Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution.

Human organoids recapitulating the cell-type diversity and function of their target organ are valuable for basic and translational research. We developed light-sensitive human retinal organoids with multiple nuclear and synaptic layers and functional synapses. We sequenced the RNA of 285,441 single cells from these organoids at seven developmental time points and from the periphery, fovea, pigment epithelium and choroid of light-responsive adult human retinas, and performed histochemistry. Cell types in organoids matured in vitro to a stable "developed" state at a rate similar to human retina development in vivo. Transcriptomes of organoid cell types converged toward the transcriptomes of adult peripheral retinal cell types. Expression of disease-associated genes was cell-type-specific in adult retina, and cell-type specificity was retained in organoids. We implicate unexpected cell types in diseases such as macular degeneration. This resource identifies cellular targets for studying disease mechanisms in organoids and for targeted repair in human retinas.

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.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.

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.

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays.

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

Neurons differentiate magnitude and location of mechanical stimuli.

Neuronal activity can be modulated by mechanical stimuli. To study this phenomenon quantitatively, we mechanically stimulated rat cortical neurons by shear stress and local indentation. Neurons show 2 distinct responses, classified as transient and sustained. Transient responses display fast kinetics, similar to spontaneous neuronal activity, whereas sustained responses last several minutes before returning to baseline. Local soma stimulations with micrometer-sized beads evoke transient responses at low forces of ∼220 nN and pressures of ∼5.6 kPa and sustained responses at higher forces of ∼360 nN and pressures of ∼9.2 kPa. Among the neuronal compartments, axons are highly susceptible to mechanical stimulation and predominantly show sustained responses, whereas the less susceptible dendrites predominantly respond transiently. Chemical perturbation experiments suggest that mechanically evoked responses require the influx of extracellular calcium through ion channels. We propose that subtraumatic forces/pressures applied to neurons evoke neuronal responses via nonspecific gating of ion channels.

Liraglutide protects ß-cells in novel human islet spheroid models of type 1 diabetes.

To enable accurate, high-throughput and longer-term studies of the immunopathogenesis of type 1 diabetes (T1D), we established three in-vitro islet-immune injury models by culturing spheroids derived from primary human islets with proinflammatory cytokines, activated peripheral blood mononuclear cells or HLA-A2-restricted preproinsulin-specific cytotoxic T lymphocytes. In all models, ß-cell function declined as manifested by increased basal and decreased glucose-stimulated insulin release (GSIS), and decreased intracellular insulin content. Additional hallmarks of T1D progression such as loss of the first-phase insulin response (FFIR), increased proinsulin-to-insulin ratios, HLA-class I expression, and inflammatory cytokine release were also observed. Using these models, we show that liraglutide, a glucagon-like peptide 1 receptor agonist, prevented loss of GSIS under T1D-relevant stress, by preserving the FFIR and decreasing immune cell infiltration and cytokine secretion. Our results corroborate that liraglutide mediates an anti-inflammatory effect that aids in protecting ß-cells from the immune-mediated attack that leads to T1D.

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.

A synthetic multifunctional mammalian pH sensor and CO2 transgene-control device.

All metabolic activities operate within a narrow pH range that is controlled by the CO2-bicarbonate buffering system. We hypothesized that pH could serve as surrogate signal to monitor and respond to the physiological state. By functionally rewiring the human proton-activated cell-surface receptor TDAG8 to chimeric promoters, we created a synthetic signaling cascade that precisely monitors extracellular pH within the physiological range. The synthetic pH sensor could be adjusted by organic acids as well as gaseous CO2 that shifts the CO2-bicarbonate balance toward hydrogen ions. This enabled the design of gas-programmable logic gates, provided remote control of cellular behavior inside microfluidic devices, and allowed for CO2-triggered production of biopharmaceuticals in standard bioreactors. When implanting cells containing the synthetic pH sensor linked to production of insulin into type 1 diabetic mice developing diabetic ketoacidosis, the prosthetic network automatically scored acidic pH and coordinated an insulin expression response that corrected ketoacidosis.

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 µVrms in the action potential (AP) frequency band (300 Hz-5 kHz), 246 low-noise recording channels with noise levels of 3.0 µVrms 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.

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 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.

Monolithic CMOS sensor platform featuring an array of 9'216 carbon-nanotube-sensor elements and low-noise, wide-bandwidth and wide-dynamic-range readout circuitry.

We present the design and characterization of a monolithic complementary metal-oxide-semiconductor (CMOS) biosensor platform comprising of a switch-matrix-based array of 9'216 carbon nanotube field-effect transistors (CNTFETs) and associated readout circuitry. The switch-matrix allows for flexible selection and simultaneous routing of 96 sensor elements to the corresponding readout channels. A low-noise, wide-bandwidth, wide-dynamic-range transimpedance continuous-time amplifier architecture has been implemented to facilitate resistance measurements in the range between 50 kΩ and 1 GΩ at a bandwidth of up to 1 MHz. The achieved accuracy of the resistance measurements over the whole range is 4%. The system has been successfully fabricated and tested and shows a noise performance equal to 2.14 pArms at a bandwidth of 1 kHz and 0.84 nArms at a bandwidth of 1 MHz. A batch integration of the CNTFETs has been achieved by using a dielectrophoresis (DEP)-based manipulation technique. The current-voltage curves of CNTFETs have been acquired, and the sensing capabilities of the system have been demonstrated by recording resistance changes of CNTFETs upon exposure to solutions with different pH values and different concentrations of NaCl. The smallest resolvable concentrations for the respective analytes were estimated to amount to 0.025 pH-units and 4 mM NaCl.

Carbon-Nanotube-Based Monolithic CMOS Platform for Electrochemical Detection of Neurotransmitter Glutamate.

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.

