Metabolomics Facility

Home

The Metabolomics facility at Umeå Plant Science Centre

During the last decade, the sequencing of genomes in different prokaryotic and eucaryotic species has revolutionised biology. The data that these efforts have yielded facilitate analyses that provide insights into the genetic basis of similarities and differences between diverse organisms. They also create new possibilities for investigating the fundamental biology of different organisms, as well as the genetic basis of various diseases.
In the era of post-genomics, elucidation of gene function is a main target. Analysis of gene function by targeted knockouts and mutations and the measurement of gene products such as mRNA and protein species are currently the main methods used in functional genomics. However, these methods do not provide all the information needed to determine how changes in mRNA or proteins are linked to changes in biological function.
Complex regulatory interactions occur at all levels in eukaryotic cells, and a change at one level in the network does not necessarily lead to a significant change in function or phenotype. Instead, single point mutations or alterations in gene expression may often lead to complex responses. Thus, metabolomic analysis is also needed if the final effects of upstream regulatory events on metabolism are to be determined accurately.
Techniques enabling metabolites to be identified and metabolic fluxes to be quantified are essential to complement the information provided by genetic experiments and the large-scale analysis of transcript and protein profiles in living organisms.

Access to the facility

The Metabolomics facility at Umeå Plant Science received substantial support from the Wallenberg Consortium North (WCN; www.wcn.se), and the facility is open for the universities associated to the WCN.

Funding

The facility is now receiving funding from SLU. Wallenberg and The Kempe foundation are highly acknowledged for previous funding.

Scientific director
Thomas Moritz
UPSC, SLU, Umeå.
Tel: 090-786 84 56

Research engineer mass spectrometry and contact person
Krister Lundgren
UPSC, SLU, Umeå
Tel: 090-786 82 42

Chemometrical expertise

Johan Trygg (co-director)
Research group for chemometrics, UmU, Umeå
This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Henrik Antti
Research group for chemometrics, UmU, Umeå
This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Steering committee
Not decided yet.

The steering committee will, if necessary, be responsible to allocate instrument time to the different projects.

Services at the Metabolomics facility

The main services at the facility are the following:

Advice regarding design of metabolomics experiment
Advice regarding extraction protocols
Extraction of samples (depends on the type of samples)
Mass spectrometry analysis
Basic multivariate analysis on obtained results (PCA, PLS, PLS-DA, O-PLS, OPLS-DA)
Simple presentation of obtained results. Only the differences between samples will be presented

The services at the facility will not include:

Extensive identification of unknown compounds.
Extensive multivariate analysis on obtained results.

If above mentioned services are necessary, please contact the facility for information about collaborations within different research groups at UPSC or the Chemometrical group at UmU.

Instrumentation at the metabolomics facility

Mass spectrometers funded by WCN and Kempe fundation, and dedicated to metabolomics
LECO Pegasus III, Gas chromatography - mass spectrometry/time-of-flight analyser (GC/TOFMS)
Waters Acquity UPLC - LCT premier time-of-flight (Tof) mass spectrometer (UPLC/TOFMS)
Thermo LTQ-Orbitrap XL LC/MS (UHPLC-high resolution MS/MS)
Agilent 6460 QQQ LC/MS (triple-stage quadropole mass spectrometer)
Bruker Esquire 3000 plus, Liquid chromatography mass spectrometer (LC/MS)

Mass spectrometers at the UPSC mass spectrometer facility
JEOL JMS-MStation GC/MS (magnetic-sector mass spectrometer)
Micromass Quattro Ultima LC/MS (triple-stage quadrupole mass spectrometer)
Micromass Q-TOF Ultima LC/MS (quadrupole/time-of-flight mass spectrometer)
Applied Biosystem Voyager-DE STR (Maldi-TOF)

Above instruments might, when necessary, be used within the metabolomics facility.

Book instruments online. (You need to be logged in to manage bookings)

Sample extraction
Extraction involves solvent extraction with internal standards added to the extract prior extraction. The internal standards are isotope labelled compounds, representing classes of different compounds, e.g. amino acids, amines, fatty acids, mono- and disaccharides, sterols. The internal standards can be supplied from the facility, and are included in the cost of the analyses.
Different extraction protocols for different types of samples are under development. Protocols will be on this web-page as soon as possible.

