Email updates

Keep up to date with the latest news and content from Plant Methods and BioMed Central.

Open Access Methodology

Metabolic labeling of plant cell cultures with K15NO3 as a tool for quantitative analysis of proteins and metabolites

Wolfgang R Engelsberger, Alexander Erban, Joachim Kopka and Waltraud X Schulze*

Author Affiliations

Max-Planck Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1 14476, Golm, Germany

For all author emails, please log on.

Plant Methods 2006, 2:14  doi:10.1186/1746-4811-2-14


The electronic version of this article is the complete one and can be found online at: http://www.plantmethods.com/content/2/1/14


Received:14 August 2006
Accepted:4 September 2006
Published:4 September 2006

© 2006 Engelsberger et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Strategies for robust quantitative comparison between different biological samples are of high importance in experiments that address biological questions beyond the establishment of protein lists. Here, we propose the use of 15N-KNO3 as the only nitrogen source in Arabidopsis cell cultures in order to achieve a metabolically fully labeled cell population. Proteins from such metabolically labeled culture are distinguishable from unlabeled protein populations by a characteristic mass shift that depends on the amino acid composition of the tryptic peptide analyzed. In addition, the metabolically labeled cell extracts are also suitable for comparative quantitative analysis of nitrogen-containing cellular metabolic complement. Protein extracts from unlabeled and from standardized 15N-labeled cells were combined into one sample for joined analytical processing. This has the advantage of (i) reduced experimental variability and (ii) immediate relative quantitation at the level of single extracted peptide and metabolite spectra. Together ease and accuracy of relative quantitation for profiling experiments is substantially improved. The metabolic labeling strategy has been validated by mixtures of protein extracts and metabolite extracts from the same cell cultures in known ratios of labeled to unlabeled extracts (1:1, 1:4, and 4:1). We conclude that saturating metabolic 15N-labeling provides a robust and affordable integrative strategy to answer questions in quantitative proteomics and nitrogen focused metabolomics.

Background

Plants adjust to developmental and environmental variability with respective changes in protein abundance and enzyme activity. Thus, protein expression and metabolite pools in cells can be highly dynamic and the abundance and activity of specific proteins can vary greatly during growth and development or in response to biotic and abiotic stress. Therefore, it is of great biological interest to be able to quantitatively compare the subproteomes of different developmental stages or to quantitatively examine the dependent or independent responses of metabolic or signaling pathways under specific conditions. Thus combined analysis of metabolites and proteins may yield novel information about regulatory processes in plants [1].

Liquid-chromatography coupled mass spectrometry has in the recent years become a widely applied tool in quantitative proteomic approaches also in plant sciences [2-4]. Mass spectrometry is especially powerful to identify and quantitate changes in post-translational modifications, which often play important roles in protein function and regulation [5,6]. However, quantitative comparisons between independent samples remain a challenging task. Label-free approaches termed 'protein correlation profiling' rely on the relative quantitation of ion intensities between independent LC-MS/MS runs require accurate reproducibility of retention times in combination with elaborate data analysis [7,8]. Other label-free techniques use the number or recorded spectra for each protein as a comparative measure [9,10].

Due to difficulties in peak assignments between independent LC-runs of complex mixtures, many quantitative proteomic strategies rely on the incorporation of stable isotopes into proteins or peptides, which are then quantitatively compared to an unlabeled control sample based on their mass spectra [11]. Relative protein abundance can be calculated from the intensities of the labeled versus the unlabeled labeled forms of the same tryptic peptides in the same mass spectrum (for review see [12,13]). Stable isotope labeling of protein samples can in principle be achieved by chemical modification of proteins and tryptic peptides. Initially, 18O labeling was used to differentially label peptides of protein samples during the digest with trypsin [14]. This approach is still used today for relative comparison between two samples [4,15]. Later, isotope coded affinity reactive tags were developed which tag specific amino acid species and allow for specific enrichment of the labeled tryptic peptides [16]. This variety of tags has successfully been used in an number of comparative proteomic studies involving comparisons of changes in protein abundances [17-19] or characterizing changes in posttranslational modifications [20,21]. Furthermore, stable isotope labeled synthetic peptides are being used as internal standard peptides for a variety of targeted assays involving quantitative mass spectrometry [22,23]. However, the drawbacks of chemical labeling strategies are that only a rather small subset of tryptic peptides are being tagged and that experimental variability may be introduced during the labeling processes.

The first studies making use of metabolic labeling for protein quantitation by mass spectrometry relied on growth of bacterial or yeast cells on 15N-enriched medium [24,25]. However, since the mass shift introduced by 15N labeling depends on the amino acid composition of each tryptic peptide, knowledge of the peptide sequence is necessary to calculate the expected mass difference to the labeled or unlabeled partner. Thus, full metabolic labeling has recently been widely replaced by labeling of only specific amino acids (SILAC), such as lysine, arginine or leucine to introduce a fixed mass shift between labeled and unlabeled peptide pairs [26-28]. SILAC works especially well with mammalian cell cultures to which essential amino acids can easily be supplied in a stable isotope labeled form and full incorporation into the proteome of the cells is ensured. In yeast, auxotrophic mutants inhibited in the synthesis of arginine or lysine have been used for full incorporation of stable isotope labeled amino acids. The SILAC approach has successfully been applied to quantitative proteomic studies of the formation of signal-dependent protein complexes [29], in modification-dependent protein-protein interaction screens [30,31], and to analyses of the dynamics of signal-dependent phosphorylation events [32-34]. In plants, labeling of arginine, lysine or leucine has also been achieved using cell cultures [35], and an average of 70% to 80% incorporation of the metabolic label was obtained. Possibly, by using auxotrophic mutants in certain amino acid synthesis pathways, higher degrees of labeling may be feasible. In general it has to be considered that SILAC labeling is a costly exercise, especially if high concentrations of amino acids need to be supplied, such as for plant cell cultures.

