Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Trends Cell Biol. 2018 Dec;28(12):1030-1048. doi: 10.1016/j.tcb.2018.09.002. Epub 2018 Oct 8.
Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.
细胞传统上是通过一些被认为能指定表型的蛋白质的表达水平来表征的。这需要对蛋白质进行先验选择,这可能会引入描述符偏差,并且忽略了群体中每个细胞内嵌套的大量其他分子信息,而这些信息通常使这些稀疏的描述符具有定性。最近,通过新的高通量、信息密集型实验方法和数据驱动的计算方法,实现了更无偏和定量的细胞表征。这篇综述讨论了这些定量描述符在细胞身份的三个核心概念的背景下:定义、创建和稳定性。总的来说,这些概念对于构建定量表型景观是必不可少的,这将增强我们对细胞生物学的理解,并为研究和临床应用促进细胞工程。