Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter.

Soil organic matter (SOM) is comprised of a diverse array of reactive carbon molecules, including hydrophilic and hydrophobic compounds, that impact rates of SOM formation and persistence. Despite clear importance to ecosystem science, little is known about broad-scale controls on SOM diversity and variability in soil. Here, we show that microbial decomposition drives significant variability in the molecular richness and diversity of SOM between soil horizons and across a continental-scale gradient in climate and ecosystem type (arid shrubs, coniferous, deciduous, and mixed forests, grasslands, and tundra sedges). The molecular dissimilarity of SOM was strongly influenced by ecosystem type (hydrophilic compounds: 17%, P < 0.001; hydrophobic compounds: 10% P < 0.001) and soil horizon (hydrophilic compounds: 17%, P < 0.001; hydrophobic compounds: 21%, P < 0.001), as assessed using metabolomic analysis of hydrophilic and hydrophobic metabolites. While the proportion of shared molecular features was significantly higher in the litter layer than subsoil C horizons across ecosystems (12 times and 4 times higher for hydrophilic and hydrophobic compounds, respectively), the proportion of site-specific molecular features nearly doubled from the litter layer to the subsoil horizon, suggesting greater differentiation of compounds after microbial decomposition within each ecosystem. Together, these results suggest that microbial decomposition of plant litter leads to a decrease in SOM α-molecular diversity, yet an increase in ß-molecular diversity across ecosystems. The degree of microbial degradation, determined by the position in the soil profile, exerts a greater control on SOM molecular diversity than environmental factors, such as soil texture, moisture, and ecosystem type.

Cynipid wasps systematically reprogram host metabolism and restructure cell walls in developing galls.

Many insects have evolved the ability to manipulate plant growth to generate extraordinary structures called galls, in which insect larva can develop while being sheltered and feeding on the plant. In particular, cynipid (Hymenoptera: Cynipidae) wasps have evolved to form morphologically complex galls and generate an astonishing array of gall shapes, colors, and sizes. However, the biochemical basis underlying these remarkable cellular and developmental transformations remains poorly understood. A key determinant in plant cellular development is cell wall deposition that dictates the physical form and physiological function of newly developing cells, tissues, and organs. However, it is unclear to what degree cell walls are restructured to initiate and support the formation of new gall tissue. Here, we characterize the molecular alterations underlying gall development using a combination of metabolomic, histological, and biochemical techniques to elucidate how valley oak (Quercus lobata) leaf cells are reprogrammed to form galls. Strikingly, gall development involves an exceptionally coordinated spatial deposition of lignin and xylan to form de novo gall vasculature. Our results highlight how cynipid wasps can radically change the metabolite profile and restructure the cell wall to enable the formation of galls, providing insights into the mechanism of gall induction and the extent to which plants can be entirely reprogrammed to form unique structures and organs.

Mass spectrometry imaging-based assays for aminotransferase activity reveal a broad substrate spectrum for a previously uncharacterized enzyme.

Aminotransferases (ATs) catalyze pyridoxal 5'-phosphate-dependent transamination reactions between amino donor and keto acceptor substrates and play central roles in nitrogen metabolism of all organisms. ATs are involved in the biosynthesis and degradation of both proteinogenic and nonproteinogenic amino acids and also carry out a wide variety of functions in photorespiration, detoxification, and secondary metabolism. Despite the importance of ATs, their functionality is poorly understood as only a small fraction of putative ATs, predicted from DNA sequences, are associated with experimental data. Even for characterized ATs, the full spectrum of substrate specificity, among many potential substrates, has not been explored in most cases. This is largely due to the lack of suitable high-throughput assays that can screen for AT activity and specificity at scale. Here we present a new high-throughput platform for screening AT activity using bioconjugate chemistry and mass spectrometry imaging-based analysis. Detection of AT reaction products is achieved by forming an oxime linkage between the ketone groups of transaminated amino donors and a probe molecule that facilitates mass spectrometry-based analysis using nanostructure-initiator mass spectrometry or MALDI-mass spectrometry. As a proof-of-principle, we applied the newly established method and found that a previously uncharacterized Arabidopsis thaliana tryptophan AT-related protein 1 is a highly promiscuous enzyme that can utilize 13 amino acid donors and three keto acid acceptors. These results demonstrate that this oxime-mass spectrometry imaging AT assay enables high-throughput discovery and comprehensive characterization of AT enzymes, leading to an accurate understanding of the nitrogen metabolic network.

Information theory and machine learning illuminate large-scale metabolomic responses of Brachypodium distachyon to environmental change.

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ-condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)-based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML-based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co-accumulation analysis further identified condition-specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio-Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.

Expression in poplar of dehydroshikimate dehydratase induces transcriptional and metabolic changes in the phenylpropanoid pathway.

Modification of lignin in feedstocks via genetic engineering aims to reduce biomass recalcitrance to facilitate efficient conversion processes. These improvements can be achieved by expressing exogenous enzymes that interfere with native biosynthetic pathways responsible for the production of the lignin precursors. In-planta expression of a 3-dehydroshikimate dehydratase (QsuB) in poplar trees reduced lignin content and altered their monomer composition, which enabled higher yields of sugars after cell wall polysaccharide hydrolysis. Understanding how plants respond to such genetic modifications at the transcriptional and metabolic levels is needed to facilitate further improvement and field deployment. In this work, we amassed fundamental knowledge on lignin-modified QsuB poplar using RNA-seq and metabolomics. The data clearly demonstrate that changes in gene expression and metabolite abundance can occur in a strict spatiotemporal fashion, revealing tissue-specific responses in the xylem, phloem, or periderm. In the poplar line that exhibits the strongest reduction in lignin, we found that 3% of the transcripts had altered expression levels and ~19% of the detected metabolites had differential abundance in the xylem from older stems. Changes affect predominantly the shikimate and phenylpropanoid pathways as wells as secondary cell wall metabolism, and result in significant accumulation of hydroxybenzoates derived from protocatechuate and salicylate.

Anaerobic gut fungi are an untapped reservoir of natural products.

Anaerobic fungi (class Neocallimastigomycetes) thrive as low-abundance members of the herbivore digestive tract. The genomes of anaerobic gut fungi are poorly characterized and have not been extensively mined for the biosynthetic enzymes of natural products such as antibiotics. Here, we investigate the potential of anaerobic gut fungi to synthesize natural products that could regulate membership within the gut microbiome. Complementary 'omics' approaches were combined to catalog the natural products of anaerobic gut fungi from four different representative species: Anaeromyces robustus (Arobustus), Caecomyces churrovis (Cchurrovis), Neocallimastix californiae (Ncaliforniae), and Piromyces finnis (Pfinnis). In total, 146 genes were identified that encode biosynthetic enzymes for diverse types of natural products, including nonribosomal peptide synthetases and polyketide synthases. In addition, N. californiae and C. churrovis genomes encoded seven putative bacteriocins, a class of antimicrobial peptides typically produced by bacteria. During standard laboratory growth on plant biomass or soluble substrates, 26% of total core biosynthetic genes in all four strains were transcribed. Across all four fungal strains, 30% of total biosynthetic gene products were detected via proteomics when grown on cellobiose. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of fungal supernatants detected 72 likely natural products from A. robustus alone. A compound produced by all four strains of anaerobic fungi was putatively identified as the polyketide-related styrylpyrone baumin. Molecular networking quantified similarities between tandem mass spectrometry (MS/MS) spectra among these fungi, enabling three groups of natural products to be identified that are unique to anaerobic fungi. Overall, these results support the finding that anaerobic gut fungi synthesize natural products, which could be harnessed as a source of antimicrobials, therapeutics, and other bioactive compounds.

Conservation of beneficial microbes between the rhizosphere and the cyanosphere.

Biocrusts are phototroph-driven communities inhabiting arid soil surfaces. Like plants, most photoautotrophs (largely cyanobacteria) in biocrusts are thought to exchange fixed carbon for essential nutrients like nitrogen with cyanosphere bacteria. Here, we aim to compare beneficial interactions in rhizosphere and cyanosphere environments, including finding growth-promoting strains for hosts from both environments. To examine this, we performed a retrospective analysis of 16S rRNA gene sequencing datasets, host-microbe co-culture experiments between biocrust communities/biocrust isolates and a model grass (Brachypodium distachyon) or a dominant biocrust cyanobacterium (Microcoleus vaginatus), and metabolomic analysis. All 18 microbial phyla in the cyanosphere were also present in the rhizosphere, with additional 17 phyla uniquely found in the rhizosphere. The biocrust microbes promoted the growth of the model grass, and three biocrust isolates (Bosea sp._L1B56, Pseudarthrobacter sp._L1D14 and Pseudarthrobacter picheli_L1D33) significantly promoted the growth of both hosts. Moreover, pantothenic acid was produced by Pseudarthrobacter sp._L1D14 when grown on B. distachyon exudates, and supplementation of plant growth medium with this metabolite increased B. distachyon biomass by over 60%. These findings suggest that cyanobacteria and other diverse photoautotrophic hosts can be a source for new plant growth-promoting microbes and metabolites.

