One of the most defining scientific discoveries in recent decades is the development of induced pluripotent stem cells, which lets scientists revert adult cells back into an embryonic-like blank state and then manipulating them to become a particular kind of tissue.
But now a new model could do away with this time-consuming process, taking out the middle step and directly programming cells to become whatever we want them to be.
“Cells in our body always self-specialise,” explains bioinformatics researcher Indika Rajapakse from the University of Michigan.
“What we propose could provide a shortcut to doing the same, to help any cell become a targeted cell type.”
The roots of the new framework go back further than the discovery of induced pluripotent stem cells in 20066, to when researchers at the Fred Hutchinson Cancer Research Centre in 1989 figured out how to turn adult skin cells into muscle cells.
They did this by exposing the cells to a protein called transcription factor (TF), which help regulate gene expression in cells, determining things like the kind of cells they become, plus cell division, growth, and death.
In the 1989 research, the team worked with a TF molecule called MyoD, and the team who discovered the technique to induce pluripotent stem cells did so by manipulating cells with TFs called POU5F1, SOX2, KLF4, and MYC.
Now, Rajapakse and fellow researchers have taken that research on TFs and combined it with newer insights into DNA and genome structures, to develop a mathematical algorithm that they say successfully predicts the factors known to reprogram cells.
In other words, rather than using just one or a few TFs to manipulate cells towards differentiation, their model draws upon 3D representations of the genome (called Hi-C data) to map out the correct timing and sequence for injecting TFs to produce the specific kinds of cells wanted.
“We have so much data now from RNA and transcription factor activity, and from Hi-C data of chromosome configuration that tells us how often two pieces of chromatin are near one another, that we believe we can go from the cell’s initial configuration to the desired configuration,” says Rajapakse.
It’s an incredibly exciting framework that could not only hypothetically help us produce all kinds of needed tissues – but it might also help us turn the tables on diseases like cancer and genetic disorders by helping us to reprogram the very cells that make tissue malignant or dangerous into something benign and safe.
At this point, the work is largely theoretical and hasn’t been utilised in the lab, but Rajapakse and his team are now looking to do just that, and at the same time are publishing their research so that other scientists will also be able to make use of the algorithm – whether to fight cancer, or to push the model into other fields.
“This work also has important implications for regenerative medicine and tissue engineering, since it provides a blueprint for generating any desired cell type,” explains one of the team, stem cell biologist Max Wicha.
“It also demonstrates the beauty of combining mathematics and biology to unravel the mysteries of nature.”
The findings are reported in Proceedings of the National Academy of Sciences.