Preserved and variable spatial-chemical changes of lipids across tomato leaves in response to central vein wounding reveals potential origin of linolenic acid in signal transduction cascade

Membrane lipids serve as substrates for the generation of numerous signaling lipids when plants are exposed to environmental stresses, and jasmonic acid, an oxidized product of 18-carbon unsaturated fatty acids (e.g., linolenic acid), has been recognized as the essential signal in wound-induced gene expression. Yet, the contribution of individual membrane lipids in linolenic acid generation is ill-defined. In this work, we performed spatial lipidomic experiments to track lipid changes that occur locally at the sight of leaf injury to better understand the potential origin of linolenic and linoleic acids from individual membrane lipids. The central veins of tomato leaflets were crushed using surgical forceps, leaves were cryosectioned and analyzed by two orthogonal matrix-assisted laser desorption/ionization mass spectrometry imaging platforms for insight into lipid spatial distribution. Significant changes in lipid composition are only observed 30 min after wounding, while after 60 min lipidome homeostasis has been re-established. Phosphatidylcholines exhibit a variable pattern of spatial behavior in individual plants. Among lysolipids, lysophosphatidylcholines strongly co-localize with the injured zone of wounded leaflets, while, for example, lysophosphatidylglycerol (LPG) (16:1) accumulated preferentially toward the apex in the injured zone of wounded leaflets. In contrast, two other LPGs (LPG [18:3] and LPG [18:2]) are depleted in the injured zone. Our high-resolution co-localization imaging analyses suggest that linolenic acids are predominantly released from PCs with 16_18 fatty acid composition along the entire leaf, while it seems that in the apex zone PG (16:1_18:3) significantly contributes to the linolenic acid pool. These results also indicate distinct localization and/or substrate preferences of phospholipase isoforms in leaf tissue.

A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. We describe a "follow the tissue" pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.

Storage Conditions of Human Kidney Tissue Sections Affect Spatial Lipidomics Analysis Reproducibility

Lipids often are labile, unstable, and tend to degrade overtime, so it is of the upmost importance to study these molecules in their most native state. We sought to understand the optimal storage conditions for spatial lipidomic analysis of human kidney tissue sections. Specifically, we evaluated human kidney tissue sections on several different days throughout the span of a week using our established protocol for elucidating lipids using high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We studied kidney tissue sections stored under five different conditions: open stored at -80 °C, vacuumed sealed and stored at -80 °C, with matrix preapplied before storage at -80 °C, under a nitrogen atmosphere and stored at -80 °C, and at room temperature in a desiccator. Results were compared to data obtained from kidney tissue sections that were prepared and analyzed immediately after cryosectioning. Data was processed using METASPACE. After a week of storage, the sections stored at room temperature showed the largest amount of lipid degradation, while sections stored under nitrogen and at -80 °C retained the greatest number of overlapping annotations in relation to freshly cut tissue. Overall, we found that molecular degradation of the tissue sections was unavoidable over time, regardless of storage conditions, but storing tissue sections in an inert gas at low temperatures can curtail molecular degradation within tissue sections.

Activity-Based Protein Profiling of Chitin Catabolism

The microbial catabolism of chitin, an abundant and ubiquitous environmental organic polymer, is a fundamental cog in terrestrial and aquatic carbon and nitrogen cycles. Despite the importance of this critical bio-geochemical function, there is a limited understanding of the synergy between the various hydrolytic and accessory enzymes involved in chitin catabolism. To address this deficit, we synthesized activity-based probes (ABPs) designed to target active chitinolytic enzymes by modifying the chitin subunits N-acetyl glucosamine and chitotriose. The ABPs were used to determine the active complement of chitinolytic enzymes produced over time by the soil bacterium Cellvibrio japonicus treated with various C substrates. We demonstrate the utility of these ABPs in determining the synergy between various enzymes involved in chitin catabolism. The strategy can be used to gain molecular-level insights that can be used to better understand microbial roles in soil bio-geochemical cycling in the face of a changing climate.

