three-dimensional (3D) tissue models, such as spherical microtissues, have been introduced to better reproduce physiological conditions. The hanging-drop method, used to cultivate microtissues at an air-liquid interface, proves to be effective for microtissue formation and maintenance. Using that technology, it is possible to fluidically interconnect several hanging drops hosting different models of human organs to recapitulate relevant tissue interactions. Here, we combine microfluidics with microelectronics (i.e., complementary metal-oxide-semiconductor (CMOS) technology) and present a novel multifunctional CMOS microelectrode array (MEA) integrated into an open microfluidic system. The device can be used in hanging-drop mode for in situ microtissue readouts and in standing-drop mode like a conventional MEA. The CMOS-MEA chip features two reconfigurable electrode arrays with 1024 electrodes each, and enables electrophysiology, impedance spectroscopy, and electrochemical sensing to acquire a broad spectrum of biologically relevant information. We fabricated the chip using a 0.18 μm CMOS process and developed a strategy to integrate the CMOS-MEA chip into the open microfluidic system within a larger overall effort to incorporate discrete CMOS sensors into microfluidic devices. Proof-of-concept experiments demonstrate the capability to perform electrophysiology and impedance spectroscopy of human induced pluripotent stem cell (hiPSC)-derived cardiac microtissues, as well as electrochemical sensing of different analytes including hydrogen peroxide and epinephrine.

Self-Foldable Three-Dimensional Biointerfaces by Strain Engineering of Two-Dimensional Layered Materials on Polymers

Two-dimensional layered materials (2DLMs) have received increasing attention for their potential in bioelectronics due to their favorable electrical, optical, and mechanical properties. The transformation of the planar structures of 2DLMs into complex 3D shapes is a key strategic step toward creating conformal biointerfaces with cells and applying them as scaffolds to simultaneously guide their growth to tissues and enable integrated bioelectronic monitoring. Using a strain-engineered self-foldable bilayer, we demonstrate the facile formation of predetermined 3D microstructures of 2DLMs with controllable curvatures, called microrolls. Three types of 2DLM microrolls─graphene, hexagonal boron nitride, and molybdenum disulfide─provide scaffolds to encapsulate and organize human-induced pluripotent stem cell-derived cardiomyocytes into tubular aggregates. Encapsulating cardiomyocytes in porous 2DLMs-laden microrolls allows for real-time microscopic observation and construction of precisely shaped cardiac tissues interacting with their surroundings. The ability to combine 2DLMs of diverse properties in the same structure further demonstrates the potential of this self-folding strategy for creating flexible, ultrathin bioelectronic devices that integrate seamlessly with complex biological environments, offering real-time, noninvasive monitoring of engineered tissues and organoids.

Impedance mapping with high-density microelectrode array chips reveals dynamic heterogeneity of in vitro epithelial barriers

Epithelial tissues in vitro undergo dynamic changes while differentiating heterogeneously on the culture substrate. This gives rise to diverse cellular arrangements which are undistinguished by conventional analysis approaches, such as transepithelial electrical resistance measurement or permeability assays. In this context, solid substrate-based systems with integrated electrodes and electrochemical impedance monitoring capability can address the limited spatiotemporal resolution of traditional porous membrane-based methods. This label-free technique facilitates local, continuous, long-term analysis of tissue barrier properties for organ-on-chip applications. Increasing spatial resolution requires small electrodes arranged in a dense array, known as high-density microelectrode arrays (HD-MEAs). Integrated with Complementary Metal Oxide Semiconductor (CMOS) technology for multiplexing and rapid impedance measurements, HD-MEAs can enable high spatiotemporal resolution assessments of epithelial tissues. Here, we used 16,384 CMOS-integrated HD-MEA chip with subcellular-sized electrodes (8 μm diameter, 15 μm pitch, patterned in 16 clusters each consisting of 1024 electrodes in a 32 × 32 matrix) and impedance sensing capability to monitor dynamic evolution of Caco-2 cells, such as their proliferation, barrier formation, and 3D structure development on the chip. Changes in impedance at the selected frequency of 1 kHz (|Z|1kHz) enabled monitoring and analyzing the life cycle of Caco-2 cells grown on the HD-MEA chips (up to + 453% change after 7 days in culture). The |Z|1kHz maps of proliferating Caco-2 cells and the differentiating epithelial tissue developing 3D domes aligned with the corresponding optical images at cellular resolution, which demonstrates the capability of the chip in tracking the dynamic heterogeneity of Caco-2 tissues in a label free and real-time fashion. Importantly, |Z|1kHz maps acquired during chemically induced barrier disruption showed electrodes covered with 3D cell domes experienced a stronger decrease in impedance than those covered with adherent cells (-41% ± sd 10% against -16% ± sd 10%, respectively). This method could thus, in principle, enable detection of tissue barrier disrupting and modifying agents with higher specificity. Epithelial barrier function assays benefit from using HD-MEA impedance sensors due to their increased informativity and resolution, which will be of great value in future organ-on-chip platforms.

Protosequences in brain organoids model intrinsic brain states Authors

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

Emerging cancer therapies: targeting physiological networks and cellular bioelectrical differences with non-thermal systemic electromagnetic fields in the human body - a comprehensive review

A steadily increasing number of publications support the concept of physiological networks, and how cellular bioelectrical properties drive cell proliferation and cell synchronization. All cells, especially cancer cells, are known to possess characteristic electrical properties critical for physiological behavior, with major differences between normal and cancer cell counterparts. This opportunity can be explored as a novel treatment modality in Oncology. Cancer cells exhibit autonomous oscillations, deviating from normal rhythms. In this context, a shift from a static view of cellular processes is required for a better understanding of the dynamic connections between cellular metabolism, gene expression, cell signaling and membrane polarization as states in constant flux in realistic human models. In oncology, radiofrequency electromagnetic fields have produced sustained responses and improved quality of life in cancer patients with minimal side effects. This review aims to show how non-thermal systemic radiofrequency electromagnetic fields leads to promising therapeutic responses at cellular and tissue levels in humans, supporting this newly emerging cancer treatment modality with early favorable clinical experience specifically in advanced cancer.

