Establishment and Application of a Metabolomics Workflow for Identification and Profiling of Volatiles from Leaves of <i>Vitis vinifera</i> by HS‐SPME‐GC‐MS

Weingart, Georg; Kluger, Bernhard; Forneck, Astrid; Krska, Rudolf; Schuhmacher, Rainer

doi:10.1002/pca.1364

Cited by 36 publications

(45 citation statements)

References 51 publications

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“…In general, SPME facilitates rapid sample preparation, simplifies extraction steps, enriches metabolites and introduces metabolites into an analytical instrument with one solventfree step [7]. Therefore, the application of SPME has widely enhanced the identification of targeted volatiles in numerous crop and medicinal plants, examples of which include apple [8], Vitis vinifera [9], Artemisia argyi [10], Artemisia annua [11] and tobacco.…”

Section: -Damascenone and Megastigmatrienones (Includingmentioning

confidence: 99%

Integration of GC-MS Based Non-Targeted Metabolic Profiling with Headspace Solid Phase Microextraction Enhances the Understanding of Volatile Differentiation in Tobacco Leaves from North Carolina, India and Brazil

Ma¹,

Gandra²,

Sharma³

et al. 2012

AJPS

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In this report, gas chromatography-mass spectrometry (GC-MS) based non-targeted metabolomics is used to develop appropriate headspace solid phase microextractions (HS-SPME) to enhance the understanding of volatile complexity of flue-cured tobacco leaves. Non-targeted metabolic profiling of GC-MS shows that the extraction condition of HS-SPME at 100˚C for 30 min provides a better metabolite profile than other extraction conditions tested. GC-MS and principal component analyses (PCA) show that among five types of fibers tested, 100 μm polydimethylsiloxane (PMDS), 65 μm polydimethylsiloxane/divinylbenzene (PMDS/DVB) and 75 μm carboxen/polydimethylsiloxane (CAR/ PMS) provide a better reproducible metabolite profile. Based on an appropriate PDMS extraction condition optimized, we use GC-MS analysis and PCA to compare metabolite profiles in flue-cured leaves of tobacco plants grown in North Carolina, India and Brazil, respectively. The resulting data of PCA show that the global metabolic profiles in North Carolina samples are separated from those in Brazil and India samples, two groups of which are characterized by a partially overlapped pattern. Several peaks that were differentially accumulated in samples were annotated to known metabolites by deconvolution analysis, such as norsolanadione, solavetivone and rishitin. Norsolanadione is detected only in Brazil samples. Solavetivone is detected in samples of India and Brazil but not in those of North Carolina. Rishitin is detected in samples of North Carolina and India but not in Brazil samples. These data indicate that not only can a non-targeted metabolic profiling approach enhance the understanding of volatile complexity, but also can identify marker volatile metabolites in tobacco leaves produced in different growth regions.

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Section: -Damascenone and Megastigmatrienones (Includingmentioning

confidence: 99%

Integration of GC-MS Based Non-Targeted Metabolic Profiling with Headspace Solid Phase Microextraction Enhances the Understanding of Volatile Differentiation in Tobacco Leaves from North Carolina, India and Brazil

Ma¹,

Gandra²,

Sharma³

et al. 2012

AJPS

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“…VOCs of the library can be taken from one's own measurements of typical plant samples and subsequent annotation/identification of metabolites and/or from the literature. A more detailed description of how this library is established can be found elsewhere [46][47][48]. When using more than one GC stationary phase, it is necessary to create separate libraries with RIs for each column type.…”

Section: Data Processing With Amdismentioning

confidence: 99%

“…Unfortunately, there are no standardized rules on how to identify a metabolite correctly. Different approaches are discussed, for example, in Refs [46][47][48][49][50].…”

Section: Generation Of Ri Calibration Filementioning

confidence: 99%

“…According to Refs [46][47][48], we use a minimum match factor of 90 and a maximum relative RI deviation of AE2% if the reference value is from the literature. In the case of authentic standards measured within a short time interval to the sequence containing the samples, a lower minimum mass spectral match factor of, for example, 60 and a narrower RI window of, for example, AE5 RI units has been used in the authors' laboratory.…”

Section: Generation Of Ri Calibration Filementioning

confidence: 99%

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Study of the Volatile Metabolome in Plant–Insect Interactions

Weingart

Lawo

Forneck

et al. 2013

The Handbook of Plant Metabolomics

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“…For the purpose of PyMS evaluation we have designed several experiments, including a mixture of 45 metabolites representing a variety of chemical classes (sugars, organic acids, amino acids, sugar phosphates), and a series of experiments where the sample was foetal calf serum spiked with quantitative amounts of metabolite standards. The performance of PyMS was compared to several leading software packages, including AMDIS [24], one of the most widely used freely available software for GC-MS data processing [37-39]; XCMS [15], representing the new generation software for MS data processing implemented in R; and AnalyzerPro (SpectralWorks, Runcorn, United Kingdom) a widely used commercial GC-MS software package. We show that when considered in realistic data processing scenarios PyMS performance is robust, and compares favorably to these software packages.…”

Section: Introductionmentioning

confidence: 99%

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

et al. 2012

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BackgroundGas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.ResultsPyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).ConclusionsPyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

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Establishment and Application of a Metabolomics Workflow for Identification and Profiling of Volatiles from Leaves of Vitis vinifera by HS‐SPME‐GC‐MS

Abstract: The developed workflow enabled the identification of grapevine leaf metabolites, which allowed the separation of leaves from two sampling dates by PCA.

Cited by 36 publications

References 51 publications

Integration of GC-MS Based Non-Targeted Metabolic Profiling with Headspace Solid Phase Microextraction Enhances the Understanding of Volatile Differentiation in Tobacco Leaves from North Carolina, India and Brazil

Integration of GC-MS Based Non-Targeted Metabolic Profiling with Headspace Solid Phase Microextraction Enhances the Understanding of Volatile Differentiation in Tobacco Leaves from North Carolina, India and Brazil

Study of the Volatile Metabolome in Plant–Insect Interactions

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

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