Department of Biochemistry and Molecular Genetics, University of Colorado Denver – Anschutz Medical Campus, 12801 East 17th Ave, Aurora, CO 80045, USA
RATIONALE: The implementation of mass spectrometry (MS)-based metabolomics is advancing many areas of biomedical research. The time associated with traditional chromatographic methods for resolving metabolites prior to mass analysis has limited the potential to perform large-scale, highly powered metabolomics studies and clinical applications.
METHODS: Here we describe a three-minute method for the rapid profiling of central metabolic pathways through UHPLC/MS, tracing experiments in vitro and in vivo, and targeted quantification of compounds of interest using spiked in heavy labeled standards.
RESULTS: This method has shown to be linear, reproducible, selective, sensitive, and robust for the semi-targeted analysis of central carbon and nitrogen metabolism. Isotopically labeled internal standards are used for absolute quantitation of steady-state metabolite levels and de novo synthesized metabolites in tracing studies. We further propose exploratory applications to biofluids, cell and tissue extracts derived from relevant biomedical/clinical samples.
CONCLUSIONS: While limited to the analysis of central carbon and nitrogen metabolism, this method enables the analysis of hundreds of samples per day derived from diverse biological matrices. This approach makes it possible to analyze samples from large patient populations for translational research, personalized medicine, and clinical metabolomics applications. Copyright © 2017 John Wiley & Sons, Ltd.
Mass spectrometry (MS)-based metabolomics has recently become a key analytical readout for biomedical investigations,especially in the fields of cancer research and precision medicine. Improvements in the mass accuracy and resolving capabilities of MS instrumentation have allowed this technique to complement and, often times, replace nuclear magnetic resonance (NMR)-based approaches[1,2] for metabolomics studies due to increased sensitivity, flexibility, and decreased sample requirements. However, MS-based metabolomics is still limited by time-consuming analytical workflows and issues with robustness, both of which are essential factors for the successful execution of highthroughput platforms for hypothesis generation/testing and translational clinical applications. While nanochip detection kits are poised to provide an innovative tool for the quantitative real-time assessment of a limited number of compounds in the future, MS-based workflows that enable more extensive coverage of the metabolome in under 10 min have already been described and implemented. Although additional methodology using flow-injection time-of-flight
MS (TOF-MS) offers rapid real-time analytical readouts for a theoretical limit of 1000 samples per day,[7–9] this technique is applicable primarily to liquid-based matrices and sacrifices chromatographic separation of metabolites.
We recently described a three-minute method that combines ultra-high-performance liquid chromatography (UHPLC) and MSto perform high-throughput quantitative analyses of amino acids in numerous biological matrices including biofluids, cell and tissue extracts. Here, we describe a fast method that utilizes UHPLC/MS to provide expanded coverage of key metabolic pathways while maintaining analytical times that are comparable to flow-injection TOF-MS.[8,9] Characterization of approximately 400 analytical standards has demonstrated the capability of this high-throughput approach to quantitatively analyze central carbon and nitrogen metabolism, including pathways such as glycolysis, the pentose phosphate pathway (PPP), the tricarboxylic acid (TCA) cycle, the urea cycle, amino acid, purine and pyrimidine metabolism. We demonstrate the biomedical utility of this approach by profiling clinically relevant samples such as packed red blood cell (RBC) concentrates used for transfusion, biofluids (plasma, mesenteric lymph and bronchoalveolar lavagefluid – BALF),andlung tissue biopsies isolated from rats that have undergone severe trauma and hemorrhagic shock.
Analytical platforms that infer rates of glycolysis and mitochondrial metabolism through the measurement of oxygen consumption and extracellular acidification rates have
become widely utilized due to their relatively low costs and ease of use. However, these techniques do not directly measure small molecule targets and therefore provide a limited insight into the specific utilization and coordination of central metabolic pathways. Here we demonstrate the utility of our method to directly measure steady-state levels and perform tracing experiments in vitro (e.g. RBC stored under blood bank conditions) and in vivo (e.g. bolus chase in animal models) using different stable isotope substrates (e.g. 13C1,2,3-glucose and 13C15N-glutamine). Tracing experiments provide investigators with a tool to mechanistically investigate metabolic fluxes by kinetically elucidating the incorporation of isotopically labeled carbon and nitrogen atoms into downstream metabolites.[12–15] Moreover, we demonstrate the suitability of this approach to perform absolute quantitation of endogenous metabolites, as well as quantitation of de novo synthesized metabolites in stable isotope tracing experiments using internal isotopically labeled standards that have identical chemical properties but unique molecular weights relative to the endogenous unlabeled and de novo synthesized isotopologues. Taken together, this rapid method paves the way for cost-effective and ultra-highthroughput semi-targeted metabolomics and quantitative tracing experiments, as demonstrated by the analysis of approximately 2000 samples over three distinct applications.
* Correspondence to: A. D’Alessandro, Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, 12801 East 17th Ave,Aurora, CO 80045, USA.
A significantly more detailed version of this section is provided in the Supporting Information (Materials and Methods extended). All procedures performed in this study
were in accordance with the ethical standards of the Ethics Committee of the University of Colorado Denver and in conformity with the Declarations of Helsinki.