A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring

The analysis of tissue cultures, particularly brain organoids, requires a sophisticated integration and coordination of multiple technologies for monitoring and measuring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Our approach enables continuous, communicative, non-invasive interactions within an Internet of Things (IoT) architecture among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids while measuring their neuronal activity. The organoids are cultured in custom, 3D-printed chambers affixed to commercial microelectrode arrays. Periodic feeding is achieved using programmable microfluidic pumps. We developed a computer vision fluid volume estimator used as feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a set of 7-day studies of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated protocols were validated in maintaining robust neural activity throughout the experiment. The automated system enabled hourly electrophysiology recordings for the 7-day studies. Median neural unit firing rates increased for every sample and dynamic patterns of organoid firing rates were revealed by high-frequency recordings. Surprisingly, feeding did not affect firing rate. Furthermore, performing media exchange during a recording showed no acute effects on firing rate, enabling the use of this automated platform for reagent screening studies.

RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies

With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort's true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort's performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort's functionality on different types of recording hardware and electrode configurations.

CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses

Introduction

In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing.

Methods

To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks.

Results and discussion

We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects.

68-channel neural signal processing system-on-chip with integrated feature extraction, compression, and hardware accelerators for neuroprosthetics in 22 nm FDSOI

Introduction

Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics.

Methods

We present a novel solution that leverages the high integration density of 22nm fully-depleted silicon-on-insulator technology to address these challenges. The proposed highly integrated programmable System-on-Chip (SoC) comprises 68-channel 0.41 μW/Ch recording frontends, spike detectors, 16-channel 0.87-4.39 μW/Ch action potentials and 8-channel 0.32 μW/Ch local field potential codecs, as well as a multiply-accumulate-assisted power-efficient processor operating at 25 MHz (5.19 μW/MHz). The system supports on-chip training processes for compression, training, and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48 or 94.12% depending on the utilized features. The proposed programmable SoC is optimized for reduced area (9 mm2) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation.

Discussion

Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression.

Multimodal Mapping of Electrical and Mechanical Latency of Human-Induced Pluripotent Stem Cell-Derived Cardiomyocyte Layers

The synchronization of the electrical and mechanical coupling assures the physiological pump function of the heart, but life-threatening pathologies may jeopardize this equilibrium. Recently, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a model for personalized investigation because they can recapitulate human diseased traits, such as compromised electrical capacity or mechanical circuit disruption. This research avails the model of hiPSC-CMs and showcases innovative techniques to study the electrical and mechanical properties as well as their modulation due to inherited cardiomyopathies. In this work, hiPSC-CMs carrying either Brugada syndrome (BRU) or dilated cardiomyopathy (DCM), were organized in a bilayer configuration to first validate the experimental methods and second mimic the physiological environment. High-density CMOS-based microelectrode arrays (HD-MEA) have been employed to study the electrical activity. Furthermore, mechanical function was investigated via quantitative video-based evaluation, upon stimulation with a β-adrenergic agonist. This study introduces two experimental methods. First, high-throughput mechanical measurements in the hiPSC-CM layers (xy-inspection) are obtained using both a recently developed optical tracker (OPT) and confocal reference-free traction force microscopy (cTFM) aimed to quantify cardiac kinematics. Second, atomic force microscopy (AFM) with FluidFM probes, combined with the xy-inspection methods, supplemented a three-dimensional understanding of cell-cell mechanical coupling (xyz-inspection). This particular combination represents a multi-technique approach to detecting electrical and mechanical latency among the cell layers, examining differences and possible implications following inherited cardiomyopathies. It can not only detect disease characteristics in the proposed in vitro model but also quantitatively assess its response to drugs, thereby demonstrating its feasibility as a scalable tool for clinical and pharmacological studies.

A millimetre-scale capacitive biosensing and biophysical stimulation system for emerging bioelectronic bone implants

Bioelectronic bone implants are being widely recognized as a promising technology for highly personalized bone/implant interface sensing and biophysical therapeutic stimulation. Such bioelectronic devices are based on an innovative concept with the ability to be applied to a wide range of implants, including in fixation and prosthetic systems. Recently, biointerface sensing using capacitive patterns was proposed to overcome the limitations of standard imaging technologies and other non-imaging technologies; moreover, electric stimulation using capacitive patterns was proposed to overcome the limitations of non-instrumented implants. We here provide an innovative low-power miniaturized electronic system with ability to provide both therapeutic stimulation and bone/implant interface monitoring using network-architectured capacitive interdigitated patterns. It comprises five modules: sensing, electric stimulation, processing, communication and power management. This technology was validated using in vitro tests: concerning the sensing system, its ability to detect biointerface changes ranging from tiny to severe bone-implant interface changes in target regions was validated; concerning the stimulation system, its ability to significantly enhance bone cells' full differentiation, including matrix maturation and mineralization, was also confirmed. This work provides an impactful contribution and paves the way for the development of the new generation of orthopaedic biodevices.

Laser-patterned epoxy-based 3D microelectrode arrays for extracellular recording

Microelectrode arrays are commonly used to study the electrophysiological behavior of cells. Recently, there has been a growing interest in fabricating three-dimensional microelectrode arrays. Here, we present a novel process for the fast fabrication of epoxy-based 3D microelectrode array platforms with the assistance of laser-patterning technology. To this end, we photopatterned 3D pillars as scaffolds using epoxy-based dry films. Electrodes and conductor traces were fabricated by laser patterning of sputtered platinum films on top of the 3D structures, followed by deposition of parylene-C for insulation. Microelectrodes at the tip of the 3D structures were exposed using a vertical laser ablation process. The final electrodes demonstrated a low impedance of ∼10 kΩ at 1 kHz in electrochemical impedance spectroscopy measurements under physiological conditions. We investigated the maximum compression force of the 3D structures, which could withstand approximately 0.6 N per pillar. The 3D microelectrode arrays were used to record extracellular signals from HL-1 cells in culture as a proof of principle. Our results show regular firing of action potentials recorded at the tip of the 3D structures, demonstrating the possibility of recording cell signals in non-planar environments.

BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network

Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative approaches such as electroceutics. Recent research in bioelectronics and neuromorphic engineering have fostered the development of the new generation of neuroprostheses for brain repair. However, achieving their full potential necessitates a deeper understanding of biohybrid interaction. In this study, we present a novel real-time, biomimetic, cost-effective and user-friendly neural network capable of real-time emulation for biohybrid experiments. Our system facilitates the investigation and replication of biophysically detailed neural network dynamics while prioritizing cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and a variety of biological cells as well as real-time emulation of diverse network configurations. We envision our system as a crucial step towards the development of neuromorphic-based neuroprostheses for bioelectrical therapeutics, enabling seamless communication with biological networks on a comparable timescale. Its embedded real-time functionality enhances practicality and accessibility, amplifying its potential for real-world applications in biohybrid experiments.

A model of human neural networks reveals NPTX2 pathology in ALS and FTLD

Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1, which involve human-specific mechanisms2-5 that cannot be directly studied in animal models. Here, to explore the emergence and consequences of TDP-43 pathologies, we generated induced pluripotent stem cell-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural precursors6. Single-cell transcriptomics and comparison to independent neural stem cells7 showed that iCoMoNSCs are uniquely homogenous and self-renewing. Differentiated iCoMoNSCs formed a self-organized multicellular system consisting of synaptically connected and electrophysiologically active neurons, which matured into long-lived functional networks (which we designate iNets). Neuronal and glial maturation in iNets was similar to that of cortical organoids8. Overexpression of wild-type TDP-43 in a minority of neurons within iNets led to progressive fragmentation and aggregation of the protein, resulting in a partial loss of function and neurotoxicity. Single-cell transcriptomics revealed a novel set of misregulated RNA targets in TDP-43-overexpressing neurons and in patients with TDP-43 proteinopathies exhibiting a loss of nuclear TDP-43. The strongest misregulated target encoded the synaptic protein NPTX2, the levels of which are controlled by TDP-43 binding on its 3' untranslated region. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-induced neurodegeneration. Notably, NPTX2 was consistently misaccumulated in neurons from patients with amyotrophic lateral sclerosis and frontotemporal lobar degeneration with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby revealing a TDP-43-dependent pathway of neurotoxicity.

Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review

In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.

Functional imaging of conduction dynamics in cortical and spinal axons

Mammalian axons are specialized for transmitting action potentials to targets within the central and peripheral nervous system. A growing body of evidence suggests that, besides signal conduction, axons play essential roles in neural information processing, and their malfunctions are common hallmarks of neurodegenerative diseases. The technologies available to study axonal function and structure integrally limit the comprehension of axon neurobiology. High-density microelectrode arrays (HD-MEAs) allow for accessing axonal action potentials at high spatiotemporal resolution, but provide no insights on axonal morphology. Here, we demonstrate a method for electrical visualization of axonal morphologies based on extracellular action potentials recorded from cortical and motor neurons using HD-MEAs. The method enabled us to reconstruct up to 5-cm-long axonal arbors and directly monitor axonal conduction across thousands of recording sites. We reconstructed 1.86 m of cortical and spinal axons in total and found specific features in their structure and function.

Toward the Next Generation of Neural Iontronic Interfaces

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.

A CMOS-based highly scalable flexible neural electrode interface

Perception, thoughts, and actions are encoded by the coordinated activity of large neuronal populations spread over large areas. However, existing electrophysiological devices are limited by their scalability in capturing this cortex-wide activity. Here, we developed an electrode connector based on an ultra-conformable thin-film electrode array that self-assembles onto silicon microelectrode arrays enabling multithousand channel counts at a millimeter scale. The interconnects are formed using microfabricated electrode pads suspended by thin support arms, termed Flex2Chip. Capillary-assisted assembly drives the pads to deform toward the chip surface, and van der Waals forces maintain this deformation, establishing Ohmic contact. Flex2Chip arrays successfully measured extracellular action potentials ex vivo and resolved micrometer scale seizure propagation trajectories in epileptic mice. We find that seizure dynamics in absence epilepsy in the Scn8a+/- model do not have constant propagation trajectories.

The next generation of hybrid microfluidic/integrated circuit chips: recent and upcoming advances in high-speed, high-throughput, and multifunctional lab-on-IC systems

Since the field's inception, pioneers in microfluidics have made significant progress towards realizing complete lab-on-chip systems capable of sophisticated sample analysis and processing. One avenue towards this goal has been to join forces with the related field of microelectronics, using integrated circuits (ICs) to perform on-chip actuation and sensing. While early demonstrations focused on using microfluidic-IC hybrid chips to miniaturize benchtop instruments, steady advancements in the field have enabled a new generation of devices that expand past miniaturization into high-performance applications that would not be possible without IC hybrid integration. In this review, we identify recent examples of labs-on-chip that use high-resolution, high-speed, and multifunctional electronic and photonic chips to expand the capabilities of conventional sample analysis. We focus on three particularly active areas: a) high-throughput integrated flow cytometers; b) large-scale microelectrode arrays for stimulation and multimodal sensing of cells over a wide field of view; c) high-speed biosensors for studying molecules with high temporal resolution. We also discuss recent advancements in IC technology, including on-chip data processing techniques and lens-free optics based on integrated photonics, that are poised to further advance microfluidic-IC hybrid chips.

Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology

The electrophysiological technology having a high spatiotemporal resolution at the single-cell level and noninvasive measurements of large areas provide insights on underlying neuronal function. Here, a complementary metal-oxide semiconductor (CMOS)-microelectrode array (MEA) is used that uses 236 880 electrodes each with an electrode size of 11.22 × 11.22 µm and 236 880 covering a wide area of 5.5 × 5.9 mm in presenting a detailed and single-cell-level neural activity analysis platform for brain slices, human iPS cell-derived cortical networks, peripheral neurons, and human brain organoids. Propagation pattern characteristics between brain regions changes the synaptic propagation into compounds based on single-cell time-series patterns, classification based on single DRG neuron firing patterns and compound responses, axonal conduction characteristics and changes to anticancer drugs, and network activities and transition to compounds in brain organoids are extracted. This detailed analysis of neural activity at the single-cell level using the CMOS-MEA provides a new understanding of the basic mechanisms of brain circuits in vitro and ex vivo, on human neurological diseases for drug discovery, and compound toxicity assessment.

Semiconducting electrodes for neural interfacing: a review

In the past 50 years, the advent of electronic technology to directly interface with neural tissue has transformed the fields of medicine and biology. Devices that restore or even replace impaired bodily functions, such as deep brain stimulators and cochlear implants, have ushered in a new treatment era for previously intractable conditions. Meanwhile, electrodes for recording and stimulating neural activity have allowed researchers to unravel the vast complexities of the human nervous system. Recent advances in semiconducting materials have allowed effective interfaces between electrodes and neuronal tissue through novel devices and structures. Often these are unattainable using conventional metallic electrodes. These have translated into advances in research and treatment. The development of semiconducting materials opens new avenues in neural interfacing. This review considers this emerging class of electrodes and how it can facilitate electrical, optical, and chemical sensing and modulation with high spatial and temporal precision. Semiconducting electrodes have advanced electrically based neural interfacing technologies owing to their unique electrochemical and photo-electrochemical attributes. Key operation modalities, namely sensing and stimulation in electrical, biochemical, and optical domains, are discussed, highlighting their contrast to metallic electrodes from the application and characterization perspective.

A Multimodal and Multifunctional CMOS Cellular Interfacing Array for Digital Physiology and Pathology Featuring an Ultra Dense Pixel Array and Reconfigurable Sampling Rate

The article presents a fully integrated multimodal and multifunctional CMOS biosensing/actuating array chip and system for multi-dimensional cellular/tissue characterization. The CMOS chip supports up to 1,568 simultaneous parallel readout channels across 21,952 individually addressable multimodal pixels with 13 μm × 13 μm 2-D pixel pitch along with 1,568 Pt reference electrodes. These features allow the CMOS array chip to perform multimodal physiological measurements on living cell/tissue samples with both high throughput and single-cell resolution. Each pixel supports three sensing and one actuating modalities, each reconfigurable for different functionalities, in the form of full array (FA) or fast scan (FS) voltage recording schemes, bright/dim optical detection, 2-/4-point impedance sensing (ZS), and biphasic current stimulation (BCS) with adjustable stimulation area for single-cell or tissue-level stimulation. Each multi-modal pixel contains an 8.84 μm × 11 μm Pt electrode, 4.16 μm × 7.2 μm photodiode (PD), and in-pixel circuits for PD measurements and pixel selection. The chip is fabricated in a standard 130nm BiCMOS process as a proof of concept. The on-chip electrodes are constructed by unique design and in-house post-CMOS fabrication processes, including a critical Al shorting of all pixels during fabrication and Al etching after fabrication that ensures a high-yield planar electrode array on CMOS with high biocompatibility and long-term measurement reliability. For demonstration, extensive biological testing is performed with human and mouse progenitor cells, in which multidimensional biophysiological data are acquired for comprehensive cellular characterization.

Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays

Comprehensive electrophysiological characterizations of human induced pluripotent stem cell (hiPSC)-derived neuronal networks are essential to determine to what extent these in vitro models recapitulate the functional features of in vivo neuronal circuits. High-density micro-electrode arrays (HD-MEAs) offer non-invasive recording with the best spatial and temporal resolution possible to date. For 3 months, we tracked the morphology and activity features of developing networks derived from a transgenic hiPSC line in which neurogenesis is inducible by neurogenic transcription factor overexpression. Our morphological data revealed large-scale structural changes from homogeneously distributed neurons in the first month to the formation of neuronal clusters over time. This led to a constant shift in position of neuronal cells and clusters on HD-MEAs and corresponding changes in spatial distribution of the network activity maps. Network activity appeared as scarce action potentials (APs), evolved as local bursts with longer duration and changed to network-wide synchronized bursts with higher frequencies but shorter duration over time, resembling the emerging burst features found in the developing human brain. Instantaneous firing rate data indicated that the fraction of fast spiking neurons (150–600 Hz) increases sharply after 63 days post induction (dpi). Inhibition of glutamatergic synapses erased burst features from network activity profiles and confirmed the presence of mature excitatory neurotransmission. The application of GABAergic receptor antagonists profoundly changed the bursting profile of the network at 120 dpi. This indicated a GABAergic switch from excitatory to inhibitory neurotransmission during circuit development and maturation. Our results suggested that an emerging GABAergic system at older culture ages is involved in regulating spontaneous network bursts. In conclusion, our data showed that long-term and continuous microscopy and electrophysiology readouts are crucial for a meaningful characterization of morphological and functional maturation in stem cell-derived human networks. Most importantly, assessing the level and duration of functional maturation is key to subject these human neuronal circuits on HD-MEAs for basic and biomedical applications.

Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study

Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed “homeostatic plasticity.” Recently, a highly excitable microdomain, located at the proximal end of the axon—the axon initial segment (AIS)—was found to exhibit structural modifications in response to activity perturbations. Though AIS plasticity appears to serve a homeostatic purpose, many aspects governing its expression and its functional role in regulating neuronal excitability remain elusive. A central challenge in studying the phenomenon is the rich heterogeneity of its expression (distal/proximal relocation, shortening, lengthening) and the variability of its functional role. A potential solution is to track AISs of a large number of neurons over time and attempt to induce structural plasticity in them. To this end, a promising approach is to use extracellular electrophysiological readouts to track a large number of neurons at high spatiotemporal resolution by means of high-density microelectrode arrays (HD-MEAs). However, an analysis framework that reliably identifies specific activity signatures that uniquely map on to underlying microstructural changes is missing. In this study, we assessed the feasibility of such a task and used the distal relocation of the AIS as an exemplary problem. We used sophisticated computational models to systematically explore the relationship between incremental changes in AIS positions and the specific consequences observed in simulated extracellular field potentials. An ensemble of feature changes in the extracellular fields that reliably characterize AIS plasticity was identified. We trained models that could detect these signatures with remarkable accuracy. Based on these findings, we propose a hybrid analysis framework that could potentially enable high-throughput experimental studies of activity-dependent AIS plasticity using HD-MEAs.

