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

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...

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Autores principales:	Lubos Polerecky, Meri Eichner, Takako Masuda, Tomáš Zavřel, Sophie Rabouille, Douglas A. Campbell, Kimberly Halsey
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	nanoSIMS stable isotope probing assimilation rates storage inclusions cell growth model Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/cfa4f70746bb434fabaa1ea06357d4ab
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spelling	oai:doaj.org-article:cfa4f70746bb434fabaa1ea06357d4ab2021-12-01T20:36:52ZCalculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS1664-302X10.3389/fmicb.2021.621634https://doaj.org/article/cfa4f70746bb434fabaa1ea06357d4ab2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.621634/fullhttps://doaj.org/toc/1664-302XStable 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.Lubos PolereckyMeri EichnerTakako MasudaTomáš ZavřelSophie RabouilleSophie RabouilleDouglas A. CampbellKimberly HalseyFrontiers Media S.A.articlenanoSIMSstable isotope probingassimilation ratesstorage inclusionscell growth modelMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nanoSIMS stable isotope probing assimilation rates storage inclusions cell growth model Microbiology QR1-502
spellingShingle	nanoSIMS stable isotope probing assimilation rates storage inclusions cell growth model Microbiology QR1-502 Lubos Polerecky Meri Eichner Takako Masuda Tomáš Zavřel Sophie Rabouille Sophie Rabouille Douglas A. Campbell Kimberly Halsey Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
description	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.
format	article
author	Lubos Polerecky Meri Eichner Takako Masuda Tomáš Zavřel Sophie Rabouille Sophie Rabouille Douglas A. Campbell Kimberly Halsey
author_facet	Lubos Polerecky Meri Eichner Takako Masuda Tomáš Zavřel Sophie Rabouille Sophie Rabouille Douglas A. Campbell Kimberly Halsey
author_sort	Lubos Polerecky
title	Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
title_short	Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
title_full	Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
title_fullStr	Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
title_full_unstemmed	Calculation and Interpretation of Substrate Assimilation Rates in Microbial Cells Based on Isotopic Composition Data Obtained by nanoSIMS
title_sort	calculation and interpretation of substrate assimilation rates in microbial cells based on isotopic composition data obtained by nanosims
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/cfa4f70746bb434fabaa1ea06357d4ab
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_version_	1718404600281694208

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

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