That is why the discovery that induced embryonic-like stem cells can be created from skin cells (iPS cells) was rewarded with a Nobel Prize in 2012. But the process has remained frustratingly slow and inefficient, and the resulting stem cells are not yet ready for medical use.
Research in the lab of the Weizmann Institute's Dr. Yaqub Hanna, which appears today in Nature
, dramatically changes that: He and his group revealed the "brake" that holds back the production of stem cells, and found that releasing this brake can both synchronize the process and increase its efficiency from around 1% or less today to 100%.
These findings may help facilitate the production of stem cells for medical use, as well as advancing our understanding of the mysterious process by which adult cells can revert back into their original, embryonic state.
Embryonic stem cells are those that have not undergone any "specialization"; thus they can give rise to any type of cell in the body. This is what makes them so valuable: They can be used, among other things, to repair damaged tissue, treat autoimmune disease and even grow transplant organs.
Using stem cells taken from embryos is problematic because of availability and ethical concerns, but the hopes for their use were renewed in 2006, when a team led by Shinya Yamanaka of Kyoto University discovered that it is possible to "reprogram" adult cells.
The resulting cells, called "induced pluripotent stem cells" (iPSCs), are created by inserting four genes into their DNA. Despite this breakthrough, the reprograming process is fraught with difficulty: It can take up to four weeks; the timing is not coordinated among the cells; and less than one percent of the treated cells actually end up becoming stem cells.
Hanna and his team asked: What is the main obstacle - or obstacles - that prevent successful reprograming in the majority of cells? In his postdoctoral research, Hanna had employed mathematical models to show that a single obstacle was responsible. Of course in biology, Hanna is the first to admit, experimental proof is required to back up the models.
The present study not only provides the proof, it reveals the identity of that single obstacle and shows that removing it can dramatically improve reprograming.