Early prediction of developing spontaneous activity in cultured neuronal networks

Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accurate diagnosis and medications for effective treatment are lacking, in part due to the historical use of preclinical model systems that do not translate well to the clinic for neurological disorders, such as rodents and heterologous cell lines. Human-induced pluripotent stem cells (hiPSCs) are a promising in vitro system for modeling NDDs, providing opportunities to understand mechanisms driving NDDs in human neurons. Functional assays, including patch clamping, multielectrode array, and imaging-based assays, are popular tools employed with hiPSC disease models for disease investigation. Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. In this review, we compare two-dimensional and three-dimensional hiPSC formats for disease modeling, discuss the applications of functional assays, and offer insights on incorporating ML into hiPSC-based NDD research and drug screening.

High-density multielectrode arrays bring cellular resolution to neuronal activity and network analyses of corticospinal motor neurons

Corticospinal motor neurons (CSMN), located in the motor cortex of the brain, are one of the key components of the motor neuron circuitry. They are in part responsible for the initiation and modulation of voluntary movement, and their degeneration is the hallmark for numerous diseases, such as amyotrophic lateral sclerosis (ALS), hereditary spastic paraplegia, and primary lateral sclerosis. Cortical hyperexcitation followed by in-excitability suggests the early involvement of cortical dysfunction in ALS pathology. However, a high-spatiotemporal resolution on our understanding of their functional health and connectivity is lacking. Here, we combine optical imaging with high-density microelectrode array (HD-MEA) system enabling single cell resolution and utilize UCHL1-eGFP mice to bring cell-type specificity to our understanding of the electrophysiological features of healthy CSMN, as they mature and form network connections with other cortical neurons, in vitro. This novel approach lays the foundation for future cell-type specific analyses of CSMN that are diseased due to different underlying causes with cellular precision, and it will allow the assessment of their functional response to compound treatment, especially for drug discovery efforts in upper motor neuron diseases.

autoMEA: machine learning-based burst detection for multi-electrode array datasets

Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity. At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. Available software offers semi-automated analysis for a fixed set of parameters that allow for the definition of spikes, bursts and network bursts. However, this analysis remains time-consuming, user-biased, and limited by pre-defined parameters. Here, we present autoMEA, software for machine learning-based automated burst detection in MEA datasets. We exemplify autoMEA efficacy on neuronal network activity of primary hippocampal neurons from wild-type mice monitored using 24-well multi-well MEA plates. To validate and benchmark the software, we showcase its application using wild-type neuronal networks and two different neuronal networks modeling neurodevelopmental disorders to assess network phenotype detection. Detection of network characteristics typically reported in literature, such as synchronicity and rhythmicity, could be accurately detected compared to manual analysis using the autoMEA software. Additionally, autoMEA could detect reverberations, a more complex burst dynamic present in hippocampal cultures. Furthermore, autoMEA burst detection was sufficiently sensitive to detect changes in the synchronicity and rhythmicity of networks modeling neurodevelopmental disorders as well as detecting changes in their network burst dynamics. Thus, we show that autoMEA reliably analyses neural networks measured with the multi-well MEA setup with the precision and accuracy compared to that of a human expert.

Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks

In this study, we implement large-scale nanomagnetic guidance on cortical neurons to guide dissociated neuronal networks during development. Cortical networks cultured over microelectrode arrays were exposed to functionalized magnetic nanoparticles, followed by magnetic field exposure to guide neurites over 14 days in vitro. Immunofluorescence of the axonal protein Tau revealed a greater number of neurites that were longer and aligned with the nanomagnetic force relative to nonguided networks. This was further confirmed through brightfield imaging on the microelectrode arrays during development. Spontaneous electrophysiological recordings revealed that the guided networks exhibited increased firing rates and frequency in force-aligned connectivity identified through Granger Causality. Applying this methodology across networks with nonuniform force directions increased local activity in target regions, identified as regions in the direction of the nanomagnetic force. Altogether, these results demonstrate that nanomagnetic forces guide the structure and function of dissociated cortical neuron networks at the millimeter scale.

Development of a new hazard scoring system in primary neuronal cell cultures for drug-induced acute neuronal toxicity identification in early drug discovery

We investigated drug-induced acute neuronal electrophysiological changes using Micro-Electrode arrays (MEA) to rat primary neuronal cell cultures. Data based on 6-key MEA parameters were analyzed for plate-to-plate vehicle variability, effects of positive and negative controls, as well as data from over 100 reference drugs, mostly known to have pharmacological phenotypic and clinical outcomes. A Least Absolute Shrinkage and Selection Operator (LASSO) regression, coupled with expert evaluation helped to identify the 6-key parameters from many other MEA parameters to evaluate the drug-induced acute neuronal changes. Calculating the statistical tolerance intervals for negative-positive control effects on those 4-key parameters helped us to develop a new weighted hazard scoring system on drug-induced potential central nervous system (CNS) adverse effects (AEs). The weighted total score, integrating the effects of a drug candidate on the identified six-pivotal parameters, simply determines if the testing compound/concentration induces potential CNS AEs. Hereto, it uses four different categories of hazard scores: non-neuroactive, neuroactive, hazard, or high hazard categories. This new scoring system was successfully applied to differentiate the new compounds with or without CNS AEs, and the results were correlated with the outcome of in vivo studies in mice for one internal program. Furthermore, the Random Forest classification method was used to obtain the probability that the effect of a compound is either inhibitory or excitatory. In conclusion, this new neuronal scoring system on the cell assay is actively applied in the early de-risking of drug development and reduces the use of animals and associated costs.

