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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗
DOE OSTI2024

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. Here, this paper introduces “molecular hypernetworks” (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nutrient and moisture limitations reveal keystone metabolites linking rhizosphere metabolomes and microbiomes

Plants release a wealth of metabolites into the rhizosphere that can shape the composition and activity of microbial communities in response to environmental stress. The connection between rhizodeposition and rhizosphere microbiome succession has been suggested, particularly under environmental stress conditions, yet definitive evidence is scarce. In this study, we investigated the relationship between rhizosphere chemistry, microbiome dynamics, and abiotic stress in the bioenergy crop switchgrass grown in a marginal soil under nutrient-limited, moisture-limited, and nitrogen (N)-replete, phosphorus (P)-replete, and NP-replete conditions. We combined 16S rRNA amplicon sequencing and LC-MS/MS-based metabolomics to link rhizosphere microbial communities and metabolites. We identified significant changes in rhizosphere metabolite profiles in response to abiotic stress and linked them to changes in microbial communities using network analysis. N-limitation amplified the abundance of aromatic acids, pentoses, and their derivatives in the rhizosphere, and their enhanced availability was linked to the abundance of bacterial lineages from Acidobacteria, Verrucomicrobia, Planctomycetes, and Alphaproteobacteria. Conversely, N-amended conditions increased the availability of N-rich rhizosphere compounds, which coincided with proliferation of Actinobacteria. Treatments with contrasting N availability differed greatly in the abundance of potential keystone metabolites; serotonin and ectoine were particularly abundant in N-replete soils, while chlorogenic, cinnamic, and glucuronic acids were enriched in N-limited soils. Serotonin, the keystone metabolite we identified with the largest number of links to microbial taxa, significantly affected root architecture and growth of rhizosphere microorganisms, highlighting its potential to shape microbial community and mediate rhizosphere plant–microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Metabolomics analysis of P. tremula × P. alba ‘717-1B4’ tissue culture hybrids grown in liquid media supplemented with EGGC and d4-EGGC

A metabolite linked to ethylene metabolism in Populus was recently structurally characterized as 2-hydroxyethyl β-D-glucopyranoside, an ethylene glycol glucose conjugate (EGGC). The dataset presented here is associated with the metabolomics analysis of various tissues (i.e., roots, stems/leaves) of plants grown in liquid media supplemented with EGGC and stable-isotope labelled EGGC (i.e., d4-EGGC). Data were collected using a Thermo Scientific gas chromatograph (GC) coupled to a Q Exactive Orbitrap mass spectrometer (MS). Samples were collected at three different timepoints and silylated prior to GCMS analysis.

09 BIOMASS FUELS↗
DOE OSTI2024

RNA-seq and metabolomic analyses of beneficial plant phenol biochemical pathways in red alder

Red alder ( Alnus rubra ) has highly desirable wood, dye pigment, and (traditional) medicinal properties which have been capitalized on for thousands of years, including by Pacific West Coast Native Americans. A rapidly growing tree species native to North American western coastal and riparian regions, it undergoes symbiosis with actinobacterium Frankia via their nitrogen-fixing root nodules. Red alder’s desirable properties are, however, largely attributed to its bioactive plant phenol metabolites, including for plant defense, for its attractive wood and bark coloration, and various beneficial medicinal properties. Integrated transcriptome and metabolome data analyses were carried out using buds, leaves, stems, roots, and root nodules from greenhouse grown red alder saplings with samples collected during different time-points (Spring, Summer, and Fall) of the growing season. Pollen and catkins were collected from field grown mature trees. Overall plant phenol biochemical pathways operative in red alder were determined, with a particular emphasis on potentially identifying candidates for the long unknown gateway entry points to the proanthocyanidin (PA) and ellagitannin metabolic classes, as well as in gaining better understanding of the biochemical basis of diarylheptanoid formation, i.e. that help define red alder’s varied medicinal uses, and its extensive wood and dye usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating inkjet dispenser/liquid vortex capture-mass spectrometry for single-cell metabolomics in Hep G2 steatosis caused by tamoxifen

