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Stem cells have the potential to become almost any cell in the body, because they contain their genes. Scientists have been working for decades to harness this power to use as medicine, which could treat or cure many diseases.

Stem cell therapies have not been found to be effective in the early days of regeneration. Scientists don't fully understand how a stem cell transforms into a blood cell or a heart cell Scientists can't use that process as a therapy if they don't understand it.

How a cell's genes are expressed over time is one of the questions that is still being asked. The genome responds to signals from the environment that tell it what to say and when to say it, by making changes to the structure of the tightly wound bundle of the DNA. There are signals in the epigenome.

For a gene to be expressed, it needs to be transcribed intoRNA. The transcriptome is the read of a cell'sRNA.

"Which changes first, the transcriptome or the epigenome, is the big question in the field," said Joshua Welch, an assistant professor in the Department of Computational Medicine and Bioinformatics. There is a mathematical model that may be used to estimate this timing.

Researchers were not able to see the expression of genes in a single cell. They are now able thanks to single cell techniques. Measuring the cell can make it hard to see the timing of changes.

An approach based on models in basic physics was developed to address this problem.

The model predicts the direction of the changes the cells are making.

"Our model can tell us how long it will take for the first to ramp up and how long it will take for the second to start."

Four types of stem cells from the brain, blood and skin were used to verify the method. The technique provides an additional, critical layer of insight to cellular atlases, which are being developed using single cell sequencing to visualize the various cell types and genes in different body systems.

Researchers are closer to steering the development of stem cells for use as therapies if they understand the timing.

The artificially differentiated cells created in the lab never make it to full replicas of their real-life counterparts. What are the epigenetic barriers to fully converting the cells into whatever you want them to be, is the biggest potential for this model.

There are additional authors on this paper.

More information: 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 Journal information: Nature Biotechnology