GC-MS metabolomics is applicable for qualitative and quantitative analysis of unknown components in complex mixtures. It allows for the determination of molecular structures and accurate measurement of molecular weights, making it suitable for trace analysis of metabolites in plant samples. For volatile components, direct qualitative and quantitative analysis is feasible, while for non-volatile and thermally unstable substances, appropriate derivatization can be performed prior to analysis. With advantages such as high sensitivity, robust qualitative capabilities, and reliability, GC-MS enables the simultaneous separation and identification of target components. The application of electron ionization in GC-MS results in the generation of extensive and highly reproducible fragmentation patterns. Even when mass spectral data is not available in databases, these fragmentation patterns can be utilized to obtain additional information about the qualitative aspects or types of metabolites or compounds.
Creative Proteomics is a leading provider of comprehensive GC-MS plant metabolomics services. With a commitment to precision and innovation, our GC-MS plant metabolomics services facilitate both qualitative and quantitative analyses of unknown components within complex mixtures in plant samples. Leveraging the sensitivity, robust qualitative capabilities, and reliability of GC-MS technology, Creative Proteomics delivers accurate insights into the molecular structures and weights of metabolites present in plants. Our services cover a broad spectrum, from trace analysis of volatile components to the examination of non-volatile and thermally unstable substances through suitable derivatization methods. With a focus on high-standardized electron ionization applications, our GC-MS services generate extensive and reproducible fragmentation patterns, providing valuable information even when mass spectral data is not available in databases.
Wide Analytical Range: GC-MS excels in detecting diverse compounds, addressing challenges with thermally unstable substances. This capability ensures comprehensive compound identification.
Strong Separation Capability: In situations lacking complete chromatogram separation, MS enables effective component differentiation. This facilitates precise qualitative and quantitative analyses.
Reliable Qualitative Analysis: Simultaneous molecular weight and structural information by GC-MS ensure reliable qualitative analysis, revealing compound identity and structure.
Low Detection Limits: High sensitivity and Selected Ion Monitoring (SIM) in GC-MS significantly lower detection limits, identifying trace amounts for enhanced accuracy.
Rapid Analysis: Efficient gas chromatography with narrow-bore columns reduces analysis time in GC-MS, allowing high-throughput studies without compromising data quality.
High Automation Level: GC-MS's automation streamlines processes, enhancing efficiency and minimizing human error for reproducible and reliable results.
Case. Metabolomic Profiling of Volatile Organic Compounds Secreted by Plant Growth-Promoting Rhizobacteria
Background
Plant Growth-Promoting Rhizobacteria (PGPR) play a crucial role in enhancing plant growth and providing protection against pathogens. Understanding the specific mechanisms of PGPR, particularly the diverse volatile organic compounds (VOCs) they secrete, is essential for harnessing their potential in sustainable agriculture. This study focuses on the metabolomic profiling of VOCs to uncover strain-specific features influencing plant-microbe interactions.
Samples
Four strains of PGPR were investigated: Ps. fluorescens (N04), Ps. koreensis (N19), Pa. alvei (T19), and L. sphaericus (T22). The study aimed to compare their volatile profiles and identify unique characteristics contributing to their plant growth-promoting and pathogen-inhibiting capabilities.
Technological Methods
The study employed high-resolution GC–HR–time-of-flight–MS (Gas Chromatography-High Resolution-Time of Flight-Mass Spectrometry) for metabolomic analysis of the VOCs. This technology allows for concurrent detection of multiple analytes with high sensitivity. Data pre-processing and chemometric techniques were utilized to extract underlying features contributing to observed differences in volatile profiles. Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Orthogonal Projection to Latent Discriminant Analysis (OPLS-DA) were employed for data exploration, clustering, and identification of significant features.
Results
The results revealed complex and strain-specific VOC profiles among the investigated PGPR strains. PCA showed distinct groupings, with Ps. koreensis (N19) and Ps. fluorescens (N04) clustering closely, while L. sphaericus (T22) exhibited a unique profile. HCA further divided strains into clusters, emphasizing differences in volatile profiles. OPLS-DA models were used to extract features responsible for dissimilarities, and permutation tests validated model predictability.
In total, 121 VOCs were identified, belonging to various classes such as aldehydes, alcohols, ketones, acids, and terpenoids. The comparison of Pseudomonas strains revealed shared and unique VOCs, including plant hormone derivatives like methyl salicylate. Additionally, the study highlighted Pa. alvei (T22) as having the highest number of unique VOCs, potentially contributing to its efficacy in inducing/enhancing resistance against phytopathogens.
This research underscores the specificity of VOC profiles among PGPR strains and emphasizes the potential of metabolomic approaches for deeper insights into their plant-protective mechanisms.
Exploratory data analysis of volatile organic compounds produced by the four PGPR strains (N19, N04, T19, T22) with unsupervised chemometric methods.
OPLS-DA modeling and variable/feature selection of Lysinibacillus sphaericus (T22) data acquired on GC–TOF–MS.
Venn diagram comparing the volatile organic compounds of the two Pseudomonas strains.
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