The Scripps Research Institute (TSRI) has teamed up with two German research centers to improve the quality of induced pluripotent stem cells (iPSCs). Researchers at Scripps and its collaborators will apply next-generation sequencing to the task of analyzing iPSCs that could improve the accuracy of preclinical disease and toxicity models.
La Jolla, CA-based Scripps is working with the Center for Integrated Psychiatry Kiel (ZIP) and the Fraunhofer Institute for Molecular Biology and Applied Ecology IME. The transatlantic collaboration is, in part, a consequence of the globetrotting career of Franz-Josef Müller. Müller now works at ZIP but during an earlier stint at Scripps collaborated with Professor Jeanne Loring on PluriTest, a bioinformatic assay to gauge the pluripotency human cells. Having each pocketed $1.8 million in funding, the former colleagues are now set to work on the development of PluriTest2.
"Quality control is our major goal," TSRI's Loring said in a statement. "We must ensure that the neurons and other cells derived from iPSCs for clinical use and drug discovery are the ideal cell type for the application. As an analogy, imagine the development of a classical drug treatment. The pills that are provided to patients must contain the right amount of the right drug. Our work applies the same quality control principles to stem cells." The original work on PluriTest led to a publication in Nature Methods in 2011, since when the technology used in genomics has advanced quickly.
Müller sees these advances having a notable effect on the capabilities of PluriTest2. "We will take the next technological step, next generation sequencing, in order to close gaps in stem cell quality control," he said. "In contrast to previous microarray-based technology, we can now see every gene that is expressed in the cell in much more detail. This allows us to draw even more extensive and reliable conclusions on pluripotency, validity of our in vitro models and patient safety." The partners see these advances improving preclinical predictions and leading to lower R&D attrition rates.
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