Single-cell mass spectrometry (MS) is advancing our understanding of metabolic pathways in heterogeneous cell populations; however, many techniques are slow or require disruptive sample preparations. This study evaluated coupling a modified HP D100 single-cell inkjet dispenser with liquid vortex capture-mass spectrometry (D100/LVC-MS). The D100 is a single-cell inkjet dispenser capable of titrating solutions and isolating single cells via disposable cassettes equipped with microfluidic channels and an impedance sensor. The LVC-MS enables high-throughput capture, lysis, and ionization of analytes for mass spectrometric analysis. The D100/LVC-MS system was characterized through titration and single-cell experiments. Propranolol titration demonstrated linearity across a broad concentration range using the D100/LVC-MS system. Additionally, Hep G2 hepatocarcinoma cells and Chlamydomonas reinhardtii algae were used to showcase the D100’s high-throughput or low-buffer-volume single-cell dispensing strategies. The D100/LVC-MS system’s performance was validated by evaluating tamoxifen-induced steatosis in Hep G2 cells. Tamoxifen, associated with nonalcoholic fatty liver disease in breast cancer patients following long-term use, was tested in Hep G2 cells at 20 µM for 72 h against DMSO-treated controls. High-throughput analysis of 500 cells per condition, completed in 25 min per run, demonstrated the system’s efficiency. The D100/LVC-MS system simultaneously quantified tamoxifen and measured triglycerides and phosphatidylcholines. Triglycerides were upregulated in the tamoxifen-treated cells and the results indicated two distinct cell populations, differing in tamoxifen and phosphatidylcholines levels, suggesting heterogeneity within the treated population. In conclusion, these findings highlight the D100/LVC-MS system as a cost-effective, high-throughput platform for single-cell metabolomics and lipidomics, with significant potential for evaluating metabolic alterations.

Chromatography↗
DOE OSTI2025

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗
DOE OSTI2024

Cuticle development and the underlying transcriptome–metabolome associations during early seedling establishment

Abstract The plant cuticle is a complex extracellular lipid barrier that has multiple protective functions. This study investigated cuticle deposition by integrating metabolomics and transcriptomics data gathered from six different maize seedling organs of four genotypes, the inbred lines B73 and Mo17, and their reciprocal hybrids. These datasets captured the developmental transition of the seedling from heterotrophic skotomorphogenic growth to autotrophic photomorphogenic growth, a transition that is highly vulnerable to environmental stresses. Statistical interrogation of these data revealed that the predominant determinant of cuticle composition is seedling organ type, whereas the seedling genotype has a smaller effect on this phenotype. Gene-to-metabolite associations assessed by integrated statistical analyses identified three gene networks associated with the deposition of different elements of the cuticle: cuticular waxes; monomers of lipidized cell wall biopolymers, including cutin and suberin; and both of these elements. These gene networks reveal three metabolic programs that appear to support cuticle deposition, including processes of chloroplast biogenesis, lipid metabolism, and molecular regulation (e.g. transcription factors, post-translational regulators, and phytohormones). This study demonstrates the wider physiological metabolic context that can determine cuticle deposition and lays the groundwork for new targets for modulating the properties of this protective barrier.

Plant Sciences↗
DOE OSTI2024

New Onset Type 1 Diabetes Urine Metabolomics

Gas chromatography-mass spectrometry-based metabolomics to identify molecular signatures of Type 1 Diabetes (T1D) in urine. We utilize three cohorts in different stages post-diagnosis: (1) new onset, (2) within one year of diagnosis and (3) after 6 years of diagnosis. There were 91 metabolites identified in all three datasets with complete data represented in each cohort dataset. Cohort 1 (CNMC): 32 T1D cases; 32 healthy controls siblings Cohort 2 (BDCD): 27 T1D cases; 27 healthy controls siblings Cohort 3 (IUSOM): 12 T1D cases; 20 healthy controls Cases and controls matched on sex and age

Bramer, Lisa↗
NASA NTRS2017

Developmental Research in Space: Predicting Adult Neurobehavioral Phenotypes via Metabolomic Imaging

As human habitation and eventual colonization of space becomes an inevitable reality, there is a necessity to understand how organisms develop over the life span in the space environment. Microgravity, altered CO2, radiation and psychological stress are some of the key factors that could affect mammalian reproduction and development in space, however there is a paucity of information on this topic. Here we combine early (neonatal) in vivo spectroscopic imaging with an adult emotionality assay following a common obstetric complication (prenatal asphyxia) likely to occur during gestation in space. The neural metabolome is sensitive to alteration by degenerative changes and developmental disorders, thus we hypothesized that that early neonatal neurometabolite profiles can predict adult response to novelty. Late gestation fetal rats were exposed to moderate asphyxia by occluding the blood supply feeding one of the rats pair uterine horns for 15min. Blood supply to the opposite horn was not occluded (within-litter cesarean control). Further comparisons were made with vaginal (natural) birth controls. In one-week old neonates, we measured neurometabolites in three brain areas (i.e., striatum, prefrontal cortex, and hippocampus). Adult perinatally-asphyxiated offspring exhibited greater anxiety-like behavioral phenotypes (as measured the composite neurobehavioral assay involving open field activity, responses to novel object, quantification of fecal droppings, and resident-intruder tests of social behavior). Further, early neurometabolite profiles predicted adult responses. Non-invasive MRS screening of mammalian offspring is likely to advance ground-based space analogue studies informing mammalian reproduction in space, and achieving high-priority multigenerational research that will enable studies of the first truly space-developed mammals.

