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Interactions between autotrophs and heterotrophs are central to carbon (C) exchange across trophic levels in essentially all ecosystems and metabolite exchange is a frequent mechanism for distributing C within spatially structured ecosystems. Yet, despite the importance of C exchange, the timescales at which fixed C is transferred in microbial communities is poorly understood. We employed a stable isotope tracer combined with spatially resolved isotope analysis to quantify photoautotrophic uptake of bicarbonate and track subsequent exchanges across a vertical depth gradient in a stratified microbial mat over a light-driven diel cycle. We observed that C mobility, both across the vertical strata and between taxa, was highest during periods of active photoautotrophy. Parallel experiments with 13C-labeled organic substrates (acetate and glucose) showed comparably less exchange of C within the mat. Metabolite analysis showed rapid incorporation of 13C into molecules that can both comprise a portion of the extracellular polymeric substances in the system and serve to transport C between photoautotrophs and heterotrophs. Stable isotope proteomic analysis revealed rapid C exchange between cyanobacterial and associated heterotrophic community members during the day with decreased exchange at night. We observed strong diel control on the spatial exchange of freshly fixed C within tightly interacting mat communities suggesting a rapid redistribution, both spatially and taxonomically, primarily during daylight periods.

Proton affinity is a major factor in the atmospheric pressure chemical ionization of illicit drugs. The detection of illicit drugs by mass spectrometry and ion mobility spectrometry relies on the analytes having greater proton affinities than background species. Evaluating proton affinities for fentanyl and its analogues is informative for predicting the likelihood of ionization in different environments and for optimizing the compounds' ionization and detection, such as through the addition of dopant chemicals. Herein, density functional theory was used to computationally determine the proton affinity and gas-phase basicity of 15 fentanyl compounds and several relevant molecules as a reference point. The range of proton affinities for the fentanyl compounds was from 1018 to 1078 kJ/mol. Fentanyl compounds with the higher proton affinity values appeared to form a bridge between the oxygen on the amide and the protonated nitrogen on the piperidine ring based on models and calculated bond distances. Experiments with fragmentation of proton-bound clusters using atmospheric flow tube-mass spectrometry (AFT-MS) provided estimates of relative proton affinities and showed proton affinity values of fentanyl compounds1000 kJ/mol, which were consistent with the computational results. The high proton affinities of fentanyl compounds facilitate their detection by ambient ionization techniques in complex environments. The detection limits of the fentanyl compounds with AFT-MS are in the low femtogram range, which demonstrates the feasibility of trace vapor drug detection.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data to illustrate the advantages of a multidimensional approach in each data processing step.

Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standa...

Understanding the complex chemical nature of root-soil interactions is essential for building the next generation of sustainable agricultural systems and facilitating long-term environmental remediation strategies. Techniques currently suited to investigate spatial controls on nutrient exchange in plant rhizospheres, however, are hindered by limitations in throughput, cost, analytical scope, and sample preparation needs. We describe here a method for rapid, high-resolution (∼100 μm), multi-element imaging of both organic content and inorganic constituents in root-rhizosphere-soil systems using laser-induced breakdown spectroscopy (LIBS). A switchgrass assemblage (Panicum virgatum) was grown in compact rhizotrons containing a sandy loam Alfisol from the Kellogg Biological Station (KBS), Michigan, USA. Root-soil samples were extracted using custom plastic coring devices for live root sampling and a modified drill press for sectioning frozen stabilized soil. A 266 nm, Nd:YAG laser was rastered over ∼10 mm2 sample surfaces and broadband spectra from single-pulse ablations were collected and mapped to discrete XY spatial coordinates for simultaneous imaging of 17 macronutrients, micronutrients and matrix elements. In order to rapidly process LIBS raster data and investigate chemical trends in the rhizosphere, an open-source Python module was developed, in which we used a novel calibration-free masking algorithm (based on principal components analysis (PCA) of normalized spectral intensities) to discriminate soil mineral grains, root fragments, and associated rhizosphere regions. We observed fine-scale chemical gradients within only a millimeter of switchgrass roots, consistent with rhizodeposition of organic compounds and proximal uptake of inorganic nutrients. Detection of trace element and carbon accumulations with diagnostic spectral signatures in the rhizosphere suggested the presence of residues (detritusphere) that could serve as sites of preferential microbial accumulation. These results highlight potential applications of LIBS within the growing field of spatially-resolved plant-soil analysis and extend its versatile imaging capabilities to the complex root-rhizosphere-soil network.

High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .

Mass-spectrometry based omics technologies - namely proteomics, metabolomics and lipidomics - have enabled the molecular level systems biology investigation of organisms in unprecedented detail. There has been increasing interest for gaining a thorough, functional understanding of the biological consequences associated with cellular heterogeneity in a wide variety of research areas such as developmental biology, precision medicine, cancer research and microbiome science. Recent advances in mass spectrometry (MS) instrumentation and sample handling strategies are quickly making comprehensive omics analyses of single cells feasible, but key breakthroughs are still required to push through remaining bottlenecks. In this review, we discuss the challenges faced by single cell MS-based omics analyses and highlight recent technological advances that collectively can contribute to comprehensive and high throughput omics analyses in single cells. We provide a vision of the potential of integrating pioneering technologies such as Structures for Lossless Ion Manipulations (SLIM) for improved sensitivity and resolution, novel peptide identification tactics and standards free metabolomics approaches for future applications in single cell analysis.