Tracing Carbon Metabolism with Stable Isotope Metabolomics Reveals the Legacy of Diverse Carbon Sources in Soil.

Tracking the metabolic activity of whole soil communities can improve our understanding of the transformation and fate of carbon in soils. We used stable isotope metabolomics to trace 13C from nine labeled carbon sources into the water-soluble metabolite pool of an agricultural soil over time. Soil was amended with a mixture of all nine sources, with one source isotopically labeled in each treatment. We compared changes in the 13C enrichment of metabolites with respect to carbon source and time over a 48-day incubation and contrasted differences between soluble sources (glucose, xylose, amino acids, etc.) and insoluble sources (cellulose and palmitic acid). Whole soil metabolite profiles varied singularly by time, while the composition of 13C-labeled metabolites differed primarily by carbon source (R2 = 0.68) rather than time (R2 = 0.07), with source-specific differences persisting throughout incubations. The 13C labeling of metabolites from insoluble carbon sources occurred slower than that from soluble sources but yielded a higher average atom percent (atom%) 13C in metabolite markers of biomass (amino acids and nucleic acids). The 13C enrichment of metabolite markers of biomass stabilized between 5 and 15 atom% 13C by the end of incubations. Temporal patterns in the 13C enrichment of tricarboxylic acid cycle intermediates, nucleobases (uracil and thymine), and by-products of DNA salvage (allantoin) closely tracked microbial activity. Our results demonstrate that metabolite production in soils is driven by the carbon source supplied to the community and that the fate of carbon in metabolites do not generally converge over time as a result of ongoing microbial processing and recycling. IMPORTANCE Carbon metabolism in soil remains poorly described due to the inherent difficulty of obtaining information on the microbial metabolites produced by complex soil communities. Our study demonstrates the use of stable isotope probing (SIP) to study carbon metabolism in soil by tracking 13C from supplied carbon sources into metabolite pools and biomass. We show that differences in the metabolism of sources influence the fate of carbon in soils. Heterogeneity in 13C-labeled metabolite profiles corresponded with compositional differences in the metabolically active populations, providing a basis for how microbial community composition correlates with the quality of soil carbon. Our study demonstrates the application of SIP-metabolomics in studying soils and identifies several metabolite markers of growth, activity, and other aspects of microbial function.

Regulation of Oxygenic Photosynthesis during Trophic Transitions in the Green Alga Chromochloris zofingiensis.

Light and nutrients are critical regulators of photosynthesis and metabolism in plants and algae. Many algae have the metabolic flexibility to grow photoautotrophically, heterotrophically, or mixotrophically. Here, we describe reversible Glc-dependent repression/activation of oxygenic photosynthesis in the unicellular green alga Chromochloris zofingiensis. We observed rapid and reversible changes in photosynthesis, in the photosynthetic apparatus, in thylakoid ultrastructure, and in energy stores including lipids and starch. Following Glc addition in the light, C. zofingiensis shuts off photosynthesis within days and accumulates large amounts of commercially relevant bioproducts, including triacylglycerols and the high-value nutraceutical ketocarotenoid astaxanthin, while increasing culture biomass. RNA sequencing reveals reversible changes in the transcriptome that form the basis of this metabolic regulation. Functional enrichment analyses show that Glc represses photosynthetic pathways while ketocarotenoid biosynthesis and heterotrophic carbon metabolism are upregulated. Because sugars play fundamental regulatory roles in gene expression, physiology, metabolism, and growth in both plants and animals, we have developed a simple algal model system to investigate conserved eukaryotic sugar responses as well as mechanisms of thylakoid breakdown and biogenesis in chloroplasts. Understanding regulation of photosynthesis and metabolism in algae could enable bioengineering to reroute metabolism toward beneficial bioproducts for energy, food, pharmaceuticals, and human health.

Metabolomic Patterns of Septoria Canker Resistant and Susceptible Populus trichocarpa Genotypes 24 Hours Postinoculation.