Therefore, in plant biology, quantitative proteomic approaches so far mainly relied on chemical modification of proteins to study membrane lipid rafts [36], on 18O labeling in tryptic digests, e.g. to characterize purity of plant plasma membrane purifications [4], or on label-free quantitation by spectrum count as comparison between independent samples [2]. More recently, the iTRAQ reagent can be used in studies comparing more than two samples [37]. However, metabolic labeling strategies of whole proteomes have the advantage over chemical labeling that samples to be compared can be processed together through the complete analytical process from extraction of combined biological samples, isolation of metabolic or proteomic complements to quantitative analysis. Since biochemical techniques, such as the isolation of proteins, their fractionation and enrichment introduce much more heterogeneity than what is known from analyses of mRNA, reduction of sample variability is of great importance [13]. Also for metabolites, comparative quantitation to date relies on the analysis of a large number of replica samples [38]. Only recently has the use of 13C-labeling been proposed as a strategy for quantitative analysis of metabolite profiles [39,40]. In addition N labeling in plants has been successfully applied to flux studies and tracer analysis indicating the feasibility of generating saturated N labeled metabolomes [41,42].

In complementation to metabolic N-flux analysis, we propose the use of full 15N-labeling of plant cell cultures in comparative quantitative proteomics and metabolomic approaches as a cheaper alternative to SILAC labeling and a more robust and accurate method for quantitation compared to label-free protein correlation profiling. For the reasons noted above, 15N-labeling of intact plant has yet only been applied to analysis of proteins by NMR [43] or metabolic flux studies [41,42], which do not require full incorporation of the label. In addition, the approach described here is the first example of a labeling strategy that can be robustly applied to the protein and metabolite complement of the same sample.

Results and discussion

Since first proteomic experiments involving metabolic labeling with 15N involved growth of yeast cells in 14N and 15N medium for the quantitation of phosphorylation [24], the technique of 15N-labeling for proteomic research had mostly remained in the field of microbiology. Exceptions are studies using 15N-labeled microorganisms as a food source for metabolically labeling Drosophila [44] and 15N-labeling of rats by the use of 15N-labeled algal food [45]. In this proof-of-principle study, we use 15N-labeling to quantify protein and metabolite abundance in known mixtures of labeled and unlabeled cells.

Quantitative analysis of known mixtures of protein extracts

Metabolic 15N-labeling introduces the stable isotope in each nitrogen atom in the labeled organism. Therefore, every observable peptide from mixtures of unlabeled (14N) and 15N-labeled samples should occur in pairs and thus be suitable for quantification. In a first step, labeled protein extracts were analyzed for full incorporation of 15N into all proteins after 14 days of continuous growth in presence of K15NO3. Comparison of protein identifications in database searches with and without 15N as a parameter for fixed modification (Tab. 1) showed that 98.3% of all proteins of the labeled cells were identified only in their labeled form, while for 2% of the proteins, a residual minor peak was observed also for the 14N form. This indicates that 15N incorporation into the proteome of plant cell cultures is as complete as it can be achieved with 98 atom% enriched KNO3. Most importantly, we observed no significant protein to protein variation for the incorporation of the label.

Table 1. Proteins and the corresponding peptides identified in unlabeled protein extract (14N extract) and 15N labeled extract (15N extract) in two consecutive database searches without (14N search) and with (15N search) 15N as a fixed modification.

In the following experiments, protein extracts of unlabeled (14N) cell cultures were mixed with extracts of 15N-labeled cell cultures at known mixing ratios of 1:1, 1:4 and 4:1. Quantitative analysis of the intensity ratios of labeled and unlabeled peptide pairs indeed corresponded to the mixing ratios (Fig. 1). Taking the doubly-charged peptide ALGVDTVPVLVGPVSYLLLSK as an example, the quantitation revealed that in unlabeled cell extracts no pairing peak was found at the expected position of the labeled peptide (Fig. 1A). In contrast, the 15N-labeled extract contained no visible peak at the expected position of the 14N-form of the respective peptide (Fig. 1B). In the 1:1 mixture of the two protein extracts, labeled and unlabeled form of the peptide were clearly identified and quantified with a ratio of 0.92 (Fig. 1C), in the 1:4 mixture the intensity ratio of labeled and unlabeled form was 2.5 (Fig. 1D), while in the 4:1 mixture the ratio was found to be 0.18 (Fig. 1E). In average in all of the above protein extract mixtures, 85% of all identified proteins were quantified, and the average intensity ratios of all quantified proteins were not significantly different (χ2-test) from the expected values (Tab. 2). However, it has to be kept in mind, that ratio quantitation can become limited by the signal-to-noise ratio of the mass spectrometer [26] especially if there are large differences between the two samples. In those cases, it will not be possible to accurately reproduce the precise mixing ratios in the measured ratios of the peak pairs.

thumbnailFigure 1. Mass spectra of the peptide ALGVDTVPVLVGPVSYLLLSK in protein extracts form unlabeled cells (A), 15N-labeled extracts (B), and in mixtures of unlabeled and labeled extract at a ratio of 1:1 (C), a ratio of 1:4 (D), a 4:1 ratio (E). The expected mass to charge ratios of unlabeled and labeled peptides are indicated by arrows.

