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.

Zn deficiency disrupts Cu and S homeostasis in Chlamydomonas resulting in over accumulation of Cu and Cysteine.

Growth of Chlamydomonas reinhardtii in zinc (Zn) limited medium leads to disruption of copper (Cu) homeostasis, resulting in up to 40-fold Cu over-accumulation relative to its typical Cu quota. We show that Chlamydomonas controls its Cu quota by balancing Cu import and export, which is disrupted in a Zn deficient cell, thus establishing a mechanistic connection between Cu and Zn homeostasis. Transcriptomics, proteomics and elemental profiling revealed that Zn-limited Chlamydomonas cells up-regulate a subset of genes encoding "first responder" proteins involved in sulfur (S) assimilation and consequently accumulate more intracellular S, which is incorporated into L-cysteine, γ-glutamylcysteine, and homocysteine. Most prominently, in the absence of Zn, free L-cysteine is increased ∼80-fold, corresponding to ∼2.8 × 109 molecules/cell. Interestingly, classic S-containing metal binding ligands like glutathione and phytochelatins do not increase. X-ray fluorescence microscopy showed foci of S accumulation in Zn-limited cells that co-localize with Cu, phosphorus and calcium, consistent with Cu-thiol complexes in the acidocalcisome, the site of Cu(I) accumulation. Notably, cells that have been previously starved for Cu do not accumulate S or Cys, causally connecting cysteine synthesis with Cu accumulation. We suggest that cysteine is an in vivo Cu(I) ligand, perhaps ancestral, that buffers cytosolic Cu.

Cysteine: an ancestral Cu binding ligand in green algae?

Growth of Chlamydomonas reinhardtii in zinc (Zn) limited medium leads to disruption of copper (Cu) homeostasis, resulting in up to 40-fold Cu over-accumulation relative to its typical Cu quota. We show that Chlamydomonas controls its Cu quota by balancing Cu import and export, which is disrupted in a Zn deficient cell, thus establishing a mechanistic connection between Cu and Zn homeostasis. Transcriptomics, proteomics and elemental profiling revealed that Zn-limited Chlamydomonas cells up-regulate a subset of genes encoding "first responder" proteins involved in sulfur (S) assimilation and consequently accumulate more intracellular S, which is incorporated into L-cysteine, γ-glutamylcysteine and homocysteine. Most prominently, in the absence of Zn, free L-cysteine is increased ~80-fold, corresponding to ~ 2.8 × 10 9 molecules/cell. Interestingly, classic S-containing metal binding ligands like glutathione and phytochelatins do not increase. X-ray fluorescence microscopy showed foci of S accumulation in Zn-limited cells that co-localize with Cu, phosphorus and calcium, consistent with Cu-thiol complexes in the acidocalcisome, the site of Cu(I) accumulation. Notably, cells that have been previously starved for Cu do not accumulate S or Cys, causally connecting cysteine synthesis with Cu accumulation. We suggest that cysteine is an in vivo Cu(I) ligand, perhaps ancestral, that buffers cytosolic Cu.

Development of platforms for functional characterization and production of phenazines using a multi-chassis approach via CRAGE.

Phenazines (Phzs), a family of chemicals with a phenazine backbone, are secondary metabolites with diverse properties such as antibacterial, anti-fungal, or anticancer activity. The core derivatives of phenazine, phenazine-1-carboxylic acid (PCA) and phenazine-1,6-dicarboxylic acid (PDC), are themselves precursors for various other derivatives. Recent advances in genome mining tools have enabled researchers to identify many biosynthetic gene clusters (BGCs) that might produce novel Phzs. To characterize the function of these BGCs efficiently, we performed modular construct assembly and subsequent multi-chassis heterologous expression using chassis-independent recombinase-assisted genome engineering (CRAGE). CRAGE allowed rapid integration of a PCA BGC into 23 diverse γ-proteobacteria species and allowed us to identify top PCA producers. We then used the top five chassis hosts to express four partially refactored PDC BGCs. A few of these platforms produced high levels of PDC. Specifically, Xenorhabdus doucetiae and Pseudomonas simiae produced PDC at a titer of 293 mg/L and 373 mg/L, respectively, in minimal media. These titers are significantly higher than those previously reported. Furthermore, selectivity toward PDC production over PCA production was improved by up to 9-fold. The results show that these strains are promising chassis for production of PCA, PDC, and their derivatives, as well as for function characterization of Phz BGCs identified via bioinformatics mining.

