数以百计的不同人类细胞系被统称为“干细胞”。它们可以来自胚胎、胎儿或者成年人体。而且，它们是多能的，即能够产生一系列不同细胞，或者产生种类有限的细胞类型。
Müller等人试图根据从超过150个细胞样品获取的一个转录谱数据库建立一个“干细胞诊断”体系，来统一人类干细胞的定性和分类。
生物信息分析显示，多能干细胞系有很多共性，它们都有一个典型的蛋白-蛋白网络，称之为“PluriNe”。其他细胞类型，包括来自大脑的神经干细胞系，要多样化得多。
这些结果为对干细胞进行分类提供了一个新策略，并且支持这样一个观点：多能性和自我更新能力受特定分子网络的严格控制。（生物谷Bioon.com）
生物谷推荐原始出处：
Nature 455, 401-405 (18 September 2008) | doi:10.1038/nature07213
Regulatory networks define phenotypic classes of human stem cell lines
Franz-Josef Müller1,2, Louise C. Laurent1,3, Dennis Kostka4,13, Igor Ulitsky5, Roy Williams6, Christina Lu1, In-Hyun Park7, Mahendra S. Rao8,9, Ron Shamir5, Philip H. Schwartz10,11, Nils O. Schmidt12 & Jeanne F. Loring1,6
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 medicine1, 2 has highlighted the need for a general, reproducible method for classification of these cells3. 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 method4, 5 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 analysis6 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.
