Advanced neural activity mapping in brain organoids via field potential imaging with ultra-high-density CMOS microelectrodes.
Yokoi, R.; Matsuda, N.; Ishibashi, Y.; Suzuki, I.
Show abstract
Human iPSC-derived brain organoids and assembloids have emerged as promising in vitro models for recapitulating human brain development, neurological disorders, and drug responses. However, detailed analysis of their electrophysiological properties requires advanced measurement techniques. Here, we present a novel analytical approach utilizing ultra-high-density (UHD) CMOS microelectrode arrays (MEAs) containing 236,880 electrodes (10.52 m x 10.52 m each) distributed over a broad sensing area of 32.45 mm2 for field potential imaging (FPI) of brain organoids. Neuronal activity was recorded simultaneously from over 46,000 electrodes interfaced with brain organoids, allowing for the identification of single-cell firing events and the assessment of neuronal network connectivity based on individual spikes. In midbrain organoids, administration of L-DOPA revealed both excitatory and inhibitory cellular responses, with a dose-dependent increase in the proportion of excitatory responses, suggesting enhanced network connectivity. Capitalizing on the spatial and temporal resolution of UHD-CMOS-MEAs, we introduced new endpoints for network activity: propagation velocity and propagation area. In cortical organoids, application of the GABAA receptor antagonist picrotoxin led to increased propagation velocity, whereas the NMDA receptor antagonist MK-801 resulted in a broad reduction of propagation area, along with localized increases. As FPI enables direct recording of electrical potential waveforms, frequency-domain analyses were also conducted. Spontaneous activity in cortical organoids exhibited region-specific frequency distributions, with gamma-band activity displaying distinct patterns compared to other frequency bands. Additionally, in midbrain-striatal assembloids, electrophysiological activity was observed in both regions. Connectivity analysis showed that treatment with 4-aminopyridine enhanced inter-organoid connection strength. This large-scale, single-cell-resolved recording approach using UHD-CMOS-MEAs facilitates comprehensive analysis of network connectivity, propagation velocity and propagation area, and frequency characteristics. It represents a powerful platform for advancing our understanding of the electrophysiological functions of brain organoids and assembloids, and holds significant potential for drug screening and disease modeling in human neuroscience research.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Scalable, flexible carbon fiber electrode thread arrays for three-dimensional spatial profiling of neurochemical activity in deep brain structures of rodents 95%
- An in vitro platform for characterizing axonal electrophysiology of individual human iPSC-derived nociceptors 95%
- Development of a Microelectrode Array System for Simultaneous Measurement of Field Potential and Glutamate Release in Brain Slices 93%
Similar papers in this journal
Similar papers in this journal
- Transcranial photoacoustic imaging of NMDA-evoked focal circuit dynamics in rat hippocampus 94%
- Structure-Function Dynamics of Engineered, Modular Neuronal Networks with Controllable Afferent-Efferent Connectivity 94%
- Calcium imaging in freely-moving mice during electrical stimulation of deep brain structures 94%
Similar papers in this journal
Similar papers in this journal
- Phase-Amplitude Coupling Detection and Analysis of Human 2-Dimensional Neural Cultures in Multi-well Microelectrode Array in Vitro 95%
- Multiplex imaging of human induced pluripotent stem cell-derived neurons with CO-Detection by indEXing (CODEX) technology 92%
- A method for chronic and semi-chronic microelectrode array implantation in deep brain structures using image guided neuronavigation 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.