NpgRJ_Nmeth_1038 1..7 - The Scripps Research Institute

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© 2007 Nature Publishing Group http://www.nature.com/naturemethods

Chemoselective probes for metabolite enrichment and profiling Erin E Carlson & Benjamin F Cravatt Chemical probes that target classes of proteins based on shared functional properties have emerged as powerful tools for proteomics. The metabolome rivals, if not surpasses, the proteome in terms of size and complexity, suggesting that efforts to profile metabolites would also benefit from targeted technologies. Here we apply the principle of chemoselective probes to the metabolome, creating a general strategy to tag, enrich and profile large classes of small molecules from biological systems. Key to success was incorporation of a protease-cleavage step to release captured metabolites in a format compatible with liquid chromatography–mass spectrometry (LC-MS) analysis. This technology, termed metabolite enrichment by tagging and proteolytic release (METPR), is applicable to small molecules of any physicochemical class, including polar, labile and low-mass (o100 Da) compounds. We applied METPR to profile changes in the thiol metabolome of human cancer cells treated with the antioxidant N-acetyl-L-cysteine.

Understanding the metabolic and signaling networks that regulate health and disease is a principal goal of post-genomic research. The remarkable complexity of these biochemical pathways necessitates the integrated application of multiple molecular profiling methods to facilitate their discovery and annotation. Although a wealth of information has been gained from genomic1 and proteomic2 studies, they provide an incomplete picture of the molecular pathways that regulate cell physiology and pathology. To fully understand the composition and function of biochemical networks, analysis of the small-molecule complement of cells and tissues, commonly referred to as the metabolome, is essential3,4. Indeed, even modest variations in enzyme activity can correlate with substantial changes in metabolite concentration, making analysis of the metabolome possibly the most sensitive measurement of the biochemical state of the cell3,5,6. Whereas techniques to examine the genome and proteome have been relatively forthcoming, efforts to fully inventory the metabolome have been hindered by a unique set of challenges4,7. In contrast to transcripts and proteins, metabolites share no direct link to the genetic code, instead being synthesized by the concerted action of complex networks of enzymes. Additionally, unlike genes

and proteins, which are linear polymers comprised of a limited number of monomeric units, metabolites constitute a structurally diverse collection of molecules that vary widely in physicochemical properties, including polarity, hydrophobicity, mass, chemical stability and relative abundance. Finally, the sheer size of the metabolome is daunting; present estimates suggest that most eukaryotic organisms possess 4,000–20,000 distinct metabolites7, although the number in any given cell type may be much smaller. Given the extraordinary size and physicochemical diversity of the metabolome, it is logical to postulate that its characterization would benefit from targeted approaches capable of enriching specific subsets of small molecules from biological systems. Such targeted methods would have the advantage of reducing the molecular complexity of samples under analysis and, at the same time, facilitate the detection of lower-abundance metabolites. To date, targeted strategies for metabolomics have largely been limited to relatively crude enrichment protocols such as organic extraction to isolate hydrophobic metabolites. Such ‘lipidomics’8 endeavors have provided tantalizing evidence of the impact that smallmolecule profiling can have on our understanding of biochemical pathways, including the characterization of new classes of brain natural products9 and the annotation of lipid signaling pathways that support cancer10. Still, general methods to enrich other classes of small-molecules (for example, polar compounds) are lacking, and consequently, large fractions of the metabolome remain difficult to profile with high sensitivity and resolution. Inspired by advances in targeted proteomics, where the implementation of chemical probes has facilitated the enrichment of many classes of proteins based on activity11,12 and post-translational modification state13,14, we sought to develop a general strategy to chemically tag and enrich specific sets of metabolites based on shared functional-group composition. We demonstrate that this method, METPR, is applicable to a wide range of metabolites of any physicochemical class and can be used to comparatively profile the metabolomes of complex biological systems. RESULTS Design rationale for METPR We envisioned that the core experimental reagents for METPR would consist of a suite of chemoselective probes to tag, capture

The Skaggs Institute for Chemical Biology, and Departments of Cell Biology and Chemistry, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, California 92037, USA. Correspondence should be addressed to B.F.C. ([email protected]). RECEIVED 17 NOVEMBER 2006; ACCEPTED 8 MARCH 2007; PUBLISHED ONLINE 8 APRIL 2007; DOI:10.1038/NMETH1038