Table 2. Overview of the intensity ratio of 14N and 15N tryptic peptide pairs and the number of proteins identified in the two database searches.

There are two main reasons why proteomic quantification using full 15N-labeling is not as straight forward for automated high-throughput analysis. Firstly, the pairs of tryptic peptides are separated by a mass difference that depends on the number of 15N atoms in the respective peptide sequence. Therefore, the peptide sequence has to be determined by a database search before the mass difference to the labeled or unlabeled partner can be calculated, and this mass difference is different for each unique peptide (Tab. 3). This is actually the most critical disadvantage of the 15N-labeling method as it makes automated peak pair recognition impossible during data-dependent acquisition of mass spectra or in automated analysis of the raw files. Secondly, the isotopic distribution of the mass spectrum of a 15N-labeled peptide is slightly altered. This is so because the labeling substance, in this case KNO3, was used only in an isotopic enrichment of 98 atom% 15N. As the number of nitrogen atoms as a proportion of the total number of atoms in a peptide is rather high, the isotopic distribution of the peptide ion is influenced by the degree of enrichment of 15N. Similarly to previous work using full 15N- labeling as a method for quantification in proteomics [24,44], we do not account for this effect. However, other research groups have developed algorithms that correct for isotope enrichment effects [46] or make use of it for quantitative analysis [47]. Despite some drawbacks in the automated analysis, full 15N-labeling is a cheaper alternative to metabolic labeling with SILAC (Tab. 5). With the concentrations of amino acids used for SILAC labeling of plant cell cultures [35] and the concentrations of nitrate used in the labeling medium in this study, the K15NO3 labeling method is up to 10-times less expensive than SILAC labeling.

Table 3. A subset of tryptic peptides from glyceraldehyde-3-phosphate dehydrogenase C subunit (At3g04120) that were identified by mass spectrometry and subsequent database search (14N-search, see methods for details). For each peptide the expected mass difference was calculated based on the measured mass to charge ratio (MCR). Asterisks mark those peptides that have also been identified in the database search with 15N as fixed modification (15N-search, see Methods for details)

Table 5. Overview of the costs of SILAC labeling and full 15N-labeling for plant cell culturesa. Depending on the compounds and concentrations used in the medium, K15NO3 labeling is 7 to 12 times cheaper per liter medium.

Quantitative analysis of known mixtures of metabolic extracts

For metabolites, quantitation was based on the ion intensities of characteristic fragment ions [39]. In total, among the nitrogen-containing compounds, 14 amino acids (ala, asp, cys, glu, gly, ile, lys, met, phe, pro, thr, trp, tyr, val) and two polyamines (spermidine, putrescine) were analyzed. We estimated an average of 91% incorporation of 15N-label into metabolites, ranging from 70% for alanine to the maximally achievable 98% for tryptophan and putrescine.

Mass isotopomer analysis of the saturated 15N-labeled cells indicated the expected portion, namely approximately 2% of residual 14N in all observed 15N-containing metabolite preparations (Fig. 2A–D, 15N samples). The observed mixing ratios of the unlabeled and labeled metabolite extracts show the expected tendencies. For example, 15N/14N ratios for glycine were found to be 1.0, 4.3 and 0.3 in 1:1, 1:4, and 4:1 mixtures (Fig. 2A). However, all 4:1 ratios were over-estimated without correction for the contribution of natural abundances of 13C (Fig. 2A–D, higher isotope peak in 14N-extracts). Natural abundance of nitrogen and carbon isotopes as well as discrimination effects during metabolism may account for these observations and the differences between metabolites [48,49]. In contrast to peptide analysis, we therefore conclude that when analyzing mixing ratios of compounds with one or few N-atoms the natural isotope abundance of 13C (1.10%) needs to be considered, whereas the contribution of natural abundance of 15N (0.37%) may be neglected (Fig. 2 A–D). In addition two further sources of errors were tested: (1) The two original cell cultures, labeled and unlabeled may have different metabolite pools and (2) equal combination of labeled and non-labeled samples might be inaccurate. Exemplary metabolites which do not contain nitrogen, demonstrate slight non-significant changes in pool size and negligible errors in the 1:1, 1:4, and 4:1 mixtures (Fig. 2 E–F). While glycine, inositol or fumarate levels were unchanged in the two original cell cultures (Fig. 2.A, E–F), threonine (Fig. 2B), glutamate (Fig. 2C), and to a minor extent spermidine (Fig. 2D) had accumulated in the 15N-labeled cells. Although the two cell cultures were grown in the same medium composition and under the exact same growth conditions, changes in metabolite contents may have resulted from slight differences in handling during harvesting (e.g. intensity of washing, speed of freezing). As a consequence, we propose that those pool-size changes in the originating cells need to be experimentally considered for the purpose profiling experiments of the N-containing metabolic complement.

thumbnailFigure 2. Ion intensities of characteristic fragment ions for 14N and 15N isoforms of metabolites in unlabeled cells (14N), 15N-labeled cells (15N) and in extracts of 1:1, 1:4, and 4:1 mixtures of unlabeled to labeled cells is shown for the amino acid glycine (A), the amino acid threonine (B), the amino acid glutamic acid (C), and the polyamine spermidine (D). Non-N-containing compounds inositol (D) and fumarate (E) were used as a control for equal extract material. Each mixture was measured in five independent samples, mean of five replica ± standard deviation are shown, and ratios between 15N-form and 14N-form for each cell mixture are shown above the bars. Average labeling efficiency of all N-containing metabolites was 91.32% as estimated from the 15N to 14N ratios in the 15N-labeled extract.

