Journal of Molecular Biology
Review ArticleEmerging Bioelectronics for Brain Organoid Electrophysiology
Graphical abstract
Introduction
Our understanding of brain development has been mainly derived from animal models.1 While these models have provided significant insight into the functionality of brain tissues, they do not fully encapsulate the molecular, structural, genetic, and functional complexity of human brains. Our current knowledge of human brain development is largely based on the analysis of postmortem of pathological specimens.2, 3 Although they have provided important fundamental knowledge, these tools are not amenable to experimental manipulation in real-time over the time course of development.
Recently, the development of human stem cell-derived organoids has become a major technological advance and represents a bridge between traditional two-dimensional (2D) cell cultures and in vivo animal models.4, 5, 6, 7, 8 For example, Lancaster et al. described a novel 3D tissue model namely cerebral organoids,9 which are 3D structures generated from human pluripotent stem cells (hPSCs), capable of self-organizing to form discrete regions of the human brain.10, 11, 12 Neurons within these 3D assemblies can generate action potentials, display excitatory and inhibitory postsynaptic currents, and exhibit spontaneous network activity as measured by calcium imaging and extracellular field potential recordings.13, 14, 15 Recent work has found that organoids can generate rhythmic activity such as synchronized activities in the delta (1–4 Hz) and gamma frequency (100–400 Hz) ranges, suggesting that the human brain organoids can partially mimic the electrical functions of the human brain.16 However, technological limitations have restricted simultaneous detection of electrical activity at a high spatiotemporal resolution to small numbers of neurons over a short time. Consequently, these technical shortcomings have hindered attempts to determine electrophysiological development, evolution, and maturation among large numbers of neurons across 3D brain organoids over the time course of development for the understanding of how they collectively synchronize with rhythmic activity and study of how pharmacologic perturbations affect these electrophysiologic parameters.17
This review highlights emerging bioelectronics, capable of investigating the evolution of neural electrophysiology during human brain organoid development. In this review, we will first discuss the development and applications of brain organoids and compare different conventional technologies used to monitor their development. We will then emphasize recent advances in bioelectronics that can provide electrical monitoring at single-cell single-spike spatiotemporal resolution. In particular, we will discuss recent 2D and 3D multielectrode arrays (MEAs) for continuous measurement of brain organoids, and 3D flexible electronics for the 3D mapping of brain organoids.18, 19, 20, 21 Finally, we will discuss the further advances in tissue-like electronics that can fully integrate with brain organoids through organogenesis, which may open up new opportunities to precisely monitor and control organoid-wide development in a long-term stable manner.22
Section snippets
Development and Applications of Brain Organoids
The development of brain organoids represents a major technological advance in the field of stem cell biology and regenerative medicine, a novel bridge between traditional 2D cultures and in vivo animal models, offering tremendous applications (Figure 1). Human brain organoids are derived from human induced pluripotent stem cells (hiPSCs) and embryonic stem cells (hESCs) under 3D culture conditions with a neuronal differentiation medium (Figure 1(a)). The generation of human brain organoids
Morphological and Genetic Characterizations of Brain Organoids
Morphological characterizations are the easiest way to determine the developmental stages of brain organoids (Figure 2(a)). On the cellular level, imaging characterizations can reveal that brain organoids exhibit some similar features of the in vivo developing human brain in the early stages of development. For example, the progenitor ventricular zone (VZ) and subventricular zone (SVZ) can be easily recognized through bright-field (BF) optical imaging (Figure 2(b)). Through labeling the stage-
Electrical Characterizations of Brain Organoids
While the morphological and genetic characterizations can provide insight about the cellular and molecular level evolution over development, to examine whether the organoid culture system produces functionally active neurons and connected neural networks, it is important to determine their electrical functions during development, ideally at the single-cell single-spike spatiotemporal resolution. Figure 3 summarizes neurotechnologies developed for electrical measurement of neuronal activities
2D MEAs
To measure the electrical activities from brain organoids at millisecond resolution over development, MEAs have been used to enable a non-invasive and stable long-term bioelectronic interface with brain organoids. Previously, 2D MEA has been widely used to long-term stably monitor the electrical properties of 2D cultured neurons and neural networks either from tissue-harvested or stem cell-derived neurons.40 Brain organoids can be placed on the top of the microelectrode array on a silicon or
Conclusion
The capacity of brain organoids to differentiate, self-organize, and form distinct, complex, biologically relevant structures makes them ideal in vitro models of development, disease pathogenesis, and platforms for drug screening. They also hold the promise of better relevance for understanding human brain development and disease than current rodent models. However, to take full advantage of this potential, technologies need to be developed to probe the structural, genetic, and functional
CRediT authorship contribution statement
Kazi Tasnim: Conceptualization, Writing - original draft, Writing - review & editing. Jia Liu: Conceptualization, Writing - original draft, Writing - review & editing.
Acknowledgements
We acknowledge the helpful discussion and assistance from Qiang Li and Paul Le Floch. We acknowledge the support from the NIH/NIMH 1RF1MH123948 and the William F. Milton Fund.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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