Examples of extraction protocols:
Plant extract: This protocol is based on the possible to use a mixer mill, and doing all extraction in eppendorf tubes. (Ref: Gullberg et al. 2004. Anal. Biochem. 331: 283-295)

Weight 10-50 mg plant samples in eppendorf tubes. Keep cold!! Ice bath or cold room. Sample weight should not differ between samples (9-11 mg is fine). If large differences, the extraction volume should vary: e.g. sample weight 20mg, 1ml extraction medium, sample weight 10mg, 0,5ml extraction medium.
Add 1 ml of chloroform:methanol:H2O (20:60:20) mixture including internal standards + mixer beads. Shake 3 min in mixer (30 Hz).
Centrifuge in eppendorf centrifuge, 10 min, 14 000 rpm. Take out mixer beads before centrifugation.
Take out 200 µl of supernatant (volume depends on amount and type of plant material) and add to GC/MS vial (or LC/MS vial if samples will be analysed by LC/MS). Dry in speed-vac concentrator.
Derivatization and GC/MS analysis as described below

Plasma analysis: This protocol is based on the possible to use a mixer mill, and doing all extraction in eppendorf tubes (Ref: A et a. 2005 Anal Chem. 77: 8086-8094)

Add 100 µl of plasma into an eppendorf tube.
Add 900 µl of MeOH:H2O (9:1)including methanol soluble internal standards. Let the tubes stand on ice for 10 min.
Add mixer beads and shake in mixer for 2 min (30 Hz).
Remove the beads, and let the tubes stand in ice for 2 h.
Centrifuge in eppendorf centrifuge 5 min, 13 000 rpm.
Take out 200 µl of supernatant and add to GC/MS vial (or LC/MS vial if samples will be analysed by LC/MS). Dry in speed-vac concentrator.
Derivatization and GC/MS analysis as described below.

Derivatization for GC/MS analysis
With the GC/MS analysis we can detect organic acids, amino acids, fatty acids, amines, mono- and disaccharides, sterols and many more compounds. Since only volatile and thermo-stable compounds can be analysed by GC/MS derivatisation is performed if GC/MS is to be used to analyse metabolites that are insufficiently volatile in their native state. Although there are a number of strategies for derivatising compounds prior to GC/MS analysis, e.g. silylation, alkylation, acylation and alkoxyamination, the standard procedure in plant metabolomics is to first derivatise them using methoxyamine (CH₃-O-NH₂) in pyridine to stabilize carbonyl moieties in the metabolites, thereby suppressing keto-enol tautomerism and the formation of multiple acetal- or ketal-structures. Methoxyamination helps to reduce the numbers of derivatives of reducing sugars, and generates only two forms of the â€“N=C< derivative, (syn and anti-forms). After methoxyamination functional groups, such as -OH, -COOH, -SH or -NH groups, are converted into TMS-ethers, TMS-esters, TMS-sulfides or TMS-amines, respectively, using a trimethylsilyl (TMS) reagent, usually BSTFA or MSTFA. TMS-derivatisation has been thoroughly investigated and shown to be very efficient. However, one should be aware that derivatisation artefacts occur, including multiple derivatives of some compounds, e.g. amino acids.

Samples are dried before derivatisation. For methoxiamination, 30 µL of 15 mg/mL methoxyamine hydrochloride in dry pyridine is used at room temperature for 16 h. Afterwards, TMS derivatization is performed by the addition of 30 µL of N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA+1% TMCS) for 1 h at room temperature. Thereafter the sample can be diluted with heptane (e.g. 30 µl) to suitable dilution of sample prior GC/MS analysis. (Ref: Gullberg et al. 2004. Anal. Biochem. 331: 283-295).

GC/MS analysis
One µL of the derivatized sample is injected splitless by an Agilent 7683 autosampler into an Agilent 6890 gas chromatograph equipped with a 15 m x 0.18 mm i.d. fused silica capillary column with a chemically bonded 0.18 µm DB 5-MS stationary phase (J&W Scientific). The injector temperature is 270 °C, the purge flow-rate is 20 ml min-1 and the purge is turned on after 60 s. The gas flow rate through the column is 1 ml min-1, the column temperature is held at 70 °C for 2 minutes, then increased by 40 °C min-1 to 320 °C, and held there for 1 min. The column effluent is introduced into the ion source of a Pegasus III time-of-flight mass spectrometer, GC/TOFMS (Leco Corp., St Joseph, MI, USA). The transfer line and the ion source temperatures are 250 °C and 200°C, respectively. Ions are generated by a 70 eV electron beam at an ionization current of 2.0 mA, and 30 (15-30) spectra s-1 are recorded in the mass range 60 to 800 m/z. The acceleration voltage is turned on after a solvent delay of 170 s. The detector voltage is 1500-1700 V.

