Evaluation of automated sample preparation, retention time locked gas chromatography–mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species

Gu, Qun; David, Frank; Lynen, Frédéric; Rumpel, Klaus; Dugardeyn, Jasper; Straeten, Dominique Van Der; Xu, Guowang; Sandra, Patrick

doi:10.1016/j.chroma.2011.01.024

Cited by 36 publications

(32 citation statements)

References 32 publications

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“…On the contrary, while derivatising manually, there was a variation in waiting time between injecting the first and latter samples, which affects the reproducibility and quality of the final data [12,22]. Many metabolites are degraded or new derivatives (most probably breakdown products) may develop while the sample is waiting on the autosampler to be injected.…”

Section: Resultsmentioning

confidence: 99%

“…It increases the overall throughput because of the overlapping of samples. Thus, this method saves researcher time and also reduces the variability between samples injected at different time intervals [12,22]. …”

Section: Resultsmentioning

confidence: 99%

“…It is well known that the majority of TMS derivatives are unstable; derivatisation of more than a small batch of samples at once will result in variability among the samples injected at different times [18]. To overcome this, an automated online method can be beneficial as it allows immediate derivatisation of samples just prior to the injection [22]. The integrity and reproducibility of the data can also be increased using an automatic online derivatisation method [12].…”

Section: Introductionmentioning

confidence: 99%

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Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

et al. 2016

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Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

et al. 2016

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“…For relative quantification of metabolites, the LC-MS chromatograms were analyzed by the XCMS peak-picking/peakalignment software (14,16,17) for positive and negative ionization modes separately. The analyses were performed in two separate batches.…”

Section: Methodsmentioning

confidence: 99%

“…For the core experiment, we used four biological replicates of WEREWOLF (WER/epidermis) and SCARECROW (SCR/endodermis) and three biological replicates of PET111 (columella), CORTEX, and WOODEN LEG (WOL; stele). The metabolic profiles obtained were analyzed using the XCMS peak picking and alignment software (14,16,17) and a metabolomics data quality control (QC) procedure developed by Brodsky et al (18) and implanted by Rogachev and Aharoni (19). Briefly, the mass signals obtained were clustered (for grouping those masses associated with the same metabolite; SI Methods) and filtered from nonplant origin masses (in comparison to blank samples), and metabolites of interest were putatively identified.…”

Section: Metabolomics Assays Of Five Specific Cell-type Populations Inmentioning

confidence: 99%

High-resolution metabolic mapping of cell types in plant roots

Moussaieff

Rogachev

Brodsky

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

132

105

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Metabolite composition offers a powerful tool for understanding gene function and regulatory processes. However, metabolomics studies on multicellular organisms have thus far been performed primarily on whole organisms, organs, or cell lines, losing information about individual cell types within a tissue. With the goal of profiling metabolite content in different cell populations within an organ, we used FACS to dissect GFP-marked cells from Arabidopsis roots for metabolomics analysis. Here, we present the metabolic profiles obtained from five GFP-tagged lines representing core cell types in the root. Fifty metabolites were putatively identified, with the most prominent groups being glucosinolates, phenylpropanoids, and dipeptides, the latter of which is not yet explored in roots. The mRNA expression of enzymes or regulators in the corresponding biosynthetic pathways was compared with the relative metabolite abundance. Positive correlations suggest that the rate-limiting steps in biosynthesis of glucosinolates in the root are oxidative modifications of side chains. The current study presents a work flow for metabolomics analyses of cell-type populations.

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Analytical Platforms in Metabolomics

Sánchez

2012

Encyclopedia of Analytical Chemistry

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The present importance of metabolomics to go inside the unsolved secrets of living organisms, as a single discipline or together with other omics, has opened interest in analytical platforms to obtain proper metabolomic information. The steps involved in developing these platforms (viz sampling, sample storage, sample preparation, individual metabolites separation, if required, and detection) are critically discussed as a function of the type of organism under study, the type of sample, and the families of metabolites to be analyzed. An overview of the fundamentals of key equipment in metabolomics, such as that based on nuclear magnetic resonance (NMR) spectrometry or mass spectrometry (MS), is also given for better understanding of its usefulness in this discipline.

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Evaluation of automated sample preparation, retention time locked gas chromatography–mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species

Cited by 36 publications

References 32 publications

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

High-resolution metabolic mapping of cell types in plant roots

Analytical Platforms in Metabolomics

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