Sphaerulina musiva is an economically and ecologically important fungal pathogen that causes Septoria stem canker and leaf spot disease of Populus species. To bridge the gap between genetic markers and structural barriers previously found to be linked to Septoria canker disease resistance in poplar, we used hydrophilic interaction liquid chromatography and tandem mass spectrometry to identify and quantify metabolites involved with signaling and cell wall remodeling. Fluctuations in signaling molecules, organic acids, amino acids, sterols, phenolics, and saccharides in resistant and susceptible P. trichocarpa inoculated with S. musiva were observed. The patterns of 222 metabolites in the resistant host implicate systemic acquired resistance (SAR), cell wall apposition, and lignin deposition as modes of resistance to this hemibiotrophic pathogen. This pattern is consistent with the expected response to the biotrophic phase of S. musiva colonization during the first 24 h postinoculation. The fungal pathogen metabolized key regulatory signals of SAR, other phenolics, and precursors of lignin biosynthesis that were depleted in the susceptible host. This is the first study to characterize metabolites associated with the response to initial colonization by S. musiva between resistant and susceptible hosts.

Deciphering microbial interactions in synthetic human gut microbiome communities.

The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-dimensional consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi-species community dynamics, as opposed to higher-order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history-dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human-associated intestinal species and illuminated design principles of microbial communities.

Multilab EcoFAB study shows highly reproducible physiology and depletion of soil metabolites by a model grass.

There is a dynamic reciprocity between plants and their environment: soil physiochemical properties influence plant morphology and metabolism, and root morphology and exudates shape the environment surrounding roots. Here, we investigate the reproducibility of plant trait changes in response to three growth environments. We utilized fabricated ecosystem (EcoFAB) devices to grow the model grass Brachypodium distachyon in three distinct media across four laboratories: phosphate-sufficient and -deficient mineral media allowed assessment of the effects of phosphate starvation, and a complex, sterile soil extract represented a more natural environment with yet uncharacterized effects on plant growth and metabolism. Tissue weight and phosphate content, total root length, and root tissue and exudate metabolic profiles were consistent across laboratories and distinct between experimental treatments. Plants grown in soil extract were morphologically and metabolically distinct, with root hairs four times longer than with other growth conditions. Further, plants depleted half of the metabolites investigated from the soil extract. To interact with their environment, plants not only adapt morphology and release complex metabolite mixtures, but also selectively deplete a range of soil-derived metabolites. The EcoFABs utilized here generated high interlaboratory reproducibility, demonstrating their value in standardized investigations of plant traits.

Insulator Nanostructure Desorption Ionization Mass Spectrometry.

Surface-assisted laser desorption ionization (SALDI) is an approach for gas-phase ion generation for mass spectrometry using laser excitation on typically conductive or semiconductive nanostructures. Here, we introduce insulator nanostructure desorption ionization mass spectrometry (INDI-MS), a nanostructured polymer substrate for SALDI-MS analysis of small molecules and peptides. INDI-MS surfaces are produced through the self-assembly of a perfluoroalkyl silsesquioxane nanostructures in a single chemical vapor deposition silanization-step. We find that surfaces formed from the perfluorooctyltrichlorosilane monomer assemble semielliptical features with a 10 nm height, diameters between 10 and 50 nm, and have attomole-femtomole sensitivities for selected analytes. Surfaces prepared with silanes that either lack the trichloro or perfluoro groups, lack sensitivity. Further, we demonstrate that hydrophobic INDI regions can be micropatterned onto hydrophilic surfaces to perform on-chip self-desalting in an array format.

Web of microbes (WoM): a curated microbial exometabolomics database for linking chemistry and microbes.

BACKGROUND: As microbiome research becomes increasingly prevalent in the fields of human health, agriculture and biotechnology, there exists a need for a resource to better link organisms and environmental chemistries. Exometabolomics experiments now provide assertions of the metabolites present within specific environments and how the production and depletion of metabolites is linked to specific microbes. This information could be broadly useful, from comparing metabolites across environments, to predicting competition and exchange of metabolites between microbes, and to designing stable microbial consortia. Here, we introduce Web of Microbes (WoM; freely available at: http://webofmicrobes.org ), the first exometabolomics data repository and visualization tool. DESCRIPTION: WoM provides manually curated, direct biochemical observations on the changes to metabolites in an environment after exposure to microorganisms. The web interface displays a number of key features: (1) the metabolites present in a control environment prior to inoculation or microbial activation, (2) heatmap-like displays showing metabolite increases or decreases resulting from microbial activities, (3) a metabolic web displaying the actions of multiple organisms on a specified metabolite pool, (4) metabolite interaction scores indicating an organism's interaction level with its environment, potential for metabolite exchange with other organisms and potential for competition with other organisms, and (5) downloadable datasets for integration with other types of -omics datasets. CONCLUSION: We anticipate that Web of Microbes will be a useful tool for the greater research community by making available manually curated exometabolomics results that can be used to improve genome annotations and aid in the interpretation and construction of microbial communities.

Towards measuring growth rates of pathogens during infections by D2 O-labeling lipidomics.