NATURE METHODS | ADVANCE ONLINE PUBLICATION | 1

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and profile specific portions of the metabolome. To be a useful chemical strategy for metabolomics, METPR should meet several criteria: criterion 1, be applicable to many classes of metabolites regardless of their physicochemical properties; criterion 2, allow for the enrichment of metabolites directly from biological samples; criterion 3, permit the selective release of captured metabolites without damaging their chemical structures; and criterion 4, be compatible with direct LC-MS analysis of released metabolites. We met criterion 1 by synthesizing a complementary set of resinbound reactive groups that target distinct classes of metabolites bearing common chemical functionalities. Our initial set of reactive group–functionalized resins targeted carboxylic acids, amines, thiols, and ketones and aldehydes (Fig. 1), which collectively represent a considerable portion of the functional diversity of the metabolome. A schematic for the synthesis of the reactive group– functionalized resins is available in Supplementary Figure 1 online. To facilitate isolation of metabolites from biological samples (criterion 2), a general protocol for preparing and reacting cell and tissue metabolomes with reactive group–functionalized resins was required (Fig. 1). Previous studies have shown that treatment of biological samples with polar organic solvents results in efficient protein precipitation while also retaining a large number of metabolites in solution15,16. We found that serial treatment of cells (homogenized in phosphate buffer; pH 8) with two parts CH3CN followed by nine parts DMF removed essentially all protein as assessed by SDS-PAGE and silver staining (Supplementary Fig. 2 online). We then evaporated CH3CN from samples under a stream of nitrogen to yield a solution directly compatible with all capture reactions (90% DMF, 10% phosphate buffer; pH 8.0). Once metabolites have been captured by their cognate resin, a gentle and efficient cleavage strategy was required to effect their selective release (criterion 3). Most ‘chemical’ cleavage strategies are unduly harsh for use with metabolites, which often have labile groups that are sensitive to acid or base treatment17,18, and/or oxidation or reduction19. We therefore devised a ‘chemo-orthogonal’ cleavage step that uses the protease trypsin to release tagged metabolites from the resin (Fig. 1). We selected this protease for the robust nature of its activity, and also because trypsin recognizes a single amino acid (lysine or arginine), thus circumventing the requirement for a large recognition element in the probe structures. 2 | ADVANCE ONLINE PUBLICATION | NATURE METHODS

Figure 1 | The METPR technology. Poly(acryloylbis(aminopropyl)polyethylene glycol) (PEGA) resin was derivatized with a series of metabolite capture agents containing reactive groups (reactive element shown in red) that target specific classes of metabolites. Each reactive group–functionalized resin was reacted with a metabolome prepared by serial treatment of a cell extract (homogenized in phosphate buffer; pH 8.0) with CH3CN and DMF to precipitate proteins. Captured metabolites were selectively released with trypsin and analyzed by untargeted LC-MS. Released metabolites bear an amine tag varying in mass from 127–310 Da (note that the valine group on the amine-capture resin is included to minimize internal cyclization of the reactive group).

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To minimize the size of the residual tag attached to trypsin-cleaved metabolites, we constructed capture resins with peptides oriented in an N to C direction (Supplementary Fig. 1). Upon trypsin cleavage, the arginine moiety remains covalently attached to the solid support while the cleaved metabolites are appended with a small residual tag containing a primary amine (127–310 Da, depending on the reactive group). Notably, cleavage with trypsin also occurs in buffers that are directly compatible with LC-MS analysis (criterion 4). Precipitated proteins

Evaluating METPR performance with small-molecule standards We first assessed the ability of METPR to selectively tag and release small molecules by treating each of the capture resins with a mixture of small molecules containing representatives of the four targeted functional groups (benzyl amine (amine), Fmoc-Gly-OH (acid), p-anisaldehyde (aldehyde) and benzyl mercaptan (thiol)). We then washed the reacted resins, treated them with trypsin, separated the released small molecules on a C18 reverse phase column and detected the small molecules under positive ionization conditions using an Agilent MSD instrument operating in the broad mass-scanning mode (m/z 150–1,500; ‘untargeted’ LC-MS analysis). The predicted cleavage product for each small molecule was exclusively detected in the reaction run with its complementary capture resin, confirming the selectivity of both tagging and release of compounds by METPR (Fig. 2a). To assess whether METPR could facilitate the detection of lowerabundance compounds in a background of high-abundance molecules, we analyzed a mixture of four small molecules containing two lower-abundance acid compounds (1 mM) and two highabundance non-acid compounds (1,000 mM) by METPR with the acid-capture resin. Before tagging and enrichment, only the two high-abundance compounds were detectable by LC-MS analysis (Fig. 2b). In contrast, after METPR, the two low-abundance compounds were clearly visible by LC-MS in their tagged forms, whereas the two high-abundance compounds were no longer detectable. These results indicate that METPR, through a combination of chemoselective tagging and enrichment, can enhance the detection of lower-abundance metabolites, while, at the same time, removing highly abundant compounds that might otherwise interfere with analysis. We postulated that by introducing a mass tag onto captured metabolites, METPR might allow the detection of very small

