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Abstract Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as “up” and “down” events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.

End-stage liver diseases have an increasing impact worldwide, exacerbated by the shortage of transplantable organs. Recognized as one of the promising solutions, tissue engineering aims at recreating functional tissues and organs in vitro. The integration of bioprinting technologies with biological 3D models, such as multi-cellular spheroids, has enabled the fabrication of tissue constructs that better mimic complex structures and in vivo functionality of organs. However, the lack of methods for large-scale production of homogeneous spheroids has hindered the upscaling of tissue fabrication. In this work, we introduce a fully automated platform, designed for high-throughput sorting of 3D spheroids based on label-free analysis of brightfield images. The compact platform is compatible with standard biosafety cabinets and includes a custom-made microscope and two fluidic systems that optimize single spheroid handling to enhance sorting speed. We use machine learning to classify spheroids based on their bioprinting compatibility. This approach enables complex morphological analysis, including assessing spheroid viability, without relying on invasive fluorescent labels. Furthermore, we demonstrate the efficacy of transfer learning for biological applications, for which acquiring large datasets remains challenging. Utilizing this platform, we efficiently sort mono-cellular and multi-cellular liver spheroids, the latter being used in bioprinting applications, and confirm that the sorting process preserves viability and functionality of the spheroids. By ensuring spheroid homogeneity, our sorting platform paves the way for standardized and scalable tissue fabrication, advancing regenerative medicine applications.

IntroductionIn 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.MethodsTo 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 discussionWe 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.

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Abstract Counteracting the overactivation of glucocorticoid receptors (GR) is an important therapeutic goal in stress-related psychiatry and beyond. The only clinically approved GR antagonist lacks selectivity and induces unwanted side effects. To complement existing tools of small-molecule-based inhibitors, we present a highly potent, catalytically-driven GR degrader, KH-103, based on proteolysis-targeting chimera technology. This selective degrader enables immediate and reversible GR depletion that is independent of genetic manipulation and circumvents transcriptional adaptations to inhibition. KH-103 achieves passive inhibition, preventing agonistic induction of gene expression, and significantly averts the GR’s genomic effects compared to two currently available inhibitors. Application in primary-neuron cultures revealed the dependency of a glucocorticoid-induced increase in spontaneous calcium activity on GR. Finally, we present a proof of concept for application in vivo. KH-103 opens opportunities for a more lucid interpretation of GR functions with translational potential.

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.

Chimeric antigen receptors (CAR) are a promising option for cell-based immunotherapy for cancer and other immune diseases. Here the authors develop speedingCARs, an integrated CAR design and screening platform based on modular signaling domain shuffling and single cell transcriptomic analyses, and test its potential for identifying and validating novel CAR designs.

Brain organoids replicate cellular organization found in the developing human brain. Here, the authors utilize microelectronics to map activity in brain organoids and assemble functional circuits that mirror complexity found in brain networks in vivo.

Abstract Blood‐brain‐barrier (BBB) disruption has been associated with a variety of central‐nervous‐system diseases. In vitro BBB models enable to investigate how the barrier reacts to external injury events, commonly referred to as insults. Here, a human‐cell‐based BBB platform with integrated, transparent electrodes to monitor barrier tightness in real time at high resolution is presented. The BBB model includes human cerebral endothelial cells and primary pericytes and astrocytes in a 3D arrangement within a pump‐free, open‐microfluidic platform. With this platform, this study demonstrates that oxygen‐glucose deprivation (OGD), which mimics the characteristics of an ischemic insult, induces a rapid remodeling of the cellular actin structures and subsequent morphological changes in the endothelial cells. High‐resolution live imaging shows the formation of large actin stress‐fiber bundles in the endothelial layer during OGD application, which ultimately leads to cell shrinkage and barrier breakage. Simultaneous electrical measurements evidence a rapid decrease of the barrier electrical resistance before the appearance of stress fibers, which indicates that the barrier function is compromised already before the appearance of drastic morphological changes. The results demonstrate that the BBB platform recapitulates the main barrier functions in vitro and can be used to investigate rapid reorganization of the BBB upon application of external stimuli.

