Scientists Find Gene Expression Profile Shared by Pluripotent Stem Cells

By Mika Ono and Franz-Josef Mueller

An international team of researchers led by Professor Jeanne Loring, Ph.D., of The Scripps Research Institute has developed a novel method to identify pluripotent stem cells—cells that can differentiate into multiple distinct cell types. These pluripotent cells hold great promise for drug development and treatment of many devastating disorders.

The team's research appears in this week's online issue of the journal Nature.

Using a collection of about 150 human cell samples, the researchers created a database of global gene expression profiles using technology developed by Illumina, Inc. (San Diego). The team discovered that all of the pluripotent stem cell lines showed remarkable similarity in the analysis, while other cell types were more diverse. Further analysis revealed a protein-protein network common to pluripotent cells, pointing to what may be one of the key building blocks of the machinery that enables these transformative cells to differentiate into multiple cell types.

"Our results offer a new strategy for classifying stem cells by their molecular machinery," says Loring, who is director of the Center for Regenerative Medicine at Scripps Research. "We show that pluripotence and self-renewal are under tight control by specific molecular networks."

With rapid advances in the field of stem cells—including methods to induce pluripotence in once fully differentiated cell types, such as skin cells—the question of how to define pluripotence has become increasingly critical, especially for human cell lines, which cannot be treated as those from other species. Pluripotence in mouse cell lines, for example, has been defined experimentally, as the ability to give rise to the tissues of a mouse when injected into a mouse embryo.

"There has been no ethically acceptable equivalent test that could prove pluripotency in human cell preparations," said first author Franz-Josef Mueller, M.D., a visiting investigator at Scripps Research who is also affiliated with the Center for Psychiatry at the University Hospital Schleswig-Holstein, Campus Kiel, in Germany. "Many human cell preparations have been purported to be multi- or pluripotent, but there has been no practical way to define pluripotency in human cells."

The new Nature paper, however, may change this. The researchers dubbed the protein-protein network they identified as common to pluripotent stem cells "PluriNet,"

Predictive Power

The new study is the culmination of many years of work led by Loring, in which the team has sought to define pluripotence by high-information-content global molecular profiling using a large, carefully selected group of human embryonic cells and other stem cell populations.

In the first part of the initiative, the group compiled transcriptional profiles from several hundred samples from human stem cell preparations, including both pluripotent stem cells (with virtually unlimited capacity for proliferation and differentiation) and multi-potent stem cells (with a more limited repertory for proliferation and differentiation). These included human embryonic lines, neural stem cells, mesenchymal stem cells, differentiated cell types from donors, and differentiated cells derived from pluripotent cells. These samples were analyzed and qualified for bioinformatic analysis.

Using an unbiased, computer-learning approach, Mueller conducted the work to identify patterns ingrained in gene expression from the many different cell types. Using systems biology tools that were developed by co-authors Igor Ulitsky and Ron Shamir of the School for Computer Science, Tel Aviv University in Israel, it was possible to identify the specific profiles uniquely characteristic of the pluripotent populations, whether they came from embryonic stem cells or induced pluripotent cells. The researchers also found these profiles were shared by mouse embryonic stem cells, induced mouse pluripotent stem cells, and human oocytes.

Detailed analysis showed that the interacting protein elements can be used to predict whether genetically induced stem cells will be pluripotent.

"Stem cell preparations can now be categorized with great accuracy," Mueller said, "based on their transcriptional phenotypes without any scientists' preconceptions or bias."

Next, the researchers plan to investigate the regulation of this protein network and how it might be used to advance the development of human therapies.

In addition to Loring, Mueller, Ulitsky, and Shamir, authors of the new study, titled "Regulatory networks define phenotypic classes of human stem cell lines," were: Louise C. Laurent of The Scripps Research Institute and UCSD; Dennis Kostka of the University of California, Davis; Roy Williams of Burnham Institute for Medical Research, Christina Lu of Scripps Research; In-Hyun Park of Children's Hospital Boston and Dana Farber Cancer Institute, Mahendra S. Rao of Invitrogen Co.; Philip H. Schwartz of Children's Hospital of Orange County Research Institute and University of California, Irvine; and Nils O. Schmidt of University Medical Center Hamburg-Eppendorf. See
http://www.nature.com/nature/journal/vaop/ncurrent/abs/nature07213.html.

This study was supported by the following grants and awards to members of the research team: Christian-Abrechts University Young Investigator Award, Sleep and Plasticity Hamburger Krebsgesellschaft Grant, Edmond J. Safra Bioinformatics program fellowship at Tel-Aviv University, Converging Technologies Program of The Israel Science Foundation Grant, Raymond and Beverly Sackler Chair in Bioinformatics, Reproductive Scientist Development Program Scholar Award, California Institute for Regenerative Medicine Clinical Scholar Award, the National Institutes of Health, the Alzheimer's Association, and anonymous donations in support of stem cell research. The gene expression analysis was aided by Illumina, Inc. (San Diego), who provided access to newly developed BeadArray technology.

Send comments to: mikaono[at]scripps.edu

"Our results offer a new strategy for classifying stem cells by their molecular machinery."

—Jeanne Loring