Institute of Biomaterials and Biomedical Engineering and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 164 College Street, RS 407, Toronto, Ontario, Canada.
Biotechnol Bioeng. 2010 Jun 1;106(2):173-82. doi: 10.1002/bit.22708.
Cytokines are central factors in the control of stem cell fate decisions and, as such, they are invaluable to those interested in the manipulation of stem and progenitor cells for clinical or research purposes. In their in vivo niches or in optimized cultures, stem cells are exposed to multiple cytokines, matrix proteins and other cell types that provide individual and combinatorial signals that influence their self-renewal, proliferation and differentiation. Although the individual effects of cytokines are well-characterized in terms of increases or decreases in stem cell expansion or in the production of specific cell lineages, their interactions are often overlooked. Factorial design experiments in association with multiple linear regression is a powerful multivariate approach to derive response-surface models and to obtain a quantitative understanding of cytokine dose and interactions effects. On the other hand, cytokine interactions detected in stem cell processes can be difficult to interpret due to the fact that the cell populations examined are often heterogeneous, that cytokines can exhibit pleiotropy and redundancy and that they can also be endogenously produced. This perspective piece presents a list of possible biological mechanisms that can give rise to positive and negative two-way factor interactions in the context of in vivo and in vitro stem cell-based processes. These interpretations are based on insights provided by recent studies examining intra- and extra-cellular signaling pathways in adult and embryonic stem cells. Cytokine interactions have been classified according to four main types of molecular and cellular mechanisms: (i) interactions due to co-signaling; (ii) interactions due to sequential actions; (iii) interactions due to high-dose saturation and inhibition; and (iv) interactions due to intercellular signaling networks. For each mechanism, possible patterns of regression coefficients corresponding to the cytokine main effects, quadratic effects and two-way interactions effects are provided. Finally, directions for future mechanistic studies are presented.
细胞因子是控制干细胞命运决策的核心因素，因此，对于那些有兴趣操纵干细胞和祖细胞用于临床或研究目的的人来说，它们是非常宝贵的。在其体内生态位或优化的培养环境中，干细胞会受到多种细胞因子、基质蛋白和其他细胞类型的影响，这些细胞会提供单独和组合的信号，影响干细胞的自我更新、增殖和分化。虽然细胞因子的单独作用在增加或减少干细胞扩增或产生特定细胞谱系方面已经得到很好的描述，但它们的相互作用往往被忽视。与多元线性回归相结合的析因设计实验是一种强大的多变量方法，可以推导出响应曲面模型，并获得对细胞因子剂量和相互作用效应的定量理解。另一方面，由于所检查的细胞群体通常是异质的，细胞因子可以表现出多效性和冗余性，并且它们也可以内源性产生，因此在干细胞过程中检测到的细胞因子相互作用可能难以解释。本文提出了一个可能的生物学机制列表，这些机制可以在体内和体外基于干细胞的过程中产生正向和负向双向因子相互作用。这些解释是基于最近研究中提供的关于成体和胚胎干细胞中细胞内和细胞外信号通路的见解得出的。根据四种主要的分子和细胞机制对细胞因子相互作用进行了分类：（i）共信号转导引起的相互作用；（ii）顺序作用引起的相互作用；（iii）高剂量饱和和抑制引起的相互作用；（iv）细胞间信号网络引起的相互作用。对于每种机制，都提供了对应于细胞因子主效应、二次效应和双向相互作用效应的回归系数的可能模式。最后，提出了未来的机制研究方向。