Programmable Electrochemical Stimulation on a Large-Scale CMOS Microelectrode Array

In this paper we present spatio-temporally controlled electrochemical stimulation of aqueous samples using an integrated CMOS microelectrode array with 131,072 pixels. We demonstrate programmable gold electrodeposition in arbitrary spatial patterns, controllable electrolysis to produce microscale hydrogen bubbles, and spatially targeted electrochemical pH modulation. Dense spatially-addressable electrochemical stimulation is important for a wide range of bioelectronics applications.

Repeated and On-Demand Intracellular Recordings of Cardiomyocytes Derived from Human-Induced Pluripotent Stem Cells

Pharmaceutical compounds may have cardiotoxic properties, triggering potentially life-threatening arrhythmias. To investigate proarrhythmic effects of drugs, the patch clamp technique has been used as the gold standard for characterizing the electrophysiology of cardiomyocytes in vitro. However, the applicability of this technology for drug screening is limited, as it is complex to use and features low throughput. Recent studies have demonstrated that 3D-nanostructured electrodes enable to obtain intracellular signals from many cardiomyocytes in parallel; however, the tedious electrode fabrication and limited measurement duration still remain major issues for cardiotoxicity testing. Here, we demonstrate how porous Pt-black electrodes, arranged in high-density microelectrode arrays, can be used to record intracellular-like signals of cardiomyocytes at large scale repeatedly over an extended period of time. The developed technique, which yields highly parallelized electroporations using stimulation voltages around 1 V peak-to-peak amplitude, enabled intracellular-like recordings at high success rates without causing significant alteration in key electrophysiological features. In a proof-of-concept study, we investigated electrophysiological modulations induced by two clinically applied drugs, nifedipine and quinidine. As the obtained results were in good agreement with previously published data, we are confident that the developed technique has the potential to be routinely used in in vitro platforms for cardiotoxicity screening.

Simultaneous recording of electrical and metabolic activity of cardiac cells in vitro using an organic charge modulated field effect transistor array

In vitro electrogenic cells monitoring is an important objective in several scientific and technological fields, such as electrophysiology, pharmacology and brain machine interfaces, and can represent an interesting opportunity in other translational medicine applications. One of the key aspects of cellular cultures is the complexity of their behavior, due to the different kinds of bio-related signals, both chemical and electrical, that characterize these systems. In order to fully understand and exploit this extraordinary complexity, specific devices and tools are needed. However, at the moment this important scientific field is characterized by the lack of easy-to-use, low-cost devices for the sensing of multiple cellular parameters. To the aim of providing a simple and integrated approach for the study of in vitro electrogenic cultures, we present here a new solution for the monitoring of both the electrical and the metabolic cellular activity. In particular, we show here how a particular device called Micro Organic Charge Modulated Array (MOA) can be conveniently engineered and then used to simultaneously record the complete cell activity using the same device architecture. The system has been tested using primary cardiac rat myocytes and allowed to detect the metabolic and electrical variations thar occur upon the administration of different drugs. This first example could lay the basis for the development of a new generation of multi-sensing tools that can help to efficiently probe the multifaceted in vitro environment.

Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.

CMOS electrochemical pH localizer-imager

pH controls a large repertoire of chemical and biochemical processes in water. Densely arrayed pH microenvironments would parallelize these processes, enabling their high-throughput studies and applications. However, pH localization, let alone its arrayed realization, remains challenging because of fast diffusion of protons in water. Here, we demonstrate arrayed localizations of picoliter-scale aqueous acids, using a 256-electrochemical cell array defined on and operated by a complementary metal oxide semiconductor (CMOS)-integrated circuit. Each cell, comprising a concentric pair of cathode and anode with their current injections controlled with a sub-nanoampere resolution by the CMOS electronics, creates a local pH environment, or a pH "voxel," via confined electrochemistry. The system also monitors the spatiotemporal pH profile across the array in real time for precision pH control. We highlight the utility of this CMOS pH localizer-imager for high-throughput tasks by parallelizing pH-gated molecular state encoding and pH-regulated enzymatic DNA elongation at any selected set of cells.

Functional imaging of brain organoids using high-density microelectrode arrays

ABSTRACT

Studies have provided evidence that human cerebral organoids (hCOs) recapitulate fundamental milestones of early brain development, but many important questions regarding their functionality and electrophysiological properties persist. High-density microelectrode arrays (HD-MEAs) represent an attractive analysis platform to perform functional studies of neuronal networks at the cellular and network scale. Here, we use HD-MEAs to derive large-scale electrophysiological recordings from sliced hCOs. We record the activity of hCO slices over several weeks and probe observed neuronal dynamics pharmacologically. Moreover, we present results on how the obtained recordings can be spike-sorted and subsequently studied across scales. For example, we show how to track single neurons across several days on the HD-MEA and how to infer axonal action potential velocities. We also infer putative functional connectivity from hCO recordings. The introduced methodology will contribute to a better understanding of developing neuronal networks in brain organoids and provide new means for their functional characterization.

