Using measures of single-cell physiology and physiological state to understand organismic aging.
Using measures of single-cell physiology and physiological state to understand organismic aging.
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DOI：
10.1111/acel.12424
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发表时间：
2016-02
期刊：
影响因子：
7.8
通讯作者：
Brent R
中科院分区：
文献类型：
--
作者：
Mendenhall A;Driscoll M;Brent R
基因相同的生物在同质环境中有不同的寿命和健康跨度。这些差异通常归因于随机事件，例如突变和“突变”，DNA甲基化和染色质的变化改变了基因功能和表达。但最近10年的研究表明，与寿命和健康相关的表型差异不是由DNA的持续变化引起的，也不是由DNA或染色质的修饰确定的。这项工作证明了单细胞和整个生物体生理状态的持续差异，这些生理状态是由活细胞中报告基因信号的值定义的。虽然一些单细胞状态，如对缺氧的反应，在以前就被定义了，但其他的，如制造蛋白质的能力普遍增强，直到最近才通过直接实验揭示出来，而且还没有得到很好的理解。在这里，我们回顾了有望大大增加这些可测量的单细胞生理变量和可测量状态数量的技术进展。我们讨论了便于使用单细胞测量的概念，以提供对生理状态和状态转换的洞察。我们断言，研究人员将利用这些信息将细胞水平的生理读数与整个生物体的结果联系起来，根据不同的生理将衰老人群分层，定义预测结果的生物标志物，并阐明导致不同个体生理的分子过程。由于这些原因，单细胞生理变量和状态转变的定量研究应该为生物体如何衰老的遗传和分子解释提供有价值的补充。
Genetically identical organisms in homogeneous environments have different lifespans and healthspans. These differences are often attributed to stochastic events, such as mutations and ‘epimutations’, changes in DNA methylation and chromatin that change gene function and expression. But work in the last 10 years has revealed differences in lifespan‐ and health‐related phenotypes that are not caused by lasting changes in DNA or identified by modifications to DNA or chromatin. This work has demonstrated persistent differences in single‐cell and whole‐organism physiological states operationally defined by values of reporter gene signals in living cells. While some single‐cell states, for example, responses to oxygen deprivation, were defined previously, others, such as a generally heightened ability to make proteins, were, revealed by direct experiment only recently, and are not well understood. Here, we review technical progress that promises to greatly increase the number of these measurable single‐cell physiological variables and measureable states. We discuss concepts that facilitate use of single‐cell measurements to provide insight into physiological states and state transitions. We assert that researchers will use this information to relate cell level physiological readouts to whole‐organism outcomes, to stratify aging populations into groups based on different physiologies, to define biomarkers predictive of outcomes, and to shed light on the molecular processes that bring about different individual physiologies. For these reasons, quantitative study of single‐cell physiological variables and state transitions should provide a valuable complement to genetic and molecular explanations of how organisms age.