Abstract As 3D in vitro tissue models become more pervasive, their built-in nutrient, metabolite, compound, and waste gradients increase biological relevance at the cost of analysis simplicity. Investigating these gradients and the resulting metabolic heterogeneity requires invasive and time-consuming methods. An alternative is using electrochemical biosensors and measuring concentrations around the tissue model to obtain size-dependent metabolism data. With our hanging-drop-integrated enzymatic glucose biosensors, we conducted current measurements within hanging-drop compartments hosting spheroids formed from the human colorectal carcinoma cell line HCT116. We developed a physics-based mathematical model of analyte consumption and transport, considering (1) diffusion and enzymatic conversion of glucose to form hydrogen peroxide (H2O2) by the glucose-oxidase-based hydrogel functionalization of our biosensors at the microscale; (2) H2O2 oxidation at the electrode surface, leading to amperometric H2O2 readout; (3) glucose diffusion and glucose consumption by cancer cells in a spherical tissue model at the microscale; (4) glucose and H2O2 transport in our hanging-drop compartments at the macroscale; and (5) solvent evaporation, leading to glucose and H2O2 upconcentration. Our model relates the measured currents to the glucose concentrations generating the currents. The low limit of detection of our biosensors (0.4 ± 0.1 μM), combined with our current-fitting method, enabled us to reveal glucose dynamics within our system. By measuring glucose dynamics in hanging-drop compartments populated by cancer spheroids of various sizes, we could infer glucose distributions within the spheroid, which will help translate in vitro 3D tissue model results to in vivo.

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABAA receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings–a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABAA receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

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.

Many biomarkers including neurotransmitters are found in external body fluids, such as sweat or saliva, but at lower titration levels than they are present in blood. Efficient detection of such biomarkers thus requires, on the one hand, to use techniques offering high sensitivity, and, on the other hand, to use a miniaturized format to carry out diagnostics in a minimally invasive way. Here, we present the hybrid integration of bottom-up silicon-nanowire Schottky-junction FETs (SiNW SJ-FETs) with complementary-metal–oxide–semiconductor (CMOS) readout and amplification electronics to establish a robust biosensing platform with 32 × 32 aptasensor measurement sites at a 100 μm pitch. The applied hetero-junctions yield a selective biomolecular detection down to femtomolar concentrations. Selective and multi-site detection of dopamine is demonstrated at an outstanding sensitivity of ∼1 V/fM. The integrated platform offers great potential for detecting biomarkers at high dilution levels and could be applied, for example, to diagnosing neurodegenerative diseases or monitoring therapy progress based on patient samples, such as tear liquid, saliva, or eccrine sweat.

Summary: Schistosomiasis is a neglected tropical disease that affects over 200 million people annually. As the antischistosomal drug pipeline is currently empty, repurposing of compound libraries has become a source for accelerating drug development, which demands the implementation of high-throughput and efficient screening strategies. Here, we present a parallelized impedance-based platform for continuous and automated viability evaluation of Schistosoma mansoni schistosomula in 128 microwells during 72 h to identify antischistosomal hits in vitro. By initially screening 57 repurposed compounds against larvae, five drugs are identified, which reduce parasite viability by more than 70%. The activity profiles of the selected drugs are then investigated via real-time dose-response monitoring, and four compounds reveal high potency and rapid action, which renders them suitable candidates for follow-up tests against adult parasites. The study shows that our device is a reliable tool for real-time drug screening analysis of libraries to identify new promising therapeutics against schistosomiasis.

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.

Due to their label-free and noninvasive nature, impedance measurements have attracted increasing interest in biological research. Advances in microfabrication and integrated-circuit technology have opened a route to using large-scale microelectrode arrays for real-time, high-spatiotemporal-resolution impedance measurements of biological samples. In this review, we discuss different methods and applications of measuring impedance for cell and tissue analysis with a focus on impedance imaging with microelectrode arrays in in vitro applications. We first introduce how electrode configurations and the frequency range of the impedance analysis determine the information that can be extracted. We then delve into relevant circuit topologies that can be used to implement impedance measurements and their characteristic features, such as resolution and data-acquisition time. Afterwards, we detail design considerations for the implementation of new impedance-imaging devices. We conclude by discussing future fields of application of impedance imaging in biomedical research, in particular applications where optical imaging is not possible, such as monitoring of ex vivo tissue slices or microelectrode-based brain implants.

Existing first-line cancer therapies often fail to cope with the heterogeneity and complexity of cancers, so that new therapeutic approaches are urgently needed. Among novel alternative therapies, adoptive cell therapy (ACT) has emerged as a promising cancer treatment in recent years. The limited clinical applications of ACT, despite its advantages over standard-of-care therapies, can be attributed to (i) time-consuming and cost-intensive procedures to screen for potent anti-tumor immune cells and the corresponding targets, (ii) difficulties to translate in-vitro and animal-derived in-vivo efficacies to clinical efficacy in humans, and (iii) the lack of systemic methods for the safety assessment of ACT. Suitable experimental models and testing platforms have the potential to accelerate the development of ACT. Immunocompetent microphysiological systems (iMPS) are microfluidic platforms that enable complex interactions of advanced tissue models with different immune cell types, bridging the gap between in-vitro and in-vivo studies. Here, we present a proof-of-concept iMPS that supports a triple culture of three-dimensional (3D) colorectal tumor microtissues, 3D cardiac microtissues, and human-derived natural killer (NK) cells in the same microfluidic network. Different aspects of tumor-NK cell interactions were characterized using this iMPS including: (i) direct interaction and NK cell-mediated tumor killing, (ii) the development of an inflammatory milieu through enrichment of soluble pro-inflammatory chemokines and cytokines, and (iii) secondary effects on healthy cardiac microtissues. We found a specific NK cell-mediated tumor-killing activity and elevated levels of tumor- and NK cell-derived chemokines and cytokines, indicating crosstalk and development of an inflammatory milieu. While viability and morphological integrity of cardiac microtissues remained mostly unaffected, we were able to detect alterations in their beating behavior, which shows the potential of iMPS for both, efficacy and early safety testing of new candidate ACTs.

Understanding the pharmacokinetic/pharmacodynamic (PK/PD)-relationship of a drug candidate is key to determine effective, yet safe treatment regimens for patients. However, current testing strategies are inefficient in characterizing in vivo responses to fluctuating drug concentrations during multi-day treatment cycles. Methods based on animal models are resource-intensive and require time, while traditional in vitro cell-culturing methods usually do not provide temporally-resolved information on the effects of in vivo–like drug exposure scenarios. To address this issue, we developed a microfluidic system to 1) culture arrays of three-dimensional spheroids in vitro, to 2) apply specific dynamic drug exposure profiles, and to 3) in-situ analyze spheroid growth and the invoked drug effects in 3D by means of 2-photon microscopy at tissue and single-cell level. Spheroids of fluorescently-labeled T-47D breast cancer cells were monitored under perfusion-culture conditions at short time intervals over three days and exposed to either three 24 h-PK-cycles or a dose-matched constant concentration of the phosphatidylinositol 3-kinase inhibitor BYL719. While the overall efficacy of the two treatment regimens was similar, spheroids exposed to the PK profile displayed cycle-dependent oscillations between regression and regrowth. Spheroids treated with a constant BYL719 concentration regressed at a steady, albeit slower rate. At a single-cell level, the cell density in BYL719-treated spheroids oscillated in a concentration-dependent manner. Our system represents a versatile tool for in-depth preclinical characterization of PK/PD parameters, as it enables an evaluation of drug efficacy and/or toxicity under realistic exposure conditions.

Abstract Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or “saline height”, the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.