Using the unmixed extracts (14N-extract and 15N-extract, Fig. 2) to estimate the contribution of natural abundance of 13C to higher isotope fragmentation peaks, we calculated the expected ratios of 15N/14N for all metabolites in the 1:1 mixture (Tab. 4). The calculation also takes into account that pool sizes (ion intensities) for certain metabolites were significantly different in the unmixed original cells. It becomes apparent that in most cases the measured ratios in the 1:1 mixture are rather accurate (deviation < 10%), and only for those metabolites, which display large differences in concentration in the original unmixed cells, measured ratios were over- or underestimated. Alanine was found in higher concentrations in unlabeled cells and therefore the expected mixing ratio was strongly underestimated due to signal-to-noise limitation of the 15N/14N ratios. The contrary holds true for methionine and putrescine which show higher concentrations in the 15N-labeled cells leading to a strong over-estimation of expected ratios. These examples underline the importance of including the unmixed extracts in the analysis. It has been described also for quantitative protein analysis that large ratios lead to larger relative errors, basically because the ratio is then defined by the signal to noise ratio of the mass spectrometer [26].

Table 4. Measured ratios of 15N/14N forms of metabolite fragment peaks in 1:1 mixtures of labeled and unlabeled extracts compared to their expected values. Expected ratios were calculated based on the fragment spectra ratios in the unmixed extracts (14N-extract, 15N-extract). The difference of the measured values to the expected values is expressed as percentage of the expected value.

The examples of different metabolite pool sizes (ala, met, glu, putrescine) in the 14N-cells compared to the 15N-cells actually reflect the true experimental situation, in which two cell extracts of unknown concentrations would be mixed in a 1:1 ratio, and those compound differing significantly from a 1:1 ratio of the ion intensities are concluded to be of different pool size in the original cells. The same experimental strategy generally also applies to proteins, and has already been successfully been used to detect proteins specifically interacting with a bait protein or peptide from background interaction partners or to describe temporal changes in protein abundance [29,30,32]. However, in the experiments described here, no variation between proteins was observed, since protein extracts were mixed based on total protein content, while for metabolite analysis, frozen cell material was mixed on a fresh weight basis (see Methods for details). Thus, the labeling and mixing strategy described here is well suitable for accurate detection of small changes and also ideal for screening for large deviations from an expected ratio, e.g. in presence/absence studies.

Alternative labeling strategies

A drawback of the 15N-labeling strategy for metabolomic analyses can be seen in the fact that quantitative information can only be obtained for nitrogen-containing metabolites. Thus, for quantitative information of a full metabolite profile, metabolic labeling with 13C would be a more promising strategy, as has already been proposed in an experiment using yeast cells [39]. However, for protein analysis full 13C-labeling is not suitable: The high proportion of carbon atoms in tryptic peptides would lead to very large mass differences between labeled and unlabeled peptides making unambiguous detection of labeled and unlabeled ion pairs very difficult.

Conclusion

In this study we used K15NO3 to metabolically label plant cell cultures for comparative proteomic and metabolomic experiments. We demonstrate that quantitative comparison using mixtures of labeled and unlabeled cell extracts is (i) approximately 10-times less expensive per liter of cell culture compared to SILAC labeling for plants or the iTRAQ reagent and (ii) accurate at the level of single mass spectra. Data supporting quantitative statistics are generated at the level of single mass spectra for peptides and those metabolites which comprise the N-metabolome. It is (iii) the first approach describing possibilities of a joined quantitative analysis utilizing mass isotopomer ratios of proteins and metabolites from the same cell extract. Thus, we believe that metabolic 15N-labeling may be applied to a wide range of biological questions in plant science which require quantitative proteomic and metabolomic analysis.

Methods

Metabolic labeling of cell cultures

Arabidopsis cell cultures derived from leaves [50] were grown in JPL medium [51] with 19 mM potassium nitrate as the sole nitrogen source. Potassium nitrate was supplied either in normal form or in 15N-enriched form (98 atom% K15NO3, Sigma-Aldrich). Cells were subcultured every 7 days using 10% of the culture volume. The remaining cells were harvested by suction over a filter plate and frozen at -80°C.

Protein extracts and mixing

Frozen cells were ground and extracted in 50 mM TRIS-HCl pH 7.5, 1% NP-40, and a protease inhibitor mixture (Complete Tabs, Roche). Cell debris was pelleted by centrifugation and 200 μg of Protein was precipitated with TCA. Protein was resuspended in a small volume (approx. 20μL) of 6 M Urea/2 M thiourea pH8 and in-solution digested with trypsin after reduction in DTT, alkylation with iodoacetamide and dilution of the sample with 4 volumes of 50 mM NH4HCO3 was performed as described [30].