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bolite concentration and mass signals over at least two orders of magnitude in METPR experiments (100–10,000 pmol/107 cells; R2 values 4 0.98; Fig. 3a). We obtained equivalent detection limits and dynamic range when small molecule standards were added to a background of human urine (100 ml; Supplementary Fig. 4 online). These data indicate that METPR can measure the relative levels of metabolites in a diversity of biological samples with good sensitivity and dynamic range. We also tested the efficiency of METPR by comparing the amount of small-molecule standards enriched from cancer cell extracts to a standard curve of synthetic tagged products. We

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metabolites (o100 Da) that are difficult to 8.0 × 105 detect in untargeted LC-MS profiling 6.0 × 105 experiments (owing to ion suppression by solvent modifiers). This proved to be the 4.0 × 105 case, as representative small metabolites, 2.0 × 105 such as pyruvate and methyl glyoxal, were readily detected by METPR (Fig. 2b and 0 4 4.5 3.5 Supplementary Fig. 3 online). METPR was 1.0 × 106 also compatible with profiling chemically 8.0 × 105 labile metabolites, such as the disulfide cystamine and the ester monoethyl itaco6.0 × 105 nate (Supplementary Fig. 3). These exam4.0 × 105 ples further highlight METPR’s capacity to enrich and profile highly polar metabolites. 2.0 × 105 We next evaluated the sensitivity and 0 3.5 4 4.5 dynamic range of METPR in more detail. To simulate profiling experiments, we added small-molecule standards to a background of cancer cell metabolome (1 107 cells homogenized in 100 ml of phosphate buffer; pH 8.0). We incubated three compounds (Fmoc-Gly-OH, deoxycholic acid and prostaglandin E2) with the acid-capture resin over a range of 10–10,000 pmol/107 cells. By untargeted LC-MS analysis we detected molecules at concentrations as low as 100 pmol/107 cells (Fig. 3a). Notably, these detection limits were 5–20-fold lower than those observed by direct LC-MS analysis of metabolomic extracts of cancer cells, indicating that the tagging and enrichment afforded by METPR substantially increases the sensitivity of metabolite measurements. We observed linear relationships between meta-

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Figure 2 | Evaluating the performance of METPR. (a) METPR analysis of representative small molecules bearing different functional groups. Reaction of an equimolar mixture of benzyl amine, Fmoc-Gly-OH, p-anisaldehyde and benzyl mercaptan (100 nmol each) with one of four capture resins resulted in the detection of the predicted trypsin cleavage product for each small molecule in the reaction with its complementary resin (amine, acid, ketone and aldehyde, thiol, respectively). Data shown represent extracted ion chromatograms from untargeted LC-MS analyses. (b) LC-MS detection of unenriched and METPRenriched small molecules in compound mixtures. Four compounds were mixed, two high abundance non-acid compounds (cystamine and progesterone, each at 1,000 mM) and two lower abundance acid compounds (pyruvate and Fmoc-Gly-OH, each at 1 mM).