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

Metabolomic profiling of wild-type and mutant soybean root nodules using laser-ablation electrospray ionization mass spectrometry reveals altered metabolism

The establishment of the nitrogen-fixing symbiosis between soybean and Bradyrhizobium japonicum is a complex process. To document the changes in plant metabolism as a result of symbiosis, we utilized laser ablation electrospray ionization-mass spectrometry (LAESI-MS) for in situ metabolic profiling of wild-type nodules, nodules infected with a B. japonicum nifH mutant unable to fix nitrogen, nodules doubly infected by both strains, and nodules formed on plants mutated in the stearoyl-acyl carrier protein desaturase (sacpd-c) gene, which were previously shown to have an altered nodule ultrastructure. The results showed that the relative abundance of fatty acids, purines, and lipids was significantly changed in response to the symbiosis. The nifH mutant nodules had elevated levels of jasmonic acid, correlating with signs of nitrogen deprivation. Nodules resulting from the mixed inoculant displayed similar, overlapping metabolic distributions within the sectors of effective (fix+ ) and ineffective (nifH mutant, fix- ) endosymbionts. These data are inconsistent with the notion that plant sanctioning is cell autonomous. Nodules lacking sacpd-c displayed an elevation of soyasaponins and organic acids in the central necrotic regions. The present study demonstrates the utility of LAESI-MS for high-throughput screening of plant phenotypes. Overall, nodules disrupted in the symbiosis were elevated in metabolites related to plant defense.

Deep learning predicts microbial interactions from self-organized spatiotemporal patterns

Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

Sequential Ammonia and Carbon Dioxide Adsorption on Pyrolyzed Biomass to Recover Waste Stream Nutrients

The amine-rich surfaces of pyrolyzed human solid waste (py-HSW) can be "primed" or "regenerated" with carbon dioxide (CO₂) to enhance their adsorption of ammonia (NH₃) for use as a soil amendment. To better understand the mechanism by which CO₂ exposure facilitates NH₃ adsorption to py-HSW, we artificially enriched a model sorbent, pyrolyzed, oxidized wood (py-ox wood) with amine functional groups through exposure to NH₃. We then exposed these N-enriched materials to CO₂ and then resorbed NH₃. The high heat of CO₂ adsorption (Q _st) on py-HSW, 49 kJ mol^-1, at low surface coverage, 0.4 mmol CO₂ g^-1, showed that the naturally occurring N compounds in py-HSW have a high affinity for CO₂. The Q _st of CO₂ on py-ox wood also increased after exposure to NH₃, reaching 50 kJ mol^-1 at 0.7 mmol CO₂ g^-1, demonstrating that the incorporation of N-rich functional groups by NH₃ adsorption is favorable for CO₂ uptake. Adsorption kinetics of py-ox wood revealed continued, albeit diminishing NH₃ uptake after each CO₂ treatment, averaging 5.9 mmol NH₃ g^-1 for the first NH₃ exposure event and 3.5 and 2.9 mmol NH₃ g^-1 for the second and third; the electrophilic character of CO₂ serves as a Lewis acid, enhancing surface affinity for NH₃ uptake. Furthermore, penetration of ¹⁵NH₃ and ¹³CO₂ measured by NanoSIMS reached over 7 μm deep into both materials, explaining the large NH₃ capture. We expected similar NH₃ uptake in py-HSW sorbed with CO₂ and py-ox wood because both materials, py-HSW and py-ox wood sorbed with NH₃, had similar N contents and similarly high CO₂ uptake. Yet NH₃ sorption in py-HSW was unexpectedly low, apparently from potassium (K) bicarbonate precipitation, reducing interactions between NH₃ and sorbed CO₂; 2-fold greater surface K in py-HSW was detected after exposure to CO₂ and NH₃ than before gas exposure. We show that amine-rich pyrolyzed waste materials have high CO₂ affinity, which facilitates NH₃ uptake. However, high ash contents as found in py-HSW hinder this mechanism.

An approach for visualizing the spatial metabolome of an entire plant root system inspired by the Swiss-rolling technique

The spatial configuration and morphology of roots are commonly monitored for a better understanding of plant health and development. However, this approach provides minimal details about the biochemistry regulating the observable traits. Therefore, the ability to metabolically map the entire root structure would be of major value. Here, we developed a sample preparation approach that enables imaging of the entire root within a restricted space (width of microscope slide), which was influenced by the Swiss-rolling technique. We were able to image and confidently identify molecules along the entire root structure from rolled-root tissue sections using multiple spatially resolved mass spectrometry approaches.