IMPACT STATEMENT

Human cerebral organoids (hCOs) represent an attractive in vitro model system to study key physiological mechanisms underlying early neuronal network formation in tissue with healthy or disease-related genetic backgrounds. Despite remarkable advances in the generation of brain organoids, knowledge on the functionality of their neuronal circuits is still scarce. Here, we used complementary metal-oxide-semiconductor (CMOS)-based high-density microelectrode arrays (HD-MEAs) to perform large-scale recordings from sliced hCOs over several weeks and quantified their activity across scales. Using single-cell and network metrics, we were able to probe aspects of hCO neurophysiology that are more difficult to obtain with other techniques, such as patch clamping (lower yield) and calcium imaging (lower temporal resolution). These metrics included, for example, extracellular action potential (AP) waveform features and axonal AP velocity at the cellular level, as well as functional connectivity at the network level. Analysis was enabled by the large sensing area and the high spatiotemporal resolution provided by HD-MEAs, which allowed recordings from hundreds of neurons and spike sorting of their activity. Our results demonstrate that HD-MEAs provide a multi-purpose platform for the functional characterization of hCOs, which will be key in improving our understanding of this model system and assessing its relevance for translational research.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1557/s43577-022-00282-w.

Group-Chopping: An 8-Channel, 0.04% Gain Mismatch, 2.1 µW 0.017 mm2 Instrumentation Amplifier for Bio-Potential Recording

An 8-channel AFE with a group-chopping instrumentation amplifier (GCIA) is proposed for bio-potential recording applications. The group-chopping technique cascades chopper switches to progressively swap channels and dynamically removes gain mismatch among all channels. An 8-phase non-overlapping clocking scheme is developed and achieves excellent between-channel gain mismatch characteristics. The dynamic offsets among all channels are mitigated by the GCIA as well. The GCIA is the first work that minimizes the gain mismatch across more than two channels. With the help of the group-chopping, combined with an area-efficient open-loop structure, the GCIA shows <0.04% between-channel gain mismatch, the lowest mismatch reported to date. The chip is fabricated in 0.18µm 1P6M CMOS, occupies only 0.017 mm²/Ch., consumes 2.1 μW/Ch. under 0.5 V supply and achieves an NEF of 2.1.

An Open-Source Transparent Microelectrode Array

The micro-electrode array (MEA) is a cell-culture surface with integrated electrodes used for assays of electrically excitable cells and tissues. MEAs have been a workhorse in the study of neurons and myocytes, owing to the scalability and millisecond temporal resolution of the technology. However, traditional MEAs are opaque, precluding inverted microscope access to modern genetically encoded optical sensors and effectors. To address this gap, transparent MEAs have been developed. However, for many labs, transparent MEAs remain out of reach due to the cost of commercially available products, and the complexity of custom fabrication. Here, we describe an open-source transparent MEA based on the OpenEphys platform. This resource is designed to be accessible, requiring minimal microelectrode fabrication or circuit design experience. We include low-noise connectors for seamless integration with the Intan Technologies headstage, and a mechanically stable adaptor conforming to the 24-well plate footprint for compatibility with most inverted microscopes. We demonstrate the performance of this transparent MEA in a multiplexed electrical and optogenetic assay of primary rat hippocampal neurons.

A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μW/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μW/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.

Highly Configurable 100 Channel Recording and Stimulating Integrated Circuit for Biomedical Experiments

This paper presents the design results of a 100-channel integrated circuit dedicated to various biomedical experiments requiring both electrical stimulation and recording ability. The main design motivation was to develop an architecture that would comprise not only the recording and stimulation, but would also block allowing to meet different experimental requirements. Therefore, both the controllability and programmability were prime concerns, as well as the main chip parameters uniformity. The recording stage allows one to set their parameters independently from channel to channel, i.e., the frequency bandwidth can be controlled in the (0.3 Hz-1 kHz)-(20 Hz-3 kHz) (slow signal path) or (0.3 Hz-1 kHz)-4.7 kHz (fast signal path) range, while the voltage gain can be set individually either to 43.5 dB or 52 dB. Importantly, thanks to in-pixel circuitry, main system parameters may be controlled individually allowing to mitigate the circuitry components spread, i.e., lower corner frequency can be tuned in the 54 dB range with approximately 5% precision, and the upper corner frequency spread is only 4.2%, while the voltage gain spread is only 0.62%. The current stimulator may also be controlled in the broad range (69 dB) with its current setting precision being no worse than 2.6%. The recording channels' input-referred noise is equal to 8.5 µVRMS in the 10 Hz-4.7 kHz bandwidth. The single-pixel occupies 0.16 mm2 and consumes 12 µW (recording part) and 22 µW (stimulation blocks).

Automatic Identification of Axon Bundle Activation for Epiretinal Prosthesis

Objective:

Retinal prostheses must be able to activate cells in a selective way in order to restore high-fidelity vision. However, inadvertent activation of far-away retinal ganglion cells (RGCs) through electrical stimulation of axon bundles can produce irregular and poorly controlled percepts, limiting artificial vision. In this work, we aim to provide an algorithmic solution to the problem of detecting axon bundle activation with a bi-directional epiretinal prostheses.

Methods:

The algorithm utilizes electrical recordings to determine the stimulation current amplitudes above which axon bundle activation occurs. Bundle activation is defined as the axonal stimulation of RGCs with unknown soma and receptive field locations, typically beyond the electrode array. The method exploits spatiotemporal characteristics of electrically-evoked spikes to overcome the challenge of detecting small axonal spikes.

Results:

The algorithm was validated using large-scale, single-electrode and short pulse, ex vivo stimulation and recording experiments in macaque retina, by comparing algorithmically and manually identified bundle activation thresholds. For 88% of the electrodes analyzed, the threshold identified by the algorithm was within ±10% of the manually identified threshold, with a correlation coefficient of 0.95.

Conclusion:

This works presents a simple, accurate and efficient algorithm to detect axon bundle activation in epiretinal prostheses.

Significance:

The algorithm could be used in a closed-loop manner by a future epiretinal prosthesis to reduce poorly controlled visual percepts associated with bundle activation. Activation of distant cells via axonal stimulation will likely occur in other types of retinal implants and cortical implants, and the method may therefore be broadly applicable.

