Neural organoids have been heralded as having huge potential for advancing our knowledge of the brain in several fields. These include exploring the responses of brain tissue to drugs, investigating the effect of specific genetic mutations on neural electrical activity and characterising how neural systems develop.
In the past, viability of these systems has been limited by their scalability, reproducibility and longevity.
New research from King’s College London has succeeded in scaling up the organoid approach; providing a new system where the effects of drugs, genetic mutations, and development can be tested at higher throughput and over much longer periods of time.
“Functional genomic and pharmacological studies of neurodevelopment often depend on reliable measures of neuronal function, not just cell identity. Our approach makes it possible to follow neural network activity over time and will allow us and others to directly compare the effects of drugs or gene variants across many parallel cultures.” — Professor Deepak Srivastava, Professor of Molecular Neuroscience, King’s College London.
Current challenges in organoid research
Lab grown neural networks can be 2D or 3D and both have advantages and disadvantages. Traditional 3D neural organoids are highly variable, being made up of many different types of cells that make each organoid slightly different. While variety in neuron types is viewed as a sign of a healthy organoid, this can present challenges for reproducibility, which is particularly important when testing drugs or trying to understand the function of a specific gene.
Additionally, it is hard to record electrical activity from 3D organoids as their structure means researchers can usually only record from the surface of the organoid, or only from one neuron at a time if they want to go deeper into the tissue.
Other groups have been growing neurons in the lab in 2D, allowing researchers to record electrical activity from many neurons over time. However, these 2D networks lack the diversity of neuron types and support cells seen in 3D organoids and real brains.
The best of both worlds: making 3D organoids 2D to reduce variability
Dr Adam Pavlinek, Professor Anthony Vernon, Professor Deepak Srivastava, and colleagues wanted to keep the diversity of neurons found in 3D organoids, but have the benefits of 2D approaches, namely the ability to record changes in activity over time and to test drugs and other manipulations at scale.
To do this, the researchers first grew organoids in the lab and then broke them down into individual cells in a process called dissociation. This gave the researchers many different types of developing neurons that they could grow on a 2D plate. They then mixed cells from different organoids together to make the cell mixture less variable. This resulted in multiple neural networks next to each other on one plate, all coming from similar origins. Pooling cells from many organoids reduced variability by averaging over the variation between the original organoids.
To ensure they could record the electrical activity of the neurons, researchers let the disassociated neurons grow on a plate with electrodes on it, called a microelectrode array.
Recording over many days
On the microelectrode array, the researchers could watch the neurons develop networks over many days and record their electrical activity throughout. In two dimensions, researchers could monitor the neurons developing for much longer than with 3D organoids. They saw neurons transitioning from asynchronous electrical activity, commonly seen in developing brains, to synchronised firing as the cells matured and the connections between them formed.
Separating sources of variation
The approach taken also meant the researchers could separate the effects of technical versus biological variability. They could have many versions of the same cell types from the same neural organoids plated alongside each other and see if the neurons developed similar connections and if they responded similarly to drugs.
“The neurons in organoids have a remarkable ability of self-assembling into networks, we think the balance of neuron cell types in these networks may affect their electrical activity and may underlie the differences we see between networks.” — Dr Adam Pavlinek, first author.
Cell Reports Methods