An official website of the United States government
Tandem mass spectrometry (MS/MS) is a popular technology for identifying small molecules involved in metabolism, better known as metabolites. Coupled with liquid chromatography (LC), LC-MS/MS instruments first separate, ionize, and fragment metabolites, then measure mass-to-charge ratios (m/z) and intensities of metabolite fragments. Understanding metabolite fragmentation is crucial to develop computational tools for identifying metabolites based on this spectroscopic data. Metabolite fragmentation patterns have large variations making it especially difficult for computer scientists to design and implement metabolite identification approaches. To address this interdisciplinary challenge, this article presents FragView, a web-based application providing the web service for visualizing metabolite fragmentation. Users can break chemical bonds to produce metabolite fragments and export 3D fragment structures for 3D printing. Developing FragView is an opportunity for exposing student participants to this interdisciplinary bioinformatics project. This paper summarizes the experience of training student participants in bootcamps and designing the implementation plan based on student backgrounds. Students were exposed to project meeting discussions on coding and raw data visualization and visited a lab with an LC-MS/MS instrument. FragView is an open source, freely accessible tool, released under the GPLv3 license. We will continue to improve and update FragView in the future based on feedback.
more »
« less
This content will become publicly available on March 1, 2026
Analysis of plant metabolomics data using identification‐free approaches
Abstract Plant metabolomes are structurally diverse. One of the most popular techniques for sampling this diversity is liquid chromatography–mass spectrometry (LC‐MS), which typically detects thousands of peaks from single organ extracts, many representing true metabolites. These peaks are usually annotated using in‐house retention time or spectral libraries, in silico fragmentation libraries, and increasingly through computational techniques such as machine learning. Despite these advances, over 85% of LC‐MS peaks remain unidentified, posing a major challenge for data analysis and biological interpretation. This bottleneck limits our ability to fully understand the diversity, functions, and evolution of plant metabolites. In this review, we first summarize current approaches for metabolite identification, highlighting their challenges and limitations. We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance‐based approaches, information theory–based metrics, and discriminant analysis. Additionally, we explore their practical applications in plant science and highlight a set of useful tools to support researchers in analyzing complex plant metabolomics data. By adopting these approaches, researchers can enhance their ability to uncover new insights into plant metabolism.
more »
« less
- PAR ID:
- 10577747
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Applications in Plant Sciences
- ISSN:
- 2168-0450
- Subject(s) / Keyword(s):
- metabolomics LC-MS bioinformatics biochemistry plants
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Premise The specialized metabolites of plants are recognized as key chemical traits in mediating the ecology and evolution of sundry plant–biotic interactions, from pollination to seed predation. Intra‐ and interspecific patterns of specialized metabolite diversity have been studied extensively in leaves, but the diverse biotic interactions that contribute to specialized metabolite diversity encompass all plant organs. Focusing on two species of Psychotria shrubs, we investigated and compared patterns of specialized metabolite diversity in leaves and fruit with respect to each organ's diversity of biotic interactions. Methods To evaluate associations between biotic interaction diversity and specialized metabolite diversity, we combined UPLC‐MS metabolomic analysis of foliar and fruit specialized metabolites with existing surveys of leaf‐ and fruit‐centered biotic interactions. We compared patterns of specialized metabolite richness and variance among vegetative and reproductive tissues, among plants, and between species. Results In our study system, leaves interact with a far larger number of consumer species than do fruit, while fruit‐centric interactions are more ecologically diverse in that they involve antagonistic and mutualistic consumers. This aspect of fruit‐centric interactions was reflected in specialized metabolite richness—leaves contained more than fruit, while each organ contained over 200 organ‐specific specialized metabolites. Within each species, leaf‐ and fruit‐specialized metabolite composition varied independently of one another across individual plants. Contrasts in specialized metabolite composition were stronger between organs than between species. Conclusions As ecologically disparate plant organs with organ‐specific specialized metabolite traits, leaves and fruit can each contribute to the tremendous overall diversity of plant specialized metabolites.more » « less
-
Multispecies microbiome systems are known to be closely linked to human, animal, and plant life processes. The growing field of metabolomics presents the opportunity to detect changes in overall metabolomic profiles of microbial species interactions. These metabolomic changes provide insight into function of metabolites as they correlate to different species presence and the observed phenotypic changes, but detection of subtle changes is often difficult in samples with complex backgrounds. Natural environments such as soil and food contain many molecules that convolute mass spectrometry-based analyses, and identification of microbial metabolites amongst environmental metabolites is an informatics problem we begin to address here. Our microbes are grown on solid or liquid cheese curd media. This medium, which is necessary for microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analyses using LC-MS/MS data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analyses so that we may prioritize metabolites for further structure elucidation.more » « less
-
Abstract Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.more » « less
-
Abstract Symbiotic nitrogen (N) fixation entails successful interaction between legume hosts and rhizobia that occur in specialized organs called nodules. N-fixing legumes have a higher demand for phosphorus (P) than legumes grown on mineral N. Medicago truncatula is an important model plant for characterization of effects of P deficiency at the molecular level. Hence, a study was carried out to address the alteration in metabolite levels of M. truncatula grown aeroponically and subjected to 4 weeks of P stress. First, GC-MS-based untargeted metabolomics initially revealed changes in the metabolic profile of nodules, with increased levels of amino acids and sugars and a decline in amounts of organic acids. Subsequently, LC-MS/MS was used to quantify these compounds including phosphorylated metabolites in the whole plant. Our results showed a drastic reduction in levels of organic acids and phosphorylated compounds in –P leaves, with a moderate reduction in –P roots and nodules. Additionally, sugars and amino acids were elevated in the whole plant under P deprivation. These findings provide evidence that N fixation in M. truncatula is mediated through a N feedback mechanism that in parallel is related to carbon and P metabolism.more » « less
