The Augmentation of Microbial Ecology by High-Resolution, Automated Sensing Technologies
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The ISME Journal (2009) 3, 881–888 & 2009 International Society for Microbial Ecology All rights reserved 1751-7362/09 $32.00 www.nature.com/ismej MINI-REVIEW Can the black box be cracked? The augmentation of microbial ecology by high-resolution, automated sensing technologies Ashley Shade1, Cayelan C Carey2, Emily Kara3,4, Stefan Bertilsson5, Katherine D McMahon3,4 and Matthew C Smith6 1Microbiology Doctoral Training Program, University of Wisconsin–Madison, Madison, WI, USA; 2Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA; 3Department of Bacteriology, University of Wisconsin–Madison, Madison, WI, USA; 4Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USA; 5Limnology/Department of Ecology and Evolution, Uppsala University, Uppsala, Sweden and 6Department of Marine Science, University of Puerto Rico, Mayagu¨ez, Puerto Rico Automated sensing technologies, ‘ASTs,’ are tools that can monitor environmental or microbial- related variables at increasingly high temporal resolution. Microbial ecologists are poised to use AST data to couple microbial structure, function and associated environmental observations on temporal scales pertinent to microbial processes. In the context of aquatic microbiology, we discuss three applications of ASTs: windows on the microbial world, adaptive sampling and adaptive management. We challenge microbial ecologists to push AST potential in helping to reveal relationships between microbial structure and function. The ISME Journal (2009) 3, 881–888; doi:10.1038/ismej.2009.56; published online 21 May 2009 Keywords: automated sensing technologies; microbial communities; microbial populations; real-time data; sensors; environmental monitoring Introduction phenomena’ (Porter et al., in press), and enable a more thorough understanding of ecological or One challenge facing microbial ecology is the ability environmental systems at fine temporal and spatial to link microbial composition and function (Wal- scales. Current ASTs allow physical, chemical or drop et al., 2000; Wagner et al., 2002; Langenheder biological parameters to be monitored simulta- et al., 2005; Ahlgren et al., 2006). Microbial com- neously with extremely fine-scale resolution. ASTs munities in ecosystems are basically analogous to a have been used to monitor diverse ecological envi- ‘black box’ where unknown specific microbial ronments ranging from engineered systems such as transformations underpinning ecosystem processes wastewater treatment facilities (Spanjers and van occur. Microorganisms function at different tempor- Lier, 2006) to pristine systems in as the high-arctic al and spatial scales than larger organisms (Prosser et al., 2007), and observations of both the microbial tundra (Edwards et al., 2006). The technology exists variables and corresponding environmental para- to compile multiple variables into online accessible meters are often limited in resolution by manual databases, allowing investigators to conveniently analytical bottlenecks (Porter et al., 2005). Auto- monitor their study sites and sensor performance mated sensing technologies (ASTs) enable envi- from their office computer (Tilak et al., 2007). ronmental monitoring at temporal and spatial To maintain the momentum of these relatively resolutions relevant to microbial community and young technologies, government funding initiatives process variation, allowing us to better decipher have supported development and deployment of links between microbial composition and function. sensor technologies into the environment, as well as A promising option is to use ASTs which ‘replace, the expansion of cross-site sensor networks (Arzber- augment, or surpass human observers of ecological ger, 2004) (Supplementary Table S1). This has resulted in the development of large-scale initiatives to promote AST networks. Similarly, scientists eager Correspondence: A Shade, Microbiology Doctoral Training Program, University of Wisconsin–Madison, 5525 Microbial to be at the forefront of AST-facilitated research have Sciences, 1550 Linden Drive, Madison, WI 53706, USA. initiated grassroots networks of smaller, system- E-mail: [email protected] specific initiatives (Supplementary Table S1). Automated sensing technologies in microbial ecology A Shade et al 882 AST observations will potentially transform mi- with off-the-shelf sensors and there is much varia- crobial ecology by providing the ability to measure bility in the maturity of available technologies. microbial responses that are more relevant to microbial scales. AST data will (1) provide a Bottom–up control. Many microorganisms derive window into the black box to help correlate the energy from solar