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Addressing the Challenges of Cell Models in Research and Drug Discovery
Discovery UK 2021 featured a panel of three expert scientists working with cell models at King’s College London. The discussion, titled ‘Addressing the Challenges of Cell Models in Research and Drug Discovery’, tackled the intricacies of developing cellular models to creating and test novel therapies.
On the panel were Davide Danovi and Ivo Lieberam, both Senior Lecturers at King’s College London. Lieberam heads a lab at the university’s Centre for Stem Cells & Regenerative Medicine and Danovi also serves as Head of Cellular Phenotyping at bit.bio.
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Challenges of Current Cell Models and Overcoming Them
A critical consideration for translational applications for cell models is their relevance to – and ability to mirror – human disease. However, Danovi pointed out that striving for this relevancy can often beget bottlenecks such as lower throughput and problems with the consistency and reproducibility of the model. “There has been a shift in recent times in using stem cells and primary cells,” he explained.
Lieberam agreed that reproducibility of differentiation methods was a problem. Furthermore, he said researchers often find that cellular models used for testing drugs are not complex enough to mirror the tissue that they want to analyse. For example, most induced pluripotent stem cell (IPSC) models for the motor neurone disease ALS contain motor neurons but not myofibers because they are too difficult to culture. Therefore, the cell type of interest is analysed outside of its normal biological context.
Yet another problem that IPSCs can conjure are differences in cell culture control lines. According to one member of the panel, these differences are likely to stem from the genetic background or sex differences of the individual donor. It has recently become apparent that sex and ethnic background can significantly influence the cellular phenotype of the cells that scientists differentiate. So, there is a need to better understand and be more inclusive of what the source material is.
Cellular Models and Tools for Novel Research and Drug Discovery
The question of which cellular models and tools are the most promising is very context-dependent and will come down to the end point of the model. For example, is the purpose of the model to replicate a disorder that only affects motor neurons? Or is the goal to model the aetiology of the disorder? It should be noted that the latter option would require recapitulating the early stages of neuronal differentiation.
Lieberam explained that until recently he was almost vehemently opposed to the use of forward programming: “That’s the old fashioned view, you want the cell to undergo a normal development process.” Forward programming can be much faster, more efficient, and can produce an almost pure cell population, but it also short-circuits some genetic processes. “As a result, you get cells that are maybe 80% of what you want them to be,” said Lieberam.
One example of this occurs in motor neuron cell models. Motor neurons have a segmented identity embedded in their hox genes which allows them to determine the muscles that they innervate and types of movement that they control. This genetic identity can be retained by differentiating the cells with extrinsic factors; different extrinsic factors will lead to different segmental identities. However, forward programming will cause these identities to be lost; as Lieberam pointed out: “they are motor neurons, they have the right neurotransmitter profile, but they don’t have any identity beyond being a generic motor neuron.”
Rethinking Cell Modelling Approaches
Danovi too, explained how previously he had been hesitant to engage in forward programming in experiments at Kings, but recently, this had “changed completely.” He said: “one of the PhD students in my group was obtaining very difficult neuro spheroids with the classic differentiation methods – she was able to fast track into very reproducible neuro spheroids.”
Furthermore, Lieberam’s lab now uses forward programming for almost every cell type that they study: “myoblast, glia, all of them except motor neurons.” In the case of motor neurons, Lieberam’s team have established a programme which includes cell sorting without forward programming. Forward programmed motor neurons are easier and faster to generate than those made by directed differentiation. Lieberman said: “Even if the cells are not perfect, they are good enough for most applications and an order of magnitude easier to produce.”
In this regard, the panel suggested that directed differentiation and forward programming should be thought about as two separate camps. Instead, it may be worth considering how they can develop together to address the question that a researcher has in mind.
Small molecule agents can be used to direct a cell towards a particular lineage, and their effects can be seen on a transcriptomic level within hours. These effects could be incredibly powerful; “Coupling them with an equally powerful driver such as an overexpression of a transcription factor (as we do in forward programming), we may have a finer control over the cells’ endpoint.” Therefore, one method is not necessarily better or worse than the other, “why not use them together?”
Key Takeaways
Attempts to address the challenges of cell models in research and drug discovery is punctuated by the complexities involved in developing and testing novel therapies. Their need for relevant and reproducible cell models that mirror human disease highlights the shift towards using stem cells and primary cells.
As such, different approaches have been offered to achieve the desired cell populations, including directed differentiation and forward programming. While each method has its advantages and limitations, as the panellists proposed, a collaborative approach can combine the strengths of both strategies. Ultimately, the key takeaway is to carefully consider the objectives of the study and choose the most appropriate cellular models and tools to advance research and drug discovery efforts.
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