Microfluidic-drop networks consist of several stable drops—interconnected through microfluidic channels—in which organ models can be cultured long-term. Drop networks feature a versatile configuration and an air–liquid interface (ALI). This ALI provides ample oxygenation, rapid liquid turnover, passive degassing, and liquid-phase stability through capillary pressure. Mathematical modeling, e.g., by using computational fluid dynamics (CFD), is a powerful tool to design drop-based microfluidic devices and to optimize their operation. Although CFD is the most rigorous technique to model flow, it falls short in terms of computational efficiency. Alternatively, the hydraulic–electric analogy is an efficient “first-pass” method to explore the design and operation parameter space of microfluidic-drop networks. However, there are no direct electric analogs to a drop, due to the nonlinear nature of the capillary pressure of the ALI. Here, we present a circuit-based model of hanging- and standing-drop compartments. We show a phase diagram describing the nonlinearity of the capillary pressure of a hanging drop. This diagram explains how to experimentally ensure drop stability. We present a methodology to find flow rates and pressures within drop networks. Finally, we review several applications, where the method, outlined in this paper, was instrumental in optimizing design and operation.

Islet perifusion systems can be used to monitor the highly dynamic insulin release of pancreatic islets in glucose-stimulated insulin secretion (GSIS) assays. Here, we present a new generation of the microfluidic hanging-drop-based islet perifusion platform that was developed to study the alterations in insulin secretion dynamics from single pancreatic islet microtissues at high temporal resolution. The platform was completely redesigned to increase experimental throughput and to reduce operational complexity. The experimental throughput was increased fourfold by implementing a network of interconnected hanging drops, which allows for performing GSIS assays with four individual islet microtissues in parallel with a sampling interval of 30 s. We introduced a self-regulating drop-height mechanism that enables continuous flow and maintains a constant liquid volume in the chip, which enables simple and robust operation. Upon glucose stimulation, reproducible biphasic insulin release was simultaneously observed from all islets in the system. The measured insulin concentrations showed low sample-to-sample variation as a consequence of precise liquid handling with stable drop volumes, equal flow rates in the channels, and accurately controlled sampling volumes in all four drops. The presented device will be a valuable tool in islet and diabetes research for studying dynamic insulin secretion from individual pancreatic islets.

Mammalian brains consist of 10s of millions to 100s of billions of neurons operating at millisecond time scales, of which current recording techniques only capture a tiny fraction. Recording techniques capable of sampling neural activity at high spatiotemporal resolution have been difficult to scale. The most intensively studied mammalian neuronal networks, such as the neocortex, show a layered architecture, where the optimal recording technology samples densely over large areas. However, the need for application-specific designs as well as the mismatch between the three-dimensional architecture of the brain and largely two-dimensional microfabrication techniques profoundly limits both neurophysiological research and neural prosthetics. Here, we discuss a novel strategy for scalable neuronal recording by combining bundles of glass-ensheathed microwires with large-scale amplifier arrays derived from high-density CMOS in vitro MEA systems or high-speed infrared cameras. High signal-to-noise ratio (

Three-dimensional liver in vitro systems have recently attracted a lot of attention in drug development. These systems help to gain unprecedented insights into drug-induced liver injury (DILI), as they more closely reproduce liver biology, and as drug effects can be studied in isolated and controllable microenvironments. Many groups established human-based in vitro models but so far neglected the animal equivalent, although the availability of both models would be desirable. Animal in vitro models enable back- and forward translation of in vitro and in vivo findings, bridge the gap between rodent in vivo and human in vitro scenarios, and ultimately support the interpretation of data generated with preclinical species and humans. Since mice are often used in drug development and physiologically relevant in vitro systems are lacking, we established, for the first time, a mouse liver model that encompasses primary parenchymal and non-parenchymal cells with preserved viability and functionality over three weeks. Using our three-dimensional liver spheroids, we were able to predict the toxicity of known DILI compounds, demonstrated the interaction cascades between the different cell types and showed evidence of drug-induced steatosis and cholestasis. In summary, our mouse liver spheroids represent a valuable in vitro model that can be applied to study DILI findings, reported from mouse studies, and offers the potential to detect immune-mediated drug-induced liver toxicity.

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 μVrms, and occupies an area of 0.0045 mm2. An alternative configuration features a larger input stage to reduce low-frequency noise, achieving 8.7 μVrms 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.