Automatic spike sorting for high-density microelectrode arrays.

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the "curse of dimensionality" and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

Microarray-based MALDI-TOF mass spectrometry enables monitoring of monoclonal antibody production in batch and perfusion cell cultures.

Cell culture process monitoring in monoclonal antibody (mAb) production is essential for efficient process development and process optimization. Currently employed online, at line and offline methods for monitoring productivity as well as process reproducibility have their individual strengths and limitations. Here, we describe a matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)-based on a microarray for mass spectrometry (MAMS) technology to rapidly monitor a broad panel of analytes, including metabolites and proteins directly from the unpurified cell supernatant or from host cell culture lysates. The antibody titer is determined from the intact antibody mass spectra signal intensity relative to an internal protein standard spiked into the supernatant. The method allows a semi-quantitative determination of light and heavy chains. Intracellular mass profiles for metabolites and proteins can be used to track cellular growth and cell productivity.

A Multi-Functional Microelectrode Array Featuring 59760 Electrodes, 2048 Electrophysiology Channels, Stimulation, Impedance Measurement and Neurotransmitter Detection Channels.

Biological cells are characterized by highly complex phenomena and processes that are, to a great extent, interdependent. To gain detailed insights, devices designed to study cellular phenomena need to enable tracking and manipulation of multiple cell parameters in parallel; they have to provide high signal quality and high spatiotemporal resolution. To this end, we have developed a CMOS-based microelectrode array system that integrates six measurement and stimulation functions, the largest number to date. Moreover, the system features the largest active electrode array area to date (4.48×2.43 mm2) to accommodate 59,760 electrodes, while its power consumption, noise characteristics, and spatial resolution (13.5 µm electrode pitch) are comparable to the best state-of-the-art devices. The system includes: 2,048 action-potential (AP, bandwidth: 300 Hz to 10 kHz) recording units, 32 local-field-potential (LFP, bandwidth: 1 Hz to 300 Hz) recording units, 32 current recording units, 32 impedance measurement units, and 28 neurotransmitter detection units, in addition to the 16 dual-mode voltage-only or current/voltage-controlled stimulation units. The electrode array architecture is based on a switch matrix, which allows for connecting any measurement/stimulation unit to any electrode in the array and for performing different measurement/stimulation functions in parallel.

Automated, Multiplexed Electrical Impedance Spectroscopy Platform for Continuous Monitoring of Microtissue Spheroids.

Microtissue spheroids in microfluidic devices are increasingly used to establish novel in vitro organ models of the human body. As the spheroids are comparably sizable, it is difficult to monitor larger numbers of them by optical means. Therefore, electrical impedance spectroscopy (EIS) emerges as a viable alternative to probing spheroid properties. Current spheroid EIS systems are, however, not suitable for investigating multiple spheroids in parallel over extended time in an automated fashion. Here we address this issue by presenting an automated, multiplexed EIS (AMEIS) platform for impedance analysis in a microfluidic setting. The system was used to continuously monitor the effect of the anticancer drug fluorouracil (5-FU) on HCT116 cancer spheroids. Simultaneous EIS monitoring of up to 15 spheroids was performed in parallel over 4 days at a temporal resolution of 2 min without any need for pumps. The measurements were continuous in nature, and the setup was kept in a standard incubator under controlled conditions during the measurements. A baseline normalization method to improve robustness and to reduce the influence of slow changes in the medium conductivity on the spheroid EIS readings has been developed and validated by experiments and means of a finite-element model. The same method and platform was then used for online monitoring of cardiac spheroids. The beating frequency of each cardiac spheroid could be read out in a completely automated fashion. The developed system constitutes a promising method for simultaneously evaluating drug impact and/or toxic effects on multiple microtissue spheroids.

Seamless Combination of Fluorescence-Activated Cell Sorting and Hanging-Drop Networks for Individual Handling and Culturing of Stem Cells and Microtissue Spheroids.

Open microfluidic cell culturing devices offer new possibilities to simplify loading, culturing, and harvesting of individual cells or microtissues due to the fact that liquids and cells/microtissues are directly accessible. We present a complete workflow for microfluidic handling and culturing of individual cells and microtissue spheroids, which is based on the hanging-drop network concept: The open microfluidic devices are seamlessly combined with fluorescence-activated cell sorting (FACS), so that individual cells, including stem cells, can be directly sorted into specified culturing compartments in a fully automated way and at high accuracy. Moreover, already assembled microtissue spheroids can be loaded into the microfluidic structures by using a conventional pipet. Cell and microtissue culturing is then performed in hanging drops under controlled perfusion. On-chip drop size control measures were applied to stabilize the system. Cells and microtissue spheroids can be retrieved from the chip by using a parallelized transfer method. The presented methodology holds great promise for combinatorial screening of stem-cell and multicellular-spheroid cultures.

Robust Functionalization of Large Microelectrode Arrays by Using Pulsed Potentiostatic Deposition.

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.

Visual coding with a population of direction-selective neurons.

The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions.

Versatile, simple-to-use microfluidic cell-culturing chip for long-term, high-resolution, time-lapse imaging.

Optical long-term observation of individual cells, combined with modern data analysis tools, allows for a detailed study of cell-to-cell variability, heredity, and differentiation. We developed a microfluidic device featuring facile cell loading, simple and robust operation, and which is amenable to high-resolution life-cell imaging. Different cell strains can be grown in parallel in the device under constant or changing media perfusion without cross-talk between the cell ensembles. The culturing chamber has been optimized for use with nonadherent cells, such as Saccharomyces cerevisiae, and enables controlled colony growth over multiple generations under aerobic or anaerobic conditions. Small changes in the layout will make the device also useable with bacteria or mammalian cells. The platform can be readily set up in every laboratory with minimal additional requirements and can be operated without technology training.