Analyses were performed using a Vanquish UHPLC system coupled online to a Q Exactive mass spectrometer (both Thermo Fisher Scientific, San Jose, CA, USA). Samples were resolved over a Kinetex C18 column (2.1 × 150 mm, 1.7 μm particle size; Phenomenex, Torrance, CA, USA) equipped with a guard column (SecurityGuard™Ultracartridge –UHPLCC18 for 2.1mmID columns; AJO-8782; Phenomenex) at 25°C using an isocratic condition of 5% acetonitrile, 95% water, and 0.1% formic acid at a flow rate of 250 μL/min. The Q Exactive mass spectrometer was operated independently in positive or negative ion mode, scanning in Full MS mode (2 μscans) from 60 to 900 m/z at 70,000 resolution, with 4 kV spray voltage, 15 shealth gas, 5 auxiliary gas. Calibration was performed prior to analysis using the PierceTM Positive and Negative Ion Calibration Solutions (Thermo Fisher Scientific). Acquired data was then converted from .raw to .mzXML file format using Mass Matrix (Cleveland, OH, USA). Metabolite assignments, isotopologue distributions, and correction for expected natural abundances of deuterium, 13C, and 15N isotopes were performed using MAVEN (Princeton, NJ, USA).
Graphs, heat maps and statistical analyses (either t-test or ANOVA) were prepared with GraphPad Prism 5.0 (GraphPad Software, Inc., La Jolla, CA, USA) and GENE-E (Broad Institute, MA, USA).
Coverage and selectivity
Experimental retention times for over 400 analytical standards (MSMLS, IROATech, Bolton, MA, USA) were determined over a 4- and 3-min isocratic method (conditions described above – Supplementary Table S1, Supporting Information).
Compounds were resuspended in 240 μL of 5% methanol (v/v) according to the manufacturer’s instructions (Supplementary Table S1). Standards were then diluted 1:9
(v/v) into either 0.1% formic acid or a metabolite extraction solution consisting of methanol, acetonitrile, water (5:3:2, v/v/v), and 10 μL were injected for UHPLC/MS analysis.
Robustness of chromatography and quantitation
Reproducibility of chromatographic retention time, pressure, mass accuracy, and absence of compound carry over in blank runs was characterized over 1500 non-consecutive runs of packed RBCs and supernatants (as detailed below) spanning across 6 days of analysis. Technical mixes were prepared by pooling RBC extracts from each of the four donors at day 2 of storage. Technical mixes were injected every 15 runs and the analysis was repeated after 4 days of data acquisition.
Comparison of 10 technical mixes from day 1 to day 2 or from day 4 to day 5 were analyzed to assess intra and inter-day technical variability, expressed as coefficients of variation (CV = mean/standard deviation) to monitor analytical variables.
Quantitative analysis and matrix effects
To assess inter-day reproducibility and the linearity of quantitation, standards were analyzed at concentrations ranging over four orders of magnitude, from 0.5 to 5000 pmol
per injection (detailed in Supporting information – Sigma Aldrich, St. Louis, MO, USA). Ion suppression effects were determined with the use of uniformly labeled 13C6-glucose that was spiked into the extraction solution at four different concentrations ranging over four orders of magnitude (0.1, 1, 10, 100, 1000 μM) prior to plasma extraction (described below in Applications). Plasma samples from three control rats (see below) were run either undiluted or diluted two-fold with extraction solution.
To further characterize stability and reproducibility, CVs were determined for the quantitation of heavy-labeled and xenometabolite standards added to plasma prior to extraction. These standards included 18 13C15N-amino acids at a final concentration of 0.25 μM (Cambridge Isotopes Laboratories, Inc., Tewksbury, MA, USA) and 5-fluorouracil at a final concentration of 2.5 μM (Sigma Aldrich).
Rat model of trauma/hemorrhagic shock
Samples were kindly provided by Dr. Anthony Bacon and Anne Slaughter (University of Colorado Denver).
Briefly, nonlethal trauma/hemorrhagic shock was induced in Sprague Dawley rats (n = 4) by laparotomy and controlled hemorrhage from the femoral artery until the rats achieved mean arterial pressure (MAP) equal to 30 mm Hg (MAP 30), as previously described. Plasma, mesenteric lymph and bronchoalveolar lavage fluid (BALF) samples (20 μL) were collected before hemorrhagic shock (Shock) and immediately after (30 min of shock at MAP 30), either in the presence or absence of mesenteric lymph diversion (MLD) through cannulation of the mesenteric artery.
Samples (20 μL) of biofluids (plasma, mesenteric lymph, or BALF) were extracted in 480 μL of ice-cold extraction solution by vortex for 30 min at 4°C, as reported. Insoluble proteins and lipids were pelleted by centrifugation at 4°C for 10 min at 10,000 g and supernatants were collected and stored at –80°C until subsequent analysis.
In vivo bolus chase of 13C15N-glutamine in rat lung biopsies
Heavy labeled lung biopsies were isolated from Shock/Sham (n = 8 per group) rats generated as described above. At the 15 min time point, Shock and Sham rats received a 1 mL/kg intravenous infusion of 13C5 15N2-glutamine (Product No. CNLM-1275-H-0.1; Cambridge Isotope Laboratories, Inc.) in physiological solution (35 mM) at a rate of 0.5 mL/min.