Mass spectrometric analysis of proteins and database search

Tryptic peptide mixtures were then desalted on STAGE tips [52] before LC-MS/MS analysis using nanoflow HPLC (Proxeon Biosystems, Odense, Denmark) and a linear ion trap instrument (LTQ, Thermo Electron, San Jose, CA, USA) as mass analyzer. Peptides were eluted from the analytical column (Reprosil C18, Dr. Maisch GmbH, Tübingen, Germany) by a linear gradient running from 10 % to 50 % acetonitrile in 110 minutes and sprayed directly into the LTQ mass spectrometer. Proteins were identified by tandem mass spectrometry (MS/MS) by information-dependent acquisition of fragmentation spectra of multiple-charged peptides. Additional data-dependent fragmentation (MS3) was used to confirm identity of ambiguous proteins [53]. Fragment spectra were then searched against a non-redundant version of the Arabidopsis protein Database (TIGR6) using the Mascot algorithm (Matrix Science, UK, http://www.matrixscience.com webcite). The following search parameters were applied: Peptide mass tolerance 800 ppm, MS/MS tolerance 0.8 Da, methionine oxidation and carbamidomethylation of cysteine were set as variable modifications. For each sample, two database searches were carried out, one without fixed modifications ('14N-search') and one with 15N-amino acids as a fixed modification ('15N-search').

Quantitative analysis of protein

Quantitative Analysis was done using the open-source software MSQuant (http://msquant.sourceforge.net webcite) which provides a validation and quantitation platform for protein mass spectrometry. The MSQuant software has previously been successfully applied to quantitative analysis involving SILAC labeling [31,32]. For quantitation involving full 15N-labeling, ratios between the centroids in the ion chromatograms of the eluting 'heavy' (i.e. 15N-labeled) and 'light' (i.e. unlabeled) tryptic peptide peaks were calculated and averaged over the duration of the respective peaks in the total ion chromatogram [26]. Labeled and unlabeled forms of peptides were found to co-elute (Fig. 3) and the quantification was based on the average of independently determined ratios for each peptide. Ratios obtained from different peptides identifying the same protein were averaged. A final standard deviation for protein ratios was calculated from the ratios of the individual peptides or, when the identification was based on a single peptide, from the ratios obtained from the different mass spectra of this peptide pair.

thumbnailFigure 3. Co-Elution of unlabeled and labeled peptides. Solid lines mark the elution peak of the unlabeled form of each peptide, dashed lines mark the elution peak of the corresponding labeled peptide.

The labeled or unlabeled counterparts to peptides identified in the database searches were detected automatically in MSQuant based on their amino acid sequence and mass to charge ratio (Fig. 4). For example, if a peptide was identified in the 14N-search, the mass to charge ratio of the corresponding peak in the 15N-labeled form was calculated from the expected mass shift based on the amino acid sequence of the 14N-isoform of the identified peptide and its charge state.

thumbnailFigure 4. Mass spectra of unlabeled and labeled peptides of from glyceraldehyde-3-phosphate dehydrogenase C subunit (At3g04120) in a 1:1 mixture of unlabeled and labeled protein extracts. For each peptide the mass difference between unlabeled and labeled form of the peptide is different as indicated by the arrows.

Metabolite extraction and sample preparation

Metabolites were extracted from 120 mg cell material, which was shock frozen in liquid nitrogen and ground to a fine powder. Alternately different ratios of labeled and non labeled powder were combined to a final amount of 120 mg and subsequently treated as a single sample. Care was taken to keep samples frozen during manipulation. Frozen material was extracted with 360 μL methanol. Extraction temperature was 70°C. 200 μL chloroform and 400 μL water were added for partitioning polar metabolites as described [54]. Polar extracts were dried in a vacuum concentrator over night. Derivatization was conducted with 10 μL 20 mg mL-1 methoxyamine hydrochloride/pyridine, and 17.5 μL 20 mg mL-1 N-methyl-N-(trimethylsilyl)-trifluoroacetamide. A mixture of alkanes was added for calculation of retention time indices [55].

Mass spectrometric analysis of metabolites

GC-MS based metabolite profiling performed according to the procedure described previously [56]. Briefly, 1μL of sample was injected at 230°C into a GC 6890 (Agilent Technologies, Palo Alto, CA, USA) gas chromatograph mounted with a Rtx-5Sil MS capillary column, 30 m × 0.25 mm inner diameter with 0.25 μm film thickness, and a 10 m integrated guard column (Restek GmbH, Bad Homburg, Germany). The temperature program comprised 1 min at 70°C, a 9°C min-1 ramp to 350°C, and 5 min at 350°C. The transfer line to the mass-spectrometer was set to 250°C. Carrier gas was helium at a flow rate of 0.6 mL min-1 and operated at constant flow. A time-of-flight mass-spectrometer was used, Pegasus III TOF-MS system (Leco, St. Joseph, MI, USA) with the electron impact ionization source set to -70 eV and 250°C. Data acquisition was set to 20 spectra s-1 and the mass range restricted to m/z = 70–600. All other settings of the mass spectrometer were according to manufacturer's instructions. Chromatograms were acquired, deconvoluted and processed with ChromaTOF™ software (LECO, St. Joseph, MI, USA). Metabolites were identified using the GMD library (Golm Metabolome Database, http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html webcite; [57,58]). Mass spectral and RI comparison was performed by NIST02 (National Institute of Standards and Technology, Gaithersburg, MD, USA) mass spectral search and comparison software accepting mass spectral matches >650 on a scale of 1000 and retention time index matches ± 5.0 RI units. Mass spectra and fragmentation pattern of amino acid derivatives were interpreted as described [59] in order to identify 15N-labeled forms of the respective amino acids.