Response Surface Methodology As a New Approach for Finding Optimal MALDI Matrix Spraying Parameters for Mass Spectrometry Imaging

Automated spraying devices have become ubiquitous in laboratories employing matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in part because they permit control of a number of matrix application parameters that can easily be reproduced for intra- and interlaboratory studies. Determining the optimal parameters for MALDI matrix application, such as temperature, flow rate, spraying velocity, number of spraying cycles, and solvent composition for matrix application, is critical for obtaining high-quality MALDI-MSI data. However, there are no established approaches for optimizing these multiple parameters simultaneously. Instead optimization is performed iteratively (i.e., one parameter at a time), which is time-consuming and can lead to overall nonoptimal settings. In this report, we demonstrate the use a novel experimental design and the response surface methodology to optimize five parameters of MALDI matrix application using a robotic sprayer. Thirty-two combinations of MALDI matrix spraying conditions were tested, which allowed us to elucidate relationships between each of the application parameters as determined by MALDI-MS (specifically, using a 15 T Fourier transform ion cyclotron resonance mass spectrometer). As such, we were able to determine the optimal automated spraying parameters that minimized signal delocalization and enabled high MALDI sensitivity. We envision this optimization strategy can be utilized for other matrix application approaches and MALDI-MSI analyses of other molecular classes and tissue types.

In-Situ Metabolomic Analysis of Setaria viridis Roots Colonized by Beneficial Endophytic Bacteria

Over the past decades, crop yields have risen in parallel with increasing use of fossil fuel-derived nitrogen (N) fertilizers but with concomitant negative impacts on climate and water resources. There is a need for more sustainable agricultural practices, and biological nitrogen fixation (BNF) could be part of the solution. A variety of nitrogen-fixing, epiphytic, and endophytic plant growth-promoting bacteria (PGPB) are known to stimulate plant growth. However, compared with the rhizobium-legume symbiosis, little mechanistic information is available as to how PGPB affect plant metabolism. Therefore, we investigated the metabolic changes in roots of the model grass species Setaria viridis upon endophytic colonization by Herbaspirillum seropedicae SmR1 (fix⁺) or a fix^- mutant strain (SmR54) compared with uninoculated roots. Endophytic colonization of the root is highly localized and, hence, analysis of whole-root segments dilutes the metabolic signature of those few cells impacted by the bacteria. Therefore, we utilized in-situ laser ablation electrospray ionization mass spectrometry to sample only those root segments at or adjacent to the sites of bacterial colonization. Metabolites involved in purine, zeatin, and riboflavin pathways were significantly more abundant in inoculated plants, while metabolites indicative of nitrogen, starch, and sucrose metabolism were reduced in roots inoculated with the fix^- strain or uninoculated, presumably due to N limitation. Interestingly, compounds, involved in indole-alkaloid biosynthesis were more abundant in the roots colonized by the fix^- strain, perhaps reflecting a plant defense response.

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

INTRODUCTION

Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.

OBJECTIVES

In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.

METHODS

DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).

RESULTS

Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.

CONCLUSION

Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.

Spatiotemporal Transformation in the Alkaloid Profile of Pinus Roots in Response to Mycorrhization

Root alkaloids remain highly unexplored in ectomycorrhizae development studies. By employing ultrahigh mass resolution mass spectrometry imaging techniques, we showed substantial relocation and transformation of piperidine alkaloids in pine root tips in response to Suillus mycorrhization. We imaged, in the time frame of ectomycorrhizae formation, a completely different alkaloid profile in Pinus strobus, where basidiospores of Suillus spraguei induce morphogenesis of symbiotic tissues, than in Pinus taeda, where such interaction fails to induce morphogenesis. On the basis of spatial colocalization studies, we proposed some alternative routes for biosynthesis of these alkaloids that supplement existing literature data.

Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution Using Laser Ablation Electrospray Ionization 21 Tesla FTICR Mass Spectrometry

Mass spectrometry (MS) is an indispensable analytical tool to capture the array of metabolites within complex biological systems. However, conventional MS-based metabolomic workflows require extensive sample processing and separation resulting in limited throughput and potential alteration of the native molecular states in these systems. Ambient ionization methods, capable of sampling directly from tissues, circumvent some of these issues but require high-performance MS to resolve the molecular complexity within these samples. Here, we demonstrate a unique combination of laser ablation electrospray ionization (LAESI) coupled with a 21 tesla Fourier transform ion cyclotron resonance (21T-FTICR) for direct MS analysis and imaging applications. This analytical platform provides isotopic fine structure information directly from biological tissues, enabling the rapid assignment of molecular formulas and delivering a higher degree of confidence for molecular identification.

