Age-based approach to characterize the dynamics of cellular processes

Cells continuously produce and degrade multiple small and large molecules, essential for maintaining homeostasis. The study of this dynamics has gained momentum since the development of pulse-chase methods, in which cellular components are labeled with isotopic or fluorescent tracers to assess properties such as turnover rates or half-lives. Standard analyses, however, often rely on simplifications such as that molecules represent a homogeneous and immediately labeled population, which does not always hold true. Here, we present a rigorous analytical framework that refocuses the interpretation of dynamic labeling experiments toward the distribution of metabolic ages, defined as the time molecules have spent within a cell. We demonstrate how this approach ubiquitously captures different aspects of dynamic behavior, including delayed labeling and complex degradation patterns, and is less prone to biased interpretations. We further introduce a general compartmental modeling approach to quantify metabolic age distributions and to test hypotheses about the organization of metabolic networks and show that metabolic ages can be quantified by dynamic labeling requiring minimal assumptions. The utility of this framework is demonstrated by quantifying metabolic ages and determining the kinetic pool structure of budding yeast proteins at optimal and suboptimal growth temperatures, revealing, for example, distinct kinetic populations of ribosomal proteins. In addition, based on the previously published data on nuclear pore complex assembly, we exemplify how age interpretation of dynamic labeling can enable characterizing the order and time scale of the cellular processes.