Animals remained in shock for a total of 45 min (30 min after bolus injection). Ten mg of lung tissues were ground to powder in liquid nitrogen and then extracted in ice-cold lysis/extraction buffer at a concentration of 10 mg
Packed red blood cell collection and storage
Packed RBC units, kindly provided by Dr. Tatsuro Yoshida (New Health Sciences, Inc., Cambridge, MA, USA), were collected and stored as previously reported. Samples (0.5 mL) were obtained through sterile couplers at days 2, 7, 14, 21, 28, 35 and 42. RBCs and supernatants were separated by centrifugation and extracted in ice-cold metabolite extraction solution at 1:9 and 1:24 (v/v) dilutions, respectively, as previously described.
Method comparison with gradient-based C18 and HILIC methods
Extracts of RBC (10 μL) and AS-3 supernatant (20 μL) were analyzed with the 3-min method and two gradient-based methods using a C18 or HILIC column.[19,20] The two alternative chromatographic methods consisted of (i) a 9- min method on a Kinetex C18 column (2.1 × 150 mm, 1.7 μm particle size; Phenomenex) run at 400 μL/min and 30°C (mobile phases – A: H2O, 0.1% formic acid; B: acetonitrile, 0.1% formic acid); or (ii) a 15-min method on an Acquity UPLC BEH Amide column (2.1 × 100 mm,
1.8 μm particle size; Waters, Milford, MA, USA) run at 350 μL/min and 35°C (mobile phases – A: 5% acetonitrile, 10 mM ammonium acetate, pH 10.0; B: 95% acetonitrile,
10 mM ammonium acetate, pH 10.0 adjusted with MS grade ammonium hydroxide). Further analytical details are extensively described in the Supporting Information.
In vitro labeling experiments
Packed RBCs (n = 4) were collected, processed and stored in CP2D-AS-3, as described above. Before processing, AS-3 (containing 55 mM dextrose in 100 ml of solution) was supplemented with 11 mM 13C1,2,3-glucose (Product No. CLM-4673-PK; Cambridge Isotope Laboratories, Inc.).
Samples were collected on a weekly basis at storage days 2, 7, 14, 21, 28, 35 and 42.
Quantitative tracing experiments
Packed RBCs incubated with 13C1,2,3-glucose (see previous paragraph) were extracted with the addition of a standard mixture containing 18 13C,15N-labeled amino acids at a final concentration of 0.25 μM (Product No. MSK-A2-1.2; Cambridge Isotope Laboratories, Inc.).
We recently described a rapid method to analyze the amino acid profiles of distinct biological matrices such as biofluids, cell and tissue extracts in only three minutes. Here we expanded upon this method to detect and profile a wide range of compounds associated with central carbon and nitrogen metabolic pathways.
Over 400 pure analytical standards ranging from 72 to 666 Da were tested and characterized using an isocratic gradient, providing retention times for each compound (Supplementary Table S1, Supporting Information). While aqueous dilution of standards into 0.1% formic acid improved chromatographic separation and detection over a 4-min window, dilution of the same compounds into the organic metabolite extraction solution (methanol/acetonitrile/water 5:3:2 v/v/v)[10,15,17,18,21] provided enhanced compound coverage, increased signal/noise ratios, and sensitivity within a shorter 3-min retention time window. Separation was achieved for key metabolites from pathways such as glycolysis, TCA cycle, glutaminolysis/glutathione homeostasis, and polyamine metabolism (Fig. 1(C)). Despite the relatively short chromatographic resolution window, this method provided baseline separation of compound classes (Figs. 1(C)and 1(G)).We observed resolution of structural and positional isomers including, but not limited, to 2,3-cAMP and 3,5-cAMP (Fig. 1(D)); L-leucine, L-isoleucine, and norleucine; 3-aminoisobutanoate and 4-aminobutanoate; fumarate and maleic acid; betaine, L-valine, and norvaline; noradrenaline and pyridoxine; D-glucoronolactone and ascorbate; citrate and isocitrate (Supplementary Table S1, Supporting Information).
Weassessed the reproducibility of this method by analyzing the coefficients of variation (CVs) for analytical parameters such as retention time, peak shape, and ion intensity (peak area top and peak area integration) of identified metabolites in a technical replicate mixture injected every 15 samples over the course of 1200 analytical runs during six non-consecutive days. An example is presented in Fig. 1(E), showing an inter-day peak area CV = 2.7% for glutathione. Although this method does not incorporate a washing/re-equilibration segment,
Figure 1. UHPLC and MS characterization of metabolite standard compounds. Using a library of ~400 compounds, the retention times and peak areas were determined when diluted in aqueous and organic solutions (Supplementary Table S1, Supporting Information). Results are depicted as two-dimensional plots showing (A) neutral mass vs. ion intensity or (B) neutral mass vs. chromatographic retention time. (C) Metabolites critical to central carbon and nitrogen metabolism are resolved during the isocratic elution. (D) Resolution of isobaric isomers such as 2,3-cAMP and 3,5-cAMP was observed. Reproducibility of peak shape, retention time, and ion signal intensity is shown in 10 replicate injections of a technical mixture over the course of 1200 analytical runs spanning over 6 non-consecutive days (E). (F) The stability of system pressure indicates a lack of excessive analyte accumulation on the column during 1500 runs. (G) Representative 3D chromatogram showing separation of different hydrophilic compound classes over a 3-min run. Linearity of quantitation (r2 > 0.98) for key metabolites in central carbon and nitrogen pathways (H) was assessed over four orders of magnitude using a nine-point standard curve. Representative metabolites are shown for glycolysis (red - I), TCA cycle (green - J), and glutaminolysis/glutathione homeostasis (GSH/GSSG – reduced/oxidized glutathione) (blue - K).
carry over of compounds was not observed in blank runs before and after 1500 sample runs spanning over 6 days. In support, the column operational pressure was maintained during this time period (Fig. 1(F)) indicating that the guard column contributes to the stability of column performance by retaining relatively hydrophobic compounds. In addition, we determined retention time CVs of 0.28% and extremely stable system pressure (CV = 0.37%) throughout the entire analysis (Figs. 1(E) and 1(F)). The MS signal was also stable during this extensive analysis as demonstrated by preservation of sub-5 ppm mass accuracy for all standard calibration mixtures analyzed both prior to and post-analysis (data not shown).
