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.

Keywords: assimilation rate; functional heterogeneity; isotope fractionation; isotope ratio; nanoSIMS; single cell; stable isotope probing.

Figure 1

Dependence of assimilated carbon fraction K_A (11) on the final ¹³C fraction D_f simulated for the cells incubated from the inoculum with D_i = 1 at% in growth substrates with different D_gs values of ¹³C fraction.

Figure 2

Lateral distribution maps for the relative yield of monoatomic ¹²C⁻ (a), ¹³C⁻ (b), and molecular ¹²C¹⁴N⁻ (c), ¹³C¹⁴N⁻ (d), ¹²C¹⁶O⁻ (e), and ¹³C¹⁶O⁻ (f) secondary ions containing light ¹²C (a,c,e) and heavy ¹³C (b,d,f) carbon isotopes. Scale bar length is 4 μm.

Figure 3

Lateral distribution of ¹³C fraction in at% derived from the isotope ratio of single atomic C⁻ ions (a) and molecular CN⁻ ions (c) measured by nanoSIMS. Frames b and d show the depth profiles of respective ¹³C fractions (gray circles) for all defined RoIs (Ranges of Interest, white line confined) involving microbial cells and filter areas. The mean value of ¹³C fraction with its standard deviation is shown for cells (solid rectangles) and filter areas (solid circles) in each scanned plain (b,d). Scale bar length is 4 μm.

Figure 4

Distribution of microbial cell by the ¹³C fraction derived from isotope ratio of single atomic C⁻ ions (1) and molecular CN⁻ ions (3) as measured with nanoSIMS (D') and the respective distributions (2 and 4) with the ¹³C fraction (D) corrected for the dilution of ¹³C label with the chemicals used during cell fixation.

Figure 5

Carbon assimilation rate calculated per volume F_V (solid circles, [pg·μm⁻³·h⁻¹]) and per single cell F_c (open rectangles, [pg·cell⁻¹·h⁻¹]) of P. putida incubated in ¹³C-glucose medium (D_gs = 13.5 at%) for 10 h. Initial ¹³C fraction D_i has been set at 1.1 at%. Assimilation rates are shown with mean value and standard deviation for all 105 single cells in the left frame. The distributions of cell-specific (F_c) and volume-specific (F_V) assimilation rates are shown in the right frame with Min-Max whiskers, box representing the 16–84 percentile range, median value (horizontal line) and mean value (solid rectangle) inside the percentile box.