Scientists at Duke-NUS Medical School, Singapore, and Monash University, Australia, say they have devised an algorithm that can predict what molecules are needed to keep cells healthy in laboratory cultures. They developed a computational approach (EpiMogrify) that reportedly can predict the molecules needed to signal stem cells to change into specific tissue cells, which can help accelerate treatments that require growing patient cells in the lab.
“Computational biology is rapidly becoming a key enabler in cell therapy, providing a way to short-circuit otherwise expensive and time-consuming discovery approaches with cleverly designed algorithms,” said Owen Rackham, PhD, assistant professor and a computational biologist at Duke-NUS, and a senior and corresponding author of the study “EpiMogrify Models H3K4me3 Data to Identify Signaling Molecules that Improve Cell Fate Control and Maintenance,” published in Cell Systems.
In the laboratory, cells are often grown and maintained in cell cultures in medium, which contains nutrients and other molecules. It has been an ongoing challenge to identify the necessary molecules to maintain high-quality cells in culture, as well as finding molecules that can induce stem cells to convert to other cell types, according to the research team that developed EpiMogrify that successfully identified molecules to add to cell culture media to maintain healthy astrocytes and cardiomyocytes. They also used their model to successfully predict molecules that trigger stem cells to turn into astrocytes and cardiomyocytes.
“The need to derive and culture diverse cell or tissue types in vitro has prompted investigations on how changes in culture conditions affect cell states. However, the identification of the optimal conditions (e.g., signaling molecules and growth factors) required to maintain cell types or convert between cell types remains a time-consuming task,” write the investigators.
“Here, we developed EpiMogrify, an approach that leverages data from ∼100 human cell/tissue types available from ENCODE and Roadmap Epigenomics consortia to predict signaling molecules and factors that can either maintain cell identity or enhance directed differentiation (or cell conversion). EpiMogrify integrates protein-protein interaction network information with a model of the cell’s epigenetic landscape based on H3K4me3 histone modifications.”
“Using EpiMogrify-predicted factors for maintenance conditions, we were able to better potentiate the maintenance of astrocytes and cardiomyocytes in vitro. We report a significant increase in the efficiency of astrocyte and cardiomyocyte differentiation using EpiMogrify-predicted factors for conversion conditions.”
“Research at Duke-NUS is paving the road for cell therapies and regenerative medicine to enter the clinic in Singapore and worldwide; 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,” noted Enrico Petretto, PhD, an associate professor who leads the Systems Genetics group at Duke-NUS, and is a senior and corresponding author of the study.
The researchers added existing information into their model about genes tagged with epigenetic markers whose presence indicates that a gene is important for cell identity. The model then determines which of these genes actually code for proteins necessary for a cell’s identity. Additionally, the model incorporates data about proteins that bind to cell receptors to influence their activities. Together, this information is used by the computer model to predict specific proteins that will influence different cells’ identities.
“This approach facilitates the identification of the optimum cell culture conditions for converting cells and also for growing the high-quality cells required for cell therapy applications,” said Jose Polo, PhD, Australian Research Council Future Fellow Professor, from Monash University’s Biomedicine Discovery Institute and the Australian Research Medicine Institute, who is also a senior and corresponding author of the study.
The team compared cultures using protein molecules predicted by EpiMogrify to a type of commonly used cell culture that uses a large amount of unknown or undefined complex molecules and chemicals. They found the EpiMogrify-predicted cultures worked as well or even surpassed their effectiveness.
The researchers have filed for a patent on their computational approach and the cell culture factors it predicted for maintaining and controlling cell fate. EpiMogrify’s predicted molecules are available for other researchers to explore on a public database.
“We aim to continue to develop tools and technologies that can enable cell therapies and bring them to the clinic as efficiently and safely as possible,” said Rackham.
“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,” added Uma S. Kamaraj, PhD, lead author of the study and a graduate of Duke-NUS’ Integrated Biology and Medicine PhD Program.