We observed <0.5% average deviation from the theoretical ion intensity for the natural abundance of the M + 1 ion (13C1) in glutathione (Fig. 1(E), inset), indicating MS signal stability that is a key analytical feature for both the identification of compounds on the basis of the chemical formulas as well as for quantitative and tracing experiments.
We next characterized the performance of this method with respect to quantitative analyses. Relative levels of metabolites were assessed across samples by characterizing signal linearity in serial dilutions of select compounds from central pathways (Fig. 1(H)). Signal linearitywasassessed using linear regression analysis (r2), and was confirmed over four orders of magnitude that were tested for compounds reported in Figs. 1(I)–1(K) (Supplementary table S2, Supporting Information), with the lowest limit of detection (LLOD)=100 fmol andthe lowest limit of quantitation (LLOQ) = 500 fmol. By further validating signal reproducibility using internal standards such as the xenometabolite 5-fluorouracil (Fig.2(A)) and isotopically labeled 13C5 15N1-L-valine (Fig. 2(B)), we then confirmed signal linearity in the presence of a complex matrix by determining an r2 > 0.98 for 13C6-glucose that was exogenously added to human plasma as an internal standard at four log orders of concentration (0.1–1000 μM) before extraction (Figs. 2(C) and 2(D)). This experiment was performed over two nonconsecutive days to demonstrate inter-day reproducibility of the linearity of quantifications (Fig. 2(D)). Human plasma, either undiluted or diluted two-fold in extraction solution (Fig. 2(E)), was tested with the 3-min method to determine linearity of quantitation, recovery, and matrix-dependent ion suppression. Minor deviations from the expected fold changes of 2 were observed, with a CV < 2% for glucose and other representative metabolites of key pathways (Figs. 2(E)) and 2(F)).
Figure 2. Robustness of relative and absolute quantitative analysis in complex matrices. To show reproducibility of internal standards, coefficients of variation (CV = average/standard deviation) for peak area and retention time were calculated for (A) the xenometabolite 5-fluorouracil and (B) isotopically labeled 13C5,15N1-valine. (C) Interday variability, matrix effects on ionization, and signal linearity were determined by extracting plasma with the addition of 1 mM 13C6-glucose. (D) Signal linearity is shown with a five-point serial dilution in plasma over four orders of magnitude. Dilutions on 2 non-consecutive days and in extraction solution alone show inter-day reproducibility and matrix effects, respectively. (E) Dilution of plasma 1:1 (v/v) with extraction solution demonstrates linearity of fold change calculations for the relative quantitation for glucose and (F) for additional compounds in central carbon and nitrogen pathways. Fold change values are depicted using a heat map, with blue representing a fold change of 0.5 and red representing a fold change of 1. (G) Red blood cell extracts sampled over a 42-day storage period were analyzed using the 3-min method and conventionl gradient-based methods using a C18 (9-min method) and amide HILIC (15-min method). Metabolite abundances are depicted as heat maps to facilitate comparison between methods of technical reproducibility and expected trends based on previously published results.