RATIONALE: Microbial growth rate is an important physiological parameter that is challenging to measure in situ, partly because microbes grow slowly in many environments. Recently, it has been demonstrated that generation times of S. aureus in cystic fibrosis (CF) infections can be determined by D2 O-labeling of actively synthesized fatty acids. To improve species specificity and allow growth rate monitoring for a greater range of pathogens during the treatment of infections, it is desirable to accurately quantify trace incorporation of deuterium into phospholipids. METHODS: Lipid extracts of D2 O-treated E. coli cultures were measured on liquid chromatography/electrospray ionization mass spectrometry (LC/ESI-MS) instruments equipped with time-of-flight (TOF) and orbitrap mass analyzers, and used for comparison with the analysis of fatty acids by isotope-ratio gas chromatography (GC)/MS. We then developed an approach to enable tracking of lipid labeling, by following the transition from stationary into exponential growth in pure cultures. Lastly, we applied D2 O-labeling lipidomics to clinical samples from CF patients with chronic lung infections. RESULTS: Lipidomics facilitates deuterium quantification in lipids at levels that are useful for many labeling applications (>0.03 at% D). In the E. coli cultures, labeling dynamics of phospholipids depend largely on their acyl chains and between phospholipids we notice differences that are not obvious from absolute concentrations alone. For example, cyclopropyl-containing lipids reflect the regulation of cyclopropane fatty acid synthase, which is predominantly expressed at the beginning of stationary phase. The deuterium incorporation into a lipid that is specific for S. aureus in CF sputum indicates an average generation time of the pathogen on the order of one cell doubling per day. CONCLUSIONS: This study demonstrates how trace level measurement of stable isotopes in intact lipids can be used to quantify lipid metabolism in pure cultures and provides guidelines that enable growth rate measurements in microbiome samples after incubation with a low percentage of D2 O.

An integrated workflow for phenazine-modifying enzyme characterization.

Increasing availability of new genomes and putative biosynthetic gene clusters (BGCs) has extended the opportunity to access novel chemical diversity for agriculture, medicine, environmental and industrial purposes. However, functional characterization of BGCs through heterologous expression is limited because expression may require complex regulatory mechanisms, specific folding or activation. We developed an integrated workflow for BGC characterization that integrates pathway identification, modular design, DNA synthesis, assembly and characterization. This workflow was applied to characterize multiple phenazine-modifying enzymes. Phenazine pathways are useful for this workflow because all phenazines are derived from a core scaffold for modification by diverse modifying enzymes (PhzM, PhzS, PhzH, and PhzO) that produce characterized compounds. We expressed refactored synthetic modules of previously uncharacterized phenazine BGCs heterologously in Escherichia coli and were able to identify metabolic intermediates they produced, including a previously unidentified metabolite. These results demonstrate how this approach can accelerate functional characterization of BGCs.

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium.

BACKGROUND: Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. RESULTS: Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. CONCLUSIONS: Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. The OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI ( http://openmsi.nersc.gov ), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 µm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. These results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat .

Morphology-Driven Control of Metabolite Selectivity Using Nanostructure-Initiator Mass Spectrometry.

Nanostructure-initiator mass spectrometry (NIMS) is a laser desorption/ionization analysis technique based on the vaporization of a nanostructure-trapped liquid "initiator" phase. Here we report an intriguing relationship between NIMS surface morphology and analyte selectivity. Scanning electron microscopy and spectroscopic ellipsometry were used to characterize the surface morphologies of a series of NIMS substrates generated by anodic electrochemical etching. Mass spectrometry imaging was applied to compare NIMS sensitivity of these various surfaces toward the analysis of diverse analytes. The porosity of NIMS surfaces was found to increase linearly with etching time where the pore size ranged from 4 to 12 nm with corresponding porosities estimated to be 7-70%. Surface morphology was found to significantly and selectively alter NIMS sensitivity. The small molecule (<2k Da) sensitivity was found to increase with increased porosity, whereas low porosity had the highest sensitivity for the largest molecules examined. Estimation of molecular sizes showed that this transition occurs when the pore size is <3× the maximum of molecular dimensions. While the origins of selectivity are unclear, increased signal from small molecules with increased surface area is consistent with a surface area restructuring-driven desorption/ionization process where signal intensity increases with porosity. In contrast, large molecules show highest signal for the low-porosity and small-pore-size surfaces. We attribute this to strong interactions between the initiator-coated pore structures and large molecules that hinder desorption/ionization by trapping large molecules. This finding may enable us to design NIMS surfaces with increased specificity to molecules of interest.