Calcium-Dependent Hyperexcitability in Human Stem Cell-Derived Rett Syndrome Neuronal Networks

Background

Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell-derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell-derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue.

Methods

We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34).

Results

RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics.

Conclusions

During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics.

Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches

The wide application of TiO₂-based engineered nanoparticles (nTiO₂) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO₂ to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO₂ on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) - catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) - glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with high accuracy and balanced accuracy for both training sets and external validation sets. Furthermore, the best model revealed the predominant factors (exposure concentration, exposure periods, and exposure matrix) influencing the oxidative stress induced by nTiO₂. These results showed that high exposure concentrations and short exposure-intervals tended to cause oxidative damage to bivalves. In addition, gills and digestive glands could be vulnerable to nTiO₂-induced oxidative damage as tissues/organs differences were the important factors controlling MDA activity. This study provided insights into important nano-features responsible for the different indicators of oxidative stress and thereby extended the application of machine learning approaches in toxicological assessment for nanoparticles.

Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review

A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.

Spontaneous Activity Predicts Survival of Developing Cortical Neurons

Spontaneous activity plays a crucial role in brain development by coordinating the integration of immature neurons into emerging cortical networks. High levels and complex patterns of spontaneous activity are generally associated with low rates of apoptosis in the cortex. However, whether spontaneous activity patterns directly encode for survival of individual cortical neurons during development remains an open question. Here, we longitudinally investigated spontaneous activity and apoptosis in developing cortical cultures, combining extracellular electrophysiology with calcium imaging. These experiments demonstrated that the early occurrence of calcium transients was strongly linked to neuronal survival. Silent neurons exhibited a higher probability of cell death, whereas high frequency spiking and burst behavior were almost exclusively detected in surviving neurons. In local neuronal clusters, activity of neighboring neurons exerted a pro-survival effect, whereas on the functional level, networks with a high modular topology were associated with lower cell death rates. Using machine learning algorithms, cell fate of individual neurons was predictable through the integration of spontaneous activity features. Our results indicate that high frequency spiking activity constrains apoptosis in single neurons through sustained calcium rises and thereby consolidates networks in which a high modular topology is reached during early development.

Calpain-Independent Intracellular Protease Activity Is Elevated in Excitotoxic Cortical Neurons Prior to Delayed Calcium Deregulation and Mitochondrial Dysfunction

Glutamate excitotoxicity contributes to many neurodegenerative diseases. Excessive glutamate receptor-mediated calcium entry causes delayed calcium deregulation (DCD) that coincides with abrupt mitochondrial depolarization. We developed cA-TAT, a live-cell protease activity reporter based on a vimentin calpain cleavage site, to test whether glutamate increases protease activity in neuronal cell bodies prior to DCD. Treatment of rat cortical neurons with excitotoxic (100 µM) glutamate increased the low baseline rate of intracellular cA-TAT proteolysis by approximately three-fold prior to DCD and by approximately seven-fold upon calcium deregulation. The glutamate-induced rate enhancement prior to DCD was suppressed by glutamate receptor antagonists, but not by calpain or proteasome inhibitors, whereas DCD-stimulated proteolysis was partly attenuated by the proteasome inhibitor MG132. Further suggesting that cA-TAT cleavage is calpain-independent, cA-TAT fluorescence was observed in immortalized Capn4 knockout fibroblasts lacking the regulatory calpain subunit. About half of the neurons lost calcium homeostasis within two hours of a transient, 20 min glutamate receptor stimulation. These neurons had a significantly (49%) higher mean baseline cA-TAT proteolysis rate than those maintaining calcium homeostasis, suggesting that the unknown protease(s) cleaving cA-TAT may influence DCD susceptibility. Overall, the results indicate that excitotoxic glutamate triggers the activation of calpain-independent neuronal protease activity prior to the simultaneous loss of calcium homeostasis and mitochondrial bioenergetic function.

Lightsheet optical tweezer (LOT) for optical manipulation of microscopic particles and live cells

Optical trapping and patterning cells or microscopic particles is fascinating. We developed a light sheet-based optical tweezer to trap dielectric particles and live HeLa cells. The technique requires the generation of a tightly focussed diffraction-limited light-sheet realized by a combination of cylindrical lens and high NA objective lens. The resultant field is a focussed line (along x-axis) perpendicular to the beam propagation direction (z-axis). This is unlike traditional optical tweezers that are fundamentally point-traps and can trap one particle at a time. Several spherical beads undergoing Brownian motion in the solution are trapped by the lightsheet gradient potential, and the time (to reach trap-centre) is estimated from the video captured at 230 frames/s. High-speed imaging of beads with increasing laser power shows a steady increase in trap stiffness with a maximum of 0.00118 pN/nm at 52.5 mW. This is order less than the traditional point-traps, and hence may be suitable for applications requiring delicate optical forces. On the brighter side, light sheet tweezer (LOT) can simultaneously trap multiple objects with the distinct ability to manipulate them in the transverse (xy) plane via translation and rotation. However, the trapped beads displayed free movement along the light-sheet axis (x-axis), exhibiting a single degree of freedom. Furthermore, the tweezer is used to trap and pattern live HeLa cells in various shapes and structures. Subsequently, the cells were cultured for a prolonged period of time (> 18 h), and cell viability was ascertained. We anticipate that LOT can be used to study constrained dynamics of microscopic particles and help understand the patterned cell growth that has implications in optical imaging, microscopy, and cell biology.