Next, we tested the suitability of this method for applications requiring high-throughput relative and absolute quantitation of target compounds in clinically relevant samples. Metabolic analyses of RBCs are clinically relevant in that blood biochemical testing is a hallmark of clinical biochemistry. Indeed, RBCs are by far the most abundant host cell in the human body (25 out of 30 trillion host cells in the human body are RBCs) and their metabolism mirrors states of health and disease. In addition, transfusion of packed RBCs is not only a life-saving intervention for patients with hematological malignancies and hemoglobinopathies, but also for surgical patients or those suffering from traumatic injuries with hemorrhage. While RBC storage is a logistic necessity to make approximately 108 million units available for millions of recipients worldwide every year, refrigerated storage of RBCs negatively affects RBC metabolism and physiology, the effects of which are referred to as the storage lesion. RBC metabolism during storage under blood bank conditions has been extensively characterized by our group using LC/MS,[18,21,23–25] and by others via NMR and gas chromatography (GC)/MS-based approaches.[26,27] Here, four units of RBCs were sampled during storage in Additive Solution (AS)-3 (Nutricel, Haemonetics) on a weekly basis until the expiration date of the units (42 days upon collection and processing). Samples were analyzed with the 3-min method and conventional gradient-based methods using either a C18 or HILIC column.[19,20] It is worth noting that the HILIC method was validated in-house using the same IROA standard library (Supplementary Table S1, Supporting Information). Samples were run in randomized order, while technical mixes (pool of day 0 samples from each of the four units) were run every 15 samples to ensure stability of the system under each analytical condition (Supplementary Table S3, Supporting Information). Metabolite levels were graphed as a heat map in order to facilitate comparison with the existing literature that extensively describes the metabolic impairments associated with the storage lesion (Fig. 2(G); a vectorial version of the heat maps including metabolite names is provided in Supplementary Fig. S1, Supporting Information).[18,21,26,27] The storage lesion is in part characterized by the progressive accumulation of lactate, increased purine catabolism, depletion of intermediates of the pentose phosphate pathway (PPP), and impaired glutathione synthesis. Decreased levels of citrate, adenine, urate, ribose phosphate, glutamine, reduced and oxidized glutathione (GSH and GSSG), and increased levels of hypoxanthine and 5-oxoproline, are also observed in stored RBCs.[18,21,26,27] Recently, we identified eight metabolic markers of the RBC storage lesion that were validated by two independent laboratories with at least three different analytical setups. Comparison of the results for those biomarkers (three representative markers are shown in Supplementary Fig, S2, Supporting Information) generated by the three methods used here indicates that the methods are indeed comparable among each other and are consistent with previously observed RBC metabolic trends during storage.[18,21,26,27] For example, accumulation of the glycolytic catabolite lactate and purine oxidation product hypoxanthine has been shown using C18- and HILIC-based UHPLC/MS and NMR by independent laboratories[18,21,26,27] and here confirmed with all methods (Supplementary Fig. S2, Supporting Information). Analogously, all three methods highlighted comparable storage-dependent citrate consumption in RBCs stored in AS-3 (Supplementary Fig. S2), consistent with recent reports identifying a role for citrate metabolism in stored RBCs and suggesting this metabolite as a marker of the RBC storage lesion.[21,24] Furthermore, the 3-min method maintained superior reproducibility across technical replicates relative to the amide HILIC column despite lacking a conventional elution gradient, indicated by the analysis of technical mixes run every 15 samples during the entire analysis (Supplementary Table S3, Supporting Information).
In the last 18 months we have applied this method to thousands of samples from a variety of biological matrices and species ranging from E. coli to humans (Fig. 3(A)). We present here a few representative applications that demonstrate the suitability of this method to assess metabolic phenotypes in diverse biological matrices, including cultured cells, human and mouse biofluids, and tissue extracts.
As noted above, the transfusion of packed RBCs is a life-saving intervention for several categories of recipients, but especially for massively transfused trauma patients with severe hemorrhage. To highlight the clinical importance of such treatment, it is worth noting that the most recent data from the Center for Disease Control indicate that trauma is the leading cause of death under the age of 46 and results in more life years lost than both cancer and cardiovascular disease combined. Acute mortality in trauma is dependent on three main factors: hypothermia, acidosis, and coagulopathy. Late death by trauma ensues hours to week after the injury and is principally caused by multiple organ failure or acute lung injury, which account for approximately 20% of total mortality. Surgical procedures that divert the mesenteric lymph in trauma/hemorrhagic shock patients have been shown to attenuate acute lung injury. We recently documented significant alterations of the human and rat plasma metabolome in response to trauma and hemorrhage. Here we sought to investigate shock-induced metabolic alterations to mesenteric lymph and BALF, which both undergo significant exchange with plasma. Plasma, lymph, and BALF were isolated from rats, either hemorrhaged to a mean arterial pressure (MAP) of 30 mmHg (MAP 30) by cannulation of the mesenteric artery (Shock group – Fig. 3(B)), or that had undergone laparotomy without hemorrhage (Sham group). The 3-min method was used to observe that the metabolic phenotypes of all three biofluids were affected by severe hemorrhage, which altered fluid compositions with respect to amino acids, nucleosides, and lactate, the latter of which is a key driver of post-shock acidosis and a marker of poor prognosis in the trauma population (Fig. 3(C)). Extended vectorial versions of the heat maps in Fig. 3 – also including metabolite names – are provided in Supplementary Fig. S3, while measurement data are provided in Supplementary
Figure 3. Analysis of biofluids using the 3-min method. (A) This method has been used to analyze cells, tissues and biofluids from a wide range of species. (B) As an example of biofluid analysis, plasma, mesenteric lymph, and bronchiolar lavage fluid (BALF) were isolated from rats hemorrhaged until shock (Shock group) at a mean arterial pressure equal to 30 mmHg (MAP 30), or unhemorrhaged (Sham group), with and without a mesenteric lymph diversion (MLD). (C) Metabolite abundances in plasma, mesenteric lymph, and BALF are graphed as heat maps with blue representing the row minimum and red representing the row maximum. The abundances of multiple metabolites are affected by shock and MLD, (D) such as the case with the proinflammatory metabolite succinate.
Table S4 (see Supporting Information). The accumulation of the TCA cycle intermediate succinate in post-shock plasma has been previously associated with coagulopathy/ fibrinolysis and inflammatory responses due to its potential role in the activation of neutrophils, which in turn contribute to the etiology of acute lung injury.[17,30] Here we show that mesenteric lymph diversion does not correct the shockinduced accumulation of succinate, and instead promotes its accumulation in the mesenteric lymph and the BALF (Fig. 3(D)).