The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 ppm and 7.1 ppm, respectively, and below 0.5 ppm and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

A series of Wrightia hanleyi extracts was screened for activity against Mycobacterium tuberculosis H37Rv. One active fraction contained a compound that initially appeared to be either the isoflavonoid wrightiadione or the alkaloid tryptanthrin, both of which have been previously reported in other Wrightia species. Characterization by NMR and MS, as well as evaluation of the literature describing these compounds, led to the conclusion that wrightiadione (1) was misidentified in the first report of its isolation from W. tomentosa in 1992 and again in 2015 when reported in W. pubescens and W. religiosa. Instead, the molecule described in these reports and in the present work is almost certainly the isobaric (same nominal mass) and isosteric (same number of atoms, valency, and shape) tryptanthrin (2), a well-known quinazolinone alkaloid found in a variety of plants including Wrightia species. Tryptanthrin (2) is also accessible synthetically via several routes and has been thoroughly characterized. Wrightiadione (1) has been synthesized and characterized and may have useful biological activity; however, this compound can no longer be said to be known to exist in Nature. To our knowledge, this misidentification of wrightiadione (1) has heretofore been unrecognized.

Motivation Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses. Results We demonstrate the utility of this approach by automatically extracting arrival times and calculating the associated CCSs for a set of endogenous metabolites and xenobiotics. The PIXiE-generated CCS values were within error of those calculated using commercially available instrument vendor software. Availability and implementation PIXiE is an open-source tool, freely available on Github. The documentation, source code of the software, and a GUI can be found at https://github.com/PNNL-Comp-Mass-Spec/PIXiE and the source code of the backend workflow library used by PIXiE can be found at https://github.com/PNNL-Comp-Mass-Spec/IMS-Informed-Library. Supplementary information Supplementary data are available at Bioinformatics online.

Due to the recently uncovered health benefits and anti-HIV activities of dicaffeoylquinic acids (diCQAs), understanding their structures and functions is of great interest for drug discovery efforts. DiCQAs are analytically challenging to identify and quantify since they commonly exist as a diverse mixture of positional and geometric (cis/trans) isomers. In this work, we utilized ion mobility spectrometry coupled with mass spectrometry to separate the various isomers before and after UV irradiation. The experimental collision cross sections were then compared with theoretical structures to differentiate and identify the diCQA isomers. Our analyses found that naturally the diCQAs existed predominantly as trans/trans isomers, but after 3 h of UV irradiation, cis/cis, cis/trans, trans/cis, and trans/trans isomers were all present in the mixture. This is the first report of successful differentiation of cis/trans diCQA isomers individually, which shows the great promise of IMS coupled with theoretical calculations for determining the structure and activity relationships of different isomers in drug discovery studies.

We present the numerical optimization and experimental characterization of two microstrip-based nuclear magnetic resonance (NMR) detectors. The first detector, introduced in our previous work, was a flat wire detector with a strip resting on a substrate, and the second detector was created by adding a ground plane on top of the strip conductor, separated by a sample-carrying capillary and a thin layer of insulator. The dimensional parameters of the detectors were optimized using numerical simulations with regards to radio frequency (RF) sensitivity and homogeneity, with particular attention given to the effect of the ground plane. The influence of copper surface finish and substrate surface on the spectral resolution was investigated, and a resolution of 0.8-1.5 Hz was obtained on 1 nL deionized water depending on sample positioning. For 0.13 nmol sucrose (0.2 M in 0.63 nL H2O) encapsulated between two Fluorinert plugs, high RF homogeneity (A810°/A90° = 70-80%) and high sensitivity (expressed in the limit of detection nLODm = 0.73-1.21 nmol s1/2) were achieved, allowing for high-performance 2D NMR spectroscopy of subnanoliter samples.

Measuring the exposome remains a challenge due to the range and number of anthropogenic molecules that are encountered in our daily lives, as well as the complex systemic responses to these exposures. One option for improving the coverage, dynamic range and throughput of measurements is to incorporate ion mobility spectrometry (IMS) into current MS-based analytical methods. The implementation of IMS in exposomics studies will lead to more frequent observations of previously undetected chemicals and metabolites. LC-IMS-MS will provide increased overall measurement dynamic range, resulting in detections of lower abundance molecules. Alternatively, the throughput of IMS-MS alone will provide the opportunity to analyze many thousands of longitudinal samples over lifetimes of exposure, capturing evidence of transitory accumulations of chemicals or metabolites. The volume of data corresponding to these new chemical observations will almost certainly outpace the generation of reference data to enable their confident identification. In this perspective, we briefly review the state-of-the-art in measuring the exposome, and discuss the potential use for IMS-MS and the physico-chemical property of collisional cross section in both exposure assessment and molecular identification.

