Looking inside the box: using Raman microspectroscopy to deconstruct microbial biomass stoichiometry one cell at a time

The ISME Journal (2011) 5, 196–208 & 2011 International Society for Microbial Ecology All rights reserved 1751-7362/11 www.nature.com/ismej ORIGINAL ARTICLE Looking inside the box: using Raman microspectroscopy to deconstruct microbial biomass stoichiometry one cell at a time Edward K Hall1, Gabriel A Singer1, Marvin Pölzl1, Ieda Hämmerle2, Christian Schwarz1, Holger Daims3, Frank Maixner3,4 and Tom J Battin1 1 Department of Limnology and WasserKluster Lunz GmbH, University of Vienna, Vienna, Austria; Department of Chemical Ecology, University of Vienna, Vienna, Austria and 3Department of Microbial Ecology, Vienna Ecology Center, University of Vienna, Vienna, Austria 2 Stoichiometry of microbial biomass is a key determinant of nutrient recycling in a wide variety of ecosystems. However, little is known about the underlying causes of variance in microbial biomass stoichiometry. This is primarily because of technological constraints limiting the analysis of macromolecular composition to large quantities of microbial biomass. Here, we use Raman microspectroscopy (MS), to analyze the macromolecular composition of single cells of two species of bacteria grown on minimal media over a wide range of resource stoichiometry. We show that macromolecular composition, determined from a subset of identified peaks within the Raman spectra, was consistent with macromolecular composition determined using traditional analytical methods. In addition, macromolecular composition determined by Raman MS correlated with total biomass stoichiometry, indicating that analysis with Raman MS included a large proportion of a cell’s total macromolecular composition. Growth phase (logarithmic or stationary), resource stoichiometry and species identity each influenced each organism’s macromolecular composition and thus biomass stoichiometry. Interestingly, the least variable peaks in the Raman spectra were those responsible for differentiation between species, suggesting a phylogenetically specific cellular architecture. As Raman MS has been previously shown to be applicable to cells sampled directly from complex environments, our results suggest Raman MS is an extremely useful application for evaluating the biomass stoichiometry of environmental microorganisms. This includes the ability to partition microbial biomass into its constituent macromolecules and increase our understanding of how microorganisms in the environment respond to resource heterogeneity. The ISME Journal (2011) 5, 196–208; doi:10.1038/ismej.2010.115; published online 12 August 2010 Subject Category: microbial population and community ecology Keywords: ecological stoichiometry; macromolecular composition; Raman microspectroscopy; resource allocation Introduction Microbial biomass stoichiometry (specifically carbon (C):nitrogen (N) and phosphorus (P) stoichiometry) is a primary determinant of whether mineral nutrients are sequestered in microbial biomass or released to the environment during decomposition (Manzoni et al., 2008). However, little is known about the how microbial physiology and environmental parameters interact to constrain the range and variance of microbial biomass stoichiometry. Microbial biomass is composed of a vast array of Correspondence: EK Hall, Department of Limnology, University of Vienna, Althanstrasse 14, Vienna 1090, Austria. E-mal: 4 Current address: Institute for Mummies and the Iceman, EURAC research, Viale Druso 1, 39100 Bolzano, Italy. Received 24 February 2010; revised 3 June 2010; accepted 10 June 2010; published online 12 August 2010 macromolecules, each containing a wide range of specific functions. These macromolecules can be assigned to relatively few classes (for example, carbohydrates, proteins and nucleic acids), each with a constrained elemental content that can be linked to its dominant element (Elser et al., 1996). Proteins are on average relatively rich in N (53% C, 17% N, 0% P by weight), nucleic acids are rich in P (32.7% C, 14.5% N and 8.7% P), while carbohydrates (37% C, 0% N, 0% P) are rich in C and contain no N or P (Sterner and Elser, 2002). Shifts in the relative concentration of these constituent macromolecule pools ultimately determine the stoichiometry of microbial biomass. From this perspective, carbohydrate content should be positively correlated with biomass C:P and C:N, protein content should be inversely correlated with biomass C:N, while nucleic acid content should be inversely correlated with both biomass C:P and N:P. While Deconstructing microbial biomass with Raman MS EK Hall et al 197 previous studies have shown a relationship between microbial biomass P and RNA content in culture (Makino et al., 2003; Makino and Cotner, 2004) and in the environment (Hall et al., 2009), few if any studies have evaluated the effect of changes in other macromolecular pools on microbial biomass stoichiometry in an ecological context. Determining how shifts in constituent macromolecules are related to changes in biomass stoichiometry will lead to a more mechanistic understanding of what controls or constrains microbial biomass stoichiometry in nature. Microbial biomass stoichiometry has been shown to change in response to physical (for example, temperature), chemical (for example, resource stoichiometry) and physiological (for example, growth rate) factors (Makino et al., 2003; Makino and Cotner, 2004; Cotner et al., 2006). How microbial biomass changes in response to resource stoichiometry is of particular interest because the relationship between biomass stoichiometry and resource stoichiometry ultimately determines how microorganisms recycle limiting nutrients (Manzoni et al., 2008), which can markedly affect the growth and community composition of the surrounding organisms (Danger et al., 2007; Cherif and Loreau, 2009). The current dearth of information on the relationship between resource stoichiometry and microbial biomass stoichiometry is due to multiple logistical constraints. First, most environmental microorganisms cannot be cultured; therefore it is not possible to follow the response of their biomass stoichiometry to experimentally-manipulated resource treatments. Second, the resource pool of environmental microorganisms is notoriously hard to define, thus relating resource stoichiometry to biomass stoichiometry in situ is not feasible. Third, and perhaps most important, determining macromolecular biomass composition of microorganisms has traditionally required large amounts of biomass and therefore requires culturing or enrichment of the organisms of interest or analysis of undifferentiated microbial communities. Various technological advances have helped to overcome these constraints and now allow for direct measurement of microorganisms and to some extent their in situ resource pool. For example, advances in quantitative chemical methods permit high-resolution analysis of the dissolved organic carbon pool in aquatic environments (Kim et al., 2006). 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