BALF only offers an indirect measurement of the lung metabolome. We thus sought to determine the actual extent of succinate accumulation within the lung that represents a potential marker of inflammatory lung injury, and also to mechanistically identify the metabolic substrate that fuels the production of this pro-inflammatory dicarboxylic acid in the lung. Therefore, we injected rats with a bolus of 13C5 15N2-glutamine right after laparotomy alone (Sham), or with hemorrhage to MAP 30 (Shock) (Fig. 4(A)). After a 45- min incubation period to allow for tissue uptake and metabolism of heavy glutamine, lungs were harvested from sacrificed animals for the detection of steady-state isotopes in glutamine catabolites (Fig. 4(B)). Based on our preliminary results indicating that less than 10% of succinate produced de novo after shock is derived from 13C6-glucose, we anticipated that a larger portion of shock-dependent accumulation of succinate would be produced via glutaminolysis. Indeed, heavy-labeled glutamine and TCA cycle intermediates, including uniformly labeled 13C4- succinate, were elevated by 3- to 10-fold in Shock lungs compared to Sham (Fig. 4(C)). This accumulation resulted in ~15% enrichment of the heavy succinate isotopologue in the Shock group and a < 2% incorporation of glutamine-derived heavy carbon atoms into succinate in the Sham group as early as 30 min after bolus injection. These results indicate that glutaminolysis contributes significantly to succinate accumulation in response to hemorrhagic shock and suggests that enzymes central to this pathway, such as glutaminase, may represent potential therapeutic targets to improve patient resuscitation strategies in trauma.
Based on the suitability of the 3-min method to perform (i) high-throughput tracing experiments and (ii) absolute quantification of compounds using the heavy isotope labeled internal standards, we sought to combine these two approaches to achieve absolute quantification of heavy isotopologues produced de novo during a tracing experiment by using orthogonally labeled stable isotope compounds for quantitation. To test the feasibility of this approach, four leukocyte and platelet-filtered packed RBC units were stored under routine blood bank conditions for 42 days in the presence of 11 mM 13C1,2,3-glucose (Fig. 5(A)). Samples were extracted using the metabolite extraction solution supplemented with a commercially available standard mixture of 18 13C15N-amino acids (for details, see Experimental section). The presence of 15N atoms in these
Figure 4. In vivo tracing experiments of lung tissue harvested from rats undergoing hemorrhagic shock. (A) Eight rats underwent laparotomy to induce trauma with and without induction of hemorrhage after 30 min to mean arterial pressure = 30mmHG(MAP 30). At the 30-min time point, a bolus of 13C5 15N2-glutamine was injected into circulation and allowed to metabolize for 45 min, at which point the rat lungs were isolated and analyzed by the 3-min method. (B) A scheme for heavy atom incorporation into TCA cycle intermediates is shown. Accumulation of succinate in particular is known to activate neutrophils and lead to induction of acute lung injury (ALI). (C) The metabolism of 13C5,15N2-glutamine to fuel the TCA cycle in the lung is shown to be shockdependent.
Three-minute semi-targeted metabolomics
Figure 5. In vitro quantitative tracing experiments of stored red blood cells. (A) Red blood cells were stored for 42 days in the presence of 13C1,2,3-glucose and were sampled on a weekly basis. (B) The predicted isotopologue distribution of 13C3 15N2-alanine, which was included as an internal standard in the metabolite extraction solution. Formulas to calculate the absolute quantity of alanine are depicted in the inset, and in (C) a schematic showing the metabolism of 13C1,2,3- glucose into alanine and the expected chromatographic overlap of the various alanine isotopologues. (D) Absolute quantities of total alanine in the cells and in the additive solution superntant throughout storage are shown. (E) Absolute quantities of de novo synthesized alanine during storage are shown.
internal standard compounds provided masses that were unique to those of endogenous unlabeled or solely 13C-labeled isotopologues that were synthesized by RBCs through the metabolism of 13C1,2,3-glucose. Absolute levels of amino acids could thus be determined by comparing the peak areas of endogenous light/13C-only-labeled amino acids to those of the fully labeled 13C15N heavy isotopologues added at a known concentration. An example for L-alanine is described in Fig. 5(B), which shows the formula to quantify total levels of alanine in addition to the de novo synthesized 13C3-alanine isotopologue that is derived from pyruvate transamination (scheme presented in Fig. 5(C)). Using this approach, we were able to quantify not just the total levels of alanine in RBCs and the additive solution supernatant (Fig. 5(D)), but also the absolute levels of de novo synthesized 13C3-alanine over the 42-day storage period (Fig. 5(E)).