Biofilms are comprised of microbial cells and an extracellular polymeric substance (EPS) matrix that supports interactions between community members and with the local environment. The highly hydrated EPS matrix makes the application of many biofilm visualization techniques difficult. Hence, to better visualize how biofilms interact with their environment, there is a need for imaging techniques to monitor hydrated state biofilm dynamics. We employed an in situ dynamic approach to construct label-free images of biofilms. In situ imaging was conducted using a vacuum compatible microfluidic reactor, SALVI (System for Analysis at the Liquid Vacuum Interface), for biofilm growth; real-time confocal laser scanning microscopy analysis; and nuclear magnetic resonance (NMR) microimaging and spectroscopy. We integrated SALVI microchannel fluids and live biofilms to demonstrate in situ measurement capabilities, including velocity mapping, diffusion coefficient mapping, relaxometry, localized spectroscopy, relaxation times, porosity, and two- and three-dimensional imaging within the microchannel at high spatial resolution. We monitored organic acids adjacent to biofilms, suggesting that kinetic rate and substrate-product yield ratio studies are possible using the SALVI microfluidic reactor for growth characterizations. The integration of NMR microimaging studies into the SALVI platform demonstrates that a multimodal microfluidic platform can serve as an avenue to explore complex biological phenomena, such as biofilm attachment to surfaces, with detailed quantitative physical and chemical mapping. The further incorporation of other SALVI-compatible technologies, such as liquid time-of-flight secondary ion mass spectrometry imaging, with NMR microimaging will produce a powerful correlative approach to monitor in situ biofilm chemistry and dynamics at different spatial scales.

We report on separations of ion isotopologues and isotopomers using ultrahigh-resolution traveling wavebased Structures for Lossless Ion Manipulations with serpentine ultralong path and extended routing ion mobility spectrometry coupled to mass spectrometry (SLIM SUPER IMS-MS). Mobility separations of ions from the naturally occurring ion isotopic envelopes (e.g., [M], [M+1], [M+2], … ions) showed the first and second isotopic peaks (i.e., [M+1] and [M+2]) for various tetraalkylammonium ions could be resolved from their respective monoisotopic ion peak ([M]) after SLIM SUPER IMS with resolving powers of ~400–600. Similar separations were obtained for other compounds (e.g., tetrapeptide ions). Greater separation was obtained using argon versus helium drift gas, as expected from the greater reduced mass contribution to ion mobility described by the Mason–Schamp relationship. To more directly explore the role of isotopic substitutions, we studied a mixture of specific isotopically substituted ((15)N, (13)C, and (2)H) protonated arginine isotopologues. While the separations in nitrogen were primarily due to their reduced mass differences, similar to the naturally occurring isotopologues, their separations in helium, where higher resolving powers could also be achieved, revealed distinct additional relative mobility shifts. These shifts appeared correlated, after correction for the reduced mass contribution, with changes in the ion center of mass due to the different locations of heavy atom substitutions. The origin of these apparent mass distribution-induced mobility shifts was then further explored using a mixture of Iodoacetyl Tandem Mass Tag (iodoTMT) isotopomers (i.e., each having the same exact mass, but with different isotopic substitution sites). Again, the observed mobility shifts appeared correlated with changes in the ion center of mass leading to multiple monoisotopic mobilities being observed for some isotopomers (up to a ~0.04% difference in mobility). These mobility shifts thus appear to reflect details of the ion structure, derived from the changes due to ion rotation impacting collision frequency or momentum transfer, and highlight the potential for new approaches for ion structural characterization.

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency’s (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

Productivity is a major determinant of ecosystem diversity. Microbial ecosystems are the most diverse on the planet yet very few relationships between diversity and productivity have been reported as compared with macro-ecological studies. Here we evaluated the spatial relationships of productivity and microbiome diversity in a laboratory-cultivated photosynthetic mat. The goal was to determine how spatial diversification of microorganisms drives localized carbon and energy acquisition rates. We measured sub-millimeter depth profiles of net primary productivity and gross oxygenic photosynthesis in the context of the localized microenvironment and community structure, and observed negative correlations between species richness and productivity within the energy-replete, photic zone. Variations between localized community structures were associated with distinct taxa as well as environmental profiles describing a continuum of biological niches. Spatial regions in the photic zone corresponding to high primary productivity and photosynthesis rates had relatively low-species richness and high evenness. Hence, this system exhibited negative species-productivity and species-energy relationships. These negative relationships may be indicative of stratified, light-driven microbial ecosystems that are able to be the most productive with a relatively smaller, even distributions of species that specialize within photic zones.

Tetraoctylammonium bromide is used in the Brust–Schiffrin nanoparticle synthesis to phase-transfer chloroaurate ions from the aqueous phase to the organic phase. While it is established that the quaternary ammonium complex self-associates in the organic phase, the actual self-assembled structure is poorly understood. We have confirmed the presence of ion–ion aggregates through quantitative 1H nuclear magnetic resonance spectroscopy (NMR), pulsed field gradient, diffusion-ordered NMR (DOSY-NMR), and density functional theory (DFT) based NMR chemical shift calculations. Tetraoctylammonium complexes (TOA-X, where X = Br, Cl, AuCl4–xBrx, AuBr4/Br, and AuBr4/Cl/Br) were investigated to measure the extraction of water into deuterated chloroform. 1H NMR- and DFT-based NMR shielding calculations indicated that deshielding of water is due to hydration of the anion and not the formation of the aqueous core of a reverse micelle. DOSY-NMR results were consistent with the formation of small aggregates at typical Brust...

