Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS

Frontiers in Microbiology

Douglas A. Campbell¹  Lubos Polerecky²  Kimberly Halsey³  Tomáš Zavřel⁴  Meri Eichner⁵  Takako Masuda⁵  Sophie Rabouille⁶ 

[1] Department of Biology, Mount Allison University, Sackville, NB, Canada;Department of Earth Sciences, Utrecht University, Utrecht, Netherlands;Department of Microbiology, Oregon State University, Corvallis, OR, United States;Global Change Research Institute, Czech Academy of Sciences, Brno, Czechia;Institute of Microbiology, Czech Academy of Sciences, Centre Algatech, Třeboň, Czechia;Sorbonne Université, CNRS, Laboratoire d’Océanographie Microbienne, LOMIC, Banyuls-sur-mer, France;Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefranche, LOV, Villefranche-sur-mer, France;

关键词: nanoSIMS;    stable isotope probing;    assimilation rates;    storage inclusions;    cell growth model;   

DOI  :  10.3389/fmicb.2021.621634

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown