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Review
. 2024 Sep 23:5:0065.
doi: 10.34133/bmef.0065. eCollection 2024.

Functional Neural Networks in Human Brain Organoids

Affiliations
Review

Functional Neural Networks in Human Brain Organoids

Longjun Gu et al. BME Front. .

Abstract

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.

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Conflict of interest statement

Competing interests: The authors declare that they have no competing interests.

Figures

Fig. 1.
Fig. 1.
Brain organoid neural networks. Calcium imaging and MEA recording are 2 mainstream strategies for neural network detection in brain organoids. Brain organoid neural networks offer a versatile tool for understanding brain neural network development, disease mechanisms, and drug screening.
Fig. 2.
Fig. 2.
The formation of neural networks in brain organoids. (A) Schematic of the unguided development method for generating cerebral organoids. (B) Bright-field image of the cerebral organoid. Scale bar, 200 μm [26]. (C) Single-cell transcriptomes of human cerebral organoids (n = 3 cerebral organoids, 5,680 cells) colored by 9 cell clusters: orange as GABAergic neurons, blue as glutamatergic neurons, red as glia cells, green as astrocytes, purple as oligodendrocyte progenitors, light pink as cycling cells, dark pink as choroid plexus, and brown as hypoxic cells [31]. (D) Top: Local field potential (<500 Hz, black line) and the 4- to 8-Hz theta-filtered band (red line). Bottom: Phase of the theta oscillation [31]. (E) Schematic of the guided development method for generating cortical organoids. (F) Bright-field image of cortical organoid. Scale bar, 200 μm [40]. (G) Single-cell transcriptomes of human cerebral organoids (15,990 cells) colored by 7 cell clusters: red as GABAergic neurons, orange as glutamatergic neurons, blue as glia cells, green as intermediate progenitors, purple as progenitors, green blue as mitotic cells, and gray as others [40]. (H) Local field potential trace from cortical organoid. Comparable events between periods of quiescence (discontinuous network dynamics) are shown in human preterm neonate EEG at 35 weeks gestational age. SAT, spontaneous activity transient [40].
Fig. 3.
Fig. 3.
Strategies for neural network detection in brain organoids. (A) Two-photon microscopy imaging of cerebral cortex–ganglionic eminence fusion organoid and the activity profile shown as normalized ΔF/F values. Each line means an individual neuron and representation of the same data as a colorized amplitude plot [45]. (B) Spike activity map of a cerebral organoid slice positioned on a high-density CMOS microelectrode array to survey the electrical activity across the entire organoid. The color scale indicates the normalized number of detected spikes. Scale bar, 500 μm [31]. (C) Spatial map of the average spike amplitude (bubble size) from single-unit activity measured simultaneously across the CMOS array from the top 1,020 electrode sites based on activity. Single-unit electrode clusters are plotted using the same color (the same colors are repeated for different units) [31]. (D) Functional connectivity map showing the temporal correlation strength between spike trains of neurons (edge thickness in gray). Sorted by directionality, the in-degree and out-degree were computed per unit, defined as predominately incoming or outgoing edges, respectively, and designated “receiver” (blue) nodes and “sender” (red) nodes. All other nodes were labeled “brokers” (gray with a fixed size not indicative of node degree) [31].
Fig. 4.
Fig. 4.
Applications of brain organoid neural networks in neurological diseases. (A) MECP2-mutant cerebral cortex–ganglionic eminence fusion organoids exhibit spontaneous synchronized calcium transients that are not observed in the cerebral cortex–ganglionic eminence fusion organoids, reflected in the raw ΔF/F colorized amplitude plot (top) and synchronization amplitude plot (bottom) [45]. (B) Cluster grams following hierarchical clustering of calcium spiking data [45]. (C) Comparison of the average amplitude of synchronized transients and proportion of multi-spiking neurons [45]. (D) Single-cell tracings of spontaneous calcium surges in the migrated neurons from healthy organoids [71]. (E) Single-cell tracings of spontaneous calcium surges in the migrated neurons from AD organoids [71].

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