Localizing metabolic synthesis in microbial cultures with kinetic mass spectrometry imaging (kMSI)

Abstract

Mass spectrometry imaging (MSI) has emerged as a powerful technique enabling spatially defined imaging of metabolites within microbial biofilms. Here, we extend this approach to enable differentiation of newly synthesized versus pre-existing metabolites across a co-culture. This is accomplished by MS imaging two soil microbes, Shewanella oneidensis MR1 and Pseudomonas stutzeri RCH2, that were administered heavy water (D 2 O) during growth on agar plates. For two species-specific diglyceride (DG) lipids, isotopic analysis was performed on each spectra collected across the co-culture to determine the relative amount of newly synthesized versus pre-existing lipid. Here, highest levels of new synthesis of RCH2 lipid was localized to border regions adjacent to S. oneidensis MR1, while the MR1 lipid showed highest levels in regions further from RCH2. Interestingly, regions of high lipid abundance did not correspond to the regions with highest new lipid biosynthesis. Given the simplicity and generality of using D 2 O as a stable isotopic probe combined with the accessibility of kMSI to a range of MSI instrumentation, this approach has broad application for improving our understanding of how microbial interactions influence metabolite biosynthesis.

KineticMSI, an R-based framework for relative quantification of spatial isotopic incorporation in mass spectrometry imaging experiments

“…However, these tools lack the statistical pipelines that allow users to conduct relative quantification of tracer incorporation between two treatment groups, such as normal versus pathophysiological circumstances, which is essential for biomedical research. In addition, it is difficult to confidently measure differential tracer incorporation between two groups, when tissue(s) display spatial heterogeneity in tracer incorporation 8,26 . The development of computational tools using freely available computational software (such as R) would aid the accessibility of data analyses pipelines.…”

“…While GC/LC-MS approaches are crucial for providing higher specificity and a broader metabolome coverage, these methods typically entail averaging metabolic flux across a whole tissue containing a heterogeneous population of cells, thereby compromising spatial information. Previously developed kMSI analysis pipelines do provide visualization of isotopic ratio images 10 and spatial patterns of tracer incorporation within tissues 8,16,26 ; and enable quantitative analysis of region-specific metabolism within organs 27 . However, these tools lack the statistical pipelines that allow users to conduct relative quantification of tracer incorporation between two treatment groups, such as normal versus pathophysiological circumstances, which is essential for biomedical research.…”