Stem cells are the definition of potential: they have in their DNA the potential to become virtually any cell in the body. Scientists have been working for decades to harness this power for use as medicine — think replacing damaged cells with brand new cells — that could treat or even cure everything from diabetes to heart disease.
However, so-called regenerative medicine is still in its infancy and very few stem cell therapies have been shown to be effective. Part of the problem is that scientists still don’t fully understand how a stem cell changes into its final form, be it a blood cell or a heart cell. Without a clear understanding of that process, scientists can’t control using it as a therapy.
One of the lingering questions is how a cell’s genes are expressed over time, from DNA to RNA to protein. The genome doesn’t just follow the instructions encoded in the DNA; it responds to signals from the environment that tell it what to express and when to express it, by altering the structure of chromatin, the tightly wound bundle that contains DNA. These chemical signals are called the epigenome.
Moreover, in order to express a gene, its DNA must be transcribed into RNA. The reading of a cell’s RNA is called the transcriptome.
“The big question in the field is which one changes first, the epigenome or the transcriptome,” says Joshua Welch, Ph.D., an assistant professor in the Department of Computational Medicine and Bioinformatics at UM Medical School. A study by his team, published in Nature Biotechnologyprovides a mathematical model that can be used to estimate this timing.
Until recently, researchers could not see gene expression in an individual cell. Thanks to single cell sequencing techniques, they can now do that. But the timing of changes is still difficult to visualize because measuring the cell destroys it.
“To address this, we developed an approach based on models in basic physics,” explains Welch, “where the cells are treated as if they were masses moving through space and we try to estimate their velocity.”
The model, called MultiVelo, predicts the direction and speed of the molecular changes the cells undergo.
“Our model can tell us which things change first — epigenome or gene expression — and how long it takes the first to ramp up the second,” Welch said.
They were able to verify the method using four types of stem cells from the brain, blood and skin, and identified two ways in which the epigenome and transcriptome are out of sync. The technique provides an additional, critical layer of insight into so-called cellular atlases, which are being developed using single cell sequencing to visualize the different cell types and gene expression in different body systems.
By understanding the timing, Welch noted, researchers are closer to directing the development of stem cells for use as therapies.
“One of the big problems in the field is that the artificially differentiated cells created in the lab never quite get to full replicas of their real-life counterparts,” Welch said. “I think the greatest potential for this model is to better understand the epigenetic barriers to fully converting the cells into the target you want them to be.”
Other authors of this article are Chen Li, Maria C. Virgilio, and Kathleen L. Collins.
New algorithm uses online learning for massive cell data sets
Chen Li et al, Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction, Nature Biotechnology (2022). DOI: 10.1038/s41587-022-01476-y
Quote: Mathematical Model Could Bring Us Closer to Effective Stem Cell Therapies (2022, Oct 13) retrieved Oct 13, 2022 from https://phys.org/news/2022-10-mathematical-closer-effective-stem-cell.html
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