GC/MS is a very robust technique, suitable for routine metabolomics analysis. Identification of compounds is based on comparison with mass spectra libraries (in-house database) as well as retention index.

LC/MS analysis
Chromatography is performed on a Waters Acquity UPLC system. Ten µL aliquot of extracted plasma or plant sample (dissolved in high content H₂O) is injected onto a 2.1 x 100 mm, 1.7 µm UPLC column (C18 or C8 UPLC columns) held at 40 °C. The gradient elution buffers are A (H2O, 0.1% formic acid) and B (acetonitrile, 0.1% formic acid), and the flow-rate is 500 µl min-1. The column is eluted with a linear gradient consisted of 1-20% B over 0-4 minutes, 20-40% of B 4-6 minutes, 40-95% B 6-9 minutes, the composition was held at 95% B for 4.5 minutes, and returned to 1% B at 14.50 minutes, the composition was kept at 1% B for a further 4.5 minutes before the next injection. The UPLC is coupled to a Micromass LCT premier time-of-flight (Tof) mass spectrometer equipped with an electrospray source operating in positive ion mode with lockspray interface for accurate mass measurements. The source temperature is 120 ºC with a cone gas flow of 10 L/hr, a desolvation temperature of 300 ºC and a nebulization gas flow of 600 L/hr. The capillary voltage is set at 3 kV for positive ion mode, with a cone voltage of 0 V, a data acquisition rate of 0.1 s, an interscan delay of 0.1 s, with dynamic range enhancement (DRE) mode activated. Leucine enkephalin was employed as the lockmass compound, infused straight into the MS at a concentration of 400 pg/µL (in 50:50 ACN:water) at a flow rate of 20 µL/min. The normal lockmass in the DRE mode is the positive ion 2nd 13C peak of leucine enkephalin at 558.2829, and the extended lock mass peak is the normal positive ion peak observed at 556.2771. All mass spectral data are acquired in the centroid mode, 50 - 1000 m/z, with a data threshold value set to 3. Identification of compounds by UPLC/TOFMS is mainly performed by calculation of elemental composition and comparing peak retention time with standard compounds.

Since there is no need to derivatise compounds prior to the MS-analysis when using LC/MS fewer sample preparation steps are required in LC/MS than in GC/MS analyses, and there are generally fewer artefacts. It is also capable of analysing large numbers of compounds that cannot be analysed by GC/MS, including compounds with labile glucosidic bonds such phenylpropanoid derivatives, carotenoids and many lipids. Many compounds detected by GC/MS will also be detected by LC/MS. Since LC/MS analysis usually provides poor structural information ([M+H]⁺ or [M-H]^- are the main ions produced) high-resolution detection systems are essential, e.g. TOF, QTOF, orbi-trap or FT-MS instruments. At UPSC the TOFMS has a resolution about 12 000 FWH and a mass error of 1-3 ppm.

Data analysis
Multivariate projection methods, e.g. PCA and PLS, represent a useful and versatile technology to modelling, monitoring and prediction of complex problems and data structures encountered within metabolomics and other -omics disciplines. The common denominator is that high complexity data tables are generated and that these data tables can be analysed and interpreted by means of chemometric methods. The principal component analysis (PCA) method summarizes the variation in a data table X into a model plane (the scores T). A scatter plot of these scores gives an overview of the samples (observations) and how they relate to each other, e.g. if there are groupings or trends or deviating samples and so on. In order to interpret the patterns found in a score plot one examines the corresponding loading plot (P). The loadings P reveal how each variable contributes to the separation among samples in the model plane and also gives insights into the relative importance of each variable.