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Figure 3 | METPR analysis of metabolites in the human breast cancer line MDA-MB-231. (a) Detection of three small-molecule carboxylic acid standards was assessed in MDA-MB-231 cells. Detection limits ranged from 100–250 pmol/107 cells (R2 4 0.98). (b) Profiling endogenous metabolites from all four functional group classes in MDA-MB-231 cells. Putative metabolites containing the indicated functionalities are depicted. Metabolites are plotted based on LC retention time (y axis) and predicted mass values after subtraction of the tag (x axis). Mass signals are color coded with the spot size and intensity correlating with signal strength. Ketone and thiol metabolites identified as pyruvate (88 Da) and glutathione (307 Da) are highlighted by green and red arrows, respectively. NATURE METHODS | ADVANCE ONLINE PUBLICATION | 3

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Figure 4 | Comparison of tandem MS spectra of tagged metabolites in METPR experiments. Note that each thiol molecule (left to right: benzyl mercaptan, glutathione, b-mercaptoethanol) shows a distinct fragmentation pattern in tagged form. One prominent tag-derived fragment is observed in each case (arrow, m/z 84). Qualitatively similar tandem MS data were observed for metabolites captured by other reactive group–functionalized resins (Supplementary Fig. 5).

observed good yields (B50%) of tagged Fmoc-Gly-OH (acid capture) and benzyl mercaptan (thiol capture). Efficiency was somewhat lower for capture of benzyl amine (amine capture; B15% yield) and acetophenone (ketone capture; B30% yield; data not shown). Evaluating METPR performance with cell metabolomes We next applied METPR to profile the metabolome of the human breast cancer line MDA-MB-231. We used all four reactive group– functionalized resins, and each reaction was compared to control reactions conducted without cell metabolome (DMF and buffer alone). We averaged data sets from four independent experiments and analyzed them by the XCMS software, which aligns and quantifies relative signal intensities of mass peaks from multiple LC-MS traces20. A metabolite was scored as a ‘hit’ if its average mass signal was significantly greater in cell metabolome samples than in buffer controls (Zthreefold in magnitude, P o 0.01). These stringent criteria ensured that any potential resin-derived signals were not inadvertently designated as metabolites. Each reactive group–functionalized resin provided a unique profile of metabolites from the cancer cells (Fig. 3b). We detected the largest number of metabolites with the acid-capture resin (4250 distinct mass signals), followed by the thiol-capture and the amine-capture resins (B80 and 24 unique signals, respectively), and then the ketonealdehyde–capture resin (14 unique signals). Captured metabolites ranged in predicted mass values of 50–1,132 Da (after subtraction of the tag), ion intensities of 19,500–1,290,000 and liquid-chromatography elutions times of 4.5–23.0 min (Fig. 3b). Notably, a prominent signal at 6.7 min in the thiol metabolite profile possessed a mass corresponding to glutathione (307 Da; Fig. 3b). We confirmed the identity of this tagged metabolite by coelution with a commercial glutathione standard that was reacted with the thiol-capture resin (data not shown). Using similar methods, we confirmed an 88-Da mass ion observed at 6.7 min in the ketonealdehyde profile as pyruvate (Fig. 3b). Collectively, these results indicate that a wide range of metabolites can be enriched and profiled by METPR. Tandem MS analysis of metabolites profiled by METPR Structural identification can be one of the most challenging aspects of metabolomic experiments, and, in most untargeted studies, no more than 20–30% of metabolite structures can be assigned21. 4 | ADVANCE ONLINE PUBLICATION | NATURE METHODS

Progress has been made toward the goal of total metabolome assignment through the creation of public databases that archive tandem MS data for large collections of metabolites22. METPR could, in principle, further facilitate metabolite structure assignment, as reactivity with a capture resin specifies that a metabolite possess the complementary functional group. One potential concern, however, is that the residual tag placed on metabolites by METPR might interfere with the structure elucidation process by, for example, dominating the fragmentation patterns in tandem MS experiments. To explore this possibility, we compared the fragmentation patterns of small molecules subjected to METPR. We found that tandem MS spectra of three representative tagged thiol metabolites contained many mass fragments unique to the individual molecules (Fig. 4). Notably, we observed one prominent tagderived signal in each tandem MS spectrum (m/z 84; Fig. 4). This daughter ion likely corresponds to fragmentation of the lysine tag, resulting in the formation of a cyclic iminium species [C5H10N]+ (ref. 23). We observed qualitatively similar fragmentation data for compounds tagged with each of the other three reactive group– functionalized resins (Supplementary Fig. 5 online). A further comparison of the tandem MS spectra of tagged and untagged glutathione revealed several common mass losses (of m/z 18, 75 and 129; Supplementary Fig. 5), indicating that the fragmentation patterns of tagged metabolites preserve key features of the parent (untagged) compounds. These findings demonstrate that tags placed on metabolites in METPR studies should not unduly complicate the use of tandem MS data for structural characterization. Moreover, the presence of a single, diagnostic tag-derived fragment should aid in rapidly discriminating masses that correspond to tagged metabolites from background signals. METPR of cancer cells treated with N-acetyl-L-cysteine A principal goal of metabolomics is to measure changes in metabolite levels that occur in response to biological or pharmacological perturbations of living systems7. We have shown previously that the relative amounts of hydrophobic metabolites (enriched by organic extraction) can be quantified in biological samples using direct measurements of mass ion intensity9,10. We therefore asked whether metabolites profiled by METPR could also be comparatively quantified by mass ion intensity measurements. For this study, we profiled the metabolomes of breast cancer cells treated with the antioxidant, N-acetyl-L-cysteine.