Quantitation of metabolites

Absolute ion currents and ratios in ion intensities of metabolites were directly derived from mass spectral fragmentation pattern exported from the ChromaTOF™ software. If available, for each metabolite, more than one fragment ion was used for quantitation [39]. For each sample type (original cell cultures, and the three mixtures), five independent samples were analyzed and the ion intensities for each fragment ion were averaged across the five replica samples.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

WRE carried out the experimental work ranging from cell culture labeling to sample preparation and LC-MS/MS analysis, and quantitative evaluation of the protein dataset, and he drafted parts of the manuscript. AE carried out the metabolite mass spectrometric analysis and data evaluation. JK contributed ideas to the metabolomic applications of the labeling strategy and critically reviewed the metabolomic aspects of the manuscript. WXS designed the study and prepared major parts of the manuscript. All authors read and approved the final manuscript.

References

  1. Weckwerth W, Wenzel K, Fiehn O: Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks.

    Proteomics 2004, 4:78-83. PubMed Abstract | Publisher Full Text OpenURL

  2. Wienkoop S, Glinski M, Nobuo T, Tolstikov V, Fiehn O, Weckwerth W: Linking protein fractionation with multidimensional monolithic reversed-phase peptide chromatography/ mass spectrometry enhances protein identification from complex mixtures even in the presence of abundant proteins.

    Rapid Communications in Mass Spectrometry 2004, 18:643-650. PubMed Abstract | Publisher Full Text OpenURL

  3. Koller A, Washburn MP, Lange BM, Andon NL, Deciu C, Haynes PA, Hays L, Schieltz D, Ulaszek R, Wei J, Wolters D, Yates JRIII: Proteomic survey of metabolic pathways in rice.

    Proceedings of the National Academy of Sciences of the USA 2002, 99:11969-11974. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Nelson CJ, Hegeman AD, Harms AC, Sussman MR: A quantitative analysis of Arabidopsis plasma membrane using trypsin-catalyzed 18O labeling.

    Molecular and Cellular Proteomics 2006., in press OpenURL

  5. Mann M, Jensen ON: Proteomic analysis of post-translational modifications.

    Nature Biotechnology 2003, 21:255-261. PubMed Abstract | Publisher Full Text OpenURL

  6. Wolschin F, Wienkoop S, Weckwerth W: Enrichment of phosphorylated proteins and peptides from complex mixtures using metal oxide/hydroxide affinity chromatography (MOAC).

    Proteomics 2005, 5:4389-4397. PubMed Abstract | Publisher Full Text OpenURL

  7. Andersen JR, Lyon CE, Fox AH, Leung AKL, Lam YW, Steen H, Mann M, Lamond AI: Direct proteomic analysis of the human nucleolus.

    Current Biology 2002, 12:1-11. PubMed Abstract | Publisher Full Text OpenURL

  8. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, Mootha VK, Mann M: A mammalian organelle map by protein correlation profiling.

    Cell 2006, 125:187-199. PubMed Abstract | Publisher Full Text OpenURL

  9. Zybailov B, Coleman MK, Florens L, Washburn MP: Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling.

    Analytical Chemistry 2005, 77:6218-6224. PubMed Abstract | Publisher Full Text OpenURL

  10. Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, Mann M: Exponentially Modified Protein Abundance Index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.

    Molecular and Cellular Proteomics 2005, 4:1265-1272. Publisher Full Text OpenURL

  11. Aebersold R, Mann M: Mass spectrometry-based proteomics.

    Nature 2003, 422:198-207. PubMed Abstract | Publisher Full Text OpenURL

  12. Ong SE, Foster L, Mann M: Mass spectrometric-based approaches in quantitative proteomics.

    Methods 2002, 29:124-130. Publisher Full Text OpenURL

  13. Ong SE, Mann M: Mass spectrometry-based proteomics turns quantitative.

    Nature Chemical Biology 2005, 1:252-262. PubMed Abstract | Publisher Full Text OpenURL

  14. Desiderio DM, Kai M: Preparation of stable isotope-incorporated peptide internal standards for field desorption mass spectrometric C-terminal sequencing technique.

    Biomedicine and Mass Spectrometry 1983, 10:471-479. Publisher Full Text OpenURL

  15. Stewart II, Thomson T, Figeys D: 18O labeling: a tool for proteomics.

    Rapid Communications in Mass Spectrometry 2001, 15:2456-2465. PubMed Abstract | Publisher Full Text OpenURL

  16. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.

    Nature Biotechnology 1999, 7:994-999. Publisher Full Text OpenURL

  17. Peters EC, Horn DM, Tully DC, Brock A: A novel multifunctional labeling reagent for enhanced protein characterization with mass spectrometry.

    Rapid Communications in Mass Spectrometry 2001, 15:2387-2392. PubMed Abstract | Publisher Full Text OpenURL

  18. Olsen JV, Andersen JR, Nielsen PA, Nielsen ML, Fibeys D, Mann M, Wisniewski JR: Hystag - a novel proteomic quantification tool applied to differential display analysis of membrane proteins from distinct areas of mouse brain.

