Scientists propose a way to turn a cell of one type into any other type—and avoid all the intermediate steps involved in another, Nobel Prize-winning technique, which produces induced pluripotent stem cells.
In a new paper, they lay out a way to harness the wealth of data now available about DNA activity, and reprogram cells directly. The formula also provides a blueprint for determining the optimal combination of factors and when they should be added to accomplish this reprogramming.
“This algorithm provides a blueprint that has important implications for cancer.”
The concept, reported in PNAS, combines biological information on genome structure and gene expression with a fair bit of math, using an approach called data-guided control.
Though the paper spells out an algorithm for transforming cells—and successfully predicts factors that are already known to reprogram cells—it does not directly test the formula in the laboratory. The authors have plans to further test their method, and hope that scientists around the world can try it.
If it bears fruit, they predict it could have applications including regenerating diseased or lost tissue, and fighting cancer.
“Cells in our body naturally specialize,” says Indika Rajapakse, a bioinformatics and mathematics researcher at the University of Michigan and senior author of the new paper. “What we propose could provide a shortcut to doing the same, to help any cell become a targeted cell type.”
Rajapakse notes that the idea of direct reprogramming is not new. In the late 1980s, a team led by the late scientist Harold Weintraub turned skin cells directly into muscle cells by bathing the cells in a type of molecule that encouraged certain genes in the cells’ DNA to be “read”. Rajapakse trained with Weintraub’s colleague Mark Groudine at the Fred Hutchinson Cancer Research Center.
The new model builds on that idea by also harnessing the power of these molecules, called transcription factors or TFs.
But instead of bathing the whole cell culture in one TF, the scientists aim to target cells with specific TFs at specific crucial times in their lifespan. They lay out a mathematical control model for harnessing all the information that can now be learned about cells at the molecular level, and combining it to map out the timing and sequence for injecting TFs to get the desired cell type.
“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.
The Hi-C technique lets scientists track the location of, and contact between, portions of the DNA/protein complex called the chromatin. So even if two genes sit far apart on a long strand of DNA, they may come in close contact with one another when those looping, folding strands end up next to one another. If one of those genes gets “read,” it may produce a transcription factor that then sets in motion the “reading” of the other gene, and the production of a certain protein that plays a key role in transforming the cell in some way.
The amount of data that would come out of analyzing these “topologically associating domains” in just one type of cell is huge. But modern bioinformatics techniques make it easier to make sense of it all.
Using this ‘blueprint’
The first author of the paper is Scott Ronquist, a PhD student who began working with Rajapakse in the computational medicine and bioinformatics department as an undergraduate at the University of Michigan. He and former postdoctoral fellow Geoff Patterson led the effort to use gene expression and TAD data generated in the Rajapakse lab and publicly available gene expression and TF data to test their model. They were able to see patterns in the data that reflected the pace of normal cell differentiation.
Now, they’re working on testing the model proactively in the laboratory of Max Wicha, professor of oncology at Michigan Medicine.
“This algorithm provides a blueprint that has important implications for cancer, in that we think cancer stem cells may arise from normal stem cells via similar reprogramming pathways,” says Wicha, who is a coauthor of the paper.
“This work also has important implications for regenerative medicine and tissue engineering, since it provides a blueprint for generating any desired cell type. It also demonstrates the beauty of combining mathematics and biology to unravel the mysteries of nature.”
Contributors to the work are also from the University of Maryland and Harvard University.
The University of Michigan has filed for a patent on the algorithm. DARPA funded the work.
Source: University of Michigan