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Regulatory networks define phenotypic classes of human stem cell lines

机译：监管网络定义了人类干细胞系的表型类别

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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.
机译：干细胞被定义为可以自我更新的细胞群，可以分化为多种不同的细胞类型。然而，来自胚胎，胎儿和成年来源的成百上千种不同的人类细胞系被称为干细胞，即使它们的范围从以胚胎干细胞为代表的多能细胞（实际上能够无限增殖和分化）到成年干细胞系也是如此。 ，这可能会导致分化细胞类型的功能更加有限。关于干细胞新来源及其在再生医学中的预期价值的报道迅速增加，突显了对用于分类这些细胞的通用，可再现方法的需求。我们在这里报告了全球基因表达谱（我们称为“干细胞基质”）数据库的创建和分析，该数据库能够在多种多能，多能和分化细胞类型的背景下对培养的人类干细胞进行分类。使用无监督聚类方法对约150个细胞样本进行分类，我们发现多能干细胞系聚在一起，而其他细胞类型（包括脑源性神经干细胞系）则非常多样化。使用进一步的生物信息学分析，我们发现了多能细胞（胚胎干细胞，胚胎癌和诱导性多能细胞）共有的蛋白质-蛋白质网络（PluriNet）。对已发表数据的分析表明，PluriNet似乎是多能细胞的共同特征，包括小鼠胚胎干细胞，诱导多能细胞和人卵母细胞。我们的结果为干细胞分类提供了新的策略，并支持多能性和自我更新受到特定分子网络严格控制的观点。

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