Partial Least Squares Projections to Latent Structures (PLS) is used instead of the PCA method when additional knowledge about each sample exists, the Y matrix, e.g. genotype of each sample (wild type/mutant). PLS represents the regression analogy of PCA working with two matrices, X and Y. It is one of the most common methods when a quantitative relationship between a descriptor matrix X and a response matrix Y is sought. The Y matrix can contain both quantitative (e.g. glucose concentration) and qualitative (genotype) information. This additional sample information in Y is used by the PLS method to focus the model plane to capture the Y-related variation in X, e.g. separation between genotypes, rather than providing an overall view of all variation in the data as done by the PCA model. In addition, the PLS method can also be used to predict the properties (Y-values) of new unknown samples, e.g. predict the glucose concentration or genotype. When Y is qualitative, the PLS method is called PLS Discriminant Analysis (PLS-DA), to distinguish it from the situation when Y is quantitative. An extension of the PLS method is orthogonal projection to latent structures (OPLS/OPLS-DA) and O2-PLS.

Submitting samples to the Metabolomics facility

Before submitting samples to the facility the principle investigators must contact the facility to discuss the metabolomics analysis ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it ). The facility will participate in the design of the experiments, since the design are very important for obtaining reliable information from the metabolomics analysis. The PI’s are encouraged to send a short description of the experiment in order to speed-up the procedure. When the facility approves to perform metabolomics analysis, the applicant will have information how to send samples, estimated time when the analysis can be done, advice regarding extraction protocols if the facility can not perform the extraction.

When submitting samples, please read following document:

Submitting samples

Send samples packed in dry ice to:

Att: Krister Lundgren
SLU, skoglig genetik och växtfysiologi
KBC Lastplats
901 87 Umeå

Price

The price is subsidised via grants from SLU and other sources. The cost for analysing samples will cover cost of columns, internal standards, reagents and data storage.

For current prices, contact This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

UPSC Metabolomics database (will come)

Metabolomics analysis at UPSC

Methodology

Compound identification

Retention index
RTI (pdf-format)
Retention index system (pdf-format)
EI-TOFMS-library (will come)

Mass spectra databases
Max Planck Institute (MPI) library in Golm (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html)

Databases metabolic pathways

KEGG (http://www.genome.jp/kegg/pathway.html )
TAIR (http://www.arabidopsis.org/biocyc/ )
EcoCyc (http://biocyc.org/ECOLI/class-subs-instances?object=Pathways )

Multivariate analysis and chemometrics

http://www.chemometrics.se/

Extraction and derivatization

Gullberg J, Jonsson P, Nordström A, Sjöström M, Moritz T (2004) Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal Biochem 331: 283-295.
A, J.; Trygg, J.; Gullberg.; Johansson, A.I.; Jonsson, P.; Antti, A.; Marklund, S.L.; Moritz, T. Extraction and GC/MS analysis of the human blood plasma metabolome. Anal. Chem. 2005, 77, 8086-8094.

Data processing H-MCR

Jonsson P, Gullberg J, Nordström A, Kusano M, Kowalczyk M, Sjöström M, Moritz T (2004) A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Anal Chem 76: 1738-1745
Jonsson P, Johansson AI, Gullberg J, Trygg J, A J, Grung B, Marklund S, Sjöström M, Antti H, Moritz T (2005) High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Anal Chem 77: 5635-5642
Jonsson P, Johansson ES, Wuolikainen A, Lindberg J, Schuppe-Koistinen I, Kusano M, Sjöström M, Trygg J, Moritz T, Antti H (2006) Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data - A potential tool for multi-parametric diagnosis. J Prot Res 5: 1407-1414

Mass spectra library identification

Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T, Lundgren K, Roessner-Tunali U, Forbes MG, Willmitzer L, Fernie AR, Kopka J (2005) GC-MS libraries for the rapid identification of metabolites in complex biological samples. Febs Letters 579: 1332-1337

Multivariate analysis

Trygg J, Gullberg J, Johansson AI, Jonsson P, Moritz T (2006) Chemometrics in metabolomics-An introduction. In Plant metabolomics (Ed Saito K, Dixon RA, Willmitzer L) Spinger-Verlag.

Trygg J, Holmes E, Lundstedt T (2007) Chemometrics in metabonomics. J Proteome Res 6: 469-479

March 2010

Thursday, March 11, 2010
Random Picture (vh15.jpg)