The development of high-throughput methods for metabolomics applications is required for large-scale studies and routine clinical practice. Here we describe a 3-min method that combines the advantages of rapid flow-injection TOF-MS with the selectivity of convential chromatographybased metabolomics to cover pathways of central carbon and nitrogen metabolism. By exploiting the recent technical advancements in UHPLC and fast scanning high-resolution MS technologies, this method maintains comparable analytical performance with regards to sensitivity, selectivity, reproducibility, and robustness at the chromatographic and MS level. While this approach is not applicable for the measurement of hydrophobic compounds such as lipids and long-chain acyl carnitines, it is capable of rapidly profiling key metabolic pathways such as glycolysis, PPP, TCA cycle, urea cycle, polyamines, amino acid and nucleotide metabolism in hundreds of samples per day. Furthermore, the robustness of this approach makes it useful for the analysis of a wide range of biological matrices relevant to basic science and clinical routine practice, including biofluids, cell and tissue extracts. We describe representative applications of clinical relevance to the fields of intensive care and transfusion medicine, though the method described here can be applied to other areas including cancer, immunology, and cardiovascular research. The applications presented here highlight the capabilities of this method for the rapid and high-throughput profiling of metabolism, relative and absolute quantitation of target metabolites, and tracing experiments both in vitro and in vivo. By combining the use of heavy labeled compounds for absolute quantitation and tracing experiments, we further highlight the utility of this method to accurately quantify de novo synthesized metabolites through central carbon and nitrogen pathways upon incubation with appropriate heavy labeled substrates. The proposed method herein significantly reduces instrument time and consumable cost (i.e. columns and solvents for UHPLC phases). At the same time, it provides critical robustness and sensitivity of relative and absolute quantitation in steady-state and tracing experiments, and enables high-throughput cost-effective routine screening of central metabolic pathways for biomedical research and clinical applications, such as in the field of personalized medicine. Taken together, this method can be used to elucidate the intricacies of altered metabolism for both small-scale and highly powered clinical studies in a timeefficient and cost-effective manner.
The authors are grateful to Drs. Anthony Bacon, Annie Slaughter, and Anirban Banerjee for the rat models of trauma/hemorrhagic shock and Dr. Tatsuro Yoshida for the red cell storage tracing experiments, as detailed in the paper. The authors would also like to thank Dr. Robert Hodges for reviewing the manuscript. Research reported in this publication was supported in part by funds from the National Blood Foundation Early career grant 2016 (AD), University of Colorado Comprehensive Cancer Center Core Support (P30 CA046934-17), the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers P50GM049222 and T32GM008315, and Grant #P50 GM049222 from NIGMS, NIH (CS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
 D. S. Wishart. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 2016, DOI: 10.1038/nrd.2016.32.
 O. Beckonert, H. C. Keun, T. M. D. Ebbels, J. Bundy, E. Holmes, J. C. Lindon, J. K. Nicholson. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protocols 2007, 2, 2692.
 T. Fuhrer, N. Zamboni. High-throughput discovery metabolomics. Curr. Opin. Biotechnol. 2015, 31, 73.
 A. D’Alessandro, B. Giardina, F. Gevi, A. M. Timperio, L. Zolla. Clinical metabolomics: the next stage of clinical biochemistry. Blood Transfus. 2012, DOI: 10.2450/2012.005S.
 N. Gasilova, Q. Yu, L. Qiao, H. H. Girault. On-chip spyhole mass spectrometry for droplet-based microfluidics. Angew. Chem. Int. Ed. 2014, 53, 4408.
 A. Zhang, H. Sun, Y. Han, G. Yan, Y. Yuan, G. Song, X. Yuan, N. Xie, X. Wang. Ultraperformance liquid chromatographymass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets. Anal. Chem. 2013, 85, 7606.
 J. Draper, A. J. Lloyd, R. Goodacre, M. Beckmann. Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review. Metabolomics 2013, 9, 4.
 T. Fuhrer, D. Heer, B. Begemann, N. Zamboni. Highthroughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal. Chem. 2011, 83, 7074.
 H. Link, T. Fuhrer, L. Gerosa, N. Zamboni, U. Sauer. Realtime metabolome profiling of the metabolic switch between starvation and growth. Nat. Methods 2015, 12, 1091.
 T. Nemkov, A. D’Alessandro, K. C. Hansen. Three-minute method for amino acid analysis by UHPLC and highresolution quadrupole orbitrap mass spectrometry. Amino Acids 2015, DOI: 10.1007/s00726-015-2019-9.
 J. Zhang, E. Nuebel, D. R. R. Wisidagama, K. Setoguchi, J. S. Hong, C. M. Van Horn, S. S. Imam, L. Vergnes, C. S. Malone, C. M. Koehler, M. A. Teitell. Measuring energy metabolism in cultured cells, including human pluripotent stem cells and differentiated cells. Nat. Protocols 2012, 7, 1068.
 J. M. Buescher, M. R. Antoniewicz, L. G. Boros, S. C. Burgess, H. Brunengraber, C. B. Clish, R. J. DeBerardinis, O. Feron, C. Frezza, B. Ghesquiere, E. Gottlieb, K. Hiller, R. G. Jones, J. J. Kamphorst, R. G. Kibbey, A. C. Kimmelman, J. W. Locasale, S. Y. Lunt, O. D. K. Maddocks, C. Malloy C. M. Metallo, E. J. Meuillet, J. Munger, K. Nöh, J. D. Rabinowitz, M. Ralser, U. Sauer, G. Stephanopoulos, J. St-Pierre, D. A. Tennant, C. Wittmann, M. G. Vander Heiden, A. Vazquez, K. Vousden, J. D. Young, N. Zamboni, S.-M. Fendt. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 2015, 34, 189.
 N. Zamboni, S.-M. Fendt, M. Rühl, U. Sauer. 13C-based metabolic flux analysis. Nat. Protocols 2009, 4, 878.
 N. Zamboni, A. Saghatelian, G. J. Patti. Defining the metabolome: Size,flux, and regulation. Mol. Cell 2015, 58, 699.
 A. D’Alessandro, I. Amelio, C. R. Berkers, A. Antonov, K. H. Vousden, G. Melino, L. Zolla. Metabolic effect of TAp63α: enhanced glycolysis and pentose phosphate pathway, resulting in increased antioxidant defense. Oncotarget 2014, 5, 7722.
