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

Automatization of metabolite extraction for high-throughput metabolomics: case study on transgenic isoprene-emitting birch

Metabolomics studies are becoming increasingly common for understanding how plant metabolism responds to changes in environmental conditions, genetic manipulations and treatments. Despite the recent advances in metabolomics workflow, the sample preparation process still limits the high-throughput analysis in large-scale studies. Here, we present a highly flexible robotic system that integrates liquid handling, sonication, centrifugation, solvent evaporation and sample transfer processed in 96-well plates to automatize the metabolite extraction from leaf samples. We transferred an established manual extraction protocol performed to a robotic system, and with this, we show the optimization steps required to improve reproducibility and obtain comparable results in terms of extraction efficiency and accuracy. We then tested the robotic system to analyze the metabolomes of wild-type and four transgenic silver birch (Betula pendula Roth) lines under unstressed conditions. Birch trees were engineered to overexpress the poplar (Populus × canescens) isoprene synthase and to emit various amounts of isoprene. By fitting the different isoprene emission capacities of the transgenic trees with their leaf metabolomes, we observed an isoprene-dependent upregulation of some flavonoids and other secondary metabolites as well as carbohydrates, amino acid and lipid metabolites. By contrast, the disaccharide sucrose was found to be strongly negatively correlated to isoprene emission. The presented study illustrates the power of integrating robotics to increase the sample throughput, reduce human errors and labor time, and to ensure a fully controlled, monitored and standardized sample preparation procedure. Due to its modular and flexible structure, the robotic system can be easily adapted to other extraction protocols for the analysis of various tissues or plant species to achieve high-throughput metabolomics in plant research.

How searching against multiple libraries can lead to biased results in GC/MS-based metabolomics

RATIONALE

Databases of electron ionization mass spectra are often used in GC/MS-based untargeted metabolomics analysis. The results of the library search depend on several factors, such as the size and quality of the database, and the library search algorithm. We found out that the list of considered m/z values is another important parameter. Unfortunately, this information is not usually specified by software developers and it is hidden from the end user.

METHODS

We created synthetic data sets and figured out how several popular software products (AMDIS, ChromaTOF, MS Search, and Xcalibur) select the list of m/z values for the library search. Moreover, we considered data sets of real mass spectra (presented in both the NIST and FiehnLib libraries) and compared the library search results obtained within different software products. All programs under consideration use the NIST MS Search binaries to perform the library search using the Identity algorithm.

RESULTS

We found that AMDIS and ChromaTOF can give biased library search results under particular conditions. In untargeted metabolomics, this can happen when NIST and FiehnLib libraries are used simultaneously, the scan range of the instrument is less than 85, and the correct answer is present only in the FiehnLib library.

CONCLUSIONS

The main reason for biased results is that the information about the scan range is not stored in the metadata of library records. As a result, in the case of AMDIS and ChromaTOF software, some unrecorded peaks are considered as missing during the library search, the respective compound is penalized, and the correct answer falls outside the top five or even top 10 hits. At the same time, the default algorithm for selecting the list of considered m/z values implemented in MS Search is free from such unexpected behavior.

An Arabidopsis GCMS chemical ionization technique to quantify adaptive responses in central metabolism