    Molecular and Cellular Proteomics 2004, 3:82-92. Publisher Full Text OpenURL

  19. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.

    Molecular and Cellular Proteomics 2004, 3:1154-1169. Publisher Full Text OpenURL

  20. Hegeman AD, Harms AC, Sussman MR, Bunner AE, Harper JF: An isotope labeling strategy for quantifying the degree of phosphorylation at multiple sites in proteins.

    Journal of the American Society of Mass Spectrometry 2004, 15:647-653. Publisher Full Text OpenURL

  21. Weckwerth W, Willmitzer L, Fiehn O: Comparative quantification and identification of phosphoproteins using stable isotope labeling and liquid chromatography/mass spectrometry.

    Rapid Communications in Mass Spectrometry 2000, 14:1677-1681. PubMed Abstract | Publisher Full Text OpenURL

  22. Wienkoop S, Weckwerth W: Relative and absolute quantitative shotgun proteomics: targeting low-abundance proteins in Arabidopsis thaliana.

    Journal of Experimental Botany 2006, 57:1529-1535. PubMed Abstract | Publisher Full Text OpenURL

  23. Glinski M, Romeis T, Witte CP, Wienkoop S, Weckwerth W: Stable isotope labeling of phosphopeptides for multiparallel kinase target analysis and identification of phosphorylation sites.

    Rapid Communications in Mass Spectrometry 2003, 17:1579-1584. PubMed Abstract | Publisher Full Text OpenURL

  24. Oda Y, Huang K, Cross FR, Cowburn D, Chait BT: Accurate quantitation of protein expression and site-specific phosphorylation.

    Proceedings of the National Academy of Sciences of the USA 1999, 96:6591-6596. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  25. Langen H, Takacs B, Evers S, Berndt P, Lahm HW, Wipf B, Gray C, Fountoulakis M: Two-dimensional map of the proteome of Haemophilus influenzae.

    Electrophoresis 2000, 21:411-429. PubMed Abstract | Publisher Full Text OpenURL

  26. Ong SE, Kratchmarova I, Mann M: Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC).

    Journal of Proteome Research 2003, 2:173-181. PubMed Abstract | Publisher Full Text OpenURL

  27. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

    Molecular and Cellular Proteomics 2002, 1:376-386. Publisher Full Text OpenURL

  28. Zhu H, Pan S, Gu S, Bradbury EM, Chen X: Amino acid residue specific stable isotope labeling for quantitative proteomics.

    Rapid Communications in Mass Spectrometry 2002, 16:2115-2123. PubMed Abstract | Publisher Full Text OpenURL

  29. Blagoev B, Kratchmarova I, Ong SE, Nielsen M, Foster L, Mann M: A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling.

    Nature Biotechnology 2003, 21:315-318. PubMed Abstract | Publisher Full Text OpenURL

  30. Schulze W, Mann M: A novel proteomic screen for peptide-protein interactions.

    Journal of Biological Chemistry 2004, 279:10756-10764. PubMed Abstract | Publisher Full Text OpenURL

  31. Schulze W, Deng L, Mann M: The phosphotyrosine interactome of the ErbB-receptor kinase family.

    Molecular Systems Biology 2005, doi: 10.1038/msb4100012:E1-E13. Publisher Full Text OpenURL

  32. Blagoev B, Ong SE, Kratchmarova I, Mann M: Temporal ordering of signaling networks by quantitative proteomics.

    Nature Biotechnology 2004, 22:1139-1145. PubMed Abstract | Publisher Full Text OpenURL

  33. Ibarrola N, Kalume DE, Gronborg M, Iwahori A, Pandey A: A proteomic approach for quantitation of phosphorylation using stable isotope labeling in cell culture.

    Annals of Chemistry 2003, 75:6043-6049. Publisher Full Text OpenURL

  34. Gruhler A, Olsen JV, Mohammed S, Mortensen P, Faergman N, Mann M, Jensen ON: Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway.

    Molecular and Cellular Proteomics 2005, 4:310-327. Publisher Full Text OpenURL

  35. Gruhler A, Schulze WX, Matthiesen R, Mann M, Jensen ON: Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry.

    Molecular and Cellular Proteomics 2005, 4:1697-1702. Publisher Full Text OpenURL

  36. Dunkley TPJ, Griffin JL, Dupree P, Lilley KS: Localization of organelle proteins by isotope tagging (LOPIT).

    Molecular and Cellular Proteomics 2004, 3:1128-1134. Publisher Full Text OpenURL

  37. Jones AM, Bennett MH, Mansfield JW, Grant M: Analysis of the defence phosphoproteome of Arabidopsis thaliana using differential mass tagging.

    Proteomics 2006, 6:4155-4165. PubMed Abstract | Publisher Full Text OpenURL

  38. Morgenthal K, Weckwerth W, Steuer R: Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

    Biosystems 2005, in press. PubMed Abstract | Publisher Full Text OpenURL

  39. Birkemeyer C, Lüdemann A, Wagner C, Erban A, Kopka J: Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

    Trends in Biotechnology 2005, 23:28-33. PubMed Abstract | Publisher Full Text OpenURL

  40. Mashego MR, Wu L, Van Dam JC, Ras C, Vinke JL, Van Winden WA, Van Gulik WM, Heijnen JJ: MIRACLE: mass isotopomer ratio analysis of U-13C-labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites.

