Plant Metabolomics
Plant Metabolomics - Related Videos
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
2012
Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
13:16
Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics
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Cited by 4 •
2021
This paper presents the use of PSYCHE and SAPPHIRE-PSYCHE in the metabolic profiling of plants and includes detailed procedures for sample preparation and optimal Pure Shift NMR spectra recording. Examples through which the gain in resolution achieved by homonuclear decoupling allows a more comprehensive understanding of the system are discussed.
07:34
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
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Cited by 18 •
2013
Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.
14:18
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Cited by 28 •
2014
Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.
10:22
Transmitting Plant Viruses Using Whiteflies
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Cited by 41 •
2013
Improving our understanding of and our ability to manage many of the insect-transmitted plant viruses requires the use of the vector. Insect transmission of plant viruses is a tritrophic interaction, and as such requires the manipulation of insects, virus, and plant. Vectors must be reared in large numbers and manipulated in such a way as to insure high rates of transmission to test plants. The basics of rearing and manipulating the whitefly, Bemisa tabaci (Hemiptera: Aleyrodidae), a vector of...
FAQs
Metabolite profiles change because different tissues and developmental stages carry out distinct biological processes, while environmental conditions further modify those patterns. Comparing these profiles helps researchers associate particular metabolite changes with plant physiology, development, or adaptation. Such comparisons can reveal when and where metabolic responses occur rather than treating the plant as a chemically uniform system.
Liquid or gas chromatography separates metabolites before detection, while mass spectrometry measures their mass-related signals. Nuclear magnetic resonance spectroscopy provides another analytical route for profiling extracted compounds. Using these platforms allows researchers to examine metabolite patterns through complementary measurement approaches, helping select an analytical strategy suited to the biological comparison under investigation.
Statistical analysis identifies consistent differences or associations within metabolite measurements, and bioinformatic analysis helps relate those patterns to biological processes and metabolic pathways. When a metabolite pattern repeatedly accompanies a characteristic, it may serve as a biomarker associated with a plant trait. These associations help interpret large datasets in biological terms.
A typical workflow begins with collecting plant samples that represent the tissues, developmental stages, or environmental conditions being compared. Metabolites are then extracted and profiled with chromatography-based mass spectrometry or nuclear magnetic resonance spectroscopy. Finally, statistical and bioinformatic analyses evaluate the resulting patterns, linking differences in metabolite levels with biological processes or traits.
The approach is useful when researchers need to compare how plant metabolism changes under contrasting environmental conditions. Profiles from plants exposed to drought, pathogens, or different nutrient conditions can reveal condition-associated metabolite patterns. These results support investigation of plant responses and adaptation, while also helping identify biomarkers linked to particular environmental or physiological states.
Metabolite patterns can identify biomarkers associated with plant traits, giving researchers measurable chemical features to examine alongside crop characteristics. In crop improvement, these associations may help characterize biologically relevant variation. For food quality assessment, profiling provides information about the small-molecule composition of plant material. The same data also support broader studies of plant physiology and adaptation.