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Fold change

Reactive oxygen species have been impli3 a b Acid Thiol Amine Ket/Ald 2 cated in a variety of human pathophysiolo* ≥10 gical conditions, including the aberrant 20 8 proliferation of cancer cells24,25. N-acetylL-cysteine acts as a reactive oxygen species 6 15 scavenger by elevating the cellular concen4 Acid trations of small-molecule thiols, such as 3 Thiol 2 1 Amine cysteine and glutathione26,27. We applied 10 Ket/Ald 0 METPR to comparatively profile the thiol 1 20 2 900 metabolomes of the human breast cancer 750 15 5 600 line MCF-7 treated with N-acetyl-L-cysteine 450 0 250 500 750 1,000 300 Retention time 10 m/z m/z 150 (20 mM, 24 h). Data sets from four inde(min) 5 0 pendent experiments were analyzed by XCMS software20 to provide a list of sig- Figure 5 | METPR analysis of breast cancer cells treated with the antioxidant N-acetyl-L-cysteine. nificantly altered metabolites (defined as (a) Shown are the relative levels (fold change) of thiol metabolites that were significantly elevated in metabolites that displayed greater than N-acetyl-L-cysteine–treated versus control (buffer-treated) MCF-7 cells (19 metabolites in total). Numbers threefold changes in level and had P o highlight three thiol compounds that were structurally identified as glutathione (1), cysteine (2) and N-acetyl-L-cysteine (3). Asterisk signifies a metabolite with a fold change greater than tenfold. 0.01). We observed augmentation of both (b) Analysis of N-acetyl-L-cysteine–treated cells with all four capture agents. Putative metabolites cysteine (tenfold) and glutathione (three- containing the indicated functionalities that differed significantly in N-acetyl-L-cysteine–treated versus fold) in breast cancer cells following treat- control cells are depicted. ment with N-acetyl-L-cysteine (Fig. 5a). Notably, the magnitude of elevation of cysteine and glutathione measured by METPR matched closely these metabolites on a C18 reverse phase column (4.5–23.0 min) the values determined previously using targeted spectrophoto- argues that they represent small molecules of highly varied polarity, metric assays (eight- and fourfold, respectively)24. These data including hydrophilic compounds, which historically have been confirm that alterations in the amounts of endogenous metabolites difficult to enrich from biological samples. Consistent with this can be accurately measured by METPR. premise, we detected polar metabolites such as cysteine, glutathione In addition to detecting the anticipated changes in cysteine and and pyruvate by METPR in cancer-cell metabolomes. METPR also glutathione, METPR also identified 17 other thiol-containing proved capable of accurately measuring fluctuations in the compounds, including N-acetyl-L-cysteine, that were significantly amounts of metabolites, such as cysteine and glutathione after elevated in N-acetyl-L-cysteine–treated cells (Fig. 5a, a complete list N-acetyl-L-cysteine treatment of breast cancer cells. METPR conof altered masses is available in Supplementary Table 1 online). To currently revealed many additional changes in the metabolome of assess whether other classes of metabolites were also affected in N-acetyl-L-cysteine–treated cells that extended beyond thiol comN-acetyl-L-cysteine–treated cancer cells, we reacted their meta- pounds, underscoring the value of this technology for broadly bolomes with additional reactive group–functionalized resins, profiling metabolic pathway dynamics in biological systems. Finalresulting in the detection of 29 acid, 6 amine and 3 ketone/aldehyde ly, we provided evidence that METPR allows profiling of metabocompounds with altered profiles in N-acetyl-L-cysteine–treated lites with difficult physicochemical properties, including not only cells (Fig. 5b; a complete list of altered masses is provided in polar, but also small and unstable molecules. On this note, a recent Supplementary Table 1). Overall, these results suggest that the metabolomic analysis of bacteria using hydrophilic interaction antioxidant N-acetyl-L-cysteine induces many changes in cancer cell chromatography and mass spectrometry28 identified neither pyrmetabolomes, including not only the direct elevation of cysteine- uvate nor thiol metabolites (for example, cysteine, glutathione). In derived products, but also more global fluctuations in biochemical contrast, this previous study detected several metabolites that lack networks that likely extend beyond cysteine metabolism. reactive functional groups for profiling by METPR. We therefore view METPR and conventional LC-MS analysis as complementary DISCUSSION methods for profiling the metabolome. METPR shows several potential advantages over more conventional Looking forward, we anticipate that the METPR technology can metabolomic methods, including (i) metabolite enrichment be strengthened by optimization of several key steps. One obvious (achieved by the selective capture and release of specific classes of challenge relates to the structural characterization of metabolites metabolites), (ii) chemo-orthogonality (protease-catalyzed release profiled by METPR. This is a problem common to all metabolomic preserves metabolite structures), (iii) structural information (reac- endeavors29. All of our experiments to date have been run on an tivity with a capture resin specifies that metabolites possess the Agilent MSD SL instrument, which does not have high-mass complementary functional group), and (iv) versatility (METPR can accuracy or tandem MS capabilities. Both of these features are now available on advanced hybrid mass spectrometers such as be applied to metabolites of any physicochemical class). Initial applications of METPR to cellular systems provided the LTQ-FT and Orbitrap instruments29, which should facilitate evidence that this technology can capture and profile substantial the assignment of molecular formulas (elemental composition) portions of the metabolome. An analysis of breast cancer cells with and key structural features to metabolites. Although one might question whether structure determination in METPR experifour distinct METPR capture reagents resulted in the collective ments will be complicated by the addition of chemical tags to detection of more than 300 putative metabolites (Fig. 3b). The wide range of liquid-chromatography retention times displayed by metabolites, our initial data suggest that this feature is unlikely NATURE METHODS | ADVANCE ONLINE PUBLICATION | 5