Insights into the histology of planarian flatworm Phagocata gracilis based on location specific, intact lipid information provided by GCIB-ToF-SIMS imaging

Planarian flatworms are known as the masters of regeneration, re-growing an entire organism from as little as 1/279th part of their body. While the proteomics of these processes has been studied extensively, the planarian lipodome remains relatively unknown. In this study we investigate the lipid profile of planarian tissue sections with imaging Time-of-Flight - Secondary-Ion-Mass-Spectrometry (ToF-SIMS). ToF-SIMS is a label-free technique capable of gathering intact, location specific lipid information on a cellular scale. Lipid identities are confirmed using LC-MS/MS. Our data shows that different organ structures within planarians have unique lipid profiles. The 22-carbon atom poly unsaturated fatty acids (PUFAs) which occur in unusually high amounts in planarians are found to be mainly located in the testes. Additionally, we observe that planarians contain various odd numbered fatty acid species, that are usually found in bacteria, localized in the reproductive and ectodermal structures of the planarian. An abundance of poorly understood ether fatty acids and ether lipids were found in unique areas in planarians as well as a new, yet unidentified class of potential lipids in planarian intestines. Identifying the location of these lipids in the planarian body provides insights into their bodily functions and, in combination with knowledge about their diet and their genome, enables drawing conclusions about planarian fatty acid processing.

Towards resolving the spatial metabolome with unambiguous molecular annotations in complex biological systems by coupling mass spectrometry imaging with structures for lossless ion manipulations

We demonstrate the coupling of liquid extraction surface analysis (LESA) to structures for lossless ion manipulations in conjunction with serpentine ultralong path with extending routing (SLIM SUPER) ion mobility-mass spectrometry (IM-MS) for the unambiguous annotation of important isomeric glycoforms in carbon-fixing communities.

Observed metabolic asymmetry within soybean root nodules reflects unexpected complexity in rhizobacteria-legume metabolite exchange

In this study, the three-dimensional spatial distributions of a number of metabolites involved in regulating symbiosis and biological nitrogen fixation (BNF) within soybean root nodules were revealed using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). While many metabolites exhibited distinct spatial compartmentalization, some metabolites were asymmetrically distributed throughout the nodule (e.g., S-adenosylmethionine). These results establish a more complex metabolic view of plant-bacteria symbiosis (and BNF) within soybean nodules than previously hypothesized. Collectively these findings suggest that spatial perspectives in metabolic regulation should be considered to unravel the overall complexity of interacting organisms, like those relating to associations of nitrogen-fixing bacteria with host plants.

Observed metabolic asymmetry within soybean root nodules reflects unexpected complexity in rhizobacteria-legume metabolite exchange

In this study, the three-dimensional spatial distributions of a number of metabolites involved in regulating symbiosis and biological nitrogen fixation (BNF) within soybean root nodules were revealed using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). While many metabolites exhibited distinct spatial compartmentalization, some metabolites were asymmetrically distributed throughout the nodule (e.g., S-adenosylmethionine). These results establish a more complex metabolic view of plant-bacteria symbiosis (and BNF) within soybean nodules than previously hypothesized. Collectively these findings suggest that spatial perspectives in metabolic regulation should be considered to unravel the overall complexity of interacting organisms, like those relating to associations of nitrogen-fixing bacteria with host plants.

Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

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.

Metabolic Noise and Distinct Subpopulations Observed by Single Cell LAESI Mass Spectrometry of Plant Cells in situ