The adaptability of soil microbial communities to prolonged periods of drought is influenced by their ability to produce extracellular polymeric substances (EPS) with sufficient water retention properties. Microbial EPS have been extensively investigated as water reservoirs during drought, but it remains unknown how carbon substrate accessibility to soil microbial communities will affect the chemical properties of the EPS they generate, and whether this in turn will alter their water retention characteristics. In this work, we observed that the accessibility of carbon substrates influenced microbial community structure and, consequently, the chemical properties of EPS produced by the microbial communities. Our results demonstrated that an insoluble carbon substrate (i.e., chitin), stimulated microbial communities to produce EPS with measurably better water retention properties in emulated soil microenvironments in comparison to a soluble carbon substrate (i.e., N-acetylglucosamine; NAG). In all, this study demonstrates the importance of carbon substrate accessibility by soil microorganisms in regulating the community structure and consequently, the EPS carbon chemistry, which in turn can greatly influence the adaptability of soil microbial communities to drought.

Phosphorus (P) is an essential macronutrient for plant growth, but bioavailable P in soils is often limited due to immobilization resulting from pH and geochemical interactions. Understanding the dynamics of P in soils and elucidating the mechanisms by which plants access P from their environment are critical to evaluating productivity, particularly in nutrient poor environments. Phosphorus from organic matter can act as a major source of P for organisms in soil systems. Phosphatases, enzymes that liberate inorganic P from organic sources, are produced by both plants and microbes and are considered one of the most active classes of enzymes in soil. We developed a root blotting method to spatially image phosphatase activity in the rhizosphere. Proteins from the rhizosphere are transferred to a nitrocellulose membrane while retaining their enzymatic activity and two-dimensional spatial distribution. Subsequent application of a fluorogenic phosphatase indicator, DDAO phosphate, enables visualization of the distribution of phosphatase activity in the sample. The proteins can then be fixed to the membrane and treated with SYPRO® Ruby Protein Blot Stain, a fluorescent total protein stain, allowing for visualization of total protein distribution. Taken together, the images of phosphatase activity and total protein localization can be mapped back to the root architecture and provide insight into factors affecting the spatial distribution of enzymatic activity and protein accumulation in the rhizosphere. Notably, this method can be applied to plants growing in rhizoboxes containing soil or soilless growth mixtures (e.g., sand or various potting mixes) and, because of the non-destructive nature of this approach, be performed over time to track changes. We anticipate that this fluorescent indicator imaging technique on root blots can be used in diverse plant-microbe-soil systems to better understand the role of phosphatases in P acquisition and soil P cycling.

Plants from the Asteraceae family are known to contain a wide spectrum of phytochemicals with various nutraceutical properties. One important phytochemical, chicoric acid (CA), is reported to exist in plants, such as Sonchus oleraceus and Bidens pilosa, as stereoisomers. These CA molecules occur either as the naturally abundant RR-chicoric acid (RR-CA), or the less abundant RS-chicoric acid (RS-CA), also known as meso-chicoric acid. To date, little is known about the biological activity of RS-CA, but there is evidence of its anti-human immunodeficiency virus (HIV) properties. In this study, a reliable analytical method was developed to distinguish between the two stereoisomers detected in S. oleraceus and B. pilosa. For structure identification and characterization of CA molecules, liquid chromatography–mass spectrometry (LC-MS) was used in combination with ultraviolet radiation (UV)-induced geometrical isomerization, molecular dynamics (MD) simulations, and density functional theory (DFT) models. Optimized structures from DFT calculations were used for docking studies against the HIV-1 integrase enzyme. Different retention times on the reverse phase chromatograms revealed that the plants produce two different CA stereoisomers: S. oleraceus produced the RR-CA isomer, while B. pilosa produced the RS-CA isomer. DFT results demonstrated the RR-CA molecule was more stable than RS-CA due to the stabilizing force of intra-molecular hydrogen bonding. Differences in the HIV-1 integrase enzyme binding modes were observed, with the RR-CA being a more potent inhibitor than the RS-CA molecule. The results highlight the significance of plant metabolite structural complexity from both chemical and biological perspectives. Furthermore, the study demonstrates that induced-formation of geometrical isomers, in combination with the predictive ability of DFT models and the resolving power of the LC-MS, can be exploited to distinguish structurally closely related compounds, such as stereoisomers.

Salmonella enterica elicits intestinal inflammation to gain access to nutrients. One of these nutrients is fructose-asparagine (F-Asn). The availability of F-Asn to Salmonella during infection is dependent upon Salmonella pathogenicity islands 1 and 2, which in turn are required to provoke inflammation. Here, we determined that F-Asn is present in mouse chow at approximately 400 pmol/mg (dry weight). F-Asn is also present in the intestinal tract of germfree mice at 2,700 pmol/mg (dry weight) and in the intestinal tract of conventional mice at 9 to 28 pmol/mg. These findings suggest that the mouse intestinal microbiota consumes F-Asn. We utilized heavy-labeled precursors of F-Asn to monitor its formation in the intestine, in the presence or absence of inflammation, and none was observed. Finally, we determined that some members of the class Clostridia encode F-Asn utilization pathways and that they are eliminated from highly inflamed Salmonella -infected mice. Collectively, our studies identify the source of F-Asn as the diet and that Salmonella -mediated inflammation is required to eliminate competitors and allow the pathogen nearly exclusive access to this nutrient.