ARTICLES to pose a considerable hurdle. On the contrary, we postulate that chemical tagging may assist structure determination efforts by identifying at least one functional group on each captured metabolite. We should also emphasize that, even though METPR is already applicable to several large classes of metabolites, others remain outside the present scope of this method owing to the absence of functional groups that are reactive with our developed resins. We envision that continued efforts to create reactive group–functionalized resins that target additional functional groups will expand the scope of metabolites amenable to METPR analysis. Finally, it is likely that improvements in resolution could be achieved by varying the matrix used for LC separation. Indeed, a survey of our initial METPR profiles showed that metabolites tended to cluster into two distinct groups migrating between 4.5–7 and 15–20 min on C18 reverse phase resin (presumably representing polar and nonpolar metabolites, respectively; see Fig. 3b). Enhanced separation of the former group of metabolites should be achievable using hydrophilic interaction chromatography28,30. Here we described an advanced strategy for metabolomics, METPR, that exploits the power of chemoselective probes to tag, enrich and profile entire classes of small molecules in biological systems. Integration of the metabolomic data acquired by METPR with complementary genomic and proteomic information should facilitate ongoing efforts to fully annotate the biochemical networks that regulate physiology and pathology. These networks should in turn provide a rich source of new biomarkers and therapeutic targets for the diagnosis and treatment of human disease. METHODS Metabolome preparation for METPR analysis. We treated homogenized cell preparations (B1 107 cells per experiment homogenized by sonication (0.3 s, 10 pulses) in 100 ml of phosphate buffer (pH 8.0)) with 200 ml of CH3CN, vortexed the sample and incubated it on ice for 10 min. We then centrifuged the sample at 13,200g for 10 min. Next we added 900 ml of DMF, vortexed the sample and centrifuged it immediately. We transferred the supernatant to a test tube and evaporated it under a stream of nitrogen (B10 min) to remove the CH3CN. We added the resulting metabolite solution to the capture resin as detailed below. General procedures for reaction of metabolomes with resins. Amine capture resin: 30 mg of resin (per 107 cells) was preactivated as the succinimidyl ester by reaction with O-(7-azabenzotriazol-1-yl)-N,N,N¢,N¢-tetramethyluronium hexafluorophosphate (HATU, 3 equivalents), N-hydroxysuccinimide (NHS, 3 equivalents) and N,N-diisopropylethylamine (DIPEA, 3 equivalents) in DMF for 4 h. We rinsed the resin three times with DMF and then added the metabolite solution to the resin. Acid-capture resin: We added HBTU (2 equivalents) and 1-hydroxybenzotriazole hydrate (HOBt, 2 equivalents) to the small-molecule mixture and incubated it for 1 min. Then we added the mixture to 23 mg of the amine-functionalized resin. Ketone-aldehyde–capture resin: we added the small-molecule mixture directly to 30 mg of alkoxyl amine–functionalized resin (30 mg). Capture was performed under nitrogen to minimize reaction of the resin with ambient acetone. Thiol-capture resin: we added the small-molecule mixture directly to 23 mg of maleimide-functionalized resin. 6 | ADVANCE ONLINE PUBLICATION | NATURE METHODS