Schorn, Julia M.↗
NASA NTRS2017

Developmental Research in Space: Predicting Adult Neurobehavioral Phenotypes via Metabolomic Imaging

As human habitation and eventual colonization of space becomes an inevitable reality, there is a necessity to understand how organisms develop over the life span in the space environment. Microgravity, altered CO2, radiation and psychological stress are some of the key factors that could affect mammalian reproduction and development in space, however there is a paucity of information on this topic. Here we combine early (neonatal) in vivo spectroscopic imaging with an adult emotionality assay following a common obstetric complication (prenatal asphyxia) likely to occur during gestation in space. The neural metabolome is sensitive to alteration by degenerative changes and developmental disorders, thus we hypothesized that that early neonatal neurometabolite profiles can predict adult response to novelty. Late gestation fetal rats were exposed to moderate asphyxia by occluding the blood supply feeding one of the rats pair uterine horns for 15min. Blood supply to the opposite horn was not occluded (within-litter cesarean control). Further comparisons were made with vaginal (natural) birth controls. In one-week old neonates, we measured neurometabolites in three brain areas (i.e., striatum, prefrontal cortex, and hippocampus). Adult perinatally-asphyxiated offspring exhibited greater anxiety-like behavioral phenotypes (as measured the composite neurobehavioral assay involving open field activity, responses to novel object, quantification of fecal droppings, and resident-intruder tests of social behavior). Further, early neurometabolite profiles predicted adult responses. Non-invasive MRS screening of mammalian offspring is likely to advance ground-based space analogue studies informing mammalian reproduction in space, and achieving high-priority.

rodent development spacefligh↗
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metabolomics of related C3 and C4 Flaveria species indicate differences in the operation of photorespiration under fluctuating light

C 3 photosynthesis can be complemented with a C 4 carbon concentrating mechanism (CCM) to minimize photorespiratory losses. C 4 photosynthesis is often more efficient than C 3 under steady-state conditions. However, the C 4 CCM depends on inter-cellular metabolite concentration gradients, which must increase following increases in light intensity and could decrease rates of C 4 photosynthesis under fluctuating light. Additionally, incomplete flux through photorespiration could prove beneficial to C 4 assimilation during light induction of the CCM. Here, we compare metabolic profiles in the closely related C 3 Flaveria robusta and C 4 Flaveria bidentis during a light transient from low to high light to determine if these non-steady state accumulation patterns provide insight to the induction of the metabolite gradients needed to drive C 4 intermediate transport and if there is incomplete cycling of photorespiratory intermediates. In these C 3 and C 4 species, metabolite steady-state pool sizes suggest that C 4 transport acids maintain concentration gradients across the bundle sheath and mesophyll cell types under these light fluctuations. However, there was incomplete flux through photorespiration in the C 4 F. bidentis, which could reduce photorespiratory CO 2 loss via glycine decarboxylation and help maintain higher rates of assimilation during following induction periods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Time-resolved targeted metabolomics shows an abrupt switch from Calvin-Benson-Bassham cycle to tricarboxylic acid cycle when the light is turned off

Abstract In leaves, major CO₂ fluxes alternate between fixation by the Calvin-Benson-Bassham (CBB) cycle during light and release by the tricarboxylic acid (TCA) cycle in darkness. The speed at which leaf metabolism transitions between these pathways likely influences plant tolerance to fluctuating light conditions. To investigate these rapid metabolic shifts, we exposed leaves to ¹³CO₂ for 20 min to establish a quasi-steady state before abruptly turning off the light while maintaining ¹³CO₂ feeding. Within 10 s of dark transition, 3-phosphoglycerate levels rose significantly while most other CBB cycle intermediates decreased by more than 90%. Simultaneously, carbon accumulated in alanine, likely via pyruvate. Over the subsequent 10 min, six- and five-carbon TCA cycle intermediates steadily increased. In contrast, four-carbon TCA intermediates peaked at one minute, declined at three minutes, and rose again at 10 min, a pattern mirrored by most measured amino acids. These results reveal an exceptionally rapid metabolic reconfiguration from CO₂ fixation by the CBB cycle in light to TCA cycle activation for energy production in darkness, accompanied by substantial changes in amino acid metabolism.

Plant Sciences↗