radiation, and there are well- complexity of microbial interactions with potential developed ASTs for assessing both the amount and drivers within study systems, (2) enable the devel- quality of different wavelengths of solar radiation opment of improved sampling regimes to capture over depth and time (Palethorpe et al., 2004). These events of interest and (3) provide knowledge to data have been coupled to microbial response better manage systems. We will refer to these three variables to understand phytoplankton photosynth- applications as: windows on the microbial world, esis and related physiology (Campanella et al., 2001; adaptive sampling and adaptive management. Rodriguez et al., 2002; Podola and Melkonian, Here, we highlight the use of ASTs in aquatic 2005). microbial ecology, though we recognize the applica- Microbial heterotrophs or mixotrophs use organic tion of AST in other ecosystems. We present an matter as an energy source. Established methods to overview of the use of ASTs in microbial ecology, autonomously measure chromophoric organic mat- followed by some specific examples of subdisci- ter at high-frequency exist (Gallegos et al., 2005), but plines within microbial ecology that may benefit are poor predictors of organic electron donors from ASTs. Finally, we present some conclusions, available to heterotrophs over biologically relevant recommendations and cautions. timescales (Benner, 2003). The challenge to encom- pass the tremendous complexity of organic com- pounds presented to the microbial community, and Overview the potential niches these diverse compounds present, has been partially addressed by the tech- Windows on the microbial world: using ASTs to couple nological advances in electrochemical sensors (Rud- environmental parameters and microbial dynamics nitskaya and Legin, 2008). One example of a Microbial ecologists have a number of tools to versatile electrochemical sensor is the ‘electronic monitor a range of environmental state variables tongue’ which uses pulsed voltammetry to detect a that control cell growth and fate at high temporal wide range of redox-active organic and inorganic and spatial resolution (Figure 1). However, many compounds (Gallardo et al., 2003, 2004). Voltam- useful parameters cannot currently be measured metric microelectrodes can simultaneously measure Direct sensing of microbial components Component AST External input AST Cell abundance In situ cytometry Hydrological inflow Stream gauge Specific population presence In situ genosensing Precipitation Rain gauge Community composition Affinity biosensors Mortality Mixing/ Acoustic radar AST Pigments Optical sensors internal redistribution Predator abundance In situ cytometry MIcrozooplankton grazing NA Resources AST Microbial community Protozoan grazing NA Electron donors and acceptors Gain or population Loss Viral lysis NA Dissolved oxygen Optical sensor Organic Voltammetric electrode Export AST Inorganic Voltammetric electrode Hydrological outflow Stream gauge Solar radiation UV/PAR sensors Sedimentation NA Nutrients Sensing of cryptic ecological variables Aerosolization NA Phosphorus NA Ecological variable AST Nitrate Potentiometric electrode Trace elements pH Potentiometric electrode NA Conductivity Conductiometric electrode Temperature Thermistors Toxic compounds Mass spectrometry Particles/solid substrates Optical turbidty Figure 1 Conceptual model of variables influencing microbial communities in aquatic ecosystems, and general examples of automated sensing technologies (ASTs) available for observing these variables. In-development sensing technologies are given in italics. For high temporal resolution, reasonably priced, self-logging sondes can be equipped with sensors to measure temperature, conductivity, turbidity, dissolved oxygen, phytoplankton pigments, pH, light and oxidation reduction potential in bulk fluid. For high spatial resolution, microelectrodes can be precisely positioned to measure substrates such as oxygen, nitrogen species and sulfur species. NA indicates that the technology for automated sensing is not available. The ISME Journal Automated sensing technologies in microbial ecology A Shade et al 883 temperature, oxygen and specific ions at the sub- environmental measurements. This information is millimeter scale. These ASTs have been used in situ especially important as microbial ecology continues for real-time data collection near thermal vents, salt in this ‘omics’ age of massive, resource demanding marshes, microbial mats and in aquatic water data collection capabilities. ASTs will refine our columns (Luther et al., 2008). Together with con- adaptive sampling efforts to the most scientifically ductimetric (salinity) and potentiometric (pH, pCO2) compelling sites and times to best collect metadata. sensors, observations of