A 1024-Channel 10-Bit 36- μW/ch CMOS ROIC for Multiplexed GFET-Only Sensor Arrays in Brain Mapping

This paper presents a 1024-channel neural read-out integrated circuit (ROIC) for solution-gated GFET sensing probes in massive μECoG brain mapping. The proposed time-domain multiplexing of GFET-only arrays enables low-cost and scalable hybrid headstages. Low-power CMOS circuits are presented for the GFET analog frontend, including a CDS mechanism to improve preamplifier noise figures and 10-bit 10-kS/s A/D conversion. The 1024-channel ROIC has been fabricated in a standard 1.8-V 0.18- μm CMOS technology with 0.012 mm ² and 36 μ W per channel. An automated methodology for the in-situ calibration of each GFET sensor is also proposed. Experimental ROIC tests are reported using a custom FPGA-based μECoG headstage with 16×32 and 32×32 GFET probes in saline solution and agar substrate. Compared to state-of-art neural ROICs, this work achieves the largest scalability in hybrid platforms and it allows the recording of infra-slow neural signals.

A Low-Power Opamp-Less Second-Order Delta-Sigma Modulator for Bioelectrical Signals in 0.18 µm CMOS

This article reports on a compact and low-power CMOS readout circuit for bioelectrical signals based on a second-order delta-sigma modulator. The converter uses a voltage-controlled, oscillator-based quantizer, achieving second-order noise shaping with a single opamp-less integrator and minimal analog circuitry. A prototype has been implemented using 0.18 μm CMOS technology and includes two different variants of the same modulator topology. The main modulator has been optimized for low-noise, neural-action-potential detection in the 300 Hz–6 kHz band, with an input-referred noise of 5.0 μV_rms, and occupies an area of 0.0045 mm². An alternative configuration features a larger input stage to reduce low-frequency noise, achieving 8.7 μV_rms in the 1 Hz–10 kHz band, and occupies an area of 0.006 mm². The modulator is powered at 1.8 V with an estimated power consumption of 3.5 μW.

Extracellular Recording of Entire Neural Networks Using a Dual-Mode Microelectrode Array With 19584 Electrodes and High SNR

Electrophysiological research on neural networks and their activity focuses on the recording and analysis of large data sets that include information of thousands of neurons. CMOS microelectrode arrays (MEAs) feature thousands of electrodes at a spatial resolution on the scale of single cells and are, therefore, ideal tools to support neural-network research. Moreover, they offer high spatio-temporal resolution and signal to-noise ratio (SNR) to capture all features and subcellular resolution details of neuronal signaling. Here, we present a dual-mode (DM) MEA, which enables simultaneous: 1) full-frame readout from all electrodes and 2) high-SNR readout from an arbitrarily selectable subset of electrodes. The DM-MEA includes 19584 electrodes, 19584 full-frame recording channels with noise levels of 10.4 μV_rms in the action potential (AP) frequency band (300 Hz-5 kHz), 246 low-noise recording channels with noise levels of 3.0 μV_rms in the AP band and eight stimulation units. The capacity to simultaneously perform full-frame and high-SNR recordings endows the presented DM-MEA with great flexibility for various applications in neuroscience and pharmacology.

Recording Strategies for High Channel Count, Densely Spaced Microelectrode Arrays

Neuroscience research into how complex brain functions are implemented at an extra-cellular level requires in vivo neural recording interfaces, including microelectrodes and read-out circuitry, with increased observability and spatial resolution. The trend in neural recording interfaces toward employing high-channel-count probes or 2D microelectrodes arrays with densely spaced recording sites for recording large neuronal populations makes it harder to save on resources. The low-noise, low-power requirement specifications of the analog front-end usually requires large silicon occupation, making the problem even more challenging. One common approach to alleviating this consumption area burden relies on time-division multiplexing techniques in which read-out electronics are shared, either partially or totally, between channels while preserving the spatial and temporal resolution of the recordings. In this approach, shared elements have to operate over a shorter time slot per channel and active area is thus traded off against larger operating frequencies and signal bandwidths. As a result, power consumption is only mildly affected, although other performance metrics such as in-band noise or crosstalk may be degraded, particularly if the whole read-out circuit is multiplexed at the analog front-end input. In this article, we review the different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and suggest strategies for improving performance.

Single-Entity Electrochemistry for Digital Biosensing at Ultralow Concentrations

Quantifying ultralow analyte concentrations is a continuing challenge in the analytical sciences in general and in electrochemistry in particular. Typical hurdles for affinity sensors at low concentrations include achieving sufficiently efficient mass transport of the analyte, dealing with slow reaction kinetics, and detecting a small transducer signal against a background signal that itself fluctuates slowly in time. Recent decades have seen the advent of methods capable of detecting single analytes ranging from the nanoscale to individual molecules, representing the ultimate mass sensitivity to these analytes. However, single-entity detection does not automatically translate into a superior concentration sensitivity. This is largely because electrochemical transducers capable of such detection are themselves miniaturized, exacerbating mass transport and binding kinetic limitations. In this Perspective, we discuss how these challenges can be tackled through so-called digital sensing: large arrays of separately addressable single-entity detectors that provide real-time information on individual binding events. We discuss the advantages of this approach and the barriers to its implementation.

Inkjet-Printed and Electroplated 3D Electrodes for Recording Extracellular Signals in Cell Culture

Recent investigations into cardiac or nervous tissues call for systems that are able to electrically record in 3D as opposed to 2D. Typically, challenging microfabrication steps are required to produce 3D microelectrode arrays capable of recording at the desired position within the tissue of interest. As an alternative, additive manufacturing is becoming a versatile platform for rapidly prototyping novel sensors with flexible geometric design. In this work, 3D MEAs for cell-culture applications were fabricated using a piezoelectric inkjet printer. The aspect ratio and height of the printed 3D electrodes were user-defined by adjusting the number of deposited droplets of silver nanoparticle ink along with a continuous printing method and an appropriate drop-to-drop delay. The Ag 3D MEAs were later electroplated with Au and Pt in order to reduce leakage of potentially cytotoxic silver ions into the cellular medium. The functionality of the array was confirmed using impedance spectroscopy, cyclic voltammetry, and recordings of extracellular potentials from cardiomyocyte-like HL-1 cells.