We rotated all reactions overnight at 25 1C and then washed the resins with DMF, CH2Cl2, CH2Cl2:CH3OH (1:1), hexanes, CH3OH, H2O and PBS (all 3, 10 ml). We initiated trypsinization by addition of 4 mg of sequencing-grade modified trypsin (Promega) in PBS (pH 7.4), 5 mM CaCl2, 0.05% AcOH (105 ml/ reaction). Cleavage was carried out at 37 1C for 2 3 h with shaking. We analyzed trypsin-released molecules on an Agilent 1100 LC-MSD using a reverse phase column (Phenomenex Luna C18, 5 mm, 150 4.6 mm) detected by electrospray ionization (positive ion mode). Analysis was initiated with an isocratic elution of 100% buffer A at 0.5 ml/min for 5 min followed by a linear gradient of 0–100% buffer B over 35 min, then an isocratic elution at 100% buffer B for 9 min and re-equilibration with 100% buffer A for 4 min (buffer A: 95:5 H2O:CH3CN, 0.1% formic acid; buffer B: 95:5 CH3CN:H2O, 0.1% formic acid). Metabolomic data analysis. We averaged and analyzed data sets from four independent experiments. We identified differentially expressed metabolites between sample pairs by XCMS software20. A metabolite was scored as a hit if its average mass signal was threefold different between the sample pairs (P o 0.01). We eliminated isotopic peaks (parent ion +1, retention time within 0.04 min). We eliminated sodium salt adducts by similar criteria (parent ion +22, retention time within 0.04 min). We also eliminated signals with m/z less than the mass of the tag (we expect that these signals are likely due to doubly charged species). We analyzed hit metabolites by manual quantification by measuring the area under the peak normalized to total ion current. In the N-acetyl-L-cysteine study, we performed a control experiment with N-acetyl-L-cysteine alone to identify and discard any low-level byproduct masses observed from reaction of this compound with the resin. Additional methods. Descriptions of the synthesis, characterization and use of METPR resins, the metabolome preparations, and the tandem MS experiments are available in Supplementary Methods online. Note: Supplementary information is available on the Nature Methods website. ACKNOWLEDGMENTS We thank H.P. Benton and A. Nordstro¨m for assistance with Q-TOF experiments. We also thank G. Simon for help in generating figures, and members of the Cravatt laboratory for helpful discussion and critical review of the manuscript. This work was supported by the American Cancer Society (PF-06-009-01-CDD, to E.E.C.), the National Institutes of Health (CA087660) and the Skaggs Institute for Chemical Biology. COMPETING INTERESTS STATEMENT The authors declare no competing financial interests. Published online at http://www.nature.com/naturemethods/ Reprints and permissions information is available online at http://npg.nature.com/reprintsandpermissions 1. Brown, P.O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999). 2. Patterson, S.D. & Aebersold, R. Proteomics: the first decade and beyond. Nat. Genet. 33, 311–323 (2003). 3. Fiehn, O. Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002). 4. Saghatelian, A. & Cravatt, B.F. Global strategies to integrate the proteome and metabolome. Curr. Opin. Chem. Biol. 9, 62–68 (2005). 5. Kell, D.B. & Westerhoff, H.V. Towards a rationale approach to the optimization of flux in microbial biotransformations. Trends Biotechnol. 4, 137–142 (1986).


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