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Duke-NUS and Monash University Develop Computer Model that Optimises Cell Culture Conditions

Researchers from Duke-NUS Medical School in Singapore and Monash University in Australia have created EpiMogrify, a computer model that effectively predicts molecules required for stem cell differentiation into specific tissue types, with applications in cell therapy.

Computational methods have vastly improved methods used in many industries, as it can help to optimise processes and predict the best conditions for desired outcomes. Such approaches are applicable even to areas at the forefront of scientific research such as the development of methods for cell manufacturing, which is a key step in cell therapy.

Cell therapy refers to the practice of injecting or implanting live cells into patients, often for purposes of immunotherapy or tissue repair. Since cell therapy requires a large number of cells in order to be effective, therapeutic cells, which include immune cells and various forms of stem cells, have to be cultured, usually in bioreactors.

Attaining high yields of cells that retain their therapeutic properties, whether in terms of ensuring the right cellular secretions, maintaining the undifferentiated state of stem cells, or encouraging differentiation into specific tissue cell types, is highly challenging to researchers and manufacturers. This is because each cell type requires different growth and division conditions, including nutrient requirements, growth factors, and the environment of the growth medium. These requirements, which are themselves difficult to optimise, can also change depending on the type of equipment and materials used during cell culture, further complicating matters. A lot of research and testing is required to optimise these conditions before any cell manufacturing can take place, and even then, it can be difficult to ascertain if they have been completely optimised.

Computational biology has the potential to save valuable time and resources during this process. Researchers from Duke-NUS Medical School in Singapore and Monash University in Australia have developed EpiMogrify, a patent-pending computer model with an algorithm (also designed by the team) that identifies which molecules need to be introduced into growth media in order to maintain healthy growth of cells, or to promote the differentiation of stem cells into particular types of tissues. In this case, they studied the maintenance and production of astrocytes, which are nerve cells, and cardiomyocytes, which are heart muscle cells. This study was published in Cell Systems in November 2020.

To build their model, the team of researchers fed it information about genes with epigenetic tags. Epigenetic markers alter gene expression patterns, without changing DNA sequences. They can lead to the upregulation or downregulation of gene expression, essentially switching genes “on” or “off”, depending on which tags are present on which parts of the gene. The EpiMogrify model uses this information, as well as data about protein functions in cellular pathways, to predict which proteins are required for cell maintenance and differentiation. This approach outperformed commercially available products for cell growth and maintenance.

“The developed technology can identify cell culture conditions required to manipulate cell fate and this facilitates growing important cells in chemically-defined cultures for cell therapy applications,” explained the study’s lead author, Dr Uma S. Kamaraj.

Associate Professor Enrico Petretto, another senior and corresponding author of this study and who also leads the Systems Genetics group at Duke-NUS, added that “this study leverages our expertise in computational and systems biology to facilitate the good manufacturing practice (GMP) production of high-quality cells for these much needed therapeutic applications”, referring to the contributions of Duke-NUS researchers to the findings and to the fields of cell therapy and regenerative medicine.

The algorithm developed by these researchers could streamline processes in cell manufacturing, helping scientists and industries develop more forms of cell therapy, with improved efficiency during cell manufacturing. [APBN]