Can Better Disease Modeling Help Fight Neurological Diseases?

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The prevalence of neurological diseases has increased rapidly, and they are now the second biggest cause of death globally. With the search for treatments and cures proving difficult, change is needed. Can better disease modeling help?

Neurological diseases range from Alzheimer’s and other dementias to stroke and brain cancer. Not only are they associated with high mortality, but they are also the leading cause of disability worldwide. New treatments and cures are needed to reduce the impact of neurological diseases on patients and the huge burden they place on healthcare systems.

Researchers, biotechs, and pharma companies have struggled with developing therapies for these diseases, given the complexity of the central and peripheral nervous systems. The brain alone is made up of tens of millions of neurons, of which there are several types, as well as numerous other cell types, such as glia, oligodendrocytes, and astrocytes. Recreating this level of complexity is extremely difficult, if not impossible.

Current approaches to modeling neurological diseases

Alexander Brown, Synthego, neurological diseases, neurological disease modeling
Alexander Brown, Senior Scientist at Synthego

Many models for neurological diseases are available to researchers. “Animal models – transgenic mice, flies, and even yeast – have been one of the most popular systems for a long time. The animals have short lifespans and can be genetically modified to interrogate important biological questions over multiple generations,” explained Alexander Brown, Senior Scientist at Synthego.

However, current approaches to modeling neurological diseases have limitations, which can undermine research efforts. In the case of animal models, there is the question of human relevance. 

Brown provided an example: “For my Ph.D. I studied hedgehog signaling – an important developmental pathway in mammals. Humans with a mutation would have a single central incisor and hypothalamic malformations, often leading to diabetes. When we made the same mutation in mice, they were completely normal.

Another commonly used model system is isolated human cells. Although these can be very powerful, they have two main limitations. First, the cells are obtained from autopsies and biopsies, which can make them difficult to come by. Second, patients with neurological diseases may have had their condition for decades, rather than being recently diagnosed.This means very late-stage disease is being studied, and not the early effectors,” Brown highlighted.

Advances in neurological diseases modeling

stem cells, embryonic stem cells, iPSCs, neurological diseases

As a result of these limitations, there is a need for alternative disease models. One potential solution is to produce organoids using edited cell lines. This approach would harness the power of induced pluripotent stem cells (iPSCs) and the gene-editing tool CRISPR. 

There’s been a lot of excitement about organoids,” said Brown. Also known as ‘mini-organs in a dish’, organoids are clumps of naive cells that have been differentiated to recreate the 3D structure and mimic the key functions of an organ or tissue in vivo. Organoids can be produced using iPSCs, which are capable of differentiating into any adult cell type.

Many neurological diseases affect multiple regions of the brain and many cell types. iPSCs can become, for example, glutamatergic neurons or dopaminergic neurons. Co-culture experiments can be carried out to produce neurons along with the support cells on which they are intimately dependent, such as astrocytes and oligodendrocytes,” Brown told me.

CRISPR allows DNA to be edited simply, quickly, and affordably. Cells from an affected individual can be edited to correct the gene of interest or left unedited, then grown to form healthy and diseased models. “The edited cells from the individual can be compared with their unedited cells, There’ll be no other differences between them, which helps us to see what is really going on,” Brown explained.

However, there are still areas of improvement. The lack of reproducibility is a problem, as cells from the same sample can vary in how they grow and differentiate. Another issue is the ability to differentiate cells into the type required to model the disease. If a particular type of neuron, or astrocytes, or microglia cannot be produced, then the disease cannot be studied.

Implementing new disease modeling technologies

CRISPR, CRISPR-Cas9, genome-editing, gene therapy

Synthego specializes in genome engineering, with more than 60,000 edits completed and characterized. The company produces CRISPR-engineered iPSCs with high accuracy and efficiency, which can be used to develop physiologically relevant disease models. The aim is to help researchers, biotechs, and pharma companies to accelerate the discovery and development of new therapeutics.

The products and services offered by Synthego have several benefits for the neurological diseases field. “We can generate thousands of new clones in a very short time and offer a scale that is, I would like to think, very difficult to match. Our turnaround time is also good, and we can be nimble to meet our customer’s deadlines,” Brown said. 

The company continues to refine its approach. “Many of our competitors still use a very manual process. There are subtle differences between how individuals handle cells; how they pipette, how quickly they move the liquids. This has an impact on results. By automating the process, it’s all reproducible,” Brown explained. Additionally, Synthego is developing a platform to go from cells to data in a high-throughput manner.

Next steps for modeling neurological diseases

Brown believes research into neurological diseases is on the brink of change. “We’re no longer bound to animal models that have long generation times or dependent on individual patient samples. We can now recapitulate so much of the brain.” Organoids made with CRISPR-engineered iPSCs can supplement the models used today and allow researchers to investigate diseases in a new way.

Machine learning could be the next technology to transform the modeling of neurological diseases. “We generate a lot of data, which can be fed into machine learning models to refine them. Machine learning could be used to leverage the massive genomics dataset and generate new hypotheses,” Brown told me. 

The hope will be that these technologies can come together to help researchers better understand neurological diseases involving single genes, as well as more complex ones like Alzheimer’s.

To find out more about Synthego’s precision CRISPR editing in iPSCs, download this technical note.

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