Functional imaging of brain organoids using high-density microelectrode arrays

Highly Stretchable 3D Microelectrode Array for Noninvasive Functional Evaluation of Cardiac Spheroids and Midbrain Organoids

Organoids are 3D biological models that recapitulate the complex structures and functions of human organs. Despite the rapid growth in the generation of organoids, in vitro assay tools are still limited to 2D forms. Thus, a comprehensive and continuous functional evaluation of the electrogenic organoids remains a challenge. Here, a highly stretchable 3D multielectrode array (sMEA) with protruding microelectrodes is presented for functional evaluation of electrogenic organoids. The optimized serpentine structures with bridge structures cover the surface of the organoids conformally even in immersion. The protruding microelectrodes form a stable contact with the organoids and allow electrophysiological recordings with high signal-to-noise ratio (SNR). sMEAs are fabricated in wafer-scale for repeatable, scalable, and mass production and packed into an easy-to-use, user-friendly, and robust microwell for fast dissemination of technology. The versatility of sMEA is validated by measuring electrophysiological signals from cardiac spheroids and midbrain organoids with a wide range of sizes from 500 to 1500 µm. Also, electrophysiological signals recorded with high SNR enable functional evaluation of the effects of drugs. The proposed sMEA with high SNR and user-friendly interface could be the key player in high-throughput drug screening, 3D spatiotemporal mapping of electrogenic organoids, and standardization of protocols for quality assessment.

Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

hPSCs-derived brain organoids for disease modeling, toxicity testing and drug evaluation

Due to the differences and variances in genetic background, in vitro and animal models cannot meet the modern medical exploration of real human brain structure and function. Recently, brain organoids generated by human pluripotent stem cells (hPSCs) can mimic the structure and physiological function of human brain, being widely used in medical research. Brain organoids generated from normal hPSCs or patient-derived induced pluripotent stem cells offer a more promising approach for the study of diverse human brain diseases. More importantly, the use of the established brain organoid model for drug evaluation is conducive to shorten the clinical transformation period. Herein, we summarize methods for the identification of brain organoids from cellular diversity, morphology and neuronal activity, brain disease modeling, toxicity testing, and drug evaluation. Based on this, it is hoped that this review will provide new insights into the pathogenesis of brain diseases and drug research and development, promoting the rapid development of brain science.

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

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

Revealing single-neuron and network-activity interaction by combining high-density microelectrode array and optogenetics

The synchronous activity of neuronal networks is considered crucial for brain function. However, the interaction between single-neuron activity and network-wide activity remains poorly understood. This study explored this interaction within cultured networks of rat cortical neurons. Employing a combination of high-density microelectrode array recording and optogenetic stimulation, we established an experimental setup enabling simultaneous recording and stimulation at a precise single-neuron level that can be scaled to the level of the whole network. Leveraging our system, we identified a network burst-dependent response change in single neurons, providing a possible mechanism for the network-burst-dependent loss of information within the network and consequent cognitive impairment during epileptic seizures. Additionally, we directly recorded a leader neuron initiating a spontaneous network burst and characterized its firing properties, indicating that the bursting activity of hub neurons in the brain can initiate network-wide activity. Our study offers valuable insights into brain networks characterized by a combination of bottom-up self-organization and top-down regulation.

Functional Neural Networks in Human Brain Organoids

Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological, psychiatric, and developmental disorders. While the molecular and cellular aspects of human brain organoids have been intensively studied, their functional properties such as organoid neural networks (ONNs) are largely understudied. Here, we summarize recent research advances in understanding, characterization, and application of functional ONNs in human brain organoids. We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array (MEA) technology and calcium imaging. Moreover, we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer's disease. Finally, we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.

Standardizing a method for functional assessment of neural networks in brain organoids

During the last decade brain organoids have emerged as an attractive model system, allowing stem cells to be differentiated into complex 3D models, recapitulating many aspects of human brain development. Whilst many studies have analysed anatomical and cytoarchitectural characteristics of organoids, their functional characterisation has been limited, and highly variable between studies. Standardised, consistent methods for recording functional activity are critical to providing a functional understanding of neuronal networks at the synaptic and network level that can yield useful information about functional network phenotypes in disease and healthy states. In this study we outline a detailed methodology for calcium imaging and Multi-Electrode Array (MEA) recordings in brain organoids. To illustrate the utility of these functional interrogation techniques in uncovering induced differences in neural network activity we applied various stimulating media protocols. We demonstrate overlapping information from the two modalities, with comparable numbers of active cells in the four treatment groups and an increase in synchronous behaviour in BrainPhys treated groups. Further development of analysis pipelines to reveal network level changes in brain organoids will enrich our understanding of network formation and perturbation in these structures, and aid in the future development of drugs that target neurological disorders at the network level.