 E. Melamud, L. Vastag, J. D. Rabinowitz. Metabolomic analysis and visualization engine for LC–MS data. Anal. Chem. 2010, 82, 9818.
 A. D’Alessandro, H. B. Moore, E. E. Moore, M. Wither, T. Nemkov, E. Gonzalez, A. Slaughter, M. Fragoso, K. C. Hansen, C. C. Silliman, A. Banerjee. Early hemorrhage triggers metabolic responses that build up during prolonged shock. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2015, 308, R1034.
 A. D’Alessandro, T. Nemkov, M. Kelher, F. B. West, R. K. Schwindt, A. Banerjee, E. E. Moore, C. C. Silliman, K. C. Hansen. Routine storage of red blood cell (RBC) units in additive solution-3: a comprehensive investigation of the RBC metabolome: Metabolomics of AS-3 RBCs. Transfusion 2015, 55, 1155.
 M. Yuan, S. B. Breitkopf, X. Yang, J. M. Asara. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protocols 2012, 7, 872.
 K. Contrepois, L. Jiang, M. Snyder. Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction and reverse-phase liquid chromatography–mass spectrometry. Mol. Cell. Proteomics 2015, mcp.M114.046508.
 G. Paglia, A. D’Alessandro, Ó. Rolfsson, Ó.E. Sigurjónsson, A. Bordbar, S. Palsson, T. Nemkov, K.C. Hansen, S. Gudmundsson, B.O. Palsson. Biomarkers defining the metabolic age of red blood cells during cold storage. Blood 2016 128, e43.
 T. Nemkov, K. C. Hansen, L. J. Dumont, A. D’Alessandro. Metabolomics in transfusion medicine. Transfusion 2015, DOI: 10.1111/trf.13442.
 A. D’Alessandro, K. C. Hansen, C. C. Silliman, E. E. Moore, M. Kelher,A.Banerjee.Metabolomics of AS-5RBCsupernatants following routine storage. Vox Sang. 2015, 108, 131.
 A. D’Alessandro, T. Nemkov, T. Yoshida, A. Bordbar, B.O. Palsson, K.C. Hansen. Citrate metabolism in red blood cells stored in additive solution-3. Transfusion 2016, DOI: 10.1111/trf.13892.
 A. D’Alessandro, A. G. Kriebardis, S. Rinalducci, M. H. Antonelou, K. C. Hansen, I. S. Papassideri, L. Zolla. An update on red blood cell storage lesions, as gleaned through biochemistry and omics technologies: An omics update on RBC storage. Transfusion 2015, 55, 205.
 A. Bordbar, P. I. Johansson, G. Paglia, S. J. Harrison, K. Wichuk, M. Magnusdottir, S. Valgeirsdottir, M. Gybel-Brask, S. R. Ostrowski, S. Palsson, O. Rolfsson, O. E. Sigurjónsson, M. B. Hansen, S. Gudmundsson, B. O. Palsson. Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes. Transfusion 2016, 56, 852.
 T. A. Pertinhez, E. Casali, L. Lindner, A. Spisni, R. Baricchi, P. Berni. Biochemical assessment of red blood cells during storage by 1H nuclear magnetic resonance spectroscopy. Identification of a biomarker of their level of protection against oxidative stress. Blood Transfus. Trasfus. Sangue 2014, 12, 548.
 P. Rhee, B. Joseph, V. Pandit, H. Aziz, G. Vercruysse, N. Kulvatunyou, R. S. Friese. Increasing trauma deaths in the United States. Ann. Surg. 2014, 260, 13.
 J. M. Caruso, E. Feketeova, S. D. Dayal, D. Z. Xu, Q. Lu, E. A. Deitch. Mesenteric lymph duct diversion reduces cd11b/cd18 expression and abrogates lung injury after trauma hemorrhagic shock.: 5. Shock 2001, 15, 2.
 R. J. Gonzalez, E. E. Moore, D. J. Ciesla, W. L. Biffl, J. L. Johnson, C. C. Silliman. Mesenteric lymph is responsible for post-hemorrhagic shock systemic neutrophil priming. J. Trauma 2001, 51, 1069.
 G. M. Tannahill, A. M. Curtis, J. Adamik, E. M. Palsson-McDermott, A. F. McGettrick, G. Goel, C. Frezza, N. J. Bernard, B. Kelly, N. H. Foley, L. Zheng, A. Gardet, Z. Tong, S. S. Jany, S. C. Corr, M. Haneklaus, B. E. Caffrey, K. Pierce, S. Walmsley, F. C. Beasley, E. Cummins, V. Nizet, M. Whyte, C. T. Taylor, H. Lin, S. L. Masters, E. Gottlieb, V. P. Kelly, C. Clish, P. E. Auron, R. J. Xavier,L.A. J.O’Neill. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 2013, 496, 238.
 A. D’Alessandro, A. L. Slaughter, E. D. Peltz, E. E. Moore, C. C. Silliman, M. Wither, T. Nemkov, A. W. Bacon, M. Fragoso, A. Banerjee, K. C. Hansen. Trauma/hemorrhagic shock instigates aberrant metabolic flux through glycolytic pathways, as revealed by preliminary 13C-glucose labeling metabolomics. J. Transl. Med. 2015, 13, 253.
Additional Supporting Information may be found online in the supporting information tab for this article.