Mobaraki Michael, Deng Changhui, Zheng Jiashun, Li Hao
Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA.
Developmental Stem Cell Biology Program, University of California, San Francisco, San Francisco, CA 94158, USA.
bioRxiv. 2025 Mar 13:2025.03.11.642143. doi: 10.1101/2025.03.11.642143.
Yeast replicative aging is cell autonomous and thus a good model for mechanistic study from a dynamic systems perspective. Utilizing an engineered strain of yeast with a switchable genetic program to arrest daughter cells (without affecting mother cell divisions) and a high throughput microfluidic device, we systematically analyze the dynamic trajectories of thousands of single yeast mother cells throughout their lifespan, using fluorescent reporters that cover a range of biological processes, including some major aging hallmarks. We found that the markers of proteostasis stand out as most predictive of the lifespan of individual cells. In particular, nuclear proteasome concentration at middle age is a good predictor. We found that cell size (measured by area) grows linearly with time, and that nuclear size grows in proportion to maintain isometric scaling in young cells. As the cells become older, their nuclear size increases faster than linear and isometric size scaling breaks down. We observed that proteasome concentration in the nucleus exhibits dynamics very different from that in cytoplasm, with much more rapid decrease during aging; such dynamic behavior can be accounted for by the change of nuclear size in a simple mathematical model of transport. We hypothesize that the gradual increase of cell size and the associated nuclear size increase lead to the dilution of important nuclear factors (such as proteasome) that drives aging. We also show that perturbing proteasome changes mitochondria morphology and function, but not vice versa, potentially placing the change of proteosome upstream of the change of mitochondrial phenotypes. Our study produced large scale single cell dynamic data that can serve as a valuable resource for the aging research community to analyze the dynamics of other markers and potential causal relations between them. It is also a useful resource for building and testing physics/AI based models that identify early dynamics events predictive of lifespan and can be targets for longevity interventions.
酵母复制性衰老具有细胞自主性，因此从动态系统的角度来看，它是一个进行机制研究的良好模型。利用一种经过基因工程改造的酵母菌株，其具有可切换的遗传程序来阻止子细胞（不影响母细胞分裂），并结合高通量微流控装置，我们使用涵盖一系列生物过程（包括一些主要衰老标志）的荧光报告基因，系统地分析了数千个单个酵母母细胞在其整个生命周期中的动态轨迹。我们发现蛋白质稳态的标志物在预测单个细胞的寿命方面最为突出。特别是中年时的细胞核蛋白酶体浓度是一个很好的预测指标。我们发现细胞大小（以面积衡量）随时间呈线性增长，并且在年轻细胞中细胞核大小按比例增长以维持等比例缩放。随着细胞变老，它们的细胞核大小增长速度快于线性增长，等比例缩放关系被打破。我们观察到细胞核中的蛋白酶体浓度表现出与细胞质中非常不同的动态变化，在衰老过程中下降得更快；在一个简单的运输数学模型中，这种动态行为可以通过细胞核大小的变化来解释。我们假设细胞大小的逐渐增加以及相关的细胞核大小增加导致驱动衰老的重要核因子（如蛋白酶体）稀释。我们还表明，干扰蛋白酶体会改变线粒体的形态和功能，但反之则不然，这可能使蛋白酶体的变化位于线粒体表型变化的上游。我们的研究产生了大规模的单细胞动态数据，可为衰老研究界分析其他标志物的动态变化及其之间潜在的因果关系提供宝贵资源。它也是构建和测试基于物理/人工智能的模型的有用资源，这些模型可识别预测寿命的早期动态事件，并可作为长寿干预的靶点。
