Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.

Microbial consortia have the potential to perform complex, industrially important tasks. The design of microbial consortia requires knowledge of the substrate preferences and metabolic outputs of each member, to allow understanding of potential interactions such as competition and beneficial metabolic exchange. Here, we used exometabolite profiling to follow the resource processing by a microbial co-culture of two biotechnologically relevant microbes, the bacterial cellulose degrader Cellulomonas fimi, and the oleaginous yeast Yarrowia lipolytica. We characterized the substrate preferences of the two strains on compounds typically found in lignocellulose hydrolysates. This allowed prediction that specific sugars resulting from hemicellulose polysaccharide degradation by C. fimi may serve as a cross-feeding metabolites to Y. lipolytica in co-culture. We also showed that products of ionic liquid-treated switchgrass lignocellulose degradation by C. fimi were channeled to Y. lipolytica in a co-culture. Additionally, we observed metabolites, such as shikimic acid accumulating in the co-culture supernatants, suggesting the potential for producing interesting co-products. Insights gained from characterizing the exometabolite profiles of individual and co-cultures of the two strains can help to refine this interaction, and guide strategies for making this an industrially viable co-culture to produce valuable products from lignocellulose material.

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.

Mass spectrometry imaging (MSI) has recently emerged as a widely used technique in application areas including pharmaceutical evaluation, ecosystem physiology, high-throughput screening and many others. Mass spectrometry images are made by raster scanning a sample in two dimensions and acquiring mass spectra at each location. Although there are many ways of performing MSI and a diversity of suitable samples, data analysis steps are generally consistent across experiments. These standard analysis steps include access to and management of the raw data, processing of individual spectra with centroiding and denoising algorithms. They also include normalization approaches to both improve spot-to-spot variability and quantification of molecules. Finally, multivariate statistics approaches are used to navigate the complexity of sometimes overwhelming large volume of information. Here, we present all of these analyses approaches and discuss how and why they are used in MSI.

Metagenomic sequencing provides a window into microbial community structure and metabolic potential; however, linking these data to exogenous metabolites that microorganisms process and produce (the exometabolome) remains challenging. Previously, we observed strong exometabolite niche partitioning among bacterial isolates from biological soil crust (biocrust). Here we examine native biocrust to determine if these patterns are reproduced in the environment. Overall, most soil metabolites display the expected relationship (positive or negative correlation) with four dominant bacteria following a wetting event and across biocrust developmental stages. For metabolites that were previously found to be consumed by an isolate, 70% are negatively correlated with the abundance of the isolate's closest matching environmental relative in situ, whereas for released metabolites, 67% were positively correlated. Our results demonstrate that metabolite profiling, shotgun sequencing and exometabolomics may be successfully integrated to functionally link microbial community structure with environmental chemistry in biocrust.

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.

The cycling of soil organic matter (SOM) by microorganisms is a critical component of the global carbon cycle but remains poorly understood. There is an emerging view that much of SOM, and especially the dissolved fraction (DOM), is composed of small molecules of plant and microbial origin resulting from lysed cells and released metabolites. Unfortunately, little is known about the small molecule composition of soils and how these molecules are cycled (by microbes or plants or by adsorption to mineral surfaces). The water-extractable organic matter (WEOM) fraction is of particular interest given that this is presumably the most biologically-accessible component of SOM. Here we describe the development of a simple soil metabolomics workflow and a novel spike recovery approach using 13C bacterial lysates to assess the types of metabolites remaining in the WEOM fraction. Soil samples were extracted with multiple mass spectrometry-compatible extraction buffers (water, 10 mM K2SO4 or NH4HCO3, 10–100% methanol or isopropanol/methanol/water [3:3:2 v/v/v]) with and without prior chloroform vapor fumigation. Profiling of derivatized extracts was performed using gas chromatography/mass spectrometry (GC/MS) with 55 metabolites identified by comparing fragmentation patterns and retention times with authentic standards. As expected, fumigation, which is thought to lyse microbial cells, significantly increased the range and abundance of metabolites relative to unfumigated samples. To assess the types of microbial metabolites from lysed bacterial cells that remain in the WEOM fraction, an extract was prepared from the soil bacterium Pseudomonas stutzerii RCH2 grown on 13C acetate. This approach produced highly labeled metabolites that were easily discriminated from the endogenous soil metabolites. Comparing the composition of the fresh bacterial extract with what was recovered following a 15 min incubation with soil revealed that only 27% of the metabolites showed >50% recovery in the WEOM. Many, especially cations (polyamines) and anions, showed <10% recovery. These represent metabolites that may be inaccessible to microbes in this environment and would be most likely to accumulate as SOM presumably due to binding with minerals and negatively-charged clay particles. This study presents a simple untargeted metabolomics workflow for extractable organic matter and an approach to estimate microbial metabolite availability in soils. These methods can be used to further our understanding of SOM and DOM composition and examine the link between metabolic pathways and microbial communities to terrestrial carbon cycling.

Background

Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.

Results

Of 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.

Conclusions

To facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.

The advent of culture independent approaches has greatly facilitated insights into the vast diversity of bacteria and the ecological importance they hold in nature and human health. Recently, metagenomic surveys and other culture-independent methods have begun to describe the distribution and diversity of microbial metabolism across environmental conditions, often using 16S rRNA gene as a marker to group bacteria into taxonomic units. However, the extent to which similarity at the conserved ribosomal 16S gene correlates with different measures of phylogeny, metabolic diversity, and ecologically relevant gene content remains contentious. Here, we examine the relationship between 16S identity, core genome divergence, and metabolic gene content across the ancient and ecologically important genus Streptomyces. We assessed and quantified the high variability of average nucleotide identity (ANI) and ortholog presence/absence within Streptomyces, even in strains identical by 16S. Furthermore, we identified key differences in shared ecologically important characters, such as antibiotic resistance, carbohydrate metabolism, biosynthetic gene clusters (BGCs), and other metabolic hallmarks, within 16S identities commonly treated as the same operational taxonomic units (OTUs). Differences between common phylogenetic measures and metabolite-gene annotations confirmed this incongruence. Our results highlight the metabolic diversity and variability within OTUs and add to the growing body of work suggesting 16S-based studies of Streptomyces fail to resolve important ecological and metabolic characteristics.