Hyperosmotic agents such as maltodextrin negatively impact bacterial growth through osmotic stress without contributing to drug resistance. We hypothesized that a combination of maltodextrin (osmotic agent) and vancomycin (antibiotic) would be more effective against Staphylococcus aureus biofilms than either alone. To test our hypothesis, S. aureus was grown in a flat plate flow cell reactor. Confocal laser scanning microscopy images were analyzed to quantify changes in biofilm structure. We used dissolved oxygen microelectrodes to quantify how vancomycin and maltodextrin affected the respiration rate and oxygen penetration into the biofilm. We found that treatment with vancomycin or maltodextrin altered biofilm structure. The effect on the structure was significant when they were used simultaneously to treat S. aureus biofilms. In addition, vancomycin treatment increased the oxygen respiration rate, while maltodextrin treatment caused an increase and then a decrease. An increased maltodextrin concentration decreased the diffusivity of the antibiotic. Overall, we conclude that (1) an increased maltodextrin concentration decreases vancomycin diffusion but increases the osmotic effect, leading to the optimum treatment condition, and (2) the combination of vancomycin and maltodextrin is more effective against S. aureus biofilms than either alone. Vancomycin and maltodextrin act together to increase the effectiveness of treatment against S. aureus biofilm growth.

The aim of this work was to elucidate the mechanism of mediated microbial electrosynthesis via neutral red from an electrode to fermenting Escherichia coli cultures in a bioelectrochemical system. Chemical reduction of NAD(+) by reduced neutral red did not occur as predicted. Instead, neutral red was shown to reduce the menaquinone pool in the inner bacterial membrane. The reduced menaquinone pool altered fermentative metabolite production via the arcB redox-sensing cascade in the absence of terminal electron acceptors. When the acceptors DMSO, fumarate, or nitrate were provided, as many as 19% of the electrons trapped in the reduced acceptors were derived from the electrode. These results demonstrate the mechanism of neutral red-mediated microbial electrosynthesis during fermentation as well as how neutral red enables microbial electrosynthesis of reduced terminal electron acceptors.

A partial-thickness epidermal explant model was colonized with green fluorescent protein (GFP)-expressing Staphylococcus aureus , and the pattern of S. aureus biofilm growth was characterized using electron and confocal laser scanning microscopy. The oxygen concentration in explants was quantified using microelectrodes. The relative effective diffusivity and porosity of the epidermis were determined using magnetic resonance imaging, while hydrogen peroxide (H 2 O 2 ) concentration in explant media was measured by using microelectrodes. Secreted proteins were identified and quantified using elevated-energy mass spectrometry (MS E ). S. aureus biofilm grows predominantly in lipid-rich areas around hair follicles and associated skin folds. Dissolved oxygen was selectively depleted (2- to 3-fold) in these locations, but the relative effective diffusivity and porosity did not change between colonized and control epidermis. Histological analysis revealed keratinocyte damage across all the layers of colonized epidermis after 4 days of culture. The colonized explants released significantly ( P < 0.01) more antioxidant proteins of both epidermal and S. aureus origin, consistent with elevated H 2 O 2 concentrations found in the media from the colonized explants ( P < 0.001). Caspase-14 was also elevated significantly in the media from the colonized explants. While H 2 O 2 induces primary keratinocyte differentiation, caspase-14 is required for terminal keratinocyte differentiation and desquamation. These results are consistent with a localized biological impact from S. aureus in response to colonization of the skin surface.

We investigated ion transport limitations on 3D graphite felt electrodes by growing Geobacter sulfurreducens biofilms with advection to eliminate external mass transfer limitations. We characterized ion transport limitations by: (i) showing that serially increasing NaCl concentration up to 200 mM increased current linearly up to a total of +273% vs. 0 mM NaCl under advective conditions; (ii) growing the biofilm with a starting concentration of 200 mM NaCl, which led to a maximum current increase of 400% vs. current generation without NaCl, and (iii) showing that un-colonized surface area remained even after steady-state current was reached. After accounting for iR effects, we confirmed that the excess surface area existed despite a non-zero overpotential. The fact that the biofilm was constrained from colonizing and producing further current under these conditions confirmed the biofilms under study here were ion transport-limited. Our work demonstrates that the use of high surface area electrodes may not increase current density when the system design allows ion transport limitations to become dominant.

Biofilms alter their metabolism in response to environmental stress. This study explores the effect of a hyperosmotic agent-antibiotic treatment on the metabolism of Staphylococcus aureus biofilms through the use of nuclear magnetic resonance (NMR) techniques. To determine the metabolic activity of S. aureus, we quantified the concentrations of metabolites in spent medium using high-resolution NMR spectroscopy. Biofilm porosity, thickness, biovolume, and relative diffusion coefficient depth profiles were obtained using NMR microimaging. Dissolved oxygen concentration was measured to determine the availability of oxygen within the biofilm. Under vancomycin-only treatment, the biofilm communities switched to fermentation under anaerobic condition, as evidenced by high concentrations of formate (7.4 ± 2.7 mM), acetate (13.1 ± 0.9 mM), and lactate (3.0 ± 0.8 mM), and there was no detectable dissolved oxygen in the biofilm. In addition, we observed the highest consumption of pyruvate (0.19 mM remaining from an initial 40 mM concentration), the sole carbon source, under the vancomycin-only treatment. On the other hand, relative effective diffusion coefficients increased from 0.73 ± 0.08 to 0.88 ± 0.08 under vancomycin-only treatment but decreased from 0.71 ± 0.04 to 0.60 ± 0.07 under maltodextrin-only and from 0.73 ± 0.06 to 0.56 ± 0.08 under combined treatments. There was an increase in biovolume, from 2.5 ± 1 mm3 to 7 ± 1 mm3 , under the vancomycin-only treatment, while the maltodextrin-only and combined treatments showed no significant change in biovolume over time. This indicated that physical biofilm growth was halted during maltodextrin-only and combined treatments.