Phenotypic variations and stochastic expression of transcripts, proteins, and metabolites in biological tissues lead to cellular heterogeneity. As a result, distinct cellular subpopulations emerge. They are characterized by different metabolite expression levels and by associated metabolic noise distributions. To capture these biological variations unperturbed, highly sensitive in situ analytical techniques are needed that can sample tissue embedded single cells with minimum sample preparation. Optical fiber-based laser ablation electrospray ionization mass spectrometry (f-LAESI-MS) is a promising tool for metabolic profiling of single cells under ambient conditions. Integration of this MS-based platform with fluorescence and brightfield microscopy provides the ability to target single cells of specific type and allows for the selection of rare cells, e.g., excretory idioblasts. Analysis of individual Egeria densa leaf blade cells (n = 103) by f-LAESI-MS revealed significant differences between the prespecified subpopulations of epidermal cells (n = 97) and excretory idioblasts (n = 6) that otherwise would have been masked by the population average. Primary metabolites, e.g., malate, aspartate, and ascorbate, as well as several glucosides were detected in higher abundance in the epidermal cells. The idioblasts contained lipids, e.g., PG(16:0/18:2), and triterpene saponins, e.g., medicoside I and azukisaponin I, and their isomers. Metabolic noise for the epidermal cells were compared to results for soybean (Glycine max) root nodule cells (n = 60) infected by rhizobia (Bradyrhizobium japonicum). Whereas some primary metabolites showed lower noise in the latter, both cell types exhibited higher noise for secondary metabolites. Post hoc grouping of epidermal and root nodule cells, based on the abundance distributions for certain metabolites (e.g., malate), enabled the discovery of cellular subpopulations characterized by different mean abundance values, and the magnitudes of the corresponding metabolic noise. Comparison of prespecified populations from epidermal cells of the closely related E. densa (n = 20) and Elodea canadensis (n = 20) revealed significant differences, e.g., higher sugar content in the former and higher levels of ascorbate in the latter, and the presence of species-specific metabolites. These results demonstrate that the f-LAESI-MS single cell analysis platform has the potential to explore cellular heterogeneity and metabolic noise for hundreds of tissue-embedded cells.

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS2 Increases Confidence in Both Molecular Identification and Localization

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC-MS²) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS²I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria, and fungus. We found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.

Laser-ablation electrospray ionization mass spectrometry with ion mobility separation reveals metabolites in the symbiotic interactions of soybean roots and rhizobia

Technologies enabling in situ metabolic profiling of living plant systems are invaluable for understanding physiological processes and could be used for rapid phenotypic screening (e.g., to produce plants with superior biological nitrogen-fixing ability). The symbiotic interaction between legumes and nitrogen-fixing soil bacteria results in a specialized plant organ (i.e., root nodule) where the exchange of nutrients between host and endosymbiont occurs. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is a method that can be performed under ambient conditions requiring minimal sample preparation. Here, we employed LAESI-MS to explore the well characterized symbiosis between soybean (Glycine max L. Merr.) and its compatible symbiont, Bradyrhizobium japonicum. The utilization of ion mobility separation (IMS) improved the molecular coverage, selectivity, and identification of the detected biomolecules. Specifically, incorporation of IMS resulted in an increase of 153 differentially abundant spectral features in the nodule samples. The data presented demonstrate the advantages of using LAESI-IMS-MS for the rapid analysis of intact root nodules, uninfected root segments, and free-living rhizobia. Untargeted pathway analysis revealed several metabolic processes within the nodule (e.g., zeatin, riboflavin, and purine synthesis). Compounds specific to the uninfected root and bacteria were also detected. Lastly, we performed depth profiling of intact nodules to reveal the location of metabolites to the cortex and inside the infected region, and lateral profiling of sectioned nodules confirmed these molecular distributions. Our results established the feasibility of LAESI-IMS-MS for the analysis and spatial mapping of plant tissues, with its specific demonstration to improve our understanding of the soybean-rhizobial symbiosis.

Constant-Distance Mode Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging of Biological Samples with Complex Topography

A new approach for constant-distance mode mass spectrometry imaging (MSI) of biological samples using nanospray desorption electrospray ionization (nano-DESI) was developed by integrating a shear-force probe with the nano-DESI probe. The technical concept and basic instrumental setup, as well as the general operation of the system are described. Mechanical dampening of resonant oscillations due to the presence of shear forces between the probe and the sample surface enabled the constant-distance imaging mode via a computer-controlled closed-feedback loop. The capability of simultaneous chemical and topographic imaging of complex biological samples is demonstrated using living Bacillus subtilis ATCC 49760 colonies on agar plates. The constant-distance mode nano-DESI MSI enabled imaging of many metabolites, including nonribosomal peptides (surfactin, plipastatin, and iturin) on the surface of living bacterial colonies, ranging in diameter from 10 to 13 mm, with height variations up to 0.8 mm above the agar plate. Co-registration of ion images to topographic images provided higher-contrast images. Based on this effort, constant-mode nano-DESI MSI proved to be ideally suited for imaging biological samples of complex topography in their native states.

Quantifying element incorporation in multispecies biofilms using nanoscale secondary ion mass spectrometry image analysis

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.