Brain-on-a-chip: Improving 3D neural network analysis

brain-on-a-chip platform (Download Image)

A 3-well device (inset) allows simultaneous MEA recordings from three 3D cultures. Each well contains a 3D MEA that features 10 actuated probes to distribute 8 electrodes, for a total of 80 electrodes per array and 240 electrodes per device, spanning the height of 3D cultures. This design helps researchers monitor the entire 3D brain-on-a-chip system.

Brain-on-a-chip (BOC) systems are engineered cell-culture models that allow non-invasive, real-time monitoring of electrochemical processes. While newer 3D BOC systems have improved the neuronal viability, neural network activity, drug responses, and resemblance to disease pathology compared to their 2D counterparts, the ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue remains a critical bottleneck.

To conduct a functional assessment of a 3D system using electrophysiology, planar multi-electrode arrays (MEAs) and patch-clamp electrophysiology are used to record neuronal activity. However, these methods present limitations, with the former detecting networks solely at the bottom of the 3D construct and the latter only being accessible to cells closest to the surface of the 3D biological system—leaving a majority of the functional tissue (in the middle region) unaccounted for.

To combat these technical challenges, an LLNL team had previously developed a novel 3D MEA prototype, in a bottom-up configuration, and integrated it with a hydrogel-based neural tissue, demonstrating an unprecedented means to non-invasively monitor the extracellular field. In a critical advance, this team has now developed a computational pipeline that can be implemented to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

This research shows, for the first time, that the combination of the high spatial resolution offered by the 3D MEA and the sensitivity of the computational tools can track and quantify the change in synchronicity within subpopulations of neural networks (within and between cross sections), networks that are sensitive/insensitive to chemicals, and those that are or become inactive as a result of chemical exposures. Overall, these findings can be adapted for future studies to evaluate disease or injury in in vitro models and evaluate short- or long-term consequences of chemical and therapeutic agents on neural activity.

This work was supported by LLNL’s Laboratory Directed Research and Development program (17-SI-002 and 21-LW-014).

[D. Lam, H.A. Enright, J. Cadena, V.K. George, D.A. Soscia, A.C. Tooker, M. Triplett, S.K.G. Peters, P. Karande, A. Ladd, C. Bogguri, E.K. Wheeler, N.O. FischerSpatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode arrayFront. Cell. Neurosci. (2023), DOI: 10.3389/fncel.2023.1287089.]

Physical and Life Sciences Communications Team