A longstanding limitation of high-resolution NMR spectroscopy is the requirement for samples to have macroscopic dimensions. Commercial probes, for example, are designed for volumes of at least 5 μL, in spite of decades of work directed toward the goal of miniaturization. Progress in miniaturizing inductive detectors has been limited by a perceived need to meet two technical requirements: (1) minimal separation between the sample and the detector, which is essential for sensitivity, and (2) near-perfect magnetic-field homogeneity at the sample, which is typically needed for spectral resolution. The first of these requirements is real, but the second can be relaxed, as we demonstrate here. By using pulse sequences that yield high-resolution spectra in an inhomogeneous field, we eliminate the need for near-perfect field homogeneity and the accompanying requirement for susceptibility matching of microfabricated detector components. With this requirement removed, typical imperfections in microfabricated components can be tolerated, and detector dimensions can be matched to those of the sample, even for samples of volume ≪5 μL. Pulse sequences that are robust to field inhomogeneity thus enable small-volume detection with optimal sensitivity. We illustrate the potential of this approach to miniaturization by presenting spectra acquired with a flat-wire detector that can easily be scaled to subnanoliter volumes. In particular, we report high-resolution NMR spectroscopy of an alanine sample of volume 500 pL.

Phototrophic mat communities are model ecosystems for studying energy cycling and elemental transformations because complete biogeochemical cycles occur over millimeter-to-centimeter scales. Characterization of energy and nutrient capture within hypersaline phototrophic mats has focused on specific processes and organisms; however, little is known about community-wide distribution of and linkages between these processes. To investigate energy and macronutrient capture and flow through a structured community, the spatial and organismal distribution of metabolic functions within a compact hypersaline mat community from Hot Lake have been broadly elucidated through species-resolved metagenomics and geochemical, microbial diversity and metabolic gradient measurements. Draft reconstructed genomes of 34 abundant organisms revealed three dominant cyanobacterial populations differentially distributed across the top layers of the mat suggesting niche separation along light and oxygen gradients. Many organisms contained diverse functional profiles, allowing for metabolic response to changing conditions within the mat. Organisms with partial nitrogen and sulfur metabolisms were widespread indicating dependence on metabolite exchange. In addition, changes in community spatial structure were observed over the diel. These results indicate that organisms within the mat community have adapted to the temporally dynamic environmental gradients in this hypersaline mat through metabolic flexibility and fluid syntrophic interactions, including shifts in spatial arrangements., Depth-resolved genome profiling of a microbial mat community from a magnesium-sulfate enriched hypersaline lake reveals metabolic linkages within and between organisms across physiochemical gradients.

Although synthesized only by bacteria and archaea, cobalamin (vitamin B12) is essential for virtually all living cells. One major function is its role in methionine synthesis, as a co-factor for the B12-dependent methionine synthase MetH. However, a large number of microbes avoid requirements for B12 by encoding cobalamin-independent enzymes, such as the B12-independent methionine synthase MetE. Interestingly, many such microbes retain transporters for exogenous B12, produced by neighboring microbes. We hypothesize that selection for retention of B12 transport suggests preservation of unrevealed but critical roles for cobalamin in photoautotroph fitness. To identify the impacts of B12 on photoautotrophic metabolism, we studied the physiological and transcriptional adaptation of Cyanobacterium stanieri HL-69 to varying irradiance and oxidative stress in the presence and absence of B12. The metabolic flexibility of C. stanieri, which possesses both MetH and MetE, allows comparative analysis of cobalamin impacts on its global metabolism. As anticipated, B12 availability governed transcription of cobalamin transporter btuB, metH and a number of genes involved in the methionine-folate cycle. Surprisingly, however, B12 impacted the cell integrity, growth rate and biomass productivity of C. stanieri under conditions of likely oxidative stress due to biofilm growth or under high partial pressures of O2. Furthermore, C. stanieri response to B12 globally rewired cellular metabolic networks, including nitrogen metabolism, energy metabolism, redox homeostasis and oxidative stress response. These findings demonstrate previously-unappreciated roles for B12 metabolism beyond methionine synthesis and reveal how interactions with cobalamin-producing heterotrophs may affect phytoplankton function and dynamics in natural microbial communities. Further comprehension and mastering of the natural oxidative stress resistance mechanisms modulated by cobalamin could be used for the design and implementation of more robust algal bioprocesses retaining high biomass productivities especially under stress conditions.

Glycomics has become an increasingly important field of research since glycans play critical roles in biology processes ranging from molecular recognition and signaling to cellular communication. Glycans often conjugate with other biomolecules, such as proteins and lipids, and alter their properties and functions, so glycan characterization is essential for understanding the effects they have on cellular systems. However, the analysis of glycans is extremely difficult due to their complexity and structural diversity (i.e., the number and identity of monomer units, and configuration of their glycosidic linkages and connectivities). In this work, we coupled ion mobility spectrometry with mass spectrometry (IMS-MS) to characterize glycan standards and biologically important isomers of synthetic αGal-containing O-glycans including glycotopes of the protozoan parasite Trypanosoma cruzi, which is the causative agent of Chagas disease. IMS-MS results showed significant differences for the glycan structural isomers when analyzed in positive and negative polarity and complexed with different metal cations. These results suggest that specific metal ions or ion polarities could be used to target and baseline separate glycan isomers of interest with IMS-MS. Graphical abstract Glycan isomers, such as fructose and glucose, show distinct separations in positive and negative ion mode.

