Journal
BIOESSAYS
Volume 33, Issue 11, Pages 880-890Publisher
WILEY
DOI: 10.1002/bies.201100073
Keywords
bioinformatics; biomarkers; gene expression; machine learning; pluripotent stem cells
Categories
Funding
- Bayer Technology Services GmbH
- Deutsche Forschungsgemeinschaft [GSC 111]
- Else-Kroner Fresenius Stiftung
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We have developed a first generation tool for the unbiased identification and characterization of human pluripotent stem cells, termed PluriTest. This assay utilizes all the information contained on a microarray and abandons the conventional stem cell marker concept. Stem cells are defined by the ability to replenish themselves and to differentiate into more mature cell types. As differentiation potential is a property that cannot be directly proven in the stem cell state, biologists have to rely on correlative measurements in stem cells associated with differentiation potential. Unfortunately, most, if not all, of those markers are only valid within narrow limits of specific experimental systems. Microarray technologies and recently next-generation sequencing have revolutionized how cellular phenotypes can be characterized on a systems-wide level. Here we discuss the challenges PluriTest and similar global assays need to address to fulfill their enormous potential for industrial, diagnostic and therapeutic applications.
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