Comparing Raman and NanoSIMS for heavy water labeling of single cells

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 ${}^{12}{C}^{2}H/{}^{12}{C}^{1}H$ and ${}^{12}{C}^{2}H/{}^{12}{C}^{1}H$ 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 C₂ ²H/C₂ ¹H may be a viable alternative to using C²H/C¹H due to lower background and higher overall count rates. Furthermore, using an empirical approach to determining Raman wavenumber ranges via the 2^nd 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.