Cocultivation of Anaerobic Fungi with Rumen Bacteria Establishes an Antagonistic Relationship.

Anaerobic gut fungi (Neocallimastigomycetes) live in the digestive tract of large herbivores, where they are vastly outnumbered by bacteria. It has been suggested that anaerobic fungi challenge growth of bacteria owing to the wealth of biosynthetic genes in fungal genomes, although this relationship has not been experimentally tested. Here, we cocultivated the rumen bacteria Fibrobacter succinogenes strain UWB7 with the anaerobic gut fungi Anaeromyces robustus or Caecomyces churrovis on a range of carbon substrates and quantified the bacterial and fungal transcriptomic response. Synthetic cocultures were established for at least 24 h, as verified by active fungal and bacterial transcription. A. robustus upregulated components of its secondary metabolism in the presence of Fibrobacter succinogenes strain UWB7, including six nonribosomal peptide synthetases, one polyketide synthase-like enzyme, and five polyketide synthesis O-type methyltransferases. Both A. robustus and C. churrovis cocultures upregulated S-adenosyl-l-methionine (SAM)-dependent methyltransferases, histone methyltransferases, and an acetyltransferase. Fungal histone 3 lysine 27 trimethylation marks were more abundant in coculture, and heterochromatin protein-1 was downregulated. Together, these findings suggest that fungal chromatin remodeling occurs when bacteria are present. F. succinogenes strain UWB7 upregulated four genes in coculture encoding drug efflux pumps, which likely protect the cell against toxins. Furthermore, untargeted nonpolar metabolomics data revealed at least one novel fungal metabolite enriched in coculture, which may be a defense compound. Taken together, these data suggest that A. robustus and C. churrovis produce antimicrobials when exposed to rumen bacteria and, more broadly, that anaerobic gut fungi are a source of novel antibiotics. IMPORTANCE Anaerobic fungi are outnumbered by bacteria by 4 orders of magnitude in the herbivore rumen. Despite their numerical disadvantage, they are resilient members of the rumen microbiome. Previous studies mining the genomes of anaerobic fungi identified genes encoding enzymes to produce natural products, which are small molecules that are often antimicrobials. In this work, we cocultured the anaerobic fungus Anaeromyces robustus or Caecomyes churrovis with rumen bacteria Fibrobacter succinogenes strain UWB7 and sequenced fungal and bacterial active genes via transcriptome sequencing (RNA-seq). Consistent with production of a fungal defense compound, bacteria upregulated genes encoding drug efflux pumps, which often export toxic molecules, and fungi upregulated genes encoding biosynthetic enzymes of natural products. Furthermore, tandem mass spectrometry detected an unknown fungal metabolite enriched in the coculture. Together, these findings point to an antagonistic relationship between anaerobic fungi and rumen bacteria resulting in the production of a fungal compound with potential antimicrobial activity.

Genomics, Exometabolomics, and Metabolic Probing Reveal Conserved Proteolytic Metabolism of Thermoflexus hugenholtzii and Three Candidate Species From China and Japan.