Biofilms are core to a range of biological processes, including the bioremediation of environmental contaminants. Within a biofilm population, cells with diverse genotypes and phenotypes coexist, suggesting that distinct metabolic pathways may be expressed based on the local environmental conditions in a biofilm. However, metabolic responses to local environmental conditions in a metabolically active biofilm interacting with environmental contaminants have never been quantitatively elucidated. In this study, we monitored the spatiotemporal metabolic responses of metabolically active Shewanella oneidensis MR-1 biofilms to U(VI) (uranyl, UO(2)(2+)) and Cr(VI) (chromate, CrO(4) (2-)) using non-invasive nuclear magnetic resonance imaging (MRI) and spectroscopy (MRS) approaches to obtain insights into adaptation in biofilms during biofilm-contaminant interactions. While overall biomass distribution was not significantly altered upon exposure to U(VI) or Cr(VI), MRI and spatial mapping of the diffusion revealed localized changes in the water diffusion coefficients in the biofilms, suggesting significant contaminant-induced changes in structural or hydrodynamic properties during bioremediation. Finally, we quantitatively demonstrated that the metabolic responses of biofilms to contaminant exposure are spatially stratified, implying that adaptation in biofilms is custom-developed based on local microenvironments.

The structure of biofilms can be numerically quantified from microscopy images using structural parameters. These parameters are used in biofilm image analysis to compare biofilms, to monitor temporal variation in biofilm structure, to quantify the effects of antibiotics on biofilm structure and to determine the effects of environmental conditions on biofilm structure. It is often hypothesized that biofilms with similar structural parameter values will have similar structures; however, this hypothesis has never been tested. The main goal was to test the hypothesis that the commonly used structural parameters can characterize the differences or similarities between biofilm structures. To achieve this goal (1) biofilm image reconstruction was developed as a new tool for assessing structural parameters, (2) independent reconstructions using the same starting structural parameters were tested to see how they differed from each other, (3) the effect of the original image parameter values on reconstruction success was evaluated, and (4) the effect of the number and type of the parameters on reconstruction success was evaluated. It was found that two biofilms characterized by identical commonly used structural parameter values may look different, that the number and size of clusters in the original biofilm image affect image reconstruction success and that, in general, a small set of arbitrarily selected parameters may not reveal relevant differences between biofilm structures.

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.

Secondary ion mass spectrometry (SIMS) has become an increasingly utilized tool in biologically relevant studies. Of these, high lateral resolution methodologies using the NanoSIMS 50/50L have been especially powerful within many biological fields over the past decade. Here, the authors provide a review of this technology, sample preparation and analysis considerations, examples of recent biological studies, data analyses, and current outlooks. Specifically, the authors offer an overview of SIMS and development of the NanoSIMS. The authors describe the major experimental factors that should be considered prior to NanoSIMS analysis and then provide information on best practices for data analysis and image generation, which includes an in-depth discussion of appropriate colormaps. Additionally, the authors provide an open-source method for data representation that allows simultaneous visualization of secondary electron and ion information within a single image. Finally, the authors present a perspective on the future of this technology and where they think it will have the greatest impact in near future.

Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parameters into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.

We investigated ion transport limitations on 3D graphite felt electrodes by growing Geobacter sulfurreducens biofilms with advection to eliminate external mass transfer limitations. We characterized ion transport limitations by: 1) showing that serially increasing NaCl concentration up to 200 mM increased current linearly up to a total of +273% vs. 0 mM NaCl under advective conditions, 2) growing the biofilm with a starting concentration of 200 mM NaCl, which led to a maximum current increase of 400% vs. current generation without NaCl, and 3) showing that un-colonized surface area remained even after steady-state current was reached. After accounting for iR effects, we confirmed that the excess surface area existed despite a non-zero overpotential at the electrode surface. The fact that the biofilm was constrained from colonizing and producing further current under these conditions confirmed the biofilms under study here were ion transport-limited. Our work demonstrates that the use of high surface area electrodes may not increase current density when the system design allows ion transport limitations to become dominant.

Nuclear magnetic resonance (NMR) techniques are ideally suited for the study of biofilms and for probing their microenvironments because these techniques allow for noninvasive interrogation and in situ monitoring with high resolution. By combining NMR with simultaneous electrochemical techniques, it is possible to sustain and study live biofilms respiring on electrodes. Here, we describe a biofilm microreactor system, including a reusable and a disposable reactor, that allows for simultaneous electrochemical and NMR techniques (EC-NMR) at the microscale. Microreactors were designed with custom radio frequency resonator coils, which allowed for NMR measurements of biofilms growing on polarized gold electrodes. For an example application of this system we grew Geobacter sulfurreducens biofilms on electrodes. EC-NMR was used to investigate growth medium flow velocities and depth-resolved acetate concentration inside the biofilm. As a novel contribution we used Monte Carlo error analysis to estimate the standard deviations of the acetate concentration measurements. Overall, we found that the disposable EC-NMR microreactor provided a 9.7 times better signal-to-noise ratio over the reusable reactor. The EC-NMR biofilm microreactor system can ultimately be used to correlate extracellular electron transfer rates with metabolic reactions and explore extracellular electron transfer mechanisms.