We present a methodology to survey central metabolism in 13CO2-labeled Arabidopsis (Arabidopsis thaliana) rosettes by ammonia positive chemical ionization-gas chromatography-mass spectrometry. This technique preserves the molecular ion cluster of methyloxime/trimethylsilyl-derivatized analytes up to 1 kDa, providing unambiguous nominal mass assignment of >200 central metabolites and 13C incorporation rates into a subset of 111 from the tricarboxylic acid (TCA) cycle, photorespiratory pathway, amino acid metabolism, shikimate pathway, and lipid and sugar metabolism. In short-term labeling assays, we observed plateau labeling of ∼35% for intermediates of the photorespiratory cycle except for glyoxylate, which reached only ∼4% labeling and was also present at molar concentrations several fold lower than other photorespiratory intermediates. This suggests photorespiratory flux may involve alternate intermediate pools besides the generally accepted route through glyoxylate. Untargeted scans showed that in illuminated leaves, noncyclic TCA cycle flux and citrate export to the cytosol revert to a cyclic flux mode following methyl jasmonate (MJ) treatment. MJ also caused a block in the photorespiratory transamination of glyoxylate to glycine. Salicylic acid treatment induced the opposite effects in both cases, indicating the antagonistic relationship of these defense signaling hormones is preserved at the metabolome level. We provide complete chemical ionization spectra for 203 Arabidopsis metabolites from central metabolism, which uniformly feature the unfragmented pseudomolecular ion as the base peak. This unbiased, soft ionization technique is a powerful screening tool to identify adaptive metabolic trends in photosynthetic tissue and represents an important advance in methodology to measure plant metabolic flux.

Metabonomics Study of the Hematopoietic Effect of Medicinal Wine Maoji Jiu on a Blood Deficiency Rat Model by Ultra-High-Performance Liquid Chromatography Coupled to Quadrupole Time-of-Flight Mass Spectrometry and a Pattern Recognition Approach

Maoji Jiu (MJ) is a kind of medicinal wine that has been widely used by Chinese people for many years to nourish and promote blood circulation. The purpose of this study was to investigate the hematopoietic effect of MJ on the metabolism of blood deficient rats and to explore the underlying hematopoietic regulation mechanisms. Blood deficiency model rats were induced by subcutaneous injection of N-acetylphenylhydrazine (APH) and intraperitoneal injection of cyclophosphamide (CTX). The plasma metabolic fingerprints of blood deficiency model rats with and without MJ treatment were obtained by using metabonomics based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to evaluate the hematopoietic effect of MJ and identify potential biomarkers in the plasma of blood deficiency model rats. The levels of white blood cells (WBC), red blood cells (RBC) and hemoglobin (HGB) and the activity of antioxidant capacity showed a recovery trend to the control group after MJ treatment, while the dose of 10 mL/kg showed the best effect. In this study, thirteen potential biomarkers were identified, which were mainly related to seven metabolic pathways, including linoleic acid metabolism, d-glutamine and d-glutamate metabolism, alanine, aspartate and glutamate metabolism, tryptophan metabolism, pyrimidine metabolism, porphyrin and chlorophyll metabolism and arginine biosynthesis. Metabolomics was applied frequently to reflect the physiological and metabolic state of organisms comprehensively, indicating that the rapid plasma metabonomics may be a potentially powerful tool to reveal the efficacy and enriching blood mechanism of MJ.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Automated Trimethyl Sulfonium Hydroxide Derivatization Method for High-Throughput Fatty Acid Profiling by Gas Chromatography-Mass Spectrometry

Fatty acid profiling on gas chromatography–mass spectrometry (GC–MS) platforms is typically performed offline by manually derivatizing and analyzing small batches of samples. A GC–MS system with a fully integrated robotic autosampler can significantly improve sample handling, standardize data collection, and reduce the total hands-on time required for sample analysis. In this study, we report an optimized high-throughput GC–MS-based methodology that utilizes trimethyl sulfonium hydroxide (TMSH) as a derivatization reagent to convert fatty acids into fatty acid methyl esters. An automated online derivatization method was developed, in which the robotic autosampler derivatizes each sample individually and injects it into the GC–MS system in a high-throughput manner. This study investigated the robustness of automated TMSH derivatization by comparing fatty acid standards and lipid extracts, derivatized manually in batches and online automatically from four biological matrices. Automated derivatization improved reproducibility in 19 of 33 fatty acid standards, with nearly half of the 33 confirmed fatty acids in biological samples demonstrating improved reproducibility when compared to manually derivatized samples. In summary, we show that the online TMSH-based derivatization methodology is ideal for high-throughput fatty acid analysis, allowing rapid and efficient fatty acid profiling, with reduced sample handling, faster data acquisition, and, ultimately, improved data reproducibility.

Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry

Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

Screening for CYP3A4 inhibition and induction coupled to parallel artificial membrane permeability assay (PAMPA) for prediction of botanical-drug interactions: The case of açaí and maca

BACKGROUND

The consumption of botanical dietary supplements (BDS) is a common practice among the US population. However, the potential for botanical-drug interactions exists, and their mechanisms have not been thoroughly studied. CYP3A4 is an important enzyme that contributes to the metabolism of about 60% of clinically used drugs.

PURPOSE

To investigate the potential for botanical-drug interactions of Lepidium meyenii Walpers (maca) root and Euterpe oleracea Mart. (açaí) berries, two commonly used BDS, when co-administered with CYP3A4-metabolized drugs.

METHODS

In an attempt to decrease the general discrepancy between in vivo and in vitro studies, the absorption profiles, particularly for passive diffusion, of plant extracts were investigated. Specifically, the parallel artificial membrane permeability assay (PAMPA) model was utilized to simulate intestinal filtration of passively diffused constituents of açaí and maca extracts. These were subsequently screened for in vitro liver CYP3A4 inhibition and induction. In the inhibition assay, midazolam was used as the probe substrate on genotyped human liver microsomes (CYP3A5 null), and the production of its 1'-substituted metabolite when co-cultured with extract treatments was monitored. In the induction assay, extract treatments were applied to human primary hepatocytes, and quantitative PCR analysis was performed to determine CYP3A4 mRNA expression.

RESULTS

Passively diffused constituents of the methanol açaí extract (IC₅₀ of 28.03 µg/µl) demonstrated the highest inhibition potential, and, at 1.5 µg/µl, induced significant changes in CYP3A4 gene expression. The composition of this extract was further investigated using the chemometric tool Mass Profiler Professional (MPP) on liquid chromatography-mass spectroscopy (LC-MS) data. Subsequently, five compounds of interest characterized by high abundance or high permeability were extracted for further study. This included efforts in effective passive permeability determination and structural elucidation by tandem mass spectrometry (MS/MS).

CONCLUSION

The passively absorbable portion of a methanol açaí extract exhibited inhibition and induction effects on CYP3A4 suggesting the potential to produce botanical-drug interactions.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Comparison of sequential derivatization with concurrent methods for GC/MS-based metabolomics

The gas chromatography/mass spectrometry (GC/MS)-based metabolomics requires a two-step derivatization procedure consisting of oximation and silylation. However, due to the incomplete derivatization and degeneration of the metabolites, good repeatability is difficult to obtain during the batch derivatization, as the time between completing the derivatization process and GC analysis differs from sample to sample. In this research, we successfully obtained good repeatability for the peak areas of 52 selected metabolites by sequential derivatization and interval injection, in which the oximation and silylation times were maintained at constant values. In addition, the derivatization times and amount of reagents employed were varied to confirm that the optimal derivatization conditions differed for the various metabolites. In conventional batch derivatization, six metabolites, viz. glutamine, glutamic acid, histidine, alanine, asparagine, and tryptophan, exhibited fluctuations in their peak areas. Indeed, we found that for all six metabolites these differences originated from the silylation process, while the variations for glutamine and glutamic acid were related to the oximation process.