    Biotechnology and Bioengineering 2004, 85:620-628. Publisher Full Text OpenURL

  41. Mesnard F, Ratcliffe RG: NMR analysis of plant nitrogen metabolism.

    Photosynthesis Research 2005, 83:163-180. PubMed Abstract | Publisher Full Text OpenURL

  42. Govindarajulu M, Pfeffer PE, Jin HR, Abubaker J, Douds DD, Allen JW, Bucking H, Lammers PJ, Shachar-Hill Y: Nitrogen transfer in the arbuscular mycorrhizal symbiosis.

    Nature 2005, 435:819-823. PubMed Abstract | Publisher Full Text OpenURL

  43. Ippel JH, Pouvreau L, Kroef T, Gruppen H, Versteeg G, van den Putten P, Struik PC, van Mierlo CP: In vivo uniform (15)N-isotope labelling of plants: using the greenhouse for structural proteomics.

    Proteomics 2004, 4:226-234. PubMed Abstract | Publisher Full Text OpenURL

  44. Krijgsveld J, Ketting RF, Mahmoudi T, Johansen J, Artal-Sanz M, Verrijzer CP, Plasterk RH, Heck AJ: Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics.

    Nature Biotechnology 2003, 8:927-931. Publisher Full Text OpenURL

  45. Wu CC, MacCoss MJ, Howell KE, Matthews DE, Yates JRIII: Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis.

    Analytical Chemistry 2004, 76:4951-4959. PubMed Abstract | Publisher Full Text OpenURL

  46. MacCoss MJ, Wu CC, Matthews DE, Yates JRIII: Measurement of the isotope enrichment of stable isotope-labeled proteins using high-resolution mass spectra of peptides.

    Analytical Chemistry 2005, 77:7646-7653. PubMed Abstract | Publisher Full Text OpenURL

  47. Whitelegge JP, Katz JE, Pikhakari KA, Hale R, Aguilera R, Gomez SM, Faull KF, Vavilin D, Vermaas W: Subtle modification of isotope ratio proteomics; an integrated strategy for expression proteomics.

    Phytochemistry 2004, 65:1507-1515. PubMed Abstract | Publisher Full Text OpenURL

  48. Yoneyama T, Handley LL, Scrimgeour CM, Fisher DB, Raven JA: Variations of the natural abundances of nitrogen and carbon isotopes in Triticum aestivum L. with special references to phloem and xylem exudates.

    New Phytologist 1997, 137:205-213. Publisher Full Text OpenURL

  49. Robinson D, Handley LL, Scrimgeour CM: A theory for 15N/14N fractionation in nitrate-grown vascular plants.

    Planta 1998, 205:397-406. Publisher Full Text OpenURL

  50. Pauly M, Eberhard S, Albersheim P, Darvill A, York WS: Effects of the mur1 mutation on xyloglucans produced by suspension-cultured Arabidopsis thaliana cells.

    Planta 2001, 214:67-74. PubMed Abstract | Publisher Full Text OpenURL

  51. Jouanneau JP, Peaud-Lenoel C: Growth and synthesis of proteins in cell suspensions of a kinetin dependent tobacco.

    Physiologia Plantarum 1967, 20:834-850. Publisher Full Text OpenURL

  52. Rappsilber J, Ishihama Y, Mann M: Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics.

    Analytical Chemistry 2003, 75:663-670. PubMed Abstract | Publisher Full Text OpenURL

  53. Olsen JV, Mann M: Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation.

    Proceedings of the National Academy of Sciences of the USA 2004, 101:13417-13422. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  54. Fiehn O, Kopka J, Dörmann P, Altmann T, Trethewey RN, Willmitzer L: Metabolite profiling for plant functional genomics.

    Nature Biotechnology 2000, 18:1157-1161. PubMed Abstract | Publisher Full Text OpenURL

  55. Wagner C, Sefkow M, Kopka J: Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles.

    Phytochemistry 2003, 62:887-900. PubMed Abstract | Publisher Full Text OpenURL

  56. Erban A, Schauer N, Fernie AR, Kopka J: Nonsupervised construction and application of mass spectral and retention time index libraries from time-of-flight gas chromatography-mass spectrometry metabolite profiles. In Metabolomics: Methods and Protocols. Volume 358. Edited by Weckwerth W. Totowa, Humana Press; 2006::19-39. [Methods in Molecular Biology] OpenURL

  57. Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T, Lundgren K, Rössner-Tunali U, Forbes MG, Willmitzer L, Fernie AR, Kopka J: GC-MS libraries for the rapid identification of metabolites in complex biological samples.

    FEBS Letters 2005, 579:1332-1337. PubMed Abstract | Publisher Full Text OpenURL

  58. Kopka J, Schauer N, Krüger S, Birkemeyer C, Usadel B, Bergmüller E, Dörmann P, Gibon Y, Stitt M, Willmitzer L, Fernie AR, Steinhauser D: GMD@CSBDB: The Golm Metabolome Database.

    Bioinformatics 2005, 21:1635-1638. PubMed Abstract | Publisher Full Text OpenURL

  59. Leimer KR, Rice RH, Gehrke CW: Complete mass spectra of the per-trimethylsilylated amino acids.

    Journal of Chromatography 1977, 141:355-375. Publisher Full Text OpenURL