Regulatory networks define phenotypic classes of human stem cell lines

• Published in: Nature Vol. 455; no. 7211; pp. 401 - 405
• Main Authors: Schmidt, Nils O, Rao, Mahendra S, Williams, Roy, Park, In-Hyun, Shamir, Ron, Loring, Jeanne F, Schwartz, Philip H, Lu, Christina, Müller, Franz-Josef, Kostka, Dennis, Ulitsky, Igor, Laurent, Louise C
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.09.2008; Nature Publishing; Nature Publishing Group
• Subjects: Algorithms; Animals; Artificial Intelligence; Biological and medical sciences; Cell Differentiation; Cell differentiation, maturation, development, hematopoiesis; Cell Line; Cell physiology; Computational Biology; Databases, Factual; Embryonic Stem Cells - classification; Embryonic Stem Cells - metabolism; Evaluation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling; Genotype & phenotype; Humanities and Social Sciences; Humans; letter; Mice; Molecular and cellular biology; multidisciplinary; Multipotent Stem Cells - classification; Multipotent Stem Cells - metabolism; Oligonucleotide Array Sequence Analysis; Oocytes - classification; Oocytes - metabolism; Phenotype; Physiological aspects; Pluripotent Stem Cells - classification; Pluripotent Stem Cells - metabolism; Protein Binding; Proteins; Science; Science (multidisciplinary); Stem cell research; Stem cells; Stem Cells - classification; Stem Cells - metabolism; Human; Phenotype; Cell line; Pluripotent cell; Stem cell; Database; Unsupervised classification; Network regulation; Bioinformatics; Gene expression profile
• ISSN: 0028-0836; 1476-4687; 1476-4679
• DOI: 10.1038/nature07213

Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells-typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation-to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the 'stem cell matrix') that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein-protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.