Thermoflexus hugenholtzii JAD2T, the only cultured representative of the Chloroflexota order Thermoflexales, is abundant in Great Boiling Spring (GBS), NV, United States, and close relatives inhabit geothermal systems globally. However, no defined medium exists for T. hugenholtzii JAD2T and no single carbon source is known to support its growth, leaving key knowledge gaps in its metabolism and nutritional needs. Here, we report comparative genomic analysis of the draft genome of T. hugenholtzii JAD2T and eight closely related metagenome-assembled genomes (MAGs) from geothermal sites in China, Japan, and the United States, representing "Candidatus Thermoflexus japonica," "Candidatus Thermoflexus tengchongensis," and "Candidatus Thermoflexus sinensis." Genomics was integrated with targeted exometabolomics and 13C metabolic probing of T. hugenholtzii. The Thermoflexus genomes each code for complete central carbon metabolic pathways and an unusually high abundance and diversity of peptidases, particularly Metallo- and Serine peptidase families, along with ABC transporters for peptides and some amino acids. The T. hugenholtzii JAD2T exometabolome provided evidence of extracellular proteolytic activity based on the accumulation of free amino acids. However, several neutral and polar amino acids appear not to be utilized, based on their accumulation in the medium and the lack of annotated transporters. Adenine and adenosine were scavenged, and thymine and nicotinic acid were released, suggesting interdependency with other organisms in situ. Metabolic probing of T. hugenholtzii JAD2T using 13C-labeled compounds provided evidence of oxidation of glucose, pyruvate, cysteine, and citrate, and functioning glycolytic, tricarboxylic acid (TCA), and oxidative pentose-phosphate pathways (PPPs). However, differential use of position-specific 13C-labeled compounds showed that glycolysis and the TCA cycle were uncoupled. Thus, despite the high abundance of Thermoflexus in sediments of some geothermal systems, they appear to be highly focused on chemoorganotrophy, particularly protein degradation, and may interact extensively with other microorganisms in situ.

Decrypting bacterial polyphenol metabolism in an anoxic wetland soil.

Microorganisms play vital roles in modulating organic matter decomposition and nutrient cycling in soil ecosystems. The enzyme latch paradigm posits microbial degradation of polyphenols is hindered in anoxic peat leading to polyphenol accumulation, and consequently diminished microbial activity. This model assumes that polyphenols are microbially unavailable under anoxia, a supposition that has not been thoroughly investigated in any soil type. Here, we use anoxic soil reactors amended with and without a chemically defined polyphenol to test this hypothesis, employing metabolomics and genome-resolved metaproteomics to interrogate soil microbial polyphenol metabolism. Challenging the idea that polyphenols are not bioavailable under anoxia, we provide metabolite evidence that polyphenols are depolymerized, resulting in monomer accumulation, followed by the generation of small phenolic degradation products. Further, we show that soil microbiome function is maintained, and possibly enhanced, with polyphenol addition. In summary, this study provides chemical and enzymatic evidence that some soil microbiota can degrade polyphenols under anoxia and subvert the assumed polyphenol lock on soil microbial metabolism.

Learning representations of microbe-metabolite interactions.

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Untargeted Soil Metabolomics Using Liquid Chromatography-Mass Spectrometry and Gas Chromatography-Mass Spectrometry.

The molecular composition of soil organic matter (SOM) sets the foundation for terrestrial microbial community structures and carbon cycling dynamics. However, the specific chemical constituents of SOM are underexplored. In this chapter we present a protocol for the extraction of small molecule metabolites from soil followed by compound detection and identification using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. There are options within the protocol to assess either the extracellular pool of metabolites or the total pool (including intracellular) and either polar or nonpolar metabolites, depending on the reader's research interests. These methods can be followed individually for a more targeted analysis or all methods can be combined to obtain a more comprehensive understanding of SOM metabolite composition (such as amino acids, nucleobases, organic acids, fatty acids, carbohydrates, secondary metabolites, and antibiotics).

Rapid characterization of the activities of lignin-modifying enzymes based on nanostructure-initiator mass spectrometry (NIMS).

