Polerecky Lubos, Eichner Meri, Masuda Takako, Zavřel Tomáš, Rabouille Sophie, Campbell Douglas A, Halsey Kimberly
Department of Earth Sciences, Utrecht University, Utrecht, Netherlands.
Institute of Microbiology, Czech Academy of Sciences, Centre Algatech, Třeboň, Czechia.
Front Microbiol. 2021 Nov 30;12:621634. doi: 10.3389/fmicb.2021.621634. eCollection 2021.
Stable isotope probing (SIP) combined with nano-scale secondary ion mass spectrometry (nanoSIMS) is a powerful approach to quantify assimilation rates of elements such as C and N into individual microbial cells. Here, we use mathematical modeling to investigate how the derived rate estimates depend on the model used to describe substrate assimilation by a cell during a SIP incubation. We show that the most commonly used model, which is based on the simplifying assumptions of linearly increasing biomass of individual cells over time and no cell division, can yield underestimated assimilation rates when compared to rates derived from a model that accounts for cell division. This difference occurs because the isotopic labeling of a dividing cell increases more rapidly over time compared to a non-dividing cell and becomes more pronounced as the labeling increases above a threshold value that depends on the cell cycle stage of the measured cell. Based on the modeling results, we present formulae for estimating assimilation rates in cells and discuss their underlying assumptions, conditions of applicability, and implications for the interpretation of intercellular variability in assimilation rates derived from nanoSIMS data, including the impacts of storage inclusion metabolism. We offer the formulae as a Matlab script to facilitate rapid data evaluation by nanoSIMS users.
稳定同位素探测（SIP）与纳米尺度二次离子质谱（nanoSIMS）相结合，是一种用于量化碳、氮等元素进入单个微生物细胞同化率的强大方法。在此，我们使用数学建模来研究推导得到的速率估计值如何依赖于用于描述SIP培养期间细胞对底物同化作用的模型。我们表明，与考虑细胞分裂的模型所推导的速率相比，基于单个细胞生物量随时间线性增加且无细胞分裂这一简化假设的最常用模型，可能会得出被低估的同化率。出现这种差异的原因是，与不分裂的细胞相比，分裂细胞的同位素标记随时间增加得更快，并且随着标记增加到取决于所测细胞细胞周期阶段的阈值以上时，这种差异会变得更加明显。基于建模结果，我们给出了估算细胞同化率的公式，并讨论了其基本假设、适用条件以及对解释从nanoSIMS数据得出的细胞间同化率变异性的影响，包括储存内含物代谢的影响。我们以Matlab脚本的形式提供这些公式，以方便nanoSIMS用户进行快速数据评估。
