Functional imaging of brain organoids using high-density microelectrode arrays

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.

A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies

The analysis of tissue cultures, particularly brain organoids, takes a high degree of coordination, measurement, and monitoring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Unified by an Internet of Things (IoT) architecture, our approach enables continuous, communicative interactions 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 and monitor their neuronal activity. The organoids are cultured in custom, 3D-printed chambers attached to commercial microelectrode arrays for electrophysiology monitoring. Periodic feeding is achieved using programmable microfluidic pumps. We developed computer vision fluid volume estimations of aspirated media, achieving high accuracy, and used feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a 7-day study of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated experimental samples maintained robust neural activity throughout the experiment, comparable with the control samples. The automated system enabled hourly electrophysiology recordings that revealed dramatic temporal changes in neuron firing rates not observed in once-a-day recordings.

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.