BACKGROUND: Producing valuable fuels and chemicals from lignin is a key factor for making lignocellulosic biomass economically feasible; however, significant roadblocks exist due to our lack of detailed understanding of how lignin is enzymatically depolymerized and of the range of possible lignin fragments that can be produced. Development of suitable enzymatic assays for characterization of putative lignin active enzymes is an important step towards improving our understanding of the catalytic activities of relevant enzymes. Previously, we have successfully built an assay platform based on glycan substrates containing a charged perfluorinated tag and nanostructure-initiator mass spectrometry to study carbohydrate active enzymes, especially various glycosyl hydrolyses. Here, we extend this approach to develop a reliable and rapid assay to study lignin-modifying enzymes. RESULTS: Two ß-aryl ether bond containing model lignin dimer substrates, designed to be suitable for studying the activities of lignin-modifying enzymes (LMEs) by nanostructure-initiator mass spectrometry (NIMS), were successful synthesized. Small-angle neutron scattering experiments showed that these substrates form micelles in solution. Two LMEs, laccase from the polypore mushroom Trametes versicolor, and manganese peroxidase (MnP) from white rot fungus Nematoloma frowardii, were tested for catalytic activity against the two model substrates. We show that the reaction of laccase and MnP with phenolic substrate yields products that arise from the cleavage of the carbon-carbon single bond between the α-carbon and the adjacent aryl carbon, consistent with the mechanism for producing phenoxy radical as reaction intermediates. Reactions of the nonphenolic substrate with laccase, on the other hand, adopt a different pathway by producing an α-oxidation product; as well as the cleavage of the ß-aryl ether bond. No cleavage of the carbon-carbon bond between the α-carbon and the aryl carbon was observed. To facilitate understanding of reaction kinetics, the reaction time course for laccase activity on the phenolic substrate (I) was generated by the simultaneous measurement of all products at different time points of the reaction. Withdrawal of only a small sample aliquot (0.2 µL at each time point) ensured minimum perturbation of the reaction. The time course can help us to understand the enzyme kinetics. CONCLUSIONS: A new assay procedure has been developed for studying lignin-modifying enzymes by nanostructure-initiator mass spectrometry. Enzyme assays of a laccase and a MnP on phenolic and nonphenolic ß-aryl ether substrates revealed different primary reaction pathways due to the availability of the phenoxy radical intermediates. Our assay provides a wealth of information on bond cleavage events not available using conventional colorimetric assays and can easily be carried out in microliter volumes and the quantitative analysis of product formation and kinetics is rapidly achieved by NIMS. This is the first time that NIMS technology was applied to study the activities of lignin-modifying enzymes. Unlike other previous works, our use of amphiphilic guaiacylglycerol ß-O-4 substrate (I) enables the formation of micelles. This approach helps avoid the re-polymerization of the resulting monomeric product. As a result, our assay can clearly demonstrate the degradation pathways of phenolic guaiacylglycerol ß-O-4 type of molecules with laccase and MnP.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses.

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.

Exometabolomics Assisted Design and Validation of Synthetic Obligate Mutualism.

Synthetic microbial ecology has the potential to enhance the productivity and resiliency of biotechnology processes compared to approaches using single isolates. Engineering microbial consortia is challenging; however, one approach that has attracted significant attention is the creation of synthetic obligate mutualism using auxotrophic mutants that depend on each other for exchange or cross-feeding of metabolites. Here, we describe the integration of mutant library fitness profiling with mass spectrometry based exometabolomics as a method for constructing synthetic mutualism based on cross-feeding. Two industrially important species lacking known ecological interactions, Zymomonas mobilis and Escherichia coli, were selected as the test species. Amino acid exometabolites identified in the spent medium of Z. mobilis were used to select three corresponding E. coli auxotrophs (proA, pheA and IlvA), as potential E. coli counterparts for the coculture. A pooled mutant fitness assay with a Z. mobilis transposon mutant library was used to identify mutants with improved growth in the presence of E. coli. An auxotroph mutant in a gene (ZMO0748) with sequence similarity to cysteine synthase A (cysK), was selected as the Z. mobilis counterpart for the coculture. Exometabolomic analysis of spent E. coli medium identified glutathione related metabolites as potentially available for rescue of the Z. mobilis cysteine synthase mutant. Three sets of cocultures between the Z. mobilis auxotroph and each of the three E. coli auxotrophs were monitored by optical density for growth and analyzed by flow cytometry to confirm high cell counts for each species. Taken together, our methods provide a technological framework for creating synthetic mutualisms combining existing screening based methods and exometabolomics for both the selection of obligate mutualism partners and elucidation of metabolites involved in auxotroph rescue.

