Adult cells can be de-differentiated so that they resemble embryonic stem cells by genetically engineering them to overexpress particular genes. Such reprogrammed cells are known as induced pluripotent stem cells or iPSCs, and these cells might have the potential to cure damaged nerves, regrow limbs and organs, and precisely model a patient’s particular disease. Unfortunately, the very process of reprogramming triggers replication stress, which causes iPSCs to acquire serious genetic and epigenetic abnormalities that lower the cells’ quality and limit their therapeutic usefulness.
When iPSCs were first derived in 2006, the efficiency of their derivation was quite low, since only a fraction of a percentage of reprogrammed cells successfully grew to become cell lines. Thus some of the earliest work with iPSCs tried to increase the efficiency of reprogramming. These experiments provided a greater understanding of the reprogramming process and demonstrated that many different variables, including the ratio of reprogramming factors and the reprogramming environment, could also greatly affect the quality of the iPSCs that were derived.
A research group from the Whitehead Institute, which includes founding member Rudolf Jaenisch, in collaboration with scientists from Hebrew University, has shown that the reprogramming factors themselves greatly influence the reprogramming efficiency and the quality of the resulting cells. This work was published in the current issue of the journal Cell Stem Cell.
“Postdoctoral researcher Yosef Buganim and Research Scientist Styliani Markoulaki show that a different combination of reprogramming factors may be less efficient than the original, but can produce higher quality iPSCs,” says Jaenisch, who is also a professor of biology at MIT. “And quality is a really important issue. At this point, it doesn’t matter if we get one colony out of 10,000 or one out of 100,000 cells, as long as it is of high quality.”
In order to derive iPSCs from mature adult cells, scientists transfect adult cells to a cocktail of genes. The genes used are all active in embryonic stem cells. By pushing cells to overexpress these embryonic stem cell-specific genes, adult cells can become iPSCs, which can then be differentiated into almost any other cell type, such as nerve, liver, or muscle cells. The original gene combination included Oct4, Sox2, Klf4, and Myc or (OSKM). This combination efficiently reprograms cells, but a relatively high percentage of the resulting cells have serious genomic aberrations, including aneuploidy, and trisomy 8, which make them unsuitable for use in clinical research.
Buganim and Markoulaki used bioinformatic analysis of a network of 48 genes that are integral to the reprogramming process. With this analysis, Buganim and Markoulaki designed a new reprogramming gene cocktail: Sall4, Nanog, Esrrb, and Lin28 (SNEL). With this gene combination, approximately 80% of SNEL colonies made from mouse cells were of high quality and fulfilled the tetraploid complementation assay, which is the most stringent pluripotency test available. As a comparison, only 20-30% of high quality OSKM passed the same test. Buganim hypothesizes that SNEL reprograms cells better because, unlike OSKM, the cocktail does not rely on a potent oncogene like Myc, which might be the source of some of the genetic problems produced by the reprogramming process. Even importantly, the cocktail does not rely on the potent key master regulators Oct4 and Sox2 that seem to abnormally activate some regions in the adult cell genome.
Buganim and Markoulaki also analyzed SNEL colonies down to the genetic and epigenetic level. On their DNA, SNEL cells have deposits of the histone protein H2AX in locations very similar to those in ESCs, and the position of H2AX seems to predict the quality of the cell. This characteristic might be a fast way to quickly screen for high quality colonies.
It must be stressed that this SNEL gene combination was designed for mouse cells; it is unable to reprogram human cells, which are generally more difficult to manipulate than mouse cells. However, the same bioinformatic analysis might provide the proper insights to find the right combination for human cells that produce clinical quality iPSCs.
“We know that SNEL is not the ideal combination of factors,” says Buganim, who is currently a Principal Investigator at Hebrew University in Jerusalem. “This work is only a proof of principle that says we must find this ideal combination. SNEL is an example that shows if you use bioinformatics tools you can get better quality. Now we should be able to find the optimal combination and try it in human cells to see if it works.”