PNNL

Abstract

Elucidating nutrient exchange in microbial communities is an important step in understanding the relationships between microbial systems and global biogeochemical cycles, but these communities are complex and the interspecies interactions that occur within them are not well understood. Phototrophic consortia are useful and relevant experimental systems to investigate such interactions as they are not only prevalent in the environment, but some are cultivable in vivo and amenable to controlled scientific experimentation. High spatial resolution secondary ion mass spectrometry (NanoSIMS) is a powerful tool capable of visualizing the metabolic activities of single cells within a biofilm, but quantitative analysis of the resulting data has typically been a manual process, resulting in a task that is both laborious and susceptible to human error. Here, we describe the creation and application of a semi-automated image-processing pipeline that can analyze NanoSIMS-generated data of phototrophic biofilms. The tool employs an image analysis process, which includes both elemental and morphological segmentation, producing a final segmented image that allows for discrimination between autotrophic and heterotrophic biomass, the detection of individual cyanobacterial filaments and heterotrophic cells, the quantification of isotopic incorporation of individual heterotrophic cells, and calculation of relevant population statistics. We demonstrate the functionality of the tool by using it to analyze the uptake of 15N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. We found that the degree of 15N incorporation by individual cells was highly variable when labeled with 15NH4 +, but much more even when biofilms were labeled with 15NO3-. In the 15NH4 +-amended biofilms, the heterotrophic distribution of 15N incorporation was highly skewed, with a large population showing moderate 15N incorporation and a small number of organisms displaying very high 15N uptake. The results showed that analysis of NanoSIMS data can be performed in a way that allows for quantitation of the elemental uptake of individual cells, a technique necessary for advancing research into the metabolic networks that exist within biofilms with statistical analyses that are supported by automated, user-friendly processes.