Functional genomics of novel secondary metabolites from diverse cyanobacteria using untargeted metabolomics.

Mass spectrometry-based metabolomics has become a powerful tool for the detection of metabolites in complex biological systems and for the identification of novel metabolites. We previously identified a number of unexpected metabolites in the cyanobacterium Synechococcus sp. PCC 7002, such as histidine betaine, its derivatives and several unusual oligosaccharides. To test for the presence of these compounds and to assess the diversity of small polar metabolites in other cyanobacteria, we profiled cell extracts of nine strains representing much of the morphological and evolutionary diversification of this phylum. Spectral features in raw metabolite profiles obtained by normal phase liquid chromatography coupled to mass spectrometry (MS) were manually curated so that chemical formulae of metabolites could be assigned. For putative identification, retention times and MS/MS spectra were cross-referenced with those of standards or available sprectral library records. Overall, we detected 264 distinct metabolites. These included indeed different betaines, oligosaccharides as well as additional unidentified metabolites with chemical formulae not present in databases of metabolism. Some of these metabolites were detected only in a single strain, but some were present in more than one. Genomic interrogation of the strains revealed that generally, presence of a given metabolite corresponded well with the presence of its biosynthetic genes, if known. Our results show the potential of combining metabolite profiling and genomics for the identification of novel biosynthetic genes.

Mass spectrometry imaging for in situ kinetic histochemistry.

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are 'blind' to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

Low-dose ionizing radiation-induced blood plasma metabolic response in a diverse genetic mouse population.

Understanding the biological effects and biochemical mechanisms of low-dose ionizing radiation (LDIR) is important for setting exposure limits for the safe use of nuclear power and medical diagnostic procedures. Although several studies have highlighted the effects of ionizing radiation on metabolism, most studies have focused on uniform genetic mouse populations. Here, we report the metabolic response to LDIR (10 cGy X ray) on a genetically diverse mouse population (142 mice) generated from a cross of radiation-sensitive (BALB/c) and radiation-resistant (SPRET/EiJ) parental strains. GC-TOF profiling of plasma metabolites was used to compare exposed vs. sham animals. From this, 16 metabolites were significantly altered in the LDIR treated vs. sham group. Use of two significantly altered metabolites, thymine and 2-monostearin, was found to effectively segregate the two treatments. Multivariate statistical analysis was used to identify genetic polymorphisms correlated with metabolite abundance (e.g., amino acids, fatty acids, nucleotides and TCA cycle intermediates). Genetic analysis of metabolic phenotypes showed suggestive linkages for fatty acid and amino acid metabolism. However, metabolite abundance was found to be a function of low-dose ionizing radiation exposure, and not of the underlying genetic variation.

Multivariate analysis of a 3D mass spectral image for examining tissue heterogeneity.