Abstract We present a novel, all-electric approach to record and to precisely control the activity of tens of individual presynaptic neurons. The method allows for parallel mapping of the efficacy of multiple synapses and of the resulting dynamics of postsynaptic neurons in a cortical culture. For the measurements, we combine an extracellular high-density microelectrode array, featuring 11’000 electrodes for extracellular recording and stimulation, with intracellular patch-clamp recording. We are able to identify the contributions of individual presynaptic neurons - including inhibitory and excitatory synaptic inputs - to postsynaptic potentials, which enables us to study dendritic integration. Since the electrical stimuli can be controlled at microsecond resolution, our method enables to evoke action potentials at tens of presynaptic cells in precisely orchestrated sequences of high reliability and minimum jitter. We demonstrate the potential of this method by evoking short- and long-term synaptic plasticity through manipulation of multiple synaptic inputs to a specific neuron.

Understanding the role of axons in neuronal information processing is a fundamental task in neuroscience. Over the last years, sophisticated patch-clamp investigations have provided unexpected and exciting data on axonal phenomena and functioning, but there is still a need for methods to investigate full axonal arbors at sufficient throughput. Here, we present a new method for the simultaneous mapping of the axonal arbors of a large number of individual neurons, which relies on their extracellular signals that have been recorded with high-density microelectrode arrays (HD-MEAs). The segmentation of axons was performed based on the local correlation of extracellular signals. Comparison of the results with both, ground truth and receiver operator characteristics, shows that the new segmentation method outperforms previously used methods. Using a standard HD-MEA, we mapped the axonal arbors of 68 neurons in

Abstract The integration of metabolic competence in developmental toxicity assays in vitro is of fundamental importance to better predict adverse drug effects. Here, a microfluidic hanging‐drop platform is presented that seamlessly integrates liver metabolism into the embryonic stem cell test (EST). Primary human liver microtissues (hLiMTs) and embryoid bodies (EBs) are combined in the same fluidic network, so that hLiMT‐generated metabolites are directly transported to the EBs. Gravity‐driven flow through the network enables continuous intertissue communication, constant medium turnover, and, most importantly, immediate exchange of metabolites. As a proof of concept, the prodrug cyclophosphamide is investigated and a fourfold lower ID50 concentration (50% inhibition of EB differentiation) is found after biotransformation, which demonstrates the potentially adverse effects of metabolites on embryotoxicity. The metaEST platform provides a promising tool to increase the predictive power of the current EST assay by more comprehensively including and better reflecting physiological processes in in vitro tests.

Axons convey information in neuronal circuits via reliable conduction of action potentials (APs) from the axon initial segment (AIS) to the presynaptic terminals. Recent experimental findings increasingly evidence that the axonal function is not limited to the simple transmission of APs. Advances in subcellular-resolution recording techniques have shown that axons display activity-dependent modulation in spike shape and conduction velocity, which influence synaptic strength and latency. We briefly review here, how recent methodological developments facilitate the understanding of the axon physiology. We included the three most common methods, i.e., genetically encoded voltage imaging (GEVI), subcellular patch-clamp and high-density microelectrode arrays (HD-MEAs). We then describe the potential of using HD-MEAs in studying axonal physiology in more detail. Due to their robustness, amenability to high-throughput and high spatiotemporal resolution, HD-MEAs can provide a direct functional electrical readout of single cells and cellular ensembles at subcellular resolution. HD-MEAs can, therefore, be employed in investigating axonal pathologies, the effects of large-scale genomic interventions (e.g., with RNAi or CRISPR) or in compound screenings. A combination of extracellular microelectrode arrays (MEAs), intracellular microelectrodes and optical imaging may potentially reveal yet unexplored repertoires of axonal functions.

Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes

In vitro screening methods for compound efficacy and toxicity to date mostly include cell or tissue exposure to preset constant compound concentrations over a defined testing period. Such concentration profiles, however, do not represent realistic in vivo situations after substance uptake. Absorption, distribution, metabolism and excretion of administered substances in an organism or human body entail gradually changing pharmacokinetic concentration profiles. As concentration profile dynamics can influence drug effects on the target tissues, it is important to be able to reproduce realistic concentration profiles in in vitro systems. We present a novel design that can be integrated in tubing-free, microfluidic culture chips. These chips are actuated by tilting so that gravity-driven flow and perfusion of culture chambers can be established between reservoirs at both ends of a microfluidic channel. The design enables the realization of in vivo-like substance exposure scenarios. Compound gradients are generated through an asymmetric Y-junction of channels with different hydrodynamic resistances. Six microtissues (MTs) can be cultured and exposed in compartments along the channel. Changes of the chip design or operation parameters enable to alter the dosing profile over a large range. Modulation of, e.g., the tilting angle, changes the slope of the dosing curves, so that concentration curves can be attained that resemble the pharmacokinetic characteristics of common substances in a human body. Human colorectal cancer (HCT 116) MTs were exposed to both, gradually decreasing and constant concentrations of Staurosporine. Measurements of apoptosis induction and viability after 5 h and 24 h showed different short- and long-term responses of the MTs to dynamic and linear dosing regimes

Deep hibernators go through several cycles of profound drops in body temperature during the winter season, with core temperatures sometimes reaching near freezing. Yet unlike non-hibernating mammals, they can sustain breathing rhythms. The physiological processes that make this possible are still not understood. In this study, we focused on the medullary Ventral Respiratory Column of a facultative hibernator, the Syrian hamster. Using shortened day-lengths, we induced a “winter-adapted” physiological state, which is a prerequisite for hibernation. When recording electrophysiological signals from acute slices in the winter-adapted pre-Bötzinger complex (preBötC), spike trains showed higher spike rates, amplitudes, complexity, as well as higher temperature sensitivity, suggesting an increase in connectivity and/or synaptic strength during the winter season. We further examined action potential waveforms and found that the depolarization integral, as measured by the area under the curve, is selectively enhanced in winter-adapted animals. This suggests that a shift in the ion handling kinetics is also being induced by the winter-adaptation program. RNA sequencing of respiratory pre-motor neurons, followed by gene set enrichment analysis, revealed differential regulation and splicing in structural, synaptic, and ion handling genes. Splice junction analysis suggested that differential exon usage is occurring in a select subset of ion handling subunits (ATP1A3, KCNC3, SCN1B), and synaptic structure genes (SNCB, SNCG, RAB3A). Our findings show that the hamster respiratory center undergoes a seasonally-cued alteration in electrophysiological properties, likely protecting against respiratory failure at low temperatures.

Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26,400 bidirectional electrodes at a pitch of 17.5 μm and an electrode area of 5 × 9 μm2. We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 pC. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA, with pulse durations as short as 18 μs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation.

Dysfunction of the N-methyl-d-aspartate receptor (NMDAR) is thought to play a role in the pathophysiology of neurodevelopmental diseases like schizophrenia. To study the effects of NMDAR dysfunction on synaptic transmission and network oscillations, we used hippocampal tissue of NMDAR subunit GluN2A knockout (KO) mice. Field excitatory postsynaptic potentials were recorded in acute hippocampal slices of adult animals. Synaptic transmission was impaired in GluN2A KO slices compared to wild-type (WT) slices. Further, to investigate whether NMDAR dysfunction would alter neurodevelopment in vitro, we used organotypic hippocampal slice cultures of WT and GluN2A KO mice. Immunostaining performed with cultures kept two, seven, 14, 25 days in vitro (DIV) revealed an increasing expression of parvalbumin (PV) over time. As a functional readout, oscillatory activity induced by the cholinergic agonist carbachol was recorded in cultures kept seven, 13, and 26 DIV using microelectrode arrays. Initial analysis focused on the occurrence of delta, theta, beta and gamma oscillations over genotype, DIV and hippocampal area (CA1, CA3, dentate gyrus (DG)). In a follow-up analysis, we studied the peak frequency and the peak power of each of the four oscillation bands per condition. The occurrence of gamma oscillations displayed an increase by DIV similar to the PV immunostaining. Unlike gamma occurrence, delta, theta, and beta occurrence did not change over time in culture. The peak frequency and peak power in the different bands of the oscillations were not different in slices of WT and GluN2A KO mice. However, the level of PV expression was lower in GluN2A KO compared to WT mice. Given the role of PV-containing fast-spiking basket cells in generation of oscillations and the decreased PV expression in subjects with schizophrenia, the study of gamma oscillations in organotypic hippocampal slices represents a potentially valuable tool for the characterization of novel therapeutic drugs.

Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively.

We present a monolithic biosensor platform, based on carbon-nanotube field-effect transistors (CNTFETs), for the detection of the neurotransmitter glutamate. We used an array of 9′216 CNTFET devices with 96 integrated readout and amplification channels that was realized in complementary metal-oxide semiconductor technology (CMOS). The detection principle is based on amperometry, where electrochemically active hydrogen peroxide, a product of the enzymatic reaction of the target analyte and an enzyme that was covalently bonded to the CNTFET, modulated the conductance of the CNTFET-based sensors. We assessed the performance of the CNTs as enzymatic sensors by evaluating the minimal resolvable concentration change of glutamate in aqueous solutions. The minimal resolvable concentration change amounted to 10 µM of glutamate, which was one of the best values reported for CMOS-based systems so far.

The numerous connections between neuronal cell bodies, made by their dendrites and axons, are vital for information processing in the brain. While dendrites and synapses have been extensively studied, axons have remained elusive to a large extent. We present a novel platform to study axonal physiology and information processing based on combining an 11,011-electrode high-density complementary metal-oxide semiconductor microelectrode array with a poly(dimethylsiloxane) channel device, which isolates axons from somas and, importantly, significantly amplifies recorded axonal signals. The combination of the microelectrode array with recording and stimulation capability with the microfluidic isolation channels permitted us to study axonal signal behavior at great detail. The device, featuring two culture chambers with over 30 channels spanning in between, enabled long-term recording of single spikes from isolated axons with signal amplitudes of 100 μV up to 2 mV. Propagating signals along axons could be recorded with 10 to 50 electrodes per channel. We (i) describe the performance and capabilities of our device for axonal electrophysiology, and (ii) present novel data on axonal signals facilitated by the device. Spontaneous action potentials with characteristic shapes propagated from somas along axons between the two compartments, and these unique shapes could be used to identify individual axons within channels that contained many axonal branches. Stimulation through the electrode array facilitated the identification of somas and their respective axons, enabling interfacing with different compartments of a single cell. Complex spike shapes observed in channels were traced back to single cells, and we show that more complicated spike shapes originate from a linear superposition of multiple axonal signals rather than signal distortion by the channels.

We present a microfluidic tilting platform with gravity-driven flow that enables culturing of three-dimensional microtissues under in vivo-like drug dosing regimens. In contrast to traditional, constant dosing regimens, the chip allows for gradual changes in substance concentrations. Dosing gradients are generated through an asymmetric Y-junction of microfluidic channels of different widths. Changes in the chip operation parameters, e.g., different tilting angles, enable to alter the drug dosage on demand. The concentration changes over time matched the predicted values, and preformed microtissues could be cultured in the chip system for at least 24 h. The chip represents an easy-to-handle tool for toxicity and efficacy testing of transient drug concentrations changes.

Surface modification of microelectrodes is a central step in the development of microsensors and microsensor arrays. Here, we present an electrodeposition scheme based on voltage pulses. Key features of this method are uniformity in the deposited electrode coatings, flexibility in the overall deposition area, i.e., the sizes and number of the electrodes to be coated, and precise control of the surface texture. Deposition and characterization of four different materials are demonstrated, including layers of high-surface-area platinum, gold, conducting polymer poly(ethylenedioxythiophene), also known as PEDOT, and the non-conducting polymer poly(phenylenediamine), also known as PPD. The depositions were conducted using a fully integrated complementary metal-oxide-semiconductor (CMOS) chip with an array of 1024 microelectrodes. The pulsed potentiostatic deposition scheme is particularly suitable for functionalization of individual electrodes or electrode subsets of large integrated microelectrode arrays: the required deposition waveforms are readily available in an integrated system, the same deposition parameters can be used to functionalize the surface of either single electrodes or large arrays of thousands of electrodes, and the deposition method proved to be robust and reproducible for all materials tested.