Tomer lab introduces “brain-in-a-dish” 3D cell culture technique in Nature Communications paper
June 21, 2022
In a new paper for Nature Communications, the Tomer lab has introduced their novel “brain-in-a-dish” 3D cell culture technique. The technique, called Modular Neuronal Network (MoNNet), allows researchers to model highly complex learning and memory neuronal networks in order to non-invasively study prevalent neuropsychiatric disorders.
MoNNet reimagines years of organoid research that traditionally seeks to develop in vivo large-scale cellular interactions and organizations in a cell-culture dish by recapitulating development. Dr. Angeles Rabadan in the Tomer lab spearheaded the project, performing systematic optimization of established cell culture techniques, and discovered a methodology that allowed a cluster of cells in culture to self-organize in a way that resembles the modular adult brain and acquires complex neuronal functions such as neuronal ensembles and local-global network synchrony. These complex and highly-ordered interactions allow for the noninvasive study of neuropsychiatric disorders of different genetic etiologies that affect the same core learning and memory pathways.
“The basic idea of MoNNet approach is to better mimic adult brain network architecture and generic computational principles,” Professor Tomer reports, “this is in contrast to organoid-like approaches that aim to mimic embryonic developmental trajectories with the hope to eventually reach the matured network stage. I believe this work is the first demonstration of effective in vitro modeling of higher-order properties of brain function- in normal and diseased states- in a robust and reproducible manner.”
The ability to engineer a MoNNet with cell lines harboring classic neuropsychiatric-causative genetic mutations has far-reaching implications. Not only will researchers be able to study complex neuronal processes in a disease model, but also will have the opportunity to develop and test therapeutic targets in these noninvasive models more efficiently.