Guidelines for Manufacturing and Application of Organoids: Brain

This study offers a comprehensive overview of brain organoids for researchers. It combines expert opinions with technical summaries on organoid definitions, characteristics, culture methods, and quality control. This approach aims to enhance the utilization of brain organoids in research. Brain organoids, as three-dimensional human cell models mimicking the nervous system, hold immense promise for studying the human brain. They offer advantages over traditional methods, replicating anatomical structures, physiological features, and complex neuronal networks. Additionally, brain organoids can model nervous system development and interactions between cell types and the microenvironment. By providing a foundation for utilizing the most human-relevant tissue models, this work empowers researchers to overcome limitations of two-dimensional cultures and conduct advanced disease modeling research.

Versatile micro-electrode array to monitor human iPSC derived 3D neural tissues at air-liquid interface

Engineered 3D neural tissues made of neurons and glial cells derived from human induced pluripotent stem cells (hiPSC) are among the most promising tools in drug discovery and neurotoxicology. They represent a cheaper, faster, and more ethical alternative to in vivo animal testing that will likely close the gap between in vitro animal models and human clinical trials. Micro-Electrode Array (MEA) technology is known to provide an assessment of compound effects on neural 2D cell cultures and acute tissue preparations by real-time, non-invasive, and long-lasting electrophysiological monitoring of spontaneous and evoked neuronal activity. Nevertheless, the use of engineered 3D neural tissues in combination with MEA biochips still involves series of constraints, such as drastically limited diffusion of oxygen and nutrients within tissues mainly due to the lack of vascularization. Therefore, 3D neural tissues are extremely sensitive to experimental conditions and require an adequately designed interface that provides optimal tissue survival conditions. A well-suited technique to overcome this issue is the combination of the Air-Liquid Interface (ALI) tissue culture method with the MEA technology. We have developed a full 3D neural tissue culture process and a data acquisition system composed of high-end electronics and novel MEA biochips based on porous, flexible, thin-film membranes integrating recording electrodes, named as "Strip-MEA," to allow the maintenance of an ALI around the 3D neural tissues. The main motivation of the porous MEA biochips development was the possibility to monitor and to study the electrical activity of 3D neural tissues under different recording configurations, (i) the Strip-MEA can be placed below a tissue, (ii) or by taking advantage of the ALI, be directly placed on top of the tissue, or finally, (iii) it can be embedded into a larger neural tissue generated by the fusion of two (or more) tissues placed on both sides of the Strip-MEA allowing the recording from its inner part. This paper presents the recording and analyses of spontaneous activity from the three positioning configurations of the Strip-MEAs. Obtained results are discussed with the perspective of developing in vitro models of brain diseases and/or impairment of neural network functioning.

Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays

Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.

WaveMAP for identifying putative cell types from in vivo electrophysiology

Action potential spike widths are used to classify cell types as either excitatory or inhibitory; however, this approach obscures other differences in waveform shape useful for identifying more fine-grained cell types. Here, we present a protocol for using WaveMAP to generate nuanced average waveform clusters more closely linked to underlying cell types. We describe steps for installing WaveMAP, preprocessing data, and clustering waveform into putative cell types. We also detail cluster evaluation for functional differences and interpretation of WaveMAP output. For complete details on the use and execution of this protocol, please refer to Lee et al. (2021).1.

Human Brain Organoids-on-Chip: Advances, Challenges, and Perspectives for Preclinical Applications

There is an urgent need for predictive in vitro models to improve disease modeling and drug target identification and validation, especially for neurological disorders. Cerebral organoids, as alternative methods to in vivo studies, appear now as powerful tools to decipher complex biological processes thanks to their ability to recapitulate many features of the human brain. Combining these innovative models with microfluidic technologies, referred to as brain organoids-on-chips, allows us to model the microenvironment of several neuronal cell types in 3D. Thus, this platform opens new avenues to create a relevant in vitro approach for preclinical applications in neuroscience. The transfer to the pharmaceutical industry in drug discovery stages and the adoption of this approach by the scientific community requires the proposition of innovative microphysiological systems allowing the generation of reproducible cerebral organoids of high quality in terms of structural and functional maturation, and compatibility with automation processes and high-throughput screening. In this review, we will focus on the promising advantages of cerebral organoids for disease modeling and how their combination with microfluidic systems can enhance the reproducibility and quality of these in vitro models. Then, we will finish by explaining why brain organoids-on-chips could be considered promising platforms for pharmacological applications.