Published July 2025 | Version Published
Journal Article Open

Comparing Raman and NanoSIMS for heavy water labeling of single cells

  • 1. ROR icon Montana State University
  • 2. ROR icon Pacific Northwest National Laboratory
  • 3. ROR icon California Institute of Technology

Contributors

Abstract

Stable isotope probing (SIP) experiments in conjunction with Raman microspectroscopy (Raman) or nano-scale secondary ion mass spectrometry (NanoSIMS) are frequently used to explore single cell metabolic activity in pure cultures as well as complex microbiomes. Despite the increasing popularity of these techniques, the comparability of isotope incorporation measurements using both Raman and NanoSIMS directly on the same cell remains largely unexplored. This knowledge gap creates uncertainty about the consistency of single-cell SIP data obtained independently from each method. Here, we conducted a comparative analysis of 543 Escherichia coli cells grown in M9 minimal medium in the absence or presence of heavy water (2H2O) using correlative Raman and NanoSIMS measurements to quantify the results between the two approaches. We demonstrate that Raman and NanoSIMS yield highly comparable measurements of 2H incorporation, with varying degrees of similarity based on the mass ratios analyzed using NanoSIMS. The 12C2H/12C1H and 12C22H/12C21H mass ratios provide targeted measurements of C-H bonds but may suffer from biases and background interference, while the 2H/1H ratio captures all hydrogen with lower detection limits, making it suitable for applications requiring comprehensive 2H quantification. Importantly, despite its higher mass resolution requirements, the use of C22H/C21H may be a viable alternative to the use of C2H/C1H due to lower background and higher overall count rates. Furthermore, using an empirical approach in determining Raman wavenumber ranges via the second derivative improved the data equivalency of 2H quantification between Raman and NanoSIMS, highlighting its potential for enhancing cross-technique comparability. These findings provide a robust framework for leveraging both techniques, enabling informed experimental design and data interpretation. By enhancing cross-technique comparability, this work advances SIP methodologies for investigating microbial metabolism and interactions in diverse systems.

Copyright and License

This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.

Acknowledgement

This study was funded through NIH award 1R35GM147166-01 to R.H. G.S. was partially supported by NASA FINESST fellowship 80NSSC20K1365. J.A.C. was supported by NSF Research Traineeship Program award 2125748. Montana State University’s confocal Raman microscope was acquired with support by the NSF MRI program (DBI-1726561) and the M. J. Murdock Charitable Trust (SR-2017331). A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (awards DOI: 10.46936/fics.proj.2020.51544/60000211) and used resources at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which is a DOE Office of Science User Facilities operated under Contract No. DE-AC05-76RL01830. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). We thank Hope McWilliams (MSU) for assistance with SIP labeling and Anthony Kohtz (MSU) for helpful input on the analysis of Raman spectra.

Contributions

G.S. and R.H. designed the study. G.S. performed culturing and prepared samples for analysis. J.A.C. collected the Raman spectra, and GS analyzed the spectra. J.B.C. and J.B. performed NanoSIMS analyses. G.S. and J.B.C. compiled the data. M.M. performed the lipid extraction and esterification, conducted the IR/MS analysis, and collaborated with A.S. to analyze the data. J.A. and G.S. performed the second derivative calculations. G.S. performed statistical analyses and prepared figures. G.S., J.B.C., and R.H. wrote the manuscript draft, which was then edited by all authors.

Data Availability

All data sets were analyzed in GNU R (64) using the tidyverse, rstatix, emmeans, and ggpubr packages (6971). Statistical differences between multiple variables were determined using pairwise t-tests with a Bonferroni p-adjusted method. Calculations to determine the wavenumber range for 2H used the pspline R package (72) and in-house written R code for the calculation of second derivative inflection points of smoothed curves. Code used for analysis is deposited on Zenodo (DOI: 10.5281/zenodo.15048223).

Supplemental Material

Supplemental figures - spectrum.01659-24-s0001.pdf

Supplemental material - spectrum.01659-24-s0002.pdf

Supplemental tables - spectrum.01659-24-s0003.xlsx

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Additional details

Identifiers

Related works

Describes
Journal Article: 40445204 (PMID)
Journal Article: PMC12211084 (PMCID)
Is new version of
Discussion Paper: 10.1101/2024.07.05.602271 (DOI)
Is supplemented by
Dataset: https://zenodo.org/records/15048223 (URL)

Funding

National Institutes of Health
1R35GM147166-01
National Aeronautics and Space Administration
80NSSC20K1365
National Science Foundation
2125748
National Science Foundation
DBI-1726561
M J Murdock Charitable Trust
SR-2017331
United States Department of Energy
DE-AC05-76RL01830
United States Department of Energy
DE-AC05-00OR22725

Dates

Accepted
2025-04-23
Available
2025-05-30
Published

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Caltech groups
Division of Geological and Planetary Sciences (GPS)
Publication Status
Published