Calculation of Single Cell Assimilation Rates From SIP-NanoSIMS-Derived Isotope Ratios: A Comprehensive Approach

Abstract

The nanoSIMS-based chemical microscopy has been introduced in biology over a decade ago. The spatial distribution of elements and isotopes analyzed by nanoSIMS can be used to reconstruct images of biological samples with a resolution down to tens of nanometers, and can be also interpreted quantitatively. Currently, a unified approach for calculation of single cell assimilation rates from nanoSIMS-derived changes in isotope ratios is missing. Here we present a comprehensive concept of assimilation rate calculation with a rigorous mathematical model based on quantitative evaluation of nanoSIMS-derived isotope ratios. We provide a detailed description of data acquisition and treatment, including the selection and accumulation of nanoSIMS scans, defining regions of interest and extraction of isotope ratios. Next, we present alternative methods to determine the cellular volume and the density of the element under scrutiny. Finally, to compensate for alterations of original isotopic ratios, our model considers corrections for sample preparation methods (e.g., air dry, chemical fixation, permeabilization, hybridization), and when known, for the stable isotope fractionation associated with utilization of defined growth substrates. As proof of concept we implemented this protocol to quantify the assimilation of 13C-labeled glucose by single cells of Pseudomonas putida. In addition, we provide a calculation template where all protocol-derived formulas are directly available to facilitate routine assimilation rate calculations by nanoSIMS users.

Warming the phycosphere: Differential effect of temperature on the use of diatom‐derived carbon by two copiotrophic bacterial taxa

“…Changes in the isotope composition during stable isotope labeling measured by nanoSIMS were generally considered quantitatively to calculate assimilation rates of single cells, following the calculations provided in Stryhanyuk et al . (2018). Assimilation rates were calculated as the fraction ( K A ) of carbon or nitrogen assimilated into a cell during its incubation in isotope‐labelled growth substrate: where R gs is the isotope ratio of the growth substrate during incubation, R i is the initial isotope ratio before incubation, and R f is the isotope ratio after incubation.…”

“…To quantify the natural abundances of 13 C 14 N/ 12 C 14 N and 12 C 15 N/ 12 C 14 N in our samples, we analysed the isotopic ratios on non-labelled samples collected just before the SIP incubation of March. Changes in the isotope composition during stable isotope labeling measured by nanoSIMS were generally considered quantitatively to calculate assimilation rates of single cells, following the calculations provided in Stryhanyuk et al (2018). Assimilation rates were calculated as the fraction (K A ) of carbon or nitrogen assimilated into a cell during its incubation in isotope-labelled growth substrate:…”

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

“…Isotope ratio data were exported and further processed with OriginPro 2019 software for statistical analysis and graphing. The relative assimilation (K A ) values were calculated with the template provided in Stryhanyuk et al (2018) and used for further numerical analysis and graphical representations.…”

“…It is difficult to compare the results on metabolic activity from different studies, even when derived from SIP-nanoSIMS experiments, if a unified approach for quantitation of single-cell assimilation is not followed. A SIP-nanoSIMS-based approach has recently introduced the "relative assimilation" (K A ) as a measure to quantify the single-cell assimilation activity (Stryhanyuk et al, 2018). However, being exceptionally powerful in quantitation of isotopic composition at the single-cell level, nanoSIMS-based approaches require a long time to acquire chemical maps for a limited number of single cells (∼1-10 2 ), implying thus a considerable limitation in the method throughput.…”

“…By extending the concept of CV and considering the distribution of single-cell anabolic activity measured in K A (Stryhanyuk et al, 2018), we developed a novel approach for heterogeneity expression which returns the "heterogeneity coefficient" (HC) involving the correction for the counting statistics error (CSE) coming from nanoSIMS analysis. Since neither the CV nor the HC accounts for the outcome of subpopulations, the challenge of quantitating heterogeneity in multimodal single-cell activity had to be tackled.…”