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

16p11.2 deletion is associated with hyperactivation of human iPSC-derived dopaminergic neuron networks and is rescued by RHOA inhibition in vitro

Reciprocal copy number variations (CNVs) of 16p11.2 are associated with a wide spectrum of neuropsychiatric and neurodevelopmental disorders. Here, we use human induced pluripotent stem cells (iPSCs)-derived dopaminergic (DA) neurons carrying CNVs of 16p11.2 duplication (16pdup) and 16p11.2 deletion (16pdel), engineered using CRISPR-Cas9. We show that 16pdel iPSC-derived DA neurons have increased soma size and synaptic marker expression compared to isogenic control lines, while 16pdup iPSC-derived DA neurons show deficits in neuronal differentiation and reduced synaptic marker expression. The 16pdel iPSC-derived DA neurons have impaired neurophysiological properties. The 16pdel iPSC-derived DA neuronal networks are hyperactive and have increased bursting in culture compared to controls. We also show that the expression of RHOA is increased in the 16pdel iPSC-derived DA neurons and that treatment with a specific RHOA-inhibitor, Rhosin, rescues the network activity of the 16pdel iPSC-derived DA neurons. Our data suggest that 16p11.2 deletion-associated iPSC-derived DA neuron hyperactivation can be rescued by RHOA inhibition.

Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays

Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, HD-MEAs are used in vitro to characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinson's disease and amyotrophic lateral sclerosis. Reproducible electrophysiological network, single-cell and subcellular metrics are used for phenotype characterization and drug testing. Metrics, such as burst shape and axonal velocity, enable the distinction of healthy and diseased neurons. The HD-MEA metrics can also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, it is shown that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.

Tissue Chips and Microphysiological Systems for Disease Modeling and Drug Testing

Tissue chips (TCs) and microphysiological systems (MPSs) that incorporate human cells are novel platforms to model disease and screen drugs and provide an alternative to traditional animal studies. This review highlights the basic definitions of TCs and MPSs, examines four major organs/tissues, identifies critical parameters for organization and function (tissue organization, blood flow, and physical stresses), reviews current microfluidic approaches to recreate tissues, and discusses current shortcomings and future directions for the development and application of these technologies. The organs emphasized are those involved in the metabolism or excretion of drugs (hepatic and renal systems) and organs sensitive to drug toxicity (cardiovascular system). This article examines the microfluidic/microfabrication approaches for each organ individually and identifies specific examples of TCs. This review will provide an excellent starting point for understanding, designing, and constructing novel TCs for possible integration within MPS.

Electrophysiology Read-Out Tools for Brain-on-Chip Biotechnology

Brain-on-Chip (BoC) biotechnology is emerging as a promising tool for biomedical and pharmaceutical research applied to the neurosciences. At the convergence between lab-on-chip and cell biology, BoC couples in vitro three-dimensional brain-like systems to an engineered microfluidics platform designed to provide an in vivo-like extrinsic microenvironment with the aim of replicating tissue- or organ-level physiological functions. BoC therefore offers the advantage of an in vitro reproduction of brain structures that is more faithful to the native correlate than what is obtained with conventional cell culture techniques. As brain function ultimately results in the generation of electrical signals, electrophysiology techniques are paramount for studying brain activity in health and disease. However, as BoC is still in its infancy, the availability of combined BoC-electrophysiology platforms is still limited. Here, we summarize the available biological substrates for BoC, starting with a historical perspective. We then describe the available tools enabling BoC electrophysiology studies, detailing their fabrication process and technical features, along with their advantages and limitations. We discuss the current and future applications of BoC electrophysiology, also expanding to complementary approaches. We conclude with an evaluation of the potential translational applications and prospective technology developments.

SpikeInterface, a unified framework for spike sorting

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.

Current methods of neuronal network imaging cannot be used for continuous, long-term functional recordings. Here, the authors present a dual-mode high-density microelectrode array, which can simultaneously record in full-frame and high-signal-to-noise modes for label-free electrophysiological measurements.

The Design of a CMOS Nanoelectrode Array with 4096 Current-Clamp/Voltage-Clamp Amplifiers for Intracellular Recording/Stimulation of Mammalian Neurons

CMOS microelectrode arrays (MEAs) can record electrophysiological activities of a large number of neurons in parallel but only extracellularly with low signal-to-noise ratio. Patch clamp electrodes can perform intracellular recording with high signal-to-noise ratio but only from a few neurons in parallel. Recently we have developed and reported a neuroelectronic interface that combines the parallelism of the CMOS MEA and the intracellular sensitivity of the patch clamp. Here, we report the design and characterization of the CMOS integrated circuit (IC), a critical component of the neuroelectronic interface. Fabricated in 0.18-μm technology, the IC features an array of 4,096 platinum black (PtB) nanoelectrodes spaced at a 20 μm pitch on its surface and contains 4,096 active pixel circuits. Each active pixel circuit, consisting of a new switched-capacitor current injector--capable of injecting from ±15 pA to ±0.7 μA with a 5 pA resolution--and an operational amplifier, is highly configurable. When configured into current-clamp mode, the pixel intracellularly records membrane potentials including subthreshold activities with ∼23 μV_rms input referred noise while injecting a current for simultaneous stimulation. When configured into voltage-clamp mode, the pixel becomes a switched-capacitor transimpedance amplifier with ∼1 pA_rms input referred noise, and intracellularly records ion channel currents while applying a voltage for simultaneous stimulation. Such voltage/current-clamp intracellular recording/stimulation is a feat only previously possible with the patch clamp method. At the same time, as an array, the IC overcomes the lack of parallelism of the patch clamp method, measuring thousands of mammalian neurons in parallel, with full-frame intracellular recording/stimulation at 9.4 kHz.