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

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 dif-ferent 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 quan-titative proteomic approaches also in plant sciences [2][3][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, 18 O 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][18][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 15 N-enriched medium [24,25]. However, since the mass shift introduced by 15 N 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][27][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 modificationdependent protein-protein interaction screens [30,31], and to analyses of the dynamics of signal-dependent phosphorylation events [32][33][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 18 O 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 13 C-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 15 N-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, 15 N-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 15 N involved growth of yeast cells in 14 N and 15 N medium for the quantitation of phosphorylation [24], the technique of 15 N-labeling for proteomic research had mostly remained in the field of microbiology. Exceptions are studies using 15 N-labeled microorganisms as a food source for metabolically labeling Drosophila [44] and 15 N-labeling of rats by the use of 15 N-labeled algal food [45]. In this proof-of-principle study, we use 15 Nlabeling to quantify protein and metabolite abundance in known mixtures of labeled and unlabeled cells.

Quantitative analysis of known mixtures of protein extracts
Metabolic 15 N-labeling introduces the stable isotope in each nitrogen atom in the labeled organism. Therefore, every observable peptide from mixtures of unlabeled ( 14 N) and 15 N-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 15 N into all proteins after 14 days of continuous growth in presence of K 15 NO 3 . Comparison of protein identifications in database searches with and without 15 N 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 14 N form. This indicates that 15 N incorporation into the proteome of plant cell cultures is as complete as it can be achieved with 98 atom% enriched KNO 3 . Most importantly, we observed no significant protein to protein variation for the incorporation of the label.
In the following experiments, protein extracts of unlabeled ( 14 N) cell cultures were mixed with extracts of 15 Nlabeled 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 15 N-labeled extract contained no visible peak at the expected position of the 14   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 (χ 2 -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.
There are two main reasons why proteomic quantification using full 15 N-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 15 N 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 15 N-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 15 N-labeled peptide is slightly altered. This is so because the labeling substance, in this case KNO 3 , was used only in an isotopic enrichment of 98 atom% 15 N. 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 15 N. Similarly to previous work using full 15 N-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 15 N-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 K 15 NO 3 labeling method is up to 10-times less expensive than SILAC labeling.

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 15 N-label into metabolites, ranging from 70% for alanine to the maximally achievable 98% for tryptophan and putrescine.
Mass isotopomer analysis of the saturated 15 Fig. 2A). However, all 4:1 ratios were over-estimated without correction for the contribution of natural abundances of 13 C ( Fig.  2A-D, higher isotope peak in 14 N-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 13 C (1.10%) needs to be considered, whereas the contribution of natural abundance of 15 N (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 nonlabeled 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 spermi-  (Fig. 2D) had accumulated in the 15 N-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.
Using the unmixed extracts ( 14 N-extract and 15 N-extract, Fig. 2) to estimate the contribution of natural abundance of 13 C to higher isotope fragmentation peaks, we calculated the expected ratios of 15 N/ 14 N 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 underes-timated due to signal-to-noise limitation of the 15 N/ 14 N ratios. The contrary holds true for methionine and putrescine which show higher concentrations in the 15 Nlabeled 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].
The examples of different metabolite pool sizes (ala, met, glu, putrescine) in the 14 N-cells compared to the 15 N-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 15 N-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 13 C would be a more promising strategy, as has already been proposed in an experiment using yeast cells [39]. However, for protein analysis full 13 C-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 K 15 NO 3 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 15 N-labeling may be applied to a wide range of biological questions in plant science which require quantitative proteomic and metabolomic analysis.

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 15 N-enriched form (98 atom% K 15 NO 3 , 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 NH 4 HCO 3 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 informationdependent 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). 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 15 N-amino acids as a fixed modification ('15Nsearch').

Quantitative analysis of protein
Quantitative Analysis was done using the open-source software MSQuant (http://msquant.sourceforge.net) 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 15 N-labeling, ratios between the centroids in the ion chromatograms of the eluting 'heavy' (i.e. 15 Nlabeled) 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.
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 14 N-search, the mass to charge ratio of the corresponding peak in the 15 N-labeled form was calculated from the expected mass shift based on the amino acid sequence of the 14 N-isoform of the identified peptide and its charge state. Figure 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.

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-offlight 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; [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 15 N-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 eval-Mass spectra of unlabeled and labeled peptides of from glyc-eraldehyde-3-phosphate dehydrogenase C subunit (At3g04120) in a 1:1 mixture of unlabeled and labeled pro-tein extracts Figure 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.
uation. 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.