Summary Microbial mats are characterized by extensive metabolic interactions, rapidly changing internal geochemical gradients, and prevalent microenvironments within tightly constrained physical structures. We present laser ablation isotope ratio mass spectrometry (LA-IRMS) as a culture-independent, spatially specific technology for tracking the accumulation of 13C-labelled substrate into heterogeneous microbial mat communities. This study demonstrates the novel LA-IRMS approach by tracking labeled bicarbonate incorporation into a cyanobacteria-dominated microbial mat system. The spatial resolution of 50 μm was sufficient for distinguishing different mat strata and the approach effectively identified regions of greatest label incorporation. Sample preparation for LA-IRMS is straightforward and the spatial selectivity of LA-IRMS minimizes the volume of mat consumed, leaving material for complimentary analyses. We present analysis of DNA extracted from a sample post-ablation and suggest pigments, lipids or other biomarkers could similarly be extracted following ablation. LA-IRMS is well positioned to spatially resolve the accumulation of any 13C-labelled substrate provided to a mat, making this a versatile tool for studying carbon transfer and interspecies exchanges within the limited spatial confines of such systems.

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 vitro and amenable to controlled scientific experimentation. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful, high spatial resolution 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, the authors describe the creation and application of a semiautomated image-processing pipeline that can analyze NanoSIMS-generated data, applied to phototrophic biofilms as an example. 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. The authors demonstrate the functionality of the tool by using it to analyze the uptake of (15)N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. The authors found that the degree of (15)N incorporation by individual cells was highly variable when labeled with (15)NH4 (+), but much more even when biofilms were labeled with (15)NO3 (-). In the (15)NH4 (+)-amended biofilms, the heterotrophic distribution of (15)N incorporation was highly skewed, with a large population showing moderate (15)N incorporation and a small number of organisms displaying very high (15)N 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.

Biofilms are core to a range of biological processes, including the bioremediation of environmental contaminants. Within a biofilm population, cells with diverse genotypes and phenotypes coexist, suggesting that distinct metabolic pathways may be expressed based on the local environmental conditions in a biofilm. However, metabolic responses to local environmental conditions in a metabolically active biofilm interacting with environmental contaminants have never been quantitatively elucidated. In this study, we monitored the spatiotemporal metabolic responses of metabolically active Shewanella oneidensis MR-1 biofilms to U(VI) (uranyl, UO(2)(2+)) and Cr(VI) (chromate, CrO(4) (2-)) using non-invasive nuclear magnetic resonance imaging (MRI) and spectroscopy (MRS) approaches to obtain insights into adaptation in biofilms during biofilm-contaminant interactions. While overall biomass distribution was not significantly altered upon exposure to U(VI) or Cr(VI), MRI and spatial mapping of the diffusion revealed localized changes in the water diffusion coefficients in the biofilms, suggesting significant contaminant-induced changes in structural or hydrodynamic properties during bioremediation. Finally, we quantitatively demonstrated that the metabolic responses of biofilms to contaminant exposure are spatially stratified, implying that adaptation in biofilms is custom-developed based on local microenvironments.

This review examines the electrochemical techniques used to study extracellular electron transfer in the electrochemically active biofilms that are used in microbial fuel cells and other bioelectrochemical systems. Electrochemically active biofilms are defined as biofilms that exchange electrons with conductive surfaces: electrodes. Following the electrochemical conventions, and recognizing that electrodes can be considered reactants in these bioelectrochemical processes, biofilms that deliver electrons to the biofilm electrode are called anodic, ie electrode-reducing, biofilms, while biofilms that accept electrons from the biofilm electrode are called cathodic, ie electrode-oxidizing, biofilms. How to grow these electrochemically active biofilms in bioelec-trochemical systems is discussed and also the critical choices made in the experimental setup that affect the experimental results. The reactor configurations used in bioelectrochemical systems research are also described and the authors demonstrate how to use selected voltammetric techniques to study extracellular electron transfer in bioelectrochemical systems. Finally, some critical concerns with the proposed electron transfer mechanisms in bioelectrochemical systems are addressed together with the prospects of bioelectrochemical systems as energy-converting and energy-harvesting devices.

Direct examination of natural and engineered environments has revealed that the majority of microorganisms in these systems live in structured communities termed biofilms. In addition to microbial cells, biofilms are comprised of a poorly characterized organic matrix commonly referred to as extracellular polymeric substance (EPS) that may play roles in facilitating microbial interactions and biogeochemical reactions including extracellular electron transfer. Using highresolution electron microscopy imaging, we have shown copious amounts of highly hydrated bacterial EPS to be produced during microbial metal reduction [1,2]. The juxtaposition of extracellular electron transfer proteins and nanoparticulate reduced metal suggested that EPS played a key role in metal capture and precipitation. Determining the chemical composition of biofilm-associated EPS and understanding how it functions and interacts with inorganic substrates including metal ions and mineral surfaces is needed to connect the molecular-scale biogeochemical processes to those at the microorganism-level, and provide insight to how microbes influence larger, pore-scale biogeochemical reactions.