The tissue microenvironment critically influences the molecular characteristics of a tumor. However, as tumorous tissue is highly heterogeneous it may harbor various sub-populations with different microenvironments, greatly complicating the unambiguous analysis of tumor biology. Mass spectrometry imaging techniques allow for the direct analysis of tumors in the spatial context of their microenvironment. However, discovery of heterogeneous sub-populations often depends on the use of multivariate statistical methods. While this is routinely used for 2D images, multivariate statistical approaches are rarely seen in the context of 3D images. Here we present the automatic alignment of 2D images recorded by nanostructure-initiator mass spectrometry (NIMS) to reconstruct a 3D model of a mouse mammary tumor. Multivariate statistical analysis was applied to the whole 3D reconstruction at once, revealing distinct tumor regions, an observation that would not have been possible in such clarity through the analysis of isolated 2D sections. These sub-structures were confirmed by H&E and Oil Red O stains. This study shows that the combination of 3D imaging and multivariate statistics can be used to define tumor regions.

Dealing with the unknown: metabolomics and metabolite atlases.

Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific 'Metabolite Atlases'.

Nanostructure initiator mass spectrometry: tissue imaging and direct biofluid analysis.

Nanostructure initiator mass spectrometry (NIMS) is a recently introduced matrix-free desorption/ionization platform that requires minimal sample preparation. Its application to xenobiotics and endogenous metabolites in tissues is demonstrated, where clozapine and N-desmethylclozapine were observed from mouse and rat brain sections. It has also been applied to direct biofluid analysis where ketamine and norketamine were observed from plasma and urine. Detection of xenobiotics from biofluids was made even more effective using a novel NIMS on-surface extraction method taking advantage of the hydrophobic nature of the initiator. Linear response and limit of detection were also evaluated for xenobiotics such as methamphetamine, codeine, alprazolam, and morphine, revealing that NIMS can be used for quantitative analysis. Overall, our results demonstrate the capacity of NIMS to perform sensitive, simple, and rapid analyses from highly complex biological tissues and fluids.

Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis.

Nanostructure-initiator mass spectrometry (NIMS) is a new surface-based MS technique that uses a nanostructured surface to trap liquid ('initiator') compounds. Analyte materials adsorbed onto this 'clathrate' surface are subsequently released by laser irradiation for mass analysis. In this protocol, we describe the preparation of NIMS surfaces capable of producing low background and high-sensitivity mass spectrometric measurement using the initiator compound BisF17. Examples of analytes that adsorb to this surface are small molecules, drugs, lipids, carbohydrates and peptides. Typically, NIMS is used to analyze samples ranging from simple analytical standards and proteolytic digests to more complex samples such as tissues, cells and biofluids. Critical experimental considerations of NIMS are described. Specifically, NIMS sensitivity is examined as a function of pre-etch cleaning treatment, etching current density, etching time, initiator composition, sample concentration, sample deposition method and laser fluence. Typically, NIMS surface preparation can be completed in less than 2 h. Subsequent sample preparation requires 1-5 min, depending on sample deposition method. Mass spectrometric data acquisition typically takes 1-30 s per sample.

Clathrate nanostructures for mass spectrometry.

The ability of mass spectrometry to generate intact biomolecular ions efficiently in the gas phase has led to its widespread application in metabolomics, proteomics, biological imaging, biomarker discovery and clinical assays (namely neonatal screens). Matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization have been at the forefront of these developments. However, matrix application complicates the use of MALDI for cellular, tissue, biofluid and microarray analysis and can limit the spatial resolution because of the matrix crystal size (typically more than 10 mum), sensitivity and detection of small compounds (less than 500 Da). Secondary-ion mass spectrometry has extremely high lateral resolution (100 nm) and has found biological applications although the energetic desorption/ionization is a limitation owing to molecular fragmentation. Here we introduce nanostructure-initiator mass spectrometry (NIMS), a tool for spatially defined mass analysis. NIMS uses 'initiator' molecules trapped in nanostructured surfaces or 'clathrates' to release and ionize intact molecules adsorbed on the surface. This surface responds to both ion and laser irradiation. The lateral resolution (ion-NIMS about 150 nm), sensitivity, matrix-free and reduced fragmentation of NIMS allows direct characterization of peptide microarrays, direct mass analysis of single cells, tissue imaging, and direct characterization of blood and urine.