Novel two-step derivation method for the synchronous analysis of inherited metabolic disorders using urine

The aim of the present study was to conduct preliminary clinical screening and monitoring using a novel two-step derivatization process of urine in five categories of inherited metabolic disease (IMD). Urine samples (100 µl, containing 2.5 mmol/l creatinine) were taken from patients with IMDs. The collected urine was then treated using a two-step derivatization method (with oximation and silylation at room temperature), where urea and protein were removed. In the first step of the derivatization, α-ketoacids and α-aldehyde acids were prepared by oximation using novel oximation reagents. The second-step of the derivatization was that residues were silylated for analysis. Urine samples were examined using gas chromatography/mass spectrometry (GC/MS) and a retention time-locking technique. The simultaneous analysis and identification of >400 metabolites in >130 types of IMD was possible from the GC/MS results, where the IMDs included phenylketonuria, ornithine transcarbamylase deficiency, neonatal intrahepatic cholestasis caused by citrin deficiency, β-ureidopropionase deficiency and mitochondrial metabolic disorders. This method was demonstrated to have good repeatability. Considering α-ketoglutarate (α-KG) as an example, the relative standard deviations (RSDs) of the α-KG retention time and peak area were 0.8 and 3.9%, respectively, the blank spiked recovery rate was between 89.6 and 99.8%, and the RSD was ≤7.5% (n=5). The method facilitates the analysis of thermally non-stable and semi-volatile metabolites in urine, and greatly expands the range of materials that can be synchronously screened by GC/MS. Furthermore, it provides a comprehensive, effective and reliable biochemical analysis platform for the pathological research of IMDs.

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

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.

Metabolomics study of hematopoietic function of Angelica sinensis on blood deficiency mice model

ETHNOPHARMACOLOGICAL RELEVANCE

Angelica sinensis (AS) has been used in traditional Chinese medicine for thousands of years to enrich and invigorate blood. In this study, the aim is to investigate the influence of AS on metabolism of blood deficiency mice model and to explore its anti-blood deficiency mechanism.

MATERIALS AND METHODS

The blood deficiency mice model was induced by being hypodermically injected with N-acetyl phenylhydrazine (APH) and being intraperitoneally injected with cyclophosphamide (CTX). Gas chromatography-mass spectrometry (GC-MS), principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to identify potential biomarkers in plasma and splenic tissue.

RESULTS

The levels of white blood cell (WBC), red blood cell (RBC), hemoglobin (HGB) and platelet (PLT) showed a trend to return to control group after administrating with AS, while the dose of 10g/kg showed the best effect. Potential metabolite biomarkers (nine in the plasma and nine in the spleen homogenates) were identified in this study. These biomarkers were mainly related to five metabolic pathways, such as arachidonic acid metabolism, valine, leucine and isoleucine biosynthesis, glycine, serine and threonine metabolism, arginine and proline metabolism and TCA cycle.

CONCLUSION

Metabolomics was used to reflect an organism׳s physiological and metabolic state comprehensively, indicating that metabolomics was a potentially powerful tool to reveal the anti-blood deficiency mechanism of AS.

Wounding stress causes rapid increase in concentration of the naturally occurring 2',3'-isomers of cyclic guanosine- and cyclic adenosine monophosphate (cGMP and cAMP) in plant tissues

3',5'-Cyclic guanosine monophosphate (cGMP) and 3',5'-cyclic adenosine monophosphate (cAMP) are well reported second messenger molecules involved in cellular signal transduction, in physiological functions such as neurotransmission in animals and in the modulation of cell growth and differentiation. In plants, 3',5'-cyclic nucleotides have been implicated in the regulation of ion homeostasis, hormone and stress responses. The behavior of the 2',3'-cyclic nucleotide variants is also known in animal tissue but no quantitative information is available about 2',3'-cAMP and 2',3'-cGMP in plant material. A recently developed HILIC-SPE/LC-MS/MS method for the analysis of cyclic nucleotides in blood and animal tissue was therefore adapted to measure 2',3'-cAMP and 2',3'-cGMP concentrations in plant material. Cyclic nucleotide concentrations were measured in Arabidopsis thaliana (Col-0) leaves before and after the application of wounding stress. A significant (∼5-fold) up-regulation of 2',3'-cAMP and 2',3'-cGMP was measured in Arabidopsis leaves compared to the control samples. The results indicate a thus far unreported strong correlation between plant stress and both 2',3'-cAMP and 2',3'-cGMP levels in plant material, and may open new avenues towards understanding the role of cyclic nucleotides in plants.

Sample preparation issues in NMR-based plant metabolomics: optimisation for Vitis wood samples

INTRODUCTION

Nuclear magnetic resonance (NMR) is one of the most commonly used analytical techniques in plant metabolomics. Although this technique is very reproducible and simple to implement, sample preparation procedures have a great impact on the quality of the metabolomics data.

OBJECTIVE

Investigation of different sample preparation methods and establishment of an optimised protocol for untargeted NMR-based metabolomics of Vitis vinifera L. wood samples.

METHODS

Wood samples from two different cultivars of V. vinifera with well-defined phenotypes (Gamaret and 2091) were selected as reference materials. Different extraction solvents (successively, dichloromethane, methanol and water, as well as ethyl acetate and 7:3 methanol-water (v/v)) and deuterated solvents (methanol-d4, 7:3 chloroform-d-methanol-d4 (v/v), dimethylsulphoxide-d6 and 9:1 dimethylsulphoxide-d6-water-d2 (v/v)) were evaluated for NMR acquisition, and the spectral quality was compared. The optimal extract concentration, chemical shift stability and peak area repeatability were also investigated.

RESULTS

Ethyl acetate was found to be the most satisfactory solvent for the extraction of all representative chemical classes of secondary metabolites in V. vinifera wood. The optimal concentration of dried extract was 10 mg/mL and 7:3 chloroform-d-methanol-d4 (v/v) was the most suitable solvent system for NMR analysis. Multivariate data analysis was used to estimate the biological variation and clustering between different cultivars.

CONCLUSION

Close attention should be paid to all required procedures before NMR analysis, especially to the selection of an extraction solvent and a deuterated solvent system to perform an extensive metabolomic survey of the specific matrix.

The use of trimethylsilyl cyanide derivatization for robust and broad-spectrum high-throughput gas chromatography-mass spectrometry based metabolomics

Reproducible and quantitative gas chromatography-mass spectrometry (GC-MS)-based metabolomics analysis of complex biological mixtures requires robust and broad-spectrum derivatization. We have evaluated derivatization of complex metabolite mixtures using trimethylsilyl cyanide (TMSCN) and the most commonly used silylation reagent N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). For the comparative analysis, two metabolite mixtures, a standard complex mixture of 35 metabolites covering a range of amino acids, carbohydrates, small organic acids, phenolic acids, flavonoids and triterpenoids, and a phenolic extract of blueberry fruits were used. Four different derivatization methods, (1) direct silylation using TMSCN, (2) methoximation followed by TMSCN (M-TMSCN), (3) direct silylation using MSTFA, and (4) methoximation followed by MSTFA (M-MSTFA) were compared in terms of method sensitivity, repeatability, and derivatization reaction time. The derivatization methods were observed at 13 different derivatization times, 5 min to 60 h, for both metabolite mixtures. Fully automated sample derivatization and injection enabled excellent repeatability and precise method comparisons. At the optimal silylation times, peak intensities of 34 out of 35 metabolites of the standard mixture were up to five times higher using M-TMSCN compared with M-MSTFA. For direct silylation of the complex standard mixture, the TMSCN method was up to 54 times more sensitive than MSTFA. Similarly, all the metabolites detected from the blueberry extract showed up to 8.8 times higher intensities when derivatized using TMSCN than with MSTFA. Moreover, TMSCN-based silylation showed fewer artifact peaks, robust profiles, and higher reaction speed as compared with MSTFA. A method repeatability test revealed the following robustness of the four methods: TMSCN > M-TMSCN > M-MSTFA > MSTFA.

High-resolution metabolic mapping of cell types in plant roots

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.

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

Background

Gas